Towards autonomous system: flexible modular production system enhanced with large language model agents

Authors: Yuchen Xia, Manthan Shenoy, Nasser Jazdi, Michael Weyrich

2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
This is the pre-print draft manuscript. The peer-reviewed version will be published exclusively by IEEE after the conference, which is set to take place from September 12th to 15th, 2023. We've made several improvements to the final version of the paper based on valuable feedback and suggestions from other researchers
License: CC BY 4.0

Abstract: In this paper, we present a novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes. We retrofit the automation system for a modular production facility and create executable control interfaces of fine-granular functionalities and coarse-granular skills. Low-level functionalities are executed by automation components, and high-level skills are performed by automation modules. Subsequently, a digital twin system is developed, registering these interfaces and containing additional descriptive information about the production system. Based on the retrofitted automation system and the created digital twins, LLM-agents are designed to interpret descriptive information in the digital twins and control the physical system through service interfaces. These LLM-agents serve as intelligent agents on different levels within an automation system, enabling autonomous planning and control of flexible production. Given a task instruction as input, the LLM-agents orchestrate a sequence of atomic functionalities and skills to accomplish the task. We demonstrate how our implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations. This research highlights the potential of integrating LLMs into industrial automation systems in the context of smart factory for more agile, flexible, and adaptive production processes, while it also underscores the critical insights and limitations for future work. Demos at: https://github.com/YuchenXia/GPT4IndustrialAutomation

Submitted to arXiv on 28 Apr. 2023

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