Multi-UAV Coordinated Navigation: A Method Based on Transformer Dynamic Graph Attention Mechanism

Abstract

Addressing the challenges of real-time decision-making and environmental adaptability in multi-unmanned systems, this study introduces a Transformer-based dynamic graph attention mechanism applied to multi-UAV coordinated navigation tasks. The approach first employs Graph Neural Networks to encode UAV positions, velocities, and environmental obstacles, extracting spatio-temporal node features. A Transformer encoder then processes temporal sequences to generate global contextual representations. A dynamic fusion layer follows, using task goal semantic embeddings as queries to adaptively adjust attention weight distributions, mitigating performance degradation in uncertain environments caused by static models. Subsequently, a multi-layer perceptron decoder outputs optimized collaborative paths, culminating in end-to-end planning via a policy network. During training, reinforcement learning is combined with imitation learning auxiliary losses to enhance model robustness and generalization. This methodology effectively improves path planning efficiency and safety for multi-unmanned systems in complex dynamic scenarios. Experimental evaluations across various environments demonstrate superior success rates, reduced path lengths, and improved computational efficiency compared to baseline methods such as GNN, RL, and standard Transformers. The proposed model achieves an average success rate of over 91%, underscoring its potential for practical deployment in autonomous navigation systems.