用大模型实时解析语言指令，让无人机动态调整导航路径。
Contextualized Autonomous Drone Navigation using LLMs Deployed in Edge-Cloud Computing
- 将大模型部署在边缘-云架构中，实现导航参数实时语义调整。
- 不同大模型在动态地图重构与任务指令生成上表现差异显著。
- 为6G时代无人机自主导航提供算力部署方案参考。
自主导航通常在多种场景下离线训练，并根据真实世界经验在线微调。然而，现实环境动态多变，许多环境变化无法通过离线数据描述，甚至在线场景也难以表达。人类操作员可通过自然语言描述这些动态环境，赋予语义上下文。本研究将大语言模型（LLMs）用于实时语义代码调整，以增强无人机自主导航能力。现有文献未评估适合的模型类型及其在边缘-云计算架构中的部署位置。本文评估了不同LLMs在动态调整导航地图参数（如轮廓图重构）及生成导航任务指令集方面的性能，并进一步分析其在6G通信架构下的最优部署位置。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。