Authors:
Erenus Yildiz
1
;
Tobias Brinker
1
;
Erwan Renaudo
2
;
Jakob J. 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.
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