Yun Seok Nam

Papers

Total Citations

H-Index

About

Yun Seok Nam is a robotics researcher whose work focuses on motion planning and obstacle avoidance for mobile robots operating in dynamic environments. His primary research areas include stochastic obstacle motion prediction, artificial potential field methods, and real-time robot navigation. Nam’s most significant contribution is the development of the “view-time” concept—a temporal planning interval that allows robots to predict and react to moving obstacles in real time. In his most-cited paper (26 citations), he introduced a stochastic model that treats obstacle motion as a random walk process, enabling robots to anticipate trajectories and plan safe paths. He further advanced this approach by integrating artificial potential fields with view-time planning, creating a unified framework that generates driving forces at each planning interval to avoid collisions. This work, detailed in his second most-cited paper (12 citations), bridges reactive and predictive control strategies. Nam’s research has practical implications for autonomous vehicles, service robots, and any system requiring safe navigation among unpredictable moving objects. His methodical integration of probabilistic prediction with potential field navigation remains a foundational reference for researchers developing robust, real-time obstacle avoidance systems.