Usage Identification of Anomaly Detection in an Industrial Context
Year: 2019
Editor: Wartzack, Sandro; Schleich, Benjamin; Gon
Author: Zoghlami, Firas (1); Kurrek, Philip (1); Jocas, Mark (1); Masala, Giovanni (2); Salehi, Vahid (1)
Series: ICED
Institution: Hochschule M
Section: Systems Engineering
DOI number: https://doi.org/10.1017/dsi.2019.383
ISSN: 2220-4342
Abstract
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 learning, Industry 4.0, Artificial intelligence, Robotics perception, Anomaly detection