Fangzhou Xu

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About

Fangzhou Xu is a robotics researcher specializing in motion planning and manipulation, with a focus on developing efficient path-planning algorithms for autonomous systems. Their major contributions center on improving the Rapidly-exploring Random Tree (RRT) family of algorithms, which are foundational for robot navigation in complex environments. Xu introduced the CAF-RRT* algorithm (2022, 20 citations), which combines circular arc fillet smoothing with bidirectional search to generate shorter, smoother paths than prior RRT* variants. Building on this, the Bi-RRT* algorithm (2023, 17 citations) further enhanced bidirectional RRT* by optimizing convergence speed and path quality in two-dimensional spaces, addressing key limitations in real-time robotic applications. More recently, Xu has extended into perception-based manipulation with work on object planar grasping pose detection in low-light scenes (2024), demonstrating versatility in tackling real-world challenges like poor visibility. With over 40 combined citations, Xu’s work is recognized for bridging theoretical improvements in sampling-based planning with practical deployment needs, making their algorithms valuable for mobile robots and autonomous vehicles. Their research continues to push toward robust, computationally efficient solutions for dynamic environments.