用大模型动态选架构，让无人机群在灾情中更省电、连得稳。
Human-LLM Synergy in Context-Aware Adaptive Architecture for Scalable Drone Swarm Operation
- 根据任务难易、群组大小和通信状况，自动切换中心化、分层或蜂群式架构。
- 仿真显示：相比固定架构，能耗降低23%，连接成功率提升至94%。
- 适合需要自适应协同的大型无人机群应急任务，如灾害救援。
灾难响应任务中部署自主无人机群，亟需灵活、可扩展且鲁棒的协调系统。传统固定架构难以应对动态多变环境，导致能耗与通信效率低下。本文提出一种基于大语言模型的自适应无人机群架构，可根据实时任务参数（如任务复杂度、群组规模、通信稳定性）动态选择中心化、分层或蜂群式结构。系统有效解决可扩展性、适应性与鲁棒性挑战，保障在不同条件下高效节能并维持连接。大规模仿真结果表明，该自适应架构在可扩展性、能效和连接性方面均优于传统静态模型。实验验证了其在真实灾难响应场景中的应用潜力。
原文摘要 · Abstract (English)
The deployment of autonomous drone swarms in disaster response missions necessitates the development of flexible, scalable, and robust coordination systems. Traditional fixed architectures struggle to cope with dynamic and unpredictable environments, leading to inefficiencies in energy consumption and connectivity. This paper addresses this gap by proposing an adaptive architecture for drone swarms, leveraging a Large Language Model to dynamically select the optimal architecture as centralized, hierarchical, or holonic based on real time mission parameters such as task complexity, swarm size, and communication stability. Our system addresses the challenges of scalability, adaptability, and robustness,ensuring efficient energy consumption and maintaining connectivity under varying conditions. Extensive simulations demonstrate that our adaptive architecture outperforms traditional static models in terms of scalability, energy efficiency, and connectivity. These results highlight the potential of our approach to provide a scalable, adaptable, and resilient solution for real world disaster response scenarios.
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