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About

Xinhua Zhao is a robotics and automation researcher whose work spans parallel manipulators, flexible robotic systems, underwater robotics, and intelligent control. With a career extending from foundational studies in underactuated flexible manipulators to cutting-edge deep-sea applications, Zhao has built a diverse and impactful body of research that bridges theoretical modeling and real-world engineering challenges. Among Zhao's most influential contributions is work on smooth trajectory planning for parallel manipulators accounting for joint friction and jerk constraints (2016, 40 citations), which has become a key reference in precision motion planning. Equally notable is research applying the YOLOv3 deep learning framework to underwater pipeline leakage detection (2020, 29 citations), addressing critical demands in marine infrastructure monitoring. Zhao has also made significant strides in adaptive sliding mode neural network control for flexible spatial parallel robots (2021, 23 citations), developing algorithms that simultaneously achieve precise positioning and vibration suppression in compliant robotic structures. Zhao's broader portfolio encompasses rigid-flexible coupling dynamics, nonlinear friction modeling, spherical parallel manipulator design, and robotic micro-injection for biomedical applications — demonstrating a remarkably versatile research vision. For students exploring advanced robotics, Zhao's work offers an excellent synthesis of control theory, mechanical modeling, and applied artificial intelligence.