🤖 AI Summary
This work addresses the limited application of large language models (LLMs) in industrial process automation, particularly their inadequate support for domain-specific programming languages and proprietary development environments. We present the first systematic exploration and practical deployment of LLMs in industrial automation scenarios, focusing on representative tasks such as robotic arm motion control. To bridge the gap between general-purpose LLMs and industrial requirements, we propose a tailored approach for code generation and task automation that accommodates specialized languages and closed environments. Experimental results demonstrate that our method can automatically produce correct and executable industrial control code, substantially reducing system development time. This study not only validates the feasibility of leveraging LLMs in industrial automation but also fills a critical gap in both research and practical applications within this domain.
📝 Abstract
A growing number of publications address the best practices to use Large Language Models (LLMs) for software engineering in recent years. However, most of this work focuses on widely-used general purpose programming languages like Python due to their widespread usage training data. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, remains underexplored. This research aims to utilize and integrate LLMs in the industrial development process, solving real-life programming tasks (e.g., generating a movement routine for a robotic arm) and accelerating the development cycles of manufacturing systems.