Minho Kim

Papers

Total Citations

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About

Minho Kim is a robotics researcher whose work focuses on advancing autonomous navigation and path planning for mobile robots and unmanned ground vehicles (UGVs). His key research areas include artificial potential field methods, probabilistic mapping, and driving-characteristic-aware algorithms. Kim’s most cited work, “A path planning algorithm using artificial potential field based on probability map” (2011, 16 citations), addresses a core challenge in mobile robot control: generating simple, effective motion inputs while overcoming the difficulty of detecting exact obstacle shapes. By integrating probability maps, he enhanced the robustness of potential field navigation in uncertain environments. In his subsequent study, “Path Planning for the Shortest Driving Time Considering UGV Driving Characteristic and Driving Time and Its Driving Algorithm” (2013, 9 citations), Kim innovatively extended the classic A* algorithm beyond distance-only optimization. He incorporated driving characteristics—such as deceleration at corners—to produce paths that minimize actual driving time rather than just path length, making his approach more practical for real-world UGV operations. Though his citation counts are modest, Kim’s contributions are notable for bridging theoretical path planning with realistic vehicle dynamics, offering valuable insights for students and researchers interested in practical autonomous navigation systems.