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Dieses Bild zeigt Lukas Zeh

Lukas Zeh

Herr M.Sc.

Wissenschaftlicher Mitarbeiter "Antriebssysteme und -regelung"
Institut für Steuerungstechnik der Werkzeugmaschinen und Fertigungseinrichtungen

Kontakt

+49 711 685 84511
E-Mail
Visitenkarte (VCF)

Seidenstr. 36
70174 Stuttgart
Deutschland
Raum: 5.088

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Publikationen

Veröffentlichungen:

Ouyang, Shengzhe ; Zürn, Manuel ; Zeh, Lukas ; Lechler, Armin ; Verl, Alexander: Topology Prediction of Branched Deformable Linear Objects Using Deep Learning. In: IEEE Access Bd. 12 (2024), S. 194399–194411
- BibTeX
BibTeX
@article{10804119, abstract = {Automated wire harness handling can improve production efficiency, increase quality, and reduce assembly costs. However, due to deformation, there are an infinite number of possible wire harness configurations, making wire harness perception a challenge. Deep learning is a popular method for computer vision but lacks datasets, models, and experiments for wire harness perception. Therefore, this paper presents a novel deep learning model to predict the configuration of a wire harness using artificially generated datasets mixed with real annotated data. The model predicts keypoints which are interpolated as cubic splines to represent the wire harness configuration with reduced degrees of freedom. We benchmark our novel model against YOLOv8-Pose and experiment with different possibilities for predicting the wire harness. As a result, our proposed approach achieves mAP@50-95 of 89.8%, which could further be integrated into robotic systems to improve the automation and precision of robotic wire harness handling.}, author = {Ouyang, Shengzhe and Zürn, Manuel and Zeh, Lukas and Lechler, Armin and Verl, Alexander}, doi = {10.1109/ACCESS.2024.3518634}, issn = {2169-3536}, journal = {IEEE Access}, pages = {194399-194411}, title = {Topology Prediction of Branched Deformable Linear Objects Using Deep Learning}, volume = 12, year = 2024 }

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