Obstacle Avoidance System for Civilian Drones Based on Reinforcement Learning

The popularization of unmanned aerial vehicle (UAV) technology has made the obstacle avoidance and path planning system crucial. Traditional obstacle avoidance relies on manual operation by pilots and struggles to adapt to complex environments. In contrast, the sensor-based autonomous obstacle avoidance technology enables UAVs to avoid obstacles autonomously by using various sensors. In recent years, reinforcement learning, as an emerging artificial intelligence technology, has been widely applied to UAV obstacle avoidance. It allows UAVs to interact with the environment and automatically learn the optimal flight strategies. There is no need to hard-code control signals; only action feedback is required. This feedback guides UAVs to choose better control behaviors. Through iteration, the best strategy is found to achieve precise flight. Therefore, reinforcement learning has brought innovation to UAV obstacle avoidance and expanded the application of UAVs in various fields.