This paper introduces a novel approach to integrating large language model (LLM) agents into automated production systems, aimed at enhancing task automation and flexibility. We organize production operations within a hierarchical framework based on the automation pyramid. Atomic operation functionalities are modeled as microservices, which are executed through interface invocation within a dedicated digital twin system. This allows for a scalable and flexible foundation for orchestrating production processes. In this digital twin system, low-level, hardware-specific data is semantically enriched and made interpretable for LLMs for production planning and control tasks. Large language model agents are systematically prompted to interpret these production-specific data and knowledge. Upon receiving a user request or identifying a triggering event, the LLM agents generate a process plan. This plan is then decomposed into a series of atomic operations, executed as microservices within the real-world automation system. We implement this overall approach on an automated modular production facility at our laboratory, demonstrating how the LLMs can handle production planning and control tasks through a concrete case study. This results in an intuitive production facility with higher levels of task automation and flexibility. Finally, we reveal the several limitations in realizing the full potential of the large language models in autonomous systems and point out promising benefits. Demos of this series of ongoing research series can be accessed at: https://github.com/YuchenXia/GPT4IndustrialAutomation

翻译：本文提出了一种将大型语言模型（LLM）智能体集成到自动化生产系统中的创新方法，旨在提升任务自动化水平与系统灵活性。我们基于自动化金字塔理论，在分层框架内组织生产操作。原子操作功能被建模为微服务，通过在专用数字孪生系统中进行接口调用来执行，从而为编排生产流程提供了可扩展且灵活的基础。在该数字孪生系统中，底层的硬件特定数据经过语义增强处理，使其可被LLM解读以用于生产规划与控制任务。我们通过系统化的提示工程，引导大型语言模型智能体解析这些生产特定数据与知识。当接收到用户请求或识别到触发事件时，LLM智能体将生成工艺规划，随后将其分解为一系列原子操作，并在现实世界的自动化系统中作为微服务执行。我们在实验室的自动化模块化生产设施上实现了这一整体方案，通过具体案例研究展示了LLM如何处理生产规划与控制任务，从而构建出具有更高任务自动化程度和灵活性的直观生产设施。最后，我们揭示了在自主系统中充分发挥大型语言模型潜力所面临的若干局限，并指出了其带来的显著优势。本系列持续研究的演示可通过以下链接访问：https://github.com/YuchenXia/GPT4IndustrialAutomation