Yingpan Shi

East China University of Science and Technology

Papers

Total Citations

169

H-Index

About

Yingpan Shi is a leading researcher in intelligent manufacturing and robotics, with a primary focus on welding robot path optimization. His work addresses critical inefficiencies in industrial automation, particularly the challenge of planning optimal trajectories for spot-welding robots across numerous weld joints. Shi’s major contribution lies in developing advanced metaheuristic algorithms that significantly enhance welding efficiency. His most influential work, “Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning” (2015), has garnered 141 citations, demonstrating its substantial impact on the field. This paper introduced a hybrid GA-PSO approach that outperforms traditional manual planning methods. Building on this, Shi further refined optimization techniques with discrete elite PSO (2016, 24 citations) and partition mutation PSO (2015, 4 citations), pushing the boundaries of computational efficiency in robotic path planning. His research bridges the gap between theoretical optimization and practical industrial applications, offering scalable solutions for manufacturing. Shi’s work is essential reading for students and researchers in robotics, automation, and swarm intelligence, showcasing how algorithmic innovation can drive real-world productivity gains.