Seok‐Young Lee
Papers
4
Total Citations
27
H-Index
2
About
Seok-Young Lee is a robotics and control systems researcher whose work spans autonomous navigation, adaptive control, and underwater sensing technologies. His research primarily focuses on advancing intelligent control strategies for mobile robots and robotic manipulators, tackling real-world challenges such as sparse reward environments, model uncertainties, and dynamic disturbances. Lee's most impactful contribution to date is his 2022 work on deep deterministic policy gradient (DDPG)-based autonomous driving for mobile robots, which garnered 21 citations by innovatively combining DDPG with hindsight experience replay (HER) to overcome the persistent challenge of sparse reward signals in reinforcement learning-based path planning. His more recent investigations into adaptive sliding mode control (ASMC) for robotic manipulators demonstrate a commitment to refining robust control frameworks, introducing quasi-convex function-based adaptive laws and neural network-enhanced time-delay estimation to improve precision under uncertain dynamic conditions. Earlier work on hydrophone-based sound source localization for underwater vehicles reflects the breadth of his expertise, addressing sensor limitations unique to aquatic environments. Collectively, Lee's research bridges machine learning, classical control theory, and marine robotics, offering meaningful contributions to the development of more reliable and intelligent autonomous systems across diverse operational environments.
Research Focus
Key Achievements
Top Papers
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