A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines

Erenus Yildiz, Tobias Brinker, Erwan Renaudo, Jakob Hollenstein, Simon Haller-Seeber, Justus Piater, Florentin Wörgötter


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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