A Learning-Based Cooperative Navigation Approach for Multi-UAV Systems Under Communication Coverage

In multi-unmanned aerial vehicle (UAV) systems cooperative navigation under the communication coverage of ground base stations, UAVs encounter challenges in maintaining reliable communication, ensuring flight safety, and achieving efficient collaboration. To address these challenges, this study formulates the cooperative navigation problem as a Markov game and introduces a two-stream graph multi-agent proximal policy optimization (two-stream GMAPPO) algorithm based on the graph neural network (GNN). We model UAVs and other entities as graph-structured data, aggregate the node information of these graph-structured data through the GNN module, and extract potential features related to UAVs. This allows UAVs to obtain richer local information. Through the two-stream network structure, the extracted potential features related to UAVs are combined with the state space of UAVs to enhance adaptability to environmental changes and improve navigation safety. Simulation results demonstrate that the proposed method significantly outperforms baseline algorithms in both mean reward and convergence speed, confirming the superior performance of two-stream GMAPPO.