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About

Dr. SeungGwan Lee is a pioneering researcher in autonomous robotics, specializing in path planning, coverage algorithms, and reinforcement learning for mobile robots. His work addresses critical challenges in enabling robots to navigate complex, obstacle-filled environments efficiently. Dr. Lee’s most influential contribution is the development of B-Theta*, an online coverage algorithm for autonomous cleaning robots, which has garnered 29 citations and represents a significant advance in practical, real-time navigation. Earlier foundational studies, including his 2008 paper on obstacle avoidance using a multi-colony ant algorithm (20 citations) and his 2007 work integrating Ant-Q reinforcement learning for path planning (19 citations), established novel bio-inspired and learning-based approaches to trajectory computation. He further advanced the field by combining heuristic search with reinforcement learning (14 citations) and proposing a Q-learning-based univector field navigation method. Through these contributions, Dr. Lee has demonstrated how hybrid algorithms—merging classical search, swarm intelligence, and reinforcement learning—can overcome the limitations of traditional path planning, directly impacting the development of smarter, more autonomous cleaning and service robots.