Unsupervised Anomaly Detection for Safe Robot Operations

Somaiya, Pratik, Hanheide, Marc and Cielniak, Grzegorz (2020) Unsupervised Anomaly Detection for Safe Robot Operations. In: UKRAS20 Conference: “Robots into the real world”, April 2020, Lincoln, UK.

Full content URL: http://doi.org/10.31256/Wg7Ap8J

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Unsupervised