Leveraging Large Language Models to Empower Bayesian Networks for Reliable Human-Robot Collaborative Disassembly Sequence Planning in Remanufacturing

Abstract

Human–robot collaborative disassembly (HRCD) is a promising approach in remanufacturing, leveraging robot's efficiency and human's adaptability for disassembling end-of-life (EoL) products. However, HRCD often encounters numerous choices with uncertain outcomes, posing significant challenges. To address this issue, an HRCD sequence planning model is introduced, providing a quantitative analysis of various decisions with explanations. Initially, HRCD constraint graph is constructed for targeted EoL product based on semantic documents. Subsequently, a Dirichlet Bayesian network (DiBN) is employed to generate feasible sequences based on the HRCD constraint graph, effectively quantifying uncertainty. Then, a fine-tuned large language model (LLM) with tailored prompts is utilized to quantitatively analyze DiBN-based sequences. The DiBN is updated with high-performing sequences from LLM, mitigating the limited knowledge about specific EoL products. Furthermore, a generative adversarial network is proposed to integrate the aforementioned modules for effective training. The effectiveness of the proposed method is demonstrated through two HRCD case studies.