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  4. An Intelligent Pipeline for Localization of Industrial Components in Robotic Manufacturing Applications
 
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An Intelligent Pipeline for Localization of Industrial Components in Robotic Manufacturing Applications

Citation Link: https://doi.org/10.15480/882.14170
Publikationstyp
Conference Paper
Date Issued
2024-10-16
Sprache
English
Author(s)
Rawal, Parth  
Bildverarbeitungssysteme E-2 (H)  
Valencia, Daniel  
Hintze, Wolfgang  
Produktionsmanagement und -technik M-18  
TORE-DOI
10.15480/882.14170
TORE-URI
https://tore.tuhh.de/handle/11420/52815
First published in
Frontiers in artificial intelligence and applications  
Number in series
392
Start Page
4539
End Page
4546
Citation
27th European Conference on Artificial Intelligence, ECAI 2024
Contribution to Conference
27th European Conference on Artificial Intelligence, ECAI 2024  
Publisher DOI
10.3233/faia241046
Scopus ID
2-s2.0-85216571519
Publisher
IOS Press
ISBN
9781643685489
The rising skill shortage problem in Europe threatens the economic slowdown in the manufacturing sector. Approaches based on artificial intelligence can play a crucial role in bridging the shortage gap if they can be integrated into robot-assisted production to simplify repetitive manual tasks. Localizing components in a production cell is a familiar problem of robot-assisted production. Robots are often taught trajectories manually, which requires expertise in robot programming. Some of the existing feature-based computer vision solutions can localize a component in 3D space. However, these solutions are not versatile enough to be integrated across different components and production cells. This paper proposes an AI-based solution in the form of a pipeline for the 6D localization of components that can be integrated into multiple industrial use cases. The pipeline encompasses flows for generating synthetic images of components from their CAD model, training deep neural networks to estimate component poses, and improving their accuracy for manufacturing applications. The performance of the pipeline has been validated for components in a production-related environment. The paper also demonstrates the versatility of the pipeline by deploying it for a robotic spray coating use case. Such AI skills can empower the skilled workforce on the shop floor so that they can focus on the overall manufacturing process.
DDC Class
670: Manufacturing
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by-nc/4.0/
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FAIA-392-FAIA241046.pdf

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