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About

Jing-Kai Lin is a robotics researcher specializing in autonomous navigation, path planning, and intelligent control systems for mobile robots in complex, dynamic environments. His work bridges artificial intelligence and robotics, with a focus on developing collision-free, optimal movement strategies that enable robots to operate safely and efficiently in real-world settings. Lin’s most cited paper, “Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment” (2022, 10 citations), introduces a model-free reinforcement learning approach that allows robots to adapt to changing surroundings without pre-programmed maps—a key advancement for rescue and service robotics. He further refined spatial reasoning in “Voronoi Diagram based Collision-free A* Algorithm for Mobile Vehicle in Complex Dynamic Environment” (2022, 3 citations), combining geometric decomposition with heuristic search to improve path efficiency. His earlier work, “Q-learning based Tracking Control and Slope Climbing Strategy Design of Autonomous Mobile Robot and Flatbed Vehicle” (2021, 2 citations), extends these techniques to industrial applications, addressing the challenges of terrain adaptation and load transport central to Industry 4.0. Though early in his career, Lin’s contributions are already shaping the next generation of autonomous ground vehicles, offering practical solutions for safer, smarter robot navigation.