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About

Yon-Ping Chen is a distinguished researcher in robotics and intelligent control, with key contributions spanning sliding-mode control, path planning, and machine learning for autonomous systems. His seminal work, "Sliding-Mode Force Control of Manipulators" (1999, 20 citations), established robust methods for managing system and environmental uncertainties in robotic manipulators, laying the foundation for force/position control in non-rigid environments. This research, along with his force/position sliding-mode control study, has been instrumental in advancing manipulator safety and precision. More recently, Chen has focused on artificial intelligence-driven mobile robotics, notably through "Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment" (2022, 10 citations), which integrates reinforcement learning for autonomous navigation in complex settings. His innovative work on Voronoi diagram-based A* algorithms and Q-learning tracking control further demonstrates his commitment to practical, real-world applications, such as Industry 4.0 automated guided vehicles. With additional contributions to neural network stereo matching, Chen’s research portfolio reflects a deep expertise in merging control theory with AI, impacting fields from manufacturing to rescue robotics.