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About

Chong Jin Ong is a researcher whose work sits at the intersection of robotics, computational geometry, and motion planning. He is best known for his pioneering contributions to robot path planning, particularly through the development of the **penetration growth distance** framework — an elegant optimization-based approach that reformulates path planning by allowing controlled robot-obstacle collisions and minimizing a penetration cost along continuous paths. This foundational concept, introduced as early as 1993 and refined through the late 1990s and early 2000s, has garnered over 100 citations across multiple publications and established Ong as a key figure in collision-aware motion planning algorithms. His earlier work on penetration distances and their computation for smooth convex objects in three-dimensional space provided essential geometric tools that underpin modern collision detection systems widely used in robotics simulation and planning. More recently, Ong has expanded his research horizon into bio-inspired robotics, contributing to the emerging field of AI-driven microswimmer navigation, including chemotactic behavior modeled through hierarchical reinforcement learning — demonstrating a sustained curiosity for applying rigorous mathematical thinking to cutting-edge robotic challenges. His career reflects a consistent commitment to bridging abstract computational theory with practical robotic applications.