Won Chang Lee
Papers
3
Total Citations
9
H-Index
2
About
Won Chang Lee is a researcher specializing in robotics, machine learning, and autonomous navigation, with a particular focus on reinforcement learning applications for mobile robots and manipulators. His work addresses critical challenges in dynamic environments, where traditional control methods fall short. In his most cited paper, "Dynamic Obstacle Avoidance and Optimal Path Finding Algorithm for Mobile Robot Using Q-learning" (2017, 4 citations), Lee proposed a novel approach that enables robots to navigate and avoid moving obstacles in real time, bridging the gap between simulation-based studies and practical deployment. He further advanced the field with "Optimal Path Search for Robot Manipulator using Deep Reinforcement Learning" (2021, 2 citations), tackling the high-dimensional continuous action-state spaces that complicate manipulator control. Lee’s contributions are particularly timely, as they leverage the same reinforcement learning principles that powered AlphaGo’s historic victory, translating them into tangible robotic systems. His earlier work on ultrasonic sensor identification and distance detection (2008, 3 citations) laid foundational methods for sensor-based perception. With a career spanning over a decade, Lee continues to push the boundaries of intelligent robotics, offering practical solutions for autonomous systems in complex, unpredictable settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3