Ho-Lun Cheng

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About

Ho-Lun Cheng is a leading researcher in robotics and motion planning, best known for tackling the notoriously difficult problem of narrow passage sampling in probabilistic roadmap (PRM) planning. His seminal 2006 paper, "Multi-level free-space dilation for sampling narrow passages in PRM planning" (41 citations), introduces an innovative approach that strategically dilates free space to enable efficient pathfinding through constrained environments. Cheng’s key contribution lies in developing methods to both dilate the free space and determine the optimal amount of dilation needed, effectively overcoming a major bottleneck in PRM algorithms. This work has had lasting impact on autonomous navigation, surgical robotics, and computational geometry, providing a foundation for subsequent advances in sampling-based planning. Beyond this, Cheng’s research spans multi-level planning and geometric reasoning, where his insights continue to influence how robots navigate complex, cluttered spaces. His ability to address fundamental algorithmic challenges with elegant, practical solutions marks him as a significant figure in robotics, inspiring students and researchers to push the boundaries of motion planning.