Based Obstacle Avoidance

Based obstacle avoidance focuses on enabling autonomous agents, such as robots and drones, to safely navigate environments while avoiding collisions. Current research emphasizes diverse approaches, including deep reinforcement learning (often incorporating improved reward functions and attention mechanisms), visual SLAM enhanced with optical flow and control barrier functions, and methods utilizing bearing-distance measurements or implicit obstacle maps learned from experience. These advancements are crucial for improving the robustness and efficiency of autonomous systems in various applications, from warehouse logistics to aerial navigation and underwater exploration.