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Xuewu Wang is a leading researcher in robotic welding automation, with a particular focus on intelligent path planning, optimization algorithms, and collision avoidance for industrial welding systems. His work sits at the intersection of robotics, artificial intelligence, and advanced manufacturing, addressing one of the field's most persistent challenges: enabling welding robots to autonomously navigate complex environments with maximum efficiency. Wang's most influential contribution, his 2015 paper introducing a hybrid genetic algorithm–particle swarm optimization approach for welding robot path planning, has garnered 141 citations and established a foundational framework that subsequent researchers have widely built upon. His 2020 survey of intelligent path optimization methods (109 citations) further cemented his role as a synthesizer and authority in the field. Beyond optimization, Wang has made significant advances in real-time obstacle avoidance, adaptive motion planning, and bidirectional rapidly-exploring random tree algorithms, demonstrating a broad and evolving research agenda. His earlier work in three-dimensional vision-based sensing for gas tungsten arc welding reflects a sustained interest in integrating sensing technologies with robotic control. Collectively accumulating over 600 citations, Wang's body of work represents a substantial contribution to the intelligent automation of modern manufacturing processes, making him an essential reference for researchers and engineers working in robotic welding systems.