Papers
10
Total Citations
107
H-Index
5
About
Cheonghwa Lee is a leading researcher in intelligent robotic systems, with a focus on AI-driven control, digital twin technology, and autonomous navigation for hazardous environments. Lee’s major contributions include developing reinforcement learning and neural network-based control algorithms for self-balancing quadruped robots (28 citations) and designing a three-modular obstacle-climbing robot for building exterior window cleaning (22 citations). A standout achievement is the pioneering work on a digital twin and deep reinforcement learning-driven robotic automation system for confined workspaces, specifically for nozzle dam replacement in nuclear power plants (21 citations), which addresses critical safety challenges in dirty, dangerous, and demanding industries. Lee has also advanced posture control for 7-DOF robot manipulators (14 citations) and enhanced control performance for degraded manipulators using proximal policy optimization (12 citations). With over 100 total citations, Lee’s research consistently bridges simulation and real-world application, pushing the boundaries of robotics in extreme environments. Notable works include slip analysis for omni-directional Mecanum wheel robots and automated FOD detection systems for aviation safety, demonstrating a versatile impact across industrial, nuclear, and aerospace domains.
Research Focus
Key Achievements
Top Papers
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- 4AI-Based Posture Control Algorithm for a 7-DOF Robot Manipulator14 citations · 2022
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