Xiaohu Liu
Papers
1
Total Citations
69
H-Index
1
About
Xiaohu Liu is a leading researcher in robotic motion planning and manipulation, with a focus on developing efficient algorithms for complex 3D environments. His most influential work, the 2019 paper "A Heuristic Rapidly-Exploring Random Trees Method for Manipulator Motion Planning," has garnered 69 citations and introduced the PBG-RRT algorithm—a novel approach that integrates heuristic probabilistic sampling with bias-goal factors. This method significantly accelerates convergence in path planning while avoiding local minima, addressing a critical bottleneck in real-time robotic control. Liu’s contributions are particularly impactful for industrial manipulators, where rapid and reliable motion planning is essential for tasks like assembly and pick-and-place. By bridging the gap between theoretical sampling-based planners and practical deployment needs, his work has informed subsequent advances in heuristic-driven RRT variants. Liu’s research continues to shape the field of autonomous manipulation, offering scalable solutions for high-dimensional configuration spaces.
Research Focus
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Top Papers
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