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About

Shumei Xiao’s research centers on intelligent robotics, with a particular focus on mobile welding robots and autonomous path planning. Her major contributions lie in developing advanced control systems that enable robots to track welding seams with high precision and speed. Xiao pioneered the use of a rough set genetic algorithm (RSGA) for robot path planning, which optimizes both the speed and accuracy of navigation under grid-based models. She also designed self-tuning fuzzy controllers and fuzzy-Gaussian neural network (FGNN) controllers to coordinate the motion of cross-sliders and wheels, achieving smooth, real-time seam tracking. Her work on a prototype mobile welding robot with a side-positioned torch further advanced practical applications in automated welding. Though her citation counts are modest—with her most cited paper, “Path Planning for Mobile Robot Based on Rough Set Genetic Algorithm,” garnering 8 citations—her contributions are notable for integrating rough set theory with genetic algorithms and fuzzy-neural networks to solve complex control problems. Xiao’s research remains relevant for students and engineers interested in intelligent manufacturing, robotics control, and adaptive automation systems.