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Usage Identification of Anomaly Detection in an Industrial Context

Part of: Systems Engineering

Published online by Cambridge University Press:  26 July 2019

Firas Zoghlami ,
Philip Kurrek ,
Mark Jocas ,
Giovanni Masala  and
Vahid Salehi
Firas Zoghlami*
Affiliation:
University of Applied Sciences Munich;
Philip Kurrek
Affiliation:
University of Applied Sciences Munich;
Mark Jocas
Affiliation:
University of Applied Sciences Munich;
Giovanni Masala
Affiliation:
University of Plymouth
Vahid Salehi
Affiliation:
University of Applied Sciences Munich;
*
Contact: Zoghlami, Firas, Hochschule München, Hochschule München, Germany, [email protected]

Abstract

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The use of flexible and autonomous robotics systems is the solution for the automation task of the production and intra-logistics environments. This dynamic context requires the robot to be aware of its surroundings through the whole task, also after accomplishing the gripping action. We present an anomaly detection approach based on unsupervised learning and reconstruction fidelity of image data. We design our method to enhance the dynamic environment perception of robotics systems and apply it in a palletizing robot, in order to perceive and detect changes to its surrounding and process after the gripping step. Our proposed approach achieves the performance targeted by the considered industrial requirements.

Keywords

Machine learningIndustry 4.0Artificial intelligenceRobotics perceptionAnomaly detection
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 3761 - 3770
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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