Real-Time Task Allocation for UAV Swarms in Complex Environments via Dynamic Hierarchical Attention GNN (DHA-GNN)

This paper presents the dynamic hierarchical attention graph neural network (DHA-GNN), an innovative framework designed for real-time task allocation in UAV swarms navigating complex, dynamic environments, such as urban cityscapes and rugged mountainous terrains. DHA-GNN integrates hierarchical feature aggregation with adaptive multi-level attention mechanisms to model evolving graph structures, capturing real-time interactions among heterogeneous UAVs, tasks, and environmental dynamics. The methodology employs self-learning graph representations to optimize task prioritization and resource allocation under constraints like dynamic obstacles, communication disruptions, and shifting mission priorities. Extensive simulations across diverse scenarios, including industrial facilities, forested regions, and counter-UAV operations, demonstrate that DHA-GNN achieves a task allocation accuracy of 98.7%, outperforming traditional methods by 15% in efficiency and reducing decision latency to 0.0023 milliseconds. It excels in urban (98.2% accuracy) and rugged terrains (97.6% accuracy), ensuring robust adaptability. These results establish DHA-GNN as a leading solution for UAV swarm intelligence, significantly enhancing operational efficiency in disaster response, surveillance, and military applications.