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 language | English |
---|---|
Article number | 10834394 |
Number of pages | 10 |
Journal | IEEE Transactions on Industrial Informatics |
Early online date | 8 Jan 2025 |
DOIs | |
Publication status | E-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