Beomjoon Lee
Papers
2
Total Citations
13
H-Index
2
About
Beomjoon Lee is a leading researcher in autonomous robot navigation and terrain-adaptive mobility, with a focus on deep reinforcement learning and real-time sensor-based perception. His most-cited work, "Static and Dynamic Collision Avoidance for Autonomous Robot Navigation in Diverse Scenarios Based on Deep Reinforcement Learning" (2023, 9 citations), introduces an efficient training method that enables robots to navigate complex environments with both static and dynamic obstacles. Unlike prior approaches limited to crowded settings, Lee’s framework generalizes across diverse scenarios, significantly advancing practical autonomous navigation. In his second highly cited paper, "Geometric Recognition of Diverse Terrain in Real-Time for a Six-Wheeled Robot based on Laser Scanning Sensors" (2022, 4 citations), he tackles the critical challenge of wheeled robot stability by developing a method for real-time terrain geometry recognition using laser scanning sensors. This work enhances robot adaptability across varied surfaces, ensuring continuous wheel-ground contact. Lee’s contributions bridge the gap between simulation and real-world deployment, offering scalable solutions for field robotics. His research is highly relevant for students and engineers working on autonomous systems, collision avoidance, and mobile robot design, demonstrating clear impact through innovative, application-driven approaches.
Research Focus
Key Achievements
Top Papers
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- 2