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About

Kuan-Yu Chou is a researcher specializing in autonomous mobile robotics, with a focus on path planning, collision avoidance, and intelligent control systems. His work addresses critical challenges in dynamic and complex environments, combining reinforcement learning and geometric methods to enhance robot autonomy. Chou’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 approach that enables robots to navigate safely and efficiently without prior knowledge of the environment—a key advancement for rescue and service applications. He further extended this work with a Voronoi Diagram-based A* algorithm (2022, 3 citations) for complex dynamic settings, and a Q-learning-based tracking control and slope climbing strategy (2021, 2 citations) tailored for autonomous guided vehicles in Industry 4.0 contexts. Though early in his career, Chou’s contributions demonstrate a clear trajectory toward practical, scalable solutions for real-world robotic navigation. His integration of learning-based and geometric planning methods positions him as a promising voice in the growing field of intelligent mobile robotics.