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About

Yidong Wan is a leading researcher in intelligent robotics and trajectory optimization, with a focus on enhancing the efficiency and precision of automated systems. Their work centers on developing advanced path-planning algorithms for autonomous guided vehicles (AGVs), spraying robots, and laser engraving robots, addressing critical challenges in industrial automation. Wan’s major contributions include the creation of a hybrid artificial potential field-A* algorithm that eliminates redundant path nodes and inflection points, significantly improving AGV trajectory planning in cluttered environments. They also pioneered a multi-objective approach using hybrid polynomial interpolation and an improved HMONSGA-II algorithm to optimize spraying robot trajectories, reducing inefficiencies in offline programming. Additionally, Wan introduced a novel grasshopper optimization algorithm for tire laser engraving robots, minimizing energy consumption and enhancing motion continuity. With over 14 citations across their 2024 publications, their research has already garnered attention for its practical impact. Notably, Wan’s work on the improved A* algorithm stands out as a key achievement, offering a scalable solution for multi-static obstacle environments. Their innovative methods are paving the way for more adaptive, energy-efficient, and precise robotic systems in manufacturing.