Chin-Tan Lee
Papers
3
Total Citations
27
H-Index
3
About
Chin-Tan Lee is a robotics researcher whose work focuses on advancing autonomous navigation and control systems for wheeled mobile robots (WMRs). His primary research areas include intelligent PID controller design, deep reinforcement learning for robotics, and human-robot interaction for service applications. Lee’s most impactful contribution is the development of a deep reinforcement learning-based controller for tracking WMR systems (2022, 14 citations), which addresses the critical limitation of traditional PID controllers—their fixed gain parameters that require constant manual tuning in dynamic environments. He has also pioneered the application of the Taguchi method to optimize PID controller parameters for path tracking (2018, 10 citations), introducing a multi-objective optimization approach that enhances robot performance. In his work on high-interactive sensory robot systems (2019, 3 citations), Lee designed a natural human-machine interface using hand motion recognition and magnetic sensor guidance for indoor autonomous services. His research bridges classical control theory with modern machine learning, offering practical solutions for industrial and service robotics. Lee’s work is particularly valuable for students and engineers seeking to improve robot adaptability in real-world environments.
Research Focus
Key Achievements
Top Papers
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