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About

Xiaowei Han is a robotics researcher whose work focuses on the design, modeling, and control of intelligent robotic systems for challenging environments. Their key research areas include autonomous underwater vehicles (AUVs), robotic manipulators, and wall-climbing robots. Han’s most significant contribution is in system identification and controller design for a novel AUV, a paper that has garnered 21 citations and addresses the critical need for accurate and stable operation in underwater structure monitoring. Building on this foundation, Han has advanced the field of robotic manipulator dynamics by co-optimizing vibration suppression and data efficiency, proposing a hybrid identification method using a Broad Learning System (BLS) to overcome the limitations of nonlinear friction modeling. Additionally, their work on multi-sensor fusion localization for magnetic adhesion wall-climbing robots tackles the practical challenge of maintaining accuracy on large steel structures under visual occlusion and sensor drift. With a growing citation impact and a focus on real-world applications, Han’s research demonstrates a clear trajectory from foundational system identification to sophisticated, multi-modal robotic control.