Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Cooperative Data Collection

In the context of UAV trajectory planning for data collection, challenges such as the uncertainty of a large-scale dynamic unknown environment and the need for multi-UAV coordination are prevalent. To address these challenges, this paper proposes a UAV data collection trajectory planning algorithm based on the D3QN (Double Dueling Deep Q-Network) algorithm. The proposed algorithm enables multiple UAVs to dynamically plan their flight paths for data collection in unknown environments through centralized training and distributed application. The algorithm’s performance is improved by incorporating competition mechanisms, candidate node queues, and reward function reshaping techniques. Based on the simulation results, the proposed algorithm outperforms similar algorithms in terms of success rates and task durations.