M. N. Goodell, J. M. Anderson, K. K. Leang\r\n<\/p>
Mobile-robotic Sensor for Estimation and Localization of Multiple Chemical-gas Leaks: A Find- and-Consume Infotaxis Approach<\/a> Journal Article<\/span> <\/p>In: <\/span>Robotics and Autonomous Systems, <\/span>vol. 202, <\/span>pp. 105431, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{GoodellMN_2026,\r\ntitle = {Mobile-robotic Sensor for Estimation and Localization of Multiple Chemical-gas Leaks: A Find- and-Consume Infotaxis Approach},\r\nauthor = {M. N. Goodell, J. M. Anderson, K. K. Leang\r\n},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.robot.2026.105431},\r\nyear = {2026},\r\ndate = {2026-04-11},\r\nurldate = {2026-04-11},\r\njournal = {Robotics and Autonomous Systems},\r\nvolume = {202},\r\npages = {105431},\r\nabstract = {Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.robot.2026.105431<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>211.<\/div><\/div>J. M. Anderson; K. K. Leang<\/p> Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
In: <\/span>Robotics and Autonomous Systems, <\/span>vol. 202, <\/span>pp. 105431, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{GoodellMN_2026,\r\ntitle = {Mobile-robotic Sensor for Estimation and Localization of Multiple Chemical-gas Leaks: A Find- and-Consume Infotaxis Approach},\r\nauthor = {M. N. Goodell, J. M. Anderson, K. K. Leang\r\n},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.robot.2026.105431},\r\nyear = {2026},\r\ndate = {2026-04-11},\r\nurldate = {2026-04-11},\r\njournal = {Robotics and Autonomous Systems},\r\nvolume = {202},\r\npages = {105431},\r\nabstract = {Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.robot.2026.105431<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>211.<\/div><\/div>J. M. Anderson; K. K. Leang<\/p> Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{GoodellMN_2026,\r\ntitle = {Mobile-robotic Sensor for Estimation and Localization of Multiple Chemical-gas Leaks: A Find- and-Consume Infotaxis Approach},\r\nauthor = {M. N. Goodell, J. M. Anderson, K. K. Leang\r\n},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.robot.2026.105431},\r\nyear = {2026},\r\ndate = {2026-04-11},\r\nurldate = {2026-04-11},\r\njournal = {Robotics and Autonomous Systems},\r\nvolume = {202},\r\npages = {105431},\r\nabstract = {Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.robot.2026.105431<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>211.<\/div><\/div>J. M. Anderson; K. K. Leang<\/p> Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{GoodellMN_2026,\r\ntitle = {Mobile-robotic Sensor for Estimation and Localization of Multiple Chemical-gas Leaks: A Find- and-Consume Infotaxis Approach},\r\nauthor = {M. N. Goodell, J. M. Anderson, K. K. Leang\r\n},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.robot.2026.105431},\r\nyear = {2026},\r\ndate = {2026-04-11},\r\nurldate = {2026-04-11},\r\njournal = {Robotics and Autonomous Systems},\r\nvolume = {202},\r\npages = {105431},\r\nabstract = {Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.robot.2026.105431<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>211.<\/div><\/div>J. M. Anderson; K. K. Leang<\/p> Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div>Rapid assessment and localization of accidental or malicious chemical-gas leaks can save lives and minimize environmental impact. An approach is described that quickly estimates and localizes multiple gas leaks using a network of mobile (ground and aerial) robotic sensors. Using the concepts of foraging and consuming food, Bayesian estimation, and information-theoretic motion planning, multiple chemical-gas leaks are found, one source after another. The find-and-consume infotaxis approach makes no assumptions about the total number of sources in a prescribed search area, but it assumes multiple, spatially-distributed gas leaks of the same chemical. Through detailed simulations, metrics such as the correct number of sources identified, the speed of identification, and the source-term estimation accuracy are quantified. These measures are compared to two standard source-finding strategies: (1) raster-scanning and (2) biased-random walk. The results show that the proposed infotaxis method outperforms the two standard approaches, specifically being able to correctly identify up to 10 individual sources 74% of the time on average with an average localization error of approximately 1.2%. Finally, the results from physical experiments using up to four mobile robot platforms equipped with gas sensors (ground and aerial platforms) show successful estimation and localization of three live methane gas leaks, with an average localization error of 4.4%.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.robot.2026.105431<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>211.<\/div><\/div>J. M. Anderson; K. K. Leang<\/p> Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/GoodellMN_2026.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.robot.2026.105431<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>211.<\/div><\/div>J. M. Anderson; K. K. Leang<\/p> Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/div><\/div>211.<\/div><\/div>J. M. Anderson; K. K. Leang<\/p> Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
