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About

Yong Tao is a prominent robotics researcher whose work spans intelligent robot systems, motion planning, advanced control strategies, and human-robot interaction. Over more than a decade of prolific scholarship, Tao has made substantial contributions to both the theoretical foundations and practical applications of modern robotics. Among his most influential contributions is a comprehensive review of intelligent robot research trends (2018, 153 citations), which has become a key reference for scholars entering the field. His work on mobile service robot path planning—particularly a novel ant colony optimization approach enhanced with fuzzy control and critical obstacle influence factors (2021, 79 citations)—addresses real-world challenges in autonomous navigation. On the control side, Tao has pioneered sliding mode control methods leveraging radial basis function neural networks for industrial deburring robots (2016, 36 citations), alongside fuzzy PID control strategies that enable adaptive, real-time trajectory accuracy (2015, 31 citations). Tao has also advanced the design of variable stiffness robot joints to improve safety in human-robot collaboration and contributed kinematic analyses of hybrid palletizing robots. His research on wall-surface inspection robots and glass-aware mapping further demonstrates his commitment to translating robotics theory into practical, safety-critical applications. Collectively, his body of work reflects a sustained and impactful career at the forefront of intelligent robotics.