Leveraging LLMs for Mission Planning in Precision Agriculture
This addresses the problem of making robotic mission planning accessible to end users in precision agriculture, though it is incremental as it builds on existing standards and libraries.
The paper tackles the challenge of enabling non-technical users to assign complex data collection tasks to agricultural robots by developing an end-to-end system that uses ChatGPT to convert natural language instructions into executable mission plans encoded with an IEEE standard and executed via ROS2. The system demonstrates strengths and limitations of LLMs in spatial reasoning and routing, with implementation solutions tested through extensive experiments.
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.