J. M. Anderson; K. K. Leang<\/p>
Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance<\/a> Journal Article<\/span> <\/p>In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
In: <\/span>IEEE Robotics and Automation Letters, <\/span>vol. 11, <\/span>iss. 5, <\/span>pp. 5398-5405, <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{AndersonJM_2026_RAL,\r\ntitle = { Information-based Supervised Learning of In-proximity Effects for 3D Distance Estimation and Collision Avoidance},\r\nauthor = {J. M. Anderson and K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf},\r\ndoi = {10.1109\/LRA.2026.3665068},\r\nyear = {2026},\r\ndate = {2026-02-16},\r\nurldate = {2026-02-16},\r\njournal = {IEEE Robotics and Automation Letters},\r\nvolume = {11},\r\nissue = {5},\r\npages = {5398-5405},\r\nabstract = {In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div>In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot when it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. The proposed concept of processing onboard motor commands and inertial measurement unit (IMU) signals enables the robot to essentially ``feel'' the presence of nearby obstacles through aerodynamic interactions. The physics of IPE, along with Shannon information, are used to tailor the input space and train a deep neural network (DNN) to estimate the distance to ground, ceiling, and wall features. Simulation and physical experimental results demonstrate reliable and robust obstacle detection and collision avoidance with a median distance estimation accuracy of 93.35%, 89.22%, and 90.67% for ground, ceiling, and wall, respectively. This new form of ``sensing'' is advantageous in environments where traditional proximity sensors and vision-based obstacle detection systems may not be reliable, such as in foggy, smokey, or dusty environments where perception is limited.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2026_RAL.pdf<\/a><\/li><\/i>doi:10.1109\/LRA.2026.3665068<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/div><\/div>210.<\/div><\/div>O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli<\/p>
Engineered ionic polymer metal composites as extension sensors: Theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
In: <\/span>ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission, <\/span>vol. 6, <\/span>iss. 1, <\/span>pp. 011003 (6-pages), <\/span>2026<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{FakharianO_2026_ALDSC,\r\ntitle = {Engineered ionic polymer metal composites as extension sensors: Theory and experiments},\r\nauthor = {O. Fakharian, W. S. Nagel, K. K. Leang, M. Aureli},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf},\r\ndoi = {10.1115\/1.4069646},\r\nyear = {2026},\r\ndate = {2026-01-01},\r\nurldate = {2026-01-01},\r\njournal = {ASME Letters in Dynamic Systems and Control\/MECC 2025 Joint Submission},\r\nvolume = {6},\r\nissue = {1},\r\npages = {011003 (6-pages)},\r\nabstract = {This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div>This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer\u2013metal composite (IPMC) and engineered IPMC (eIPMC) sensors under extensional loading. To predict the sensing response of eIPMCs, a detailed model is proposed incorporating a composite layer (CL) for the abraded interface between polymer and electrode. We present open-circuit voltage and short-circuit current sensing predictions derived from this model, and we validate them via experiments on anisotropic extensional loading of IPMCs. Experimental results demonstrate that our sensors\u2019 electrical\r\noutputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/FakharianO_2025_ALDSC.pdf<\/a><\/li><\/i>doi:10.1115\/1.4069646<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/div><\/div>2025<\/h3>209.<\/div><\/div>J. M. Anderson, K. K. Leang<\/p>Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
J. M. Anderson, K. K. Leang<\/p>
Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems<\/a> Journal Article<\/span> <\/p>In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
