Papers
3
Total Citations
99
H-Index
2
About
Jaewan Choi is a robotics researcher whose work focuses on autonomous navigation, path planning, and intelligent control for mobile robots and unmanned aerial vehicles (UAVs). His most influential contribution is a reinforcement learning-based framework for dynamic obstacle avoidance integrated with path planning, which has garnered 94 citations and addresses a critical challenge in real-world robotic autonomy. Choi has also advanced path tracking for differential drive wheeled robots using nonlinear model predictive control, and proposed an A-star-guided potential field method for UAV path planning that balances optimality with collision avoidance—a technique particularly relevant for reconnaissance and surveillance missions. His research bridges classical planning algorithms with modern learning-based approaches, demonstrating practical solutions for safe and efficient robot motion in complex environments. With work spanning ground and aerial platforms, Choi’s contributions are shaping the next generation of autonomous systems capable of navigating unpredictable surroundings.
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