A Multi-UAV Cooperative Navigation Method Based on Policy Decomposition Structure
Cooperative navigation of multiple unmanned aerial vehicles (UAVs) in disaster search-and-rescue scenarios is challenging due to dense obstacles, partial observability, and strong inter-agent coupling, which often result in path conflicts, collision risks, and limited policy generalization. To address these challenges, this paper proposes a Multi-Agent Deep Deterministic Policy Gradient framework with a Graph-Attention-based Staged Actor (GS-MADDPG). Under a centralized training and decentralized execution paradigm, GNNs are employed to model local interaction relationships among UAVs, enabling effective information aggregation and cooperative decision-making under partial observability. Furthermore, the Actor network is decomposed into perception, goal-guidance, and feature fusion subnetworks, allowing hierarchical decoupling and coordinated integration of local obstacle avoidance behaviors and global navigation objectives. Simulation results conducted in a complex three-dimensional urban environment demonstrate that, compared to traditional methods, GS-MADDPG improves the navigation success rate, robustness, and generalization performance. When the obstacle density reaches 50% and the number of UAVs increases from 2 to 10, the navigation success rate of GS-MADDPG is approximately 40% higher than that of the benchmark algorithm; even in cases with higher obstacle density, GS-MADDPG still achieves a relatively high success rate. This verifies its effectiveness in multi-UAV cooperative navigation for search and rescue tasks.