In: <\/span>ASME Journal of Autonomous Vehicles and Systems, <\/span>vol. 5, <\/span>iss. 4, <\/span>no. 041002 (10 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{AndersonJM_2025_JAVS,\r\ntitle = {Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems},\r\nauthor = {J. M. Anderson, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf},\r\nyear = {2025},\r\ndate = {2025-10-21},\r\nurldate = {2025-10-21},\r\njournal = {ASME Journal of Autonomous Vehicles and Systems},\r\nvolume = {5},\r\nnumber = {041002 (10 pages)},\r\nissue = {4},\r\nabstract = {A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div>A novel smart sensor payload for uncrewed autonomous systems and emergency responders with the ability to automatically detect, estimate, and locate chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization performance. More specifically, chemical concentration is measured by a recently developed microelectromechanical-system (MEMS)-based sensor, the location and the size of a chemical leak are estimated using a Bayesian inference machine learning process, and information-theoretic motion planning is used to optimally guide the user or an autonomous mobile robot during the search process to improve the speed and accuracy of localizing and quantifying a leaking gas source. Experiments are performed that compare the device's leak-source estimation and localization performance between two different motion planning methods: (1) moving the device as instructed by the information-based, guided-motion planner and (2) randomly moving the device for search (baseline approach). By following the device's visual cues on where to take measurements (guided-motion method), on average, the smart chemical sensor locates a source over 170% faster than moving the sensor randomly (unguided motion method). Additionally, the leak localization error is less than 6.4% (0.325 m). Finally, live methane gas release experiments are performed to further demonstrate real-world application of the smart handheld chemical-sensing device.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/AndersonJM_2025_JAVS.pdf<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/div><\/div>208.<\/div><\/div>J. R. Bourne, K. K. Leang<\/p>Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
J. R. Bourne, K. K. Leang<\/p>
Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping Journal Article<\/span> <\/p>In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
In: <\/span>ASME Letters in Translational Robotics, <\/span>vol. 1, <\/span>iss. 3, <\/span>pp. 031003 (12 pages), <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{BourneJR_2026,\r\ntitle = {Intelligent Flying Chemical Sensor Network for Gas-leak Localization and Mapping},\r\nauthor = {J. R. Bourne, K. K. Leang},\r\nyear = {2025},\r\ndate = {2025-09-01},\r\nurldate = {2026-01-02},\r\njournal = {ASME Letters in Translational Robotics},\r\nvolume = {1},\r\nissue = {3},\r\npages = {031003 (12 pages)},\r\nabstract = {This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div>This paper describes the development of a network of intelligent flying robotic sensors for quick and precise localization, estimation, and mapping of chemical-gas leaks. The key advances of the technology include leveraging decentralized Bayesian inference machine learning for source-term estimation, minimizing uncertainty through coordinated bio-inspired actions between robots, incorporating open-sector collision avoidance for safe autonomous navigation during search, and utilizing a kernel-based chemical distribution process to create chemical concentration maps. Simulation and real-world outdoor field tests with propane gas demonstrate the effectiveness of the flying-sensor network. This multi-robot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.<\/div>Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/div><\/div>207.<\/div><\/div>C. G. Kou, J. R. Stoll, K. K. Leang<\/p>Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
C. G. Kou, J. R. Stoll, K. K. Leang<\/p>
Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles<\/a> Journal Article<\/span> <\/p>In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
In: <\/span>International Journal of Micro Aerial Vehicles, <\/span>vol. 17, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{KouCG_2025IJMAV,\r\ntitle = {Impact of Surface Roughness on Quasi-Steady In-Ground Effect for Hover-capable Aerial Vehicles},\r\nauthor = {C. G. Kou, J. R. Stoll, K. K. Leang},\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf},\r\ndoi = {https:\/\/doi.org\/10.1177\/175682932513500},\r\nyear = {2025},\r\ndate = {2025-06-19},\r\nurldate = {2025-06-19},\r\njournal = {International Journal of Micro Aerial Vehicles},\r\nvolume = {17},\r\nabstract = {Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div>Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.<\/div>Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/i>http:\/\/www.kam.k.leang.com\/academics\/pubs\/KouGC_2025IJMAV.pdf<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1177\/175682932513500<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Close<\/a><\/p><\/div><\/div><\/div>206.<\/div><\/div>O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
O. Fakharian, W.S. Nagel, K.K. Leang, M. Aureli<\/p>
Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments<\/a> Journal Article<\/span> <\/p>In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
In: <\/span> Smart Materials and Structures, <\/span>vol. 34, <\/span>iss. 2, <\/span>pp. 025048, <\/span>2025<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>