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

Liqiao Xia, Youxi Hu, Jiazhen Pang, Xiangying Zhang, Chao Liu

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number10834394
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Early online date8 Jan 2025
DOIs
Publication statusE-pub ahead of print - 8 Jan 2025

Bibliographical note

Copyright © 2025, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • Planning
  • Uncertainty
  • Collaboration
  • Reliability
  • Robots
  • Semantics
  • Large language models
  • Bayes methods
  • Safety
  • Robustness

Fingerprint

Dive into the research topics of 'Leveraging Large Language Models to Empower Bayesian Networks for Reliable Human-Robot Collaborative Disassembly Sequence Planning in Remanufacturing'. Together they form a unique fingerprint.

Cite this