Shenglan Liu

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About

Shenglan Liu is a robotics and autonomous systems researcher whose work centers on motion planning, collision avoidance, and intelligent control for mobile robots. His most recognized contribution lies in advancing artificial potential field (APF) methods for robot navigation, most notably through his development of the obstacle envelope modelling (OEM) technique. Published in 2016 and accumulating 42 citations, this work addressed a fundamental challenge in autonomous robotics: enabling mobile robots to safely navigate complex environments by geometrically modelling obstacles of arbitrary shape, significantly improving the reliability and generalizability of traditional APF approaches. Liu's research bridges theoretical control frameworks with practical robotic implementation, offering solutions that are computationally tractable yet geometrically sophisticated. By reformulating how obstacles are represented within potential field calculations, his approach reduces the risk of local minima and unrealistic force computations that have historically plagued APF-based systems. This contribution has resonated within the robotics community, particularly among researchers working on real-world deployment of autonomous ground vehicles and service robots. His work serves as a valuable reference for students and engineers seeking robust, geometry-aware collision avoidance strategies in dynamic and unstructured environments.