Dynamic UAV Swarm Control in Disaster Recovery via GenAI-Based Graph Reinforcement Learning

This study presents a dynamic UAV swarm framework to support ground networks in disaster zones. The framework leverages Generative AI (GenAI) for real-time hover point generation to guide waypoint-based UAV navigation and realistic task modeling, integrated with graph neural networks (GNN) for safe navigation and obstacle avoidance. A multi-agent graph reinforcement learning (MAGRL) mechanism optimizes UAV coordination, enhancing energy efficiency, task completion, and load balancing in response to environmental changes. The framework's graph attention mechanism further improves inter-UAV communication, enabling adaptive task allocation and efficient coverage of high-risk zones. Extensive simulations show that the integrated GenAI-GNN and MAGRL approach achieves superior performance in task completion, energy savings, and system utility, outperforming benchmarks including MADDPG, GCRL, PSO, and Greedy strategies in dynamic disaster scenarios.