Usage Identification of Anomaly Detection in an Industrial Context

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.

Design of a Deep Post Gripping Perception Framework for Industrial Robots

“…The anomaly detectors integrated in the planning and verifying modules are selected after comparing their performance based on the harmonic mean of precision and recall [14], also called F1-score and computed as…”

“…This data should be available in the running packaging process, since most of the items have normal quality and are ready to be stacked in the container. For the experiment in this work, three unsupervised anomaly detectors are evaluated, namely One-Class Support Vector Machines (OCSVM) [58], L2-norm deep convolution auto-encoder reconstruction based anomaly detection (L2-AE) [14,59] and a Generative Adversial Networks based anomaly detection (AnoGAN) [60]. The evaluation of the PGP planning module is conducted based on the process quality rate (QR):…”

“…Machine learning, which is the use of learning agents that improve their performance on future tasks after making observations about the world [10], and mainly deep learning, meaning to learn using artificial neural networks with hidden layers, has known a wide application spectrum [11] and an important success recently [12]. The use of this artificial intelligence ability increases the autonomy of robots and their ability to adapt to new conditions, which allow their usage in novel applications [13][14][15][16].…”