Junxiang Xu

Papers

Total Citations

H-Index

About

Junxiang Xu is a robotics researcher whose work focuses on advancing motion planning and kinematic modeling for robotic manipulators, with a particular emphasis on dual-arm systems. His most significant contribution is the development of the SDPS-RRTConnect algorithm, an improved version of the RRTConnect motion planner that integrates a sparse expansion strategy with a dead point saved strategy. This innovation dramatically reduces the number of collision detections required during path planning, thereby accelerating the speed and efficiency of manipulator motion—a critical advancement for real-time robotic applications in manufacturing and automation. Xu’s foundational paper on this algorithm has garnered 7 citations, reflecting its growing influence in the field. Additionally, his work on kinematic modeling and simulation of dual-arm robots, with 3 citations, provides essential frameworks for understanding and controlling complex, coordinated robotic movements. By tackling the computational bottlenecks in motion planning, Xu is helping to make robots faster, smarter, and more practical for dynamic environments, positioning him as a rising contributor to the robotics community.