用大模型把用户口语化指令转为可执行无人机任务，自动规划路线与调度。
LLM-DaaS: LLM-driven Drone-as-a-Service Operations from Text User Requests
- 用微调大模型解析自然语言请求，提取位置、重量等任务元数据。
- 结合实时天气优化路径，使任务准确率和效率显著提升。
- 适合需要自动化无人机服务调度的物流与应急场景。
我们提出 LLM-DaaS，一种基于大语言模型的新型无人机即服务（DaaS）框架，将用户自由文本请求转化为结构化、可执行的无人机服务任务。该方法解决了在不确定环境下理解与构建自然语言输入的关键挑战。系统包含三个核心组件：自由文本请求处理、结构化请求生成，以及动态 DaaS 选择与组合。首先，我们在包含文本请求与对应结构化任务的数据集上，对 Phi-3.5、LLaMA-3.2 7b 与 Gemma 2b 等 LLM 模型进行微调。用户以对话形式提出包裹配送需求，微调后的 LLM 能自动提取交付时间、起止地点、包裹重量等元数据。服务选择模型从可用无人机中选出最合适的单机完成从起始点到最近最优目的地的配送；服务组合模型则从最佳可用无人机集合中，协同规划从起点至最终目的地的完整配送流程。其次，系统集成实时天气数据，优化飞行路径与调度，保障安全高效运行。模拟实验表明，该系统显著提升了任务准确性与运营效率，验证了其在不确定环境下的鲁棒性，为 DaaS 运营提供可靠解决方案。
原文摘要 · Abstract (English)
We propose LLM-DaaS, a novel Drone-as-a-Service (DaaS) framework that leverages Large Language Models (LLMs) to transform free-text user requests into structured, actionable DaaS operation tasks. Our approach addresses the key challenge of interpreting and structuring natural language input to automate drone service operations under uncertain conditions. The system is composed of three main components: free-text request processing, structured request generation, and dynamic DaaS selection and composition. First, we fine-tune different LLM models such as Phi-3.5, LLaMA-3.2 7b and Gemma 2b on a dataset of text user requests mapped to structured DaaS requests. Users interact with our model in a free conversational style, discussing package delivery requests, while the fine-tuned LLM extracts DaaS metadata such as delivery time, source and destination locations, and package weight. The DaaS service selection model is designed to select the best available drone capable of delivering the requested package from the delivery point to the nearest optimal destination. Additionally, the DaaS composition model composes a service from a set of the best available drones to deliver the package from the source to the final destination. Second, the system integrates real-time weather data to optimize drone route planning and scheduling, ensuring safe and efficient operations. Simulations demonstrate the system's ability to significantly improve task accuracy, operational efficiency, and establish LLM-DaaS as a robust solution for DaaS operations in uncertain environments.
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