Enhanced Coverage Path Planning Strategies for UAV Swarms Based on SADQN Algorithm

In the current era of intelligent technologies, comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring, emergency rescue, and agricultural plant protection. Owing to their exceptional flexibility and rapid deployment capabilities, unmanned aerial vehicles (UAVs) have emerged as the ideal platforms for accomplishing these tasks. This study proposes a swarm A*-guided Deep Q-Network (SADQN) algorithm to address the coverage path planning (CPP) problem for UAV swarms in complex environments. Firstly, to overcome the dependency of traditional modeling methods on regular terrain environments, this study proposes an improved cellular decomposition method for map discretization. Simultaneously, a distributed UAV swarm system architecture is adopted, which, through the integration of multi-scale maps, addresses the issues of redundant operations and flight conflicts in multi-UAV cooperative coverage. Secondly, the heuristic mechanism of the A* algorithm is combined with full-coverage path planning, and this approach is incorporated at the initial stage of Deep Q-Network (DQN) algorithm training to provide effective guidance in action selection, thereby accelerating convergence. Additionally, a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm. To evaluate the efficacy of the proposed algorithm, simulation experiments were conducted in several irregular environments and compared with several popular algorithms. Simulation results show that the SADQN algorithm outperforms other methods, achieving performance comparable to that of the baseline prior algorithm, with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers. In addition, the algorithm demonstrates excellent generalization ability, enabling it to adapt to different environments.