A context-aware real-time human-robot collaborating reinforcement learning-based disassembly planning model under uncertainty.
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| Title: | A context-aware real-time human-robot collaborating reinforcement learning-based disassembly planning model under uncertainty. |
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| Authors: | Amirnia, Ashkan1 ashkan.amirnia@polymtl.ca, Keivanpour, Samira1 |
| Source: | International Journal of Production Research. 2024, Vol. 62 Issue 11, p3972-3993. 22p. |
| Subjects: | Deep reinforcement learning, Reinforcement learning |
| Abstract: | Herein, we present a real-time multi-agent deep reinforcement learning model as a disassembly planning framework for human-robot collaboration. This disassembly plan optimises sequences to minimise operation time and the disassembling costs of end-of-life (EoL) products. Combining different data-driven decision-making tools, the plan aims to handle the complexities and uncertainties of disassembly tasks. Based on the physical features and geometric limitations of EoL product components, we calculate product disassembly difficulty scores. Subsequently, the deep reinforcement learning model integrates these scores into planning process. The model allocates tasks in real time according to the online conditions of the human operator, cobot, and product, enabling the model to cope with uncertainties that may change the process routine. We also present different scenarios wherein a cobot collaborates with human operators with different skill levels. To evaluate model performance, we compare it with baseline models in terms of the convergence time and incorporated disassembly features. The analysis indicates that our model converges three times faster than a baseline model applied to the same case study. Moreover, our model includes more features of the disassembly problem in its decision-making process than any other baseline model. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177477934 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A context-aware real-time human-robot collaborating reinforcement learning-based disassembly planning model under uncertainty. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Amirnia%2C+Ashkan%22">Amirnia, Ashkan</searchLink><relatesTo>1</relatesTo><i> ashkan.amirnia@polymtl.ca</i><br /><searchLink fieldCode="AR" term="%22Keivanpour%2C+Samira%22">Keivanpour, Samira</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. 2024, Vol. 62 Issue 11, p3972-3993. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Herein, we present a real-time multi-agent deep reinforcement learning model as a disassembly planning framework for human-robot collaboration. This disassembly plan optimises sequences to minimise operation time and the disassembling costs of end-of-life (EoL) products. Combining different data-driven decision-making tools, the plan aims to handle the complexities and uncertainties of disassembly tasks. Based on the physical features and geometric limitations of EoL product components, we calculate product disassembly difficulty scores. Subsequently, the deep reinforcement learning model integrates these scores into planning process. The model allocates tasks in real time according to the online conditions of the human operator, cobot, and product, enabling the model to cope with uncertainties that may change the process routine. We also present different scenarios wherein a cobot collaborates with human operators with different skill levels. To evaluate model performance, we compare it with baseline models in terms of the convergence time and incorporated disassembly features. The analysis indicates that our model converges three times faster than a baseline model applied to the same case study. Moreover, our model includes more features of the disassembly problem in its decision-making process than any other baseline model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2023.2252526 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 3972 Subjects: – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: Reinforcement learning Type: general Titles: – TitleFull: A context-aware real-time human-robot collaborating reinforcement learning-based disassembly planning model under uncertainty. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Amirnia, Ashkan – PersonEntity: Name: NameFull: Keivanpour, Samira IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 62 – Type: issue Value: 11 Titles: – TitleFull: International Journal of Production Research Type: main |
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