3C Assembly Methods and Systems Based on Large Language Models

Abstract

Addressing issues such as low efficiency in semi-automated production changes, difficulty in guaranteeing product quality, and heavy reliance on manpower in the current 3C assembly industry, this paper proposes a new intelligent assembly planning method for 3C assembly manipulations. It focuses on decomposing tasks and generating operation sequences using Large Language Models (LLMs) and the Planning Domain Definition Language (PDDL). The introduction of a hierarchical clustering-based sample selection and prompt generation algorithm (HCSSPG) enhances the problem-solving capabilities of LLMs, facilitating intelligent assembly operations. Furthermore, the study investigates flexible assembly state monitoring methods for robots, which enable anomaly detection during the assembly process and the intelligent selection and execution of solutions based on these anomalies. A real mobile phone assembly platform has been developed to validate the planning and execution phases of the assembly process, thus providing essential theories and technical support for industrial applications.