Mingxiu Lin

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About

Mingxiu Lin is a robotics researcher whose work centers on motion planning and adaptive control for autonomous systems, with a particular focus on improving the efficiency and robustness of robot navigation in uncertain environments. Lin’s most influential contribution, the 2017 paper on path planning for mobile robots, introduces an enhanced A* algorithm that refines heuristic search by incorporating the influence of a node’s parent, achieving more optimal weight selection for indoor navigation. This work has garnered 82 citations, reflecting its practical value in autonomous robotics. In earlier research, Lin proposed a robust adaptive iterative learning control strategy for uncertain robot systems, integrating saturation-based control to handle modeling uncertainties, unknown parameters, and external disturbances under alignment conditions. Though less cited, this contribution demonstrates a sustained interest in bridging theoretical control methods with real-world robotic challenges. Lin’s research is notable for its applied focus—translating complex algorithmic improvements into tangible gains for autonomous parade robots and other mobile platforms—making it a valuable reference for students and engineers working on intelligent navigation and adaptive control.