Multi-UAV fire rescue path planning based on the improved DQN algorithm
TL;DR
An improved deep Q-network (DQN) algorithm is proposed, introducing a priority experience replay mechanism to improve sample utilization, and designing a composite reward function including arrival reward, step penalty, direction guidance, and safety penalty to guide the UAV to plan safe and efficient flight paths in complex urban environments.
Abstract
Urban fire rescue poses severe challenges to the real-time performance and obstacle avoidance capabilities of unmanned aerial vehicle (UAV) path planning. Existing methods (such as A*, RRT, and standard DQN) have problems such as low search efficiency, insufficient obstacle avoidance ability, or slow convergence in complex environments. This paper proposes an improved deep Q-network (DQN) algorithm, introducing a priority experience replay mechanism to improve sample utilization, and designing a composite reward function including arrival reward, step penalty, direction guidance, and safety penalty to guide the UAV to plan safe and efficient flight paths in complex urban environments. A threedimensional grid simulation environment was constructed based on the real fire incident at Chongqing California Garden. Experimental results show that the improved DQN algorithm outperforms the traditional DQN and RRT algorithms. This method provides a feasible technical solution for multi-UAV collaborative rescue in urban fire scenarios.