Digital-twin deep dynamic camera position optimisation for the V-STARS photogrammetry system based on 3D reconstruction.
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| Title: | Digital-twin deep dynamic camera position optimisation for the V-STARS photogrammetry system based on 3D reconstruction. |
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| Authors: | Likun Wang1, Zi Wang1 Sara.Wang@nottingham.ac.uk, Kendall, Peter1, Gumma, Kevin1, Turner, Alison1, Ratchev, Svetan1 |
| Source: | International Journal of Production Research. 2024, Vol. 62 Issue 11, p3932-3951. 20p. |
| Subjects: | Deep reinforcement learning, Reinforcement learning, Manufacturing processes, Photogrammetry, Digital twin |
| Abstract: | Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application. [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: 177477943 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Digital-twin deep dynamic camera position optimisation for the V-STARS photogrammetry system based on 3D reconstruction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Likun+Wang%22">Likun Wang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zi+Wang%22">Zi Wang</searchLink><relatesTo>1</relatesTo><i> Sara.Wang@nottingham.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Kendall%2C+Peter%22">Kendall, Peter</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gumma%2C+Kevin%22">Gumma, Kevin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Turner%2C+Alison%22">Turner, Alison</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ratchev%2C+Svetan%22">Ratchev, Svetan</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, p3932-3951. 20p. – 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><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Photogrammetry%22">Photogrammetry</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application. [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: 10.1080/00207543.2023.2252108 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 3932 Subjects: – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Manufacturing processes Type: general – SubjectFull: Photogrammetry Type: general – SubjectFull: Digital twin Type: general Titles: – TitleFull: Digital-twin deep dynamic camera position optimisation for the V-STARS photogrammetry system based on 3D reconstruction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Likun Wang – PersonEntity: Name: NameFull: Zi Wang – PersonEntity: Name: NameFull: Kendall, Peter – PersonEntity: Name: NameFull: Gumma, Kevin – PersonEntity: Name: NameFull: Turner, Alison – PersonEntity: Name: NameFull: Ratchev, Svetan 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 |
| ResultId | 1 |
