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Digital-twin deep dynamic camera position optimisation for the V-STARS photogrammetry system based on 3D reconstruction

Wang, Likun; Wang, Zi; Kendall, Peter; Gumma, Kevin; Turner, Alison; Ratchev, Svetan

Authors

Likun Wang

SARA WANG SARA.WANG@NOTTINGHAM.AC.UK
Research Fellow in Aerospace

Peter Kendall

Kevin Gumma

Alison Turner

Professor SVETAN RATCHEV svetan.ratchev@nottingham.ac.uk
Cripps Professor of Production Engineering & Head of Research Division



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.

Citation

Wang, L., Wang, Z., Kendall, P., Gumma, K., Turner, A., & Ratchev, S. (2024). Digital-twin deep dynamic camera position optimisation for the V-STARS photogrammetry system based on 3D reconstruction. International Journal of Production Research, 62(11), 3932-3951. https://doi.org/10.1080/00207543.2023.2252108

Journal Article Type Article
Acceptance Date Jun 13, 2023
Online Publication Date Sep 22, 2023
Publication Date 2024
Deposit Date Oct 3, 2023
Publicly Available Date Oct 19, 2023
Journal International Journal of Production Research
Print ISSN 0020-7543
Electronic ISSN 1366-588X
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 62
Issue 11
Pages 3932-3951
DOI https://doi.org/10.1080/00207543.2023.2252108
Keywords Industrial and Manufacturing Engineering, Management Science and Operations Research, Strategy and Management
Public URL https://nottingham-repository.worktribe.com/output/25644817
Publisher URL https://www.tandfonline.com/doi/full/10.1080/00207543.2023.2252108

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