人类-大语言模型协同的上下文感知自适应架构用于可扩展无人机蜂群作战
Human-LLM Synergy in Context-Aware Adaptive Architecture for Scalable Drone Swarm Operation
AI总结：
针对灾害响应中无人机蜂群协调的固定架构低效问题，提出利用大语言模型根据实时任务参数动态选择集中式、层级式或全息式架构的自适应方法，仿真验证其在可扩展性、能效和连接性上优于静态模型。
摘要：
在灾害响应任务中部署自主无人机蜂群需要开发灵活、可扩展且稳健的协调系统。传统固定架构难以应对动态且不可预测的环境，导致能源消耗和连接性方面的低效。本文通过提出一种用于无人机蜂群的自适应架构来解决这一差距，利用大语言模型根据实时任务参数（如任务复杂度、蜂群规模和通信稳定性）动态选择最优架构，包括集中式、层级式或全息式。我们的系统解决了可扩展性、适应性和稳健性的挑战，确保在不同条件下实现高效能源消耗并维持连接性。大量仿真表明，我们的自适应架构在可扩展性、能源效率和连接性方面优于传统静态模型。这些结果凸显了我们的方法为现实世界灾害响应场景提供可扩展、可适应且具有弹性的解决方案的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
