Chun Fan Goh
Papers
7
Total Citations
122
H-Index
5
About
Chun Fan Goh is a robotics researcher whose work sits at the intersection of motion planning, automated inspection, and autonomous robotic systems. His most significant contributions lie in developing intelligent planning frameworks for industrial robotic inspection, where he has tackled the challenge of enabling robots to autonomously and efficiently inspect complex 3D surfaces—work that directly addresses the need to reduce manufacturing costs and improve productivity. Goh's most-cited paper (53 citations) introduced a computational framework combining coverage planning and reinforcement learning for automatic online path generation in robotic inspection tasks, representing a notable advance in adaptive, data-driven robotics. Complementing this, his model-based and sampling-based coverage motion planning methods (22 and 21 citations respectively) offer rigorous formulations using Set Covering and Travelling Salesman Problem approaches, as well as solutions for redundant robotic systems with up to 7 degrees of freedom. Beyond inspection, Goh has contributed to autonomous welding in shipbuilding environments, designing mobility solutions for double-hull blocks and applying reinforcement learning to enable safe robot navigation in confined, hazardous spaces. His work on kinematically redundant mobile manipulators further demonstrates his breadth across motion planning and robot kinematics. Collectively, his research positions him as a thoughtful contributor to industrial automation and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 7Robot Model Learning with Gaussian Process Mixture Model2 citations · 2018