用自然语言让农民指挥机器人完成精准农业任务
Leveraging LLMs for Mission Planning in Precision Agriculture
- 通过大模型理解用户口语指令，自动生成机器人任务计划
- 采用IEEE标准编码任务，确保跨系统可复用
- 适合农业科研人员与非技术用户快速部署智能农机
机器人与人工智能在精准农业中潜力巨大。尽管机器人已成功应用于多种任务，但使其适应多样化使命仍具挑战，尤其因终端用户普遍缺乏技术背景。本文提出一个端到端系统，利用大语言模型（如ChatGPT）使用户能通过自然语言指令指派复杂的数据采集任务给自主机器人。为提升可复用性，任务计划采用现有IEEE任务规范标准编码，并通过ROS2节点将高层任务描述与已有ROS库对接执行。通过大量实验，我们揭示了大模型在此场景下的优势与局限，特别是在空间推理和复杂路径规划方面，并展示所提实现如何有效克服这些问题。
原文摘要 · Abstract (English)
Robotics and artificial intelligence hold significant potential for advancing precision agriculture. While robotic systems have been successfully deployed for various tasks, adapting them to perform diverse missions remains challenging, particularly because end users often lack technical expertise. In this paper, we present an end-to-end system that leverages large language models (LLMs), specifically ChatGPT, to enable users to assign complex data collection tasks to autonomous robots using natural language instructions. To enhance reusability, mission plans are encoded using an existing IEEE task specification standard, and are executed on robots via ROS2 nodes that bridge high-level mission descriptions with existing ROS libraries. Through extensive experiments, we highlight the strengths and limitations of LLMs in this context, particularly regarding spatial reasoning and solving complex routing challenges, and show how our proposed implementation overcomes them.
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。