Xinjin Wang

Papers

Total Citations

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About

Xinjin Wang is a leading researcher in robotics and automation, with a focus on intelligent motion planning and perception for industrial applications. His work bridges the gap between theoretical algorithms and practical robotic systems, particularly in manufacturing and logistics. Wang’s major contributions include pioneering the use of the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm for trajectory planning of redundant robot manipulators. By integrating chaotic sequences into QPSO, he achieved collision-free, optimized motion in complex environments, a breakthrough that has garnered over 11 citations and influenced subsequent work in robotic path planning. In parallel, Wang advanced laser-based perception through his feature-to-feature scan matching method for pallet recognition, which clusters laser data into line segments and extracts corner points to represent environments. This work, with over 10 citations, has been instrumental in enabling autonomous forklifts and warehouse robots to accurately detect and interact with pallets. Wang’s research exemplifies the synergy between optimization and perception, making him a notable figure in the field of robotics.