在边云计算中部署 LLM 的情境化自主无人机导航
Contextualized Autonomous Drone Navigation using LLMs Deployed in Edge-Cloud Computing
AI总结：
本研究探讨在边云计算架构中部署 LLMs，以实现自主无人机导航的实时上下文代码调整，评估了不同 LLMs 动态调整地图参数和生成指令集的能力及其在 6G 边云中的最佳部署位置。
摘要：
自主导航通常在多样场景下离线训练，并根据真实世界经验进行在线微调。然而，真实世界是动态且多变的，许多环境遭遇/影响由于难以在离线训练数据中描述，甚至在线场景中也难以描述，而无法在实时中被考虑。但我们知道，人类操作员可以通过自然语言描述这些动态环境遭遇，从而增加语义上下文。本研究旨在部署大型语言模型（LLMs），对自主导航执行实时的上下文代码调整。文献中尚未评估的挑战是：哪些 LLMs 是合适的，以及这些计算量大的算法应置于计算-通信边云计算架构中的哪个位置。本文评估了不同 LLMs 如何动态调整导航地图参数（例如，等高线地图整形）并推导导航任务指令集。随后，我们评估了哪些 LLMs 最合适，以及它们在未来 6G 电信架构的边云中应部署在何处。
英文摘要：
Autonomous navigation is usually trained offline in diverse scenarios and fine-tuned online subject to real-world experiences. However, the real world is dynamic and changeable, and many environmental encounters/effects are not accounted for in real-time due to difficulties in describing them within offline training data or hard to describe even in online scenarios. However, we know that the human operator can describe these dynamic environmental encounters through natural language, adding semantic context. The research is to deploy Large Language Models (LLMs) to perform real-time contextual code adjustment to autonomous navigation. The challenge not evaluated in literature is what LLMs are appropriate and where should these computationally heavy algorithms sit in the computation-communication edge-cloud computing architectures. In this paper, we evaluate how different LLMs can adjust both the navigation map parameters dynamically (e.g., contour map shaping) and also derive navigation task instruction sets. We then evaluate which LLMs are most suitable and where they should sit in future edge-cloud of 6G telecommunication architectures.
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。
