loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Erenus Yildiz 1 ; Tobias Brinker 1 ; Erwan Renaudo 2 ; Jakob Hollenstein 2 ; Simon Haller-Seeber 2 ; Justus Piater 2 and Florentin Wörgötter 1

Affiliations: 1 III. Physics Institute, Georg-August University of Göttingen, Germany ; 2 Department of Computer Science, University of Innsbruck, Innsbruck, Austria

Keyword(s): Object Detection, Object Classification, Automation, e-Waste, Recycling.

Abstract: As the state-of-the-art deep learning models are taking the leap to generalize and leverage automation, they are becoming useful in real-world tasks such as disassembly of devices by robotic manipulation. We address the problem of analyzing the visual scenes on industrial-grade tasks, for example, automated robotic recycling of a computer hard drive with small components and little space for manipulation. We implement a supervised learning architecture combining deep neural networks and standard pointcloud processing for detecting and recognizing hard drives parts, screws, and gaps. We evaluate the architecture on a custom hard drive dataset and reach an accuracy higher than 75% in every component used in our pipeline. Additionally, we show that the pipeline can generalize on damaged hard drives. Our approach combining several specialized modules can provide a robust description of a device usable for manipulation by a robotic system. To our knowledge, we are the pioneers to offer a complete scheme to address the entire disassembly process of the chosen device. To facilitate the pursuit of this issue of global concern, we provide a taxonomy for the target device to be used in automated disassembly scenarios and publish our collected dataset and code. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.92.28.52

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Yildiz, E.; Brinker, T.; Renaudo, E.; Hollenstein, J.; Haller-Seeber, S.; Piater, J. and Wörgötter, F. (2020). A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines. In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS, ISBN 978-989-758-479-4, pages 17-27. DOI: 10.5220/0010016000170027

@conference{robovis20,
author={Erenus Yildiz. and Tobias Brinker. and Erwan Renaudo. and Jakob Hollenstein. and Simon Haller{-}Seeber. and Justus Piater. and Florentin Wörgötter.},
title={A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines},
booktitle={Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS,},
year={2020},
pages={17-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010016000170027},
isbn={978-989-758-479-4},
}

TY - CONF

JO - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS,
TI - A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines
SN - 978-989-758-479-4
AU - Yildiz, E.
AU - Brinker, T.
AU - Renaudo, E.
AU - Hollenstein, J.
AU - Haller-Seeber, S.
AU - Piater, J.
AU - Wörgötter, F.
PY - 2020
SP - 17
EP - 27
DO - 10.5220/0010016000170027

0123movie.net