Generative AI-Augmented Graph Reinforcement Learning for Adaptive UAV Swarm Optimization

Uncrewed aerial vehicles (UAVs) are essential for providing communication and computation services in disaster recovery scenarios where traditional infrastructure is compromised. However, challenges related to energy efficiency, real-time adaptability, coverage, load balancing, and safe navigation persist, particularly in dynamic disaster environments. In this study, we propose a comprehensive framework that integrates generative AI (GenAI) with graph neural networks (GNNs) to dynamically generate hover points for waypoint-based UAV navigation and realistic task generation based on environmental conditions. The GNN-based collision avoidance mechanism further ensures safe navigation by allowing UAVs to avoid obstacles and no-fly zones while coordinating with neighboring UAVs in real time. To optimize UAV swarm operations, we introduce a multiagent graph reinforcement learning (MAGRL) framework, enabling UAVs to maximize overall system utility by refining hover point selection, task allocation, and load balancing in response to environmental changes. A graph attention mechanism enhances UAV coordination, improving communication efficiency and decision-making. Extensive simulations show that the proposed GenAI-GNN and MAGRL framework significantly outperforms existing methods in task completion, energy efficiency, and overall system utility in disaster recovery scenarios.