用边缘计算让无人机群自主决策，提升火灾救援效率
Agentic AI Meets Edge Computing in Autonomous UAV Swarms
- 将大模型智能与边缘计算结合，实现无人机群实时协同
- 火灾搜救任务中覆盖范围更大，完成时间更短，自主性更强
- 适合高风险场景下的无人系统研发与应急响应团队
将基于大语言模型（LLMs）的智能体人工智能引入无人机群，可拓展自主作业能力并推动无人机互联网愿景落地。然而，基础设施限制、动态环境及多智能体协同的计算需求，制约其在野火、灾后救援等高风险场景中的实际部署。本文研究了基于LLM的智能体AI与边缘计算融合方案，提出三种架构：独立式、边缘增强型和边缘-云混合式，分别适配不同自主性与连接条件。以野火搜救为例验证，边缘增强架构显著提升搜救覆盖率、缩短任务完成时间，并实现更高水平的自主性。最后指出该技术在关键任务应用中仍面临若干挑战。
原文摘要 · Abstract (English)
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.
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