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.

翻译：将基于大型语言模型（LLM）、具备自主推理、规划与执行能力的智能体人工智能（Agentic AI）集成到无人机（UAV）集群中，开辟了新的操作可能性，并使无人机物联网的愿景更接近现实。然而，基础设施限制、动态环境以及多智能体协同的计算需求，限制了其在野火救援和灾害响应等高风险场景中的实际部署。本文研究了基于LLM的智能体人工智能与边缘计算的融合，以实现无人机集群中可扩展且具有韧性的自主性。我们首先讨论了支持无人机集群的三种架构——独立部署、边缘赋能部署以及边缘-云混合部署——每种架构针对不同级别的自主性和连接性进行了优化。随后，设计了一个用于野外火灾搜索与救援（SAR）的用例，以展示边缘赋能架构的效率。与传统方法相比，该架构能够实现更高的SAR覆盖范围、更短的任务完成时间以及更高水平的自主性。最后，我们指出了在任务关键型无人机集群应用中集成LLM与边缘计算所面临的开放性挑战。