Abstract
The integration of agentic AI, powered by large language models (LLMs) with autonomous reasoning, planning, and execution, into unmanned aerial vehicle (UAV) swarms opens new operational possibilities and brings the vision of the Internet of Drones closer to reality. However, infrastructure constraints, dynamic environments, and the computational demands of multi-agent coordination limit real-world deployment in high-risk scenarios such as wildfires and disaster response. This paper investigates the integration of LLM-based agentic AI and edge computing to realize scalable and resilient autonomy in UAV swarms. We first discuss three architectures for supporting UAV swarms - standalone, edge-enabled, and edge-cloud hybrid deployment - each optimized for varying autonomy and connectivity levels. Then, a use case for wildfire search and rescue (SAR) is designed to demonstrate the efficiency of the edge-enabled architecture, enabling high SAR coverage, reduced mission completion times, and a higher level of autonomy compared to traditional approaches. Finally, we highlight open challenges in integrating LLMs and edge computing for mission-critical UAV-swarm applications.
Abstract (translated)
基于大型语言模型（LLMs）自主推理、规划和执行能力的代理型人工智能集成到无人飞行器（UAV）集群中，开创了新的操作可能性，并使无人机互联网的概念更加接近现实。然而，基础设施限制、动态环境以及多智能体协调计算需求限制了在野火和灾害响应等高风险场景中的实际部署。本文探讨了将基于LLM的代理型人工智能与边缘计算结合，以实现UAV集群中可扩展且具有弹性的自主性。首先，我们讨论支持UAV集群的三种架构——独立式、边缘增强式以及边缘-云混合部署，每种架构都针对不同级别的自主性和连接性进行了优化。然后，设计了一个用于野火搜索和救援（SAR）的实际案例，以展示边缘增强式架构的有效性，该架构能够提高SAR覆盖范围，减少任务完成时间，并比传统方法实现更高的自主水平。最后，我们强调了在关键任务型UAV集群应用中整合LLMs与边缘计算所面临的开放挑战。
URL
https://arxiv.org/abs/2601.14437
