DQN-based 3D path planning for UAVs in urban airspace
To address the challenges of three-dimensional (3D) flight path planning for Unmanned Aerial Vehicles (UAVs) in complex urban environments, this paper proposes a reinforcement learning approach based on the Deep Q-Network (DQN) algorithm. The method enables intelligent flight path planning within a discretized 3D urban space, dynamically avoiding obstacles in real-time through the UAV's sensory perception. The UAV agent is trained in a simulated 100×100×20 virtual urban environment, with training scenarios categorized into high, medium, and low difficulty levels to progressively enhance the agent's decision-making capabilities. Throughout the training process, a greedy strategy is adopted to balance the exploration of new potential paths and the exploitation of known optimal routes. Once over 80% of the UAV agents successfully reach their designated target points, the training program automatically advances to the next difficulty level. Experimental results validate the effectiveness of the proposed method, demonstrating its superior obstacle avoidance capabilities and exceptional energy optimization performance in complex urban settings.