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About

Jingzhou Song is a pioneering roboticist whose research spans assembly automation, medical robotics, and novel locomotion systems. His most influential work, a peg-in-hole robot assembly system using Gauss mixture models (97 citations), addresses fundamental challenges in industrial automation by enabling robots to learn and adapt to complex assembly tasks with high precision. In medical robotics, Song developed an intraocular snake robot integrated with the steady-hand eye robot for retinal microsurgery (23 citations), enhancing dexterity in confined surgical spaces—a breakthrough that expands operable areas on delicate retinal tissue. His contributions to spherical robotics include a fractional-order adaptive integral hierarchical sliding mode controller (30 citations) that achieves high-speed, high-precision linear motion, alongside self-reconfiguration strategies for modular spherical robots. Song also advanced vascular intervention with a guidewire feeding method based on deep reinforcement learning (11 citations), reducing radiation exposure for surgeons. His work on dynamic obstacle avoidance for redundant robots (10 citations) introduced the pre-selected minimum distance index, improving real-time collision avoidance. With over 200 total citations across diverse domains—from space manipulators to two-wheeled robots—Song’s research exemplifies the fusion of theoretical control methods with practical robotic systems, making him a key figure in advancing both industrial and surgical robotics.