Deployment Method for Emergency Delivery of Multi-Agent UAV Swarms Driven by Large Language Models

作者：Bing-Bing You, Hongyan Cui, Jun Tang, Zhengang Zhai · 发表于：2026 9th International Conference on Advanced Algorithms and Control Engineering (ICAACE) · 年份：2026 · DOI：10.1109/icaace69793.2026.11508815

In response to challenges in natural language instruction parsing, dynamic task scaling, and rigid optimization strategies in urban multi-UAV emergency delivery, this paper proposes a large language model (LLM)-driven multi-agent UAV deployment framework. The LLM serves as a cognitive scheduler for task understanding, structuring, and strategy selection, while Google OR-Tools performs vehicle routing optimization under capacity and priority constraints to ensure feasibility and efficiency. The system adopts a closed-loop multi-agent architecture including intent parsing, task decomposition, deployment optimization, and execution monitoring. Experiments under different task scales compare heuristic, pure LLM, and pure OR-Tools baselines. Results show that the proposed method maintains 100% task completion while reducing total path cost and improving high-priority task satisfaction, demonstrating the scalability and engineering feasibility of the hybrid LLMoptimization architecture.