Reactive Obstacle Avoidance

Reactive obstacle avoidance focuses on enabling robots to safely and efficiently navigate dynamic environments by reacting to obstacles in real-time, without relying on pre-planned paths. Current research emphasizes integrating perception, planning, and control into unified frameworks, employing techniques like model predictive control, artificial potential fields, and neural networks (including those with symmetrical architectures) to achieve fast, robust avoidance. These advancements are crucial for improving the safety and autonomy of robots in various applications, from autonomous vehicles and drones to collaborative robots in industrial settings.