About

Kam K. Leang is a Professor of Mechanical Engineering at the University of Utah. His educational background includes a Ph.D. in Mechanical Engineering from the University of Washington, obtained in 2004, a Master's degree from the University of Utah in 1999, and a Bachelor's degree from the same institution in 1997. He also holds a Certificate in Mechatronics from the University of Utah. His research interests encompass dynamic systems and control, autonomous systems and robotics, nanopositioning, electroactive materials, and mechatronics. He is actively involved in advancing the fields of soft robotics, electroactive polymer materials, and autonomous robotic systems. Professor Leang has received significant funding for his research, including NSF grants for work on electroactive polymer materials for soft robotics and control of nanpositioning systems. He is also recognized for his contributions through awards and media coverage, and he maintains a laboratory dedicated to dynamic autonomous robotics.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Structural engineering
• Real-time computing
• Mechanics
• Mathematical optimization
• Control engineering
• Aerospace engineering
• Algorithm
• Physics

Selected publications

• Mobile-robotic sensor for estimation and localization of multiple chemical-gas leaks: A find-and-consume infotaxis approach

  Robotics and Autonomous Systems · 2026-04-09

  articleOpen accessSenior authorCorresponding

  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 method makes no assumptions about the total number of sources in a prescribed search area, but 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, results from physical experiments using up to four mobile robot platforms carrying 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%. • Multiple leaking gas sources are successfully identified and the gas distribution is modeled through a find-and-consume method with Bayesian inference and information theory. • The approach does not require any knowledge of the total number of plume sources in the search area. • The algorithm can be applied to both single and multi-robot systems. • Simulation and physical experiments with live methane-gas leaks are used to validate and quantifying the performance of the approach.

  PublisherDOI

• Flying Blind: In-Ground Effect Enabled Haptic Teleoperation of Uncrewed Aerial Vehicles

  2026-03-29

  article

  Uncrewed aerial vehicles (UAVs) are increasingly deployed in environments with limited visibility, such as smoke, dust, rain, or fog. In these conditions, conventional visual or range sensors provide little useful information to the operator. To address this, we present a new approach that converts aerodynamic in-ground effect (IGE) interactions into haptic feedback cues. These cues are derived from variations in UAV power without relying on sensors that may fail in low-visibility conditions. We describe the modeling, hardware, and software used to measure and render IGE-based haptic feedback, and evaluate participants' ability to discriminate heights in a user study. Results show users can distinguish different ground distances using only the haptic cues, highlighting the potential of IGEbased feedback in UAV teleoperation. Finally, we present a demonstration experiment where a user is flying the UAV blindly along a path through IGE haptic feedback, showcasing our system's ability to aid navigation under low-visibility conditions.

  PublisherDOI

• Information-Based Supervised Learning of In-Proximity Effects for 3D Distance Estimation and Collision Avoidance

  IEEE Robotics and Automation Letters · 2026-02-16

  articleSenior author

  In-proximity effects (IPE) in 3D, specifically in-ground, in-ceiling, and in-wall effects, experienced by a rotary-wing aerial robot as it flies near obstacles are leveraged for obstacle distance estimation and collision-free motion control. Onboard motor commands and inertial measurement unit (IMU) signals are processed to enable 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 useful in environments with fog, smoke, dust, rain, or snow, where traditional proximity sensors and vision-based systems struggle to detect obstacles and determine distance.

  PublisherDOI

• Passive Spherical Capacitive-Based Resonant Sensor for Wireless Soil Moisture Monitoring

  2025-10-19 · 1 citations

  article
  PublisherDOI

• Engineered ionic polymer metal composites (eIPMCs) under dynamic compression loading conditions: theory and experiments

  Smart Materials and Structures · 2025-01-15 · 2 citations

  article

  Abstract Engineered Ionic Polymer Metal Composites (eIPMCs) represent the next generation of IPMCs, soft electro-chemo-mechanically coupled smart materials used as actuators and sensors. Recent studies indicate that eIPMC sensors, featuring unique microstructures at the interface between the ionic polymer membrane and the electrode, exhibit enhanced electrochemical behavior and sensitivity under compression, as compared to traditional IPMCs. However, a complete and experimentally-validated model of how eIPMCs behave under dynamic compression loads is currently missing. In this paper, we develop an analytical model for eIPMC sensors, elucidating the role of the engineered interface, modeled as a separate material layer with unique mechanical and electrochemical properties. Theoretical predictions focus on the mechanical-to-electrochemical transduction response under dynamic compressive loads. Experimental verification is conducted on conventional IPMC and novel eIPMC samples fabricated using the polymer abrading technique. Electrochemical impedance spectroscopy is performed to study the effect of the engineered interface on the electrochemical properties. Open-circuit (OC) voltage and short-circuit (SC) current are measured under external compressive loads in different loading scenarios to demonstrate sensing performance. Results show good qualitative agreement between experimental trends and model predictions. Experiments over the frequency range 1– <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>18</mml:mn> <mml:mstyle scriptlevel="0"/> <mml:mrow> <mml:mi>Hz</mml:mi> </mml:mrow> </mml:mrow> </mml:math> demonstrate an increase of 220%–290% in open-circuit voltage and 17%–166% in SC current sensitivity for eIPMCs over IPMCs.

  PublisherDOI

• Impact of surface roughness on quasi-steady in-ground effect for hover-capable aerial vehicles

  International Journal of Micro Air Vehicles · 2025-06-19 · 3 citations

  articleOpen accessSenior authorCorresponding

  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.

  PublisherDOI

• Smart Chemical Sensing Payload for Emergency Response Uncrewed Autonomous Systems

  Journal of Autonomous Vehicles and Systems · 2025-09-26 · 1 citations

  articleSenior author

  Abstract A novel smart-sensor payload for uncrewed autonomous systems and emergency responders that automatically detects, estimates, and locates chemical sources is presented. The smart-sensing device fuses Bayesian inference machine learning with information-theoretic motion planning for fast source estimation and localization. More specifically, chemical concentration is measured by a newly developed microelectromechanical-system (MEMS)-based sensor, the location and size of a chemical leak are estimated by 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 performance under 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 (baseline 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 the real-world application of the smart handheld chemical sensing device.

  PublisherDOI

• Engineered Ionic Polymer Metal Composites as Extension Sensors: Theory and Experiments

  ASME Letters in Dynamic Systems and Control · 2025-08-29

  article

  Abstract This article investigates analytically and experimentally the mechano-chemo-electrical behavior of ionic polymer–metal 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’ electrical outputs align well with theoretical predictions, thereby validating our findings and enhancing our understanding of eIPMC strain sensor behavior.

  PublisherDOI

• Multi-Coil-Based Wireless Stimulation and Sensing for Zero-Dc-Power Passive Spherical Soil Moisture Sensor and System Design

  2025-12-15

  article

  A multi-coil-based wireless stimulation and detection system was designed and demonstrated to enable an underground zero-DC-power passive spherical soil moisture sensor over an extended telemetry distance. A 6cm-diameter spherical soil moisture sensor incorporates three orthogonal coil windings to reduce its orientation sensitivity and ensure sufficient inductive coupling to an above-ground system. The above-ground system employs a two-coil configuration (one for stimulation and one for detection). Each coil exhibits 20cm-radius with one turn. The two-coil configuration is highly critical for suppressing interference coupled from the stimulation coil to the detection coil by electronically controlling the loss in each coil loop under each corresponding operation. The two-phase Q-switching architecture enables a substantially increased telemetry distance. The prototype system demonstrates precision sensor’s resonant frequency detection achieving a measurement accuracy with an error less than 0.2% across an extended telemetry distance of 35cm.

  PublisherDOI

• Intelligent Flying Chemical Sensor Network for Gas-Leak Localization and Mapping

  ASME Letters in Translational Robotics · 2025-09-01

  articleOpen accessSenior author

  Abstract This article 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 multirobot system can assist emergency responders in assessing and containing the spread of dangerous chemical leaks.

  PublisherOA PDFDOI

Recent grants

• GOALI/Collaborative Research: Precision Control of Nanopositioners

  NSF · $213k · 2015–2019

• A Novel Multifunctional SPM Probe with Modular Quick-Change Tips for Fully Automated Probe-Based Nanomanufacturing

  NSF · $252k · 2008–2012

• Collaborative Project: Integration of Modeling and Control of Smart Actuators for Nano/Bio Technology into Mechanical Engineering Curriculum

  NSF · $23k · 2008–2009

• COLLABORATIVE RESEARCH: HIGH-SPEED AFM IMAGING OF DYNAMICS ON BIOPOLYMERS THROUGH NON-RASTER SCANNING

  NSF · $237k · 2014–2018

• PFI:BIC: Enhanced Situational Awareness Using Unmanned Autonomous Systems for Disaster Remediation

  NSF · $806k · 2014–2018

Frequent coauthors

• Andrew J. Fleming

  University of Newcastle Australia

  32 shared

• Kwang J. Kim

  27 shared

• Santosh Devasia

  University of Washington

  24 shared

• William S. Nagel

  Widener University

  18 shared

• Yingfeng Shan

  14 shared

• Xiang He

  13 shared

• Garrett M. Clayton

  Villanova University

  13 shared

• Jake A. Steiner

  University of Utah

  12 shared

Labs

• Dynamic Autonomous Robotics (DARC) LabPI

Awards & honors

• Kingstedt, Leang, & McCarter Receive $195K RIF Award for New…
• Leang 3D Printing Electroactive Polymer Soft Robots
• Leang, Pardyjak & Nevada Nano Tech Receive US Army DOD Grant…
• Leang & Collaborators Receive New $3.8M NSF Funding to Work…
• Leang Receives NSF Grant to Work on Temporal-Spatial Control…