Hui Yang

Papers

Total Citations

458

H-Index

About

Hui Yang is a prominent researcher in the field of autonomous robotics and intelligent path planning, with particular expertise in developing advanced navigation algorithms for complex, dynamic environments. His work focuses on bridging the gap between theoretical optimization techniques and real-world robotic applications, producing solutions that are both computationally efficient and practically deployable. Among his most significant contributions is the MOD-RRT* algorithm (2020), a multiobjective dynamic rapidly exploring random tree framework designed for robot navigation in unknown dynamic environments. This work, which has garnered over 280 citations, elegantly combines path generation with adaptive replanning, enabling robots to respond intelligently to unpredictable surroundings. Complementing this, his earlier development of the Double-Layer Ant Colony Optimization algorithm (DL-ACO) in 2018 — cited over 175 times — demonstrated his innovative application of bio-inspired computing to trajectory optimization and autonomous navigation. Together, these contributions reflect Yang's consistent pursuit of robust, multi-layered algorithmic solutions that push the boundaries of robot autonomy. His research has made a meaningful impact on the robotics and artificial intelligence communities, establishing him as an influential voice in sampling-based planning and swarm-intelligence-driven navigation methodologies.