Papers
3
Total Citations
103
H-Index
3
About
Geonhee Lee is a robotics researcher whose work centers on autonomous navigation, motion planning, and surgical robotics. His most influential contribution, "Reinforcement learning-based dynamic obstacle avoidance and integration of path planning" (2021, 94 citations), addresses a critical challenge in mobile robotics: enabling robots to safely navigate dynamic environments by combining reinforcement learning with traditional path planning algorithms. This work has become a key reference for researchers developing adaptive navigation systems. Lee has also contributed to the emerging field of surgical robotics through his involvement in the SurgRIPE challenge (2025), which benchmarks vision-based instrument pose estimation—a foundational technology for autonomous surgical task execution. His earlier work on "Path Tracking with Nonlinear Model Predictive Control for Differential Drive Wheeled Robot" (2020) demonstrates his expertise in control theory for wheeled platforms. Lee’s research bridges the gap between classical control methods and modern learning-based approaches, with applications ranging from warehouse automation to minimally invasive surgery. His growing citation record reflects the practical relevance of his work in advancing both industrial and medical robotics.
Research Focus
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