Perception-Aware Cooperative Path Planning for Multi-UAV Systems in Urban Wind Fields via Deep Reinforcement Learning

The safe deployment of multiple Unmanned Aerial Vehicles (UAVs) in complex urban environments relies heavily on accurate environmental perception and efficient cooperative path planning. However, executing multi-UAV operations in low-altitude airspaces faces severe challenges due to the dual constraints of complex building clusters and steady-state wind field disturbances. These dynamic environmental factors frequently distort sensory expectations, inducing trajectory drift and degrading policy robustness. To address these limitations, this paper proposes an enhanced Dueling Double Deep Q-Network (D3QN) algorithm, termed NPD3QN, tailored for perception-aware multi-UAV cooperative path planning. By formulating the perceived environmental data (e.g., wind speed, obstacle distances, and inter-UAV states) into a Markov Decision Process, an N-step update strategy is integrated to enhance the characterization of long-term returns. Simultaneously, an improved Prioritized Experience Replay (PER) mechanism is developed to actively filter negative experiences and assign dynamic weights to critical state-action samples, thereby significantly elevating training stability. A 3D urban kinematic environment incorporating a steady-state simulated wind field is constructed. Extensive ablation and comparative results demonstrate that NPD3QN effectively maps high-dimensional state perceptions to robust control commands. In wind-disturbed scenarios, it generates highly streamlined cooperative trajectories, reducing the total path length by approximately 11.7% compared to the standard D3QN baseline. While currently evaluated within steady-state simulated constraints, this study establishes a robust, sensor-driven methodological foundation for autonomous multi-UAV cooperative path planning in wind-disturbed airspaces.