Obstacle Region

Obstacle region research focuses on efficiently and safely navigating robots and autonomous vehicles around obstacles, addressing challenges in path planning and collision avoidance. Current efforts leverage diverse approaches, including deep learning models like diffusion models and neural networks for risk assessment and path prediction, control barrier functions for safe navigation, and reinforcement learning for adapting to dynamic obstacles. These advancements are crucial for improving the autonomy and reliability of robots in complex, cluttered environments, with applications ranging from autonomous driving to search and rescue operations.