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About

Nak Yong Ko is a robotics researcher whose work spans mobile robot navigation, obstacle avoidance, and autonomous localization — areas where his contributions have demonstrably shaped both theoretical frameworks and practical implementations. His most influential work, the Lane-Curvature Method (LCM) for local obstacle avoidance (2002, 155 citations), introduced an elegant fusion of the curvature-velocity method with a novel lane-based directional approach, providing indoor mobile robots with more reliable and computationally efficient navigation. Complementing this, his development of the avoidability measure concept offered a rigorous mathematical framework for characterizing robot-obstacle collision states in dynamic environments. Ko's research evolved significantly toward underwater and indoor localization, where he investigated Kalman and particle filter techniques for pose estimation under uncertainty — work that has collectively accumulated over 100 citations. His fusion of ultrasonic beacon ranging with laser measurement further demonstrated a practical approach to robust localization in partially unknown environments. Beyond ground and underwater robotics, Ko extended his expertise to vision-based navigation for autonomous golf ball collection, illustrating the applied breadth of his research. With a career spanning from manipulator motion planning in 1993 to attitude estimation innovations in 2016, Ko represents a sustained and versatile contributor to intelligent robotics systems.