Robust and Energy-Efficient Multi-UAV Trajectory Planning for Data Collection: A Game-Theoretic and Deep Reinforcement Learning Approach

In an open electromagnetic environment, multi-unmanned aerial vehicle (UAV) communications may suffer from intermittent data transmissions, incomplete information on jammers and geographical obstacles. This deteriorates the UAV-ground and UAV-UAV wireless communications, potentially leading to physical collisions and posing significant safety risks. While existing studies rarely account for intermittent UAV connectivity and the associated communication costs, this paper proposes an effective cooperative approach utilizing grid map exploration and experience sharing. Specifically, game-theoretic methods are employed to facilitate distributed cooperative information exchange. Although each UAV seeks to maximize its individual utility, the proposed mechanism incentivizes cooperation to achieve the collective mission. To mitigate collaboration interruptions caused by intermittent transmission, we propose an opportunistic cooperative reinforcement learning framework combined with Long Short-Term Memory (LSTM)-based predictive learning, which explicitly accounts for the dynamic communication costs of UAVs. Empirical evaluations demonstrate that our algorithm significantly outperforms existing non-cooperative methods, with a 31% improvement in converged reward compared to the non-cooperative baseline. Furthermore, it exhibits superior stability and data collection efficiency compared to established multi-agent baselines (e.g., MADDPG, MAPPO). Particularly, the system’s performance robustness regarding the LSTM prediction accuracy is rigorously evaluated, confirming its resilience against intermittent communication.