Leveraging LLMs for Mission Planning in Precision Agriculture

2025-06-11

AI Summary

To address the challenge of non-expert users in precision agriculture struggling to specify complex data-collection tasks for autonomous robots, this paper proposes an end-to-end natural language task planning framework powered by large language models (LLMs). Methodologically, it introduces the first deep integration of ChatGPT into an agricultural robot’s closed-loop control system, establishing a three-layer “semantic–standard–execution” mapping: natural language instructions are parsed into IEEE-standardized task specifications and subsequently dispatched via ROS 2 nodes for robotic execution. To compensate for LLMs’ limitations in spatial reasoning and multi-objective path planning, the framework incorporates a spatial semantic parsing module and a route optimization module. Experimental evaluation in real-world farmland environments demonstrates a 92% task parsing accuracy and an 89% task execution success rate, significantly enhancing task specification usability, reusability, and cross-platform interoperability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Adapting robots for diverse missions in precision agriculture
• Enabling non-technical users to assign tasks via natural language
• Improving spatial reasoning and routing in LLM-based mission planning

Innovation

• Leveraging LLMs for natural language mission planning
• Encoding missions using IEEE task standard
• Bridging mission descriptions with ROS2 nodes