Leveraging LLMs for Mission Planning in Precision Agriculture

TL;DR

This paper presents an end-to-end system that uses large language models like ChatGPT to enable non-expert users to assign complex, natural language mission plans to autonomous robots in precision agriculture, integrating standards and ROS2 for execution.

Contribution

The paper introduces a novel system combining LLMs, IEEE task standards, and ROS2 to facilitate user-friendly mission planning for agricultural robots.

Findings

LLMs effectively interpret natural language instructions for robot missions.

The system demonstrates improved usability for non-technical users.

Limitations in spatial reasoning and routing are addressed through specific implementation strategies.

Abstract

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.