用自然语言控制多种农业机器人，让非专业人士也能轻松规划复杂任务。
One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
- 通过大模型将自然语言转为机器人可执行的中间指令
- 支持轮式机器人、机械臂与视觉任务的混合任务规划
- 无需编程即可完成复杂农事操作，适合普通农户使用
人工智能正在改变精准农业，为农民提供新工具以优化日常作业。然而，这些技术常带来额外复杂性与学习成本，对非技术人员尤为困难。本文提出一种基于自然语言的机器人任务规划系统，使非专业人士可通过统一接口控制异构机器人。该系统利用大语言模型（LLMs）和预定义动作基元，将人类语言自动转化为不同机器人平台可执行的中间描述。在本研究中，我们扩展了此前仅针对轮式机器人的规划系统，新增涉及机器人操作与计算机视觉的任务实验。结果表明，该架构具备足够的通用性以支持多样机器人，同时能有效执行复杂任务请求。这项工作推动了精准农业中机器人自动化向非技术人员的普及。
原文摘要 · Abstract (English)
Artificial intelligence is transforming precision agriculture, offering farmers new tools to streamline their daily operations. While these technological advances promise increased efficiency, they often introduce additional complexity and steep learning curves that are particularly challenging for non-technical users who must balance tech adoption with existing workloads. In this paper, we present a natural language (NL) robotic mission planner that enables non-specialists to control heterogeneous robots through a common interface. By leveraging large language models (LLMs) and predefined primitives, our architecture seamlessly translates human language into intermediate descriptions that can be executed by different robotic platforms. With this system, users can formulate complex agricultural missions without writing any code. In the work presented in this paper, we extend our previous system tailored for wheeled robot mission planning through a new class of experiments involving robotic manipulation and computer vision tasks. Our results demonstrate that the architecture is both general enough to support a diverse set of robots and powerful enough to execute complex mission requests. This work represents a significant step toward making robotic automation in precision agriculture more accessible to non-technical users.
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