Kendra Lesser
Papers
2
Total Citations
193
H-Index
2
About
Kendra Lesser is a leading researcher in autonomous robotics, specializing in safe navigation through uncertain and dynamic environments. Her work centers on developing robust algorithms for collision avoidance, particularly when dealing with hybrid dynamic obstacles that can unpredictably change their motion patterns. Lesser’s major contribution is the introduction of a stochastic reachable set-based potential field method, which probabilistically models an obstacle’s future positions to plan collision-free paths. Her most-cited paper, "Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field" (2017, 169 citations), provides a groundbreaking framework for handling obstacles that shift dynamics without warning—a critical challenge for real-world autonomous systems. This work extends her earlier 2015 paper (24 citations) on aggressive moving obstacle avoidance, demonstrating a clear trajectory of innovation. Lesser’s research has significant implications for autonomous vehicles, drones, and robotics operating in crowded, unpredictable spaces, earning her recognition as a key contributor to the field of safe autonomous navigation. Her methods offer practical solutions that balance computational efficiency with rigorous safety guarantees, making her a notable figure in robotics and control theory.
Research Focus
Key Achievements
Top Papers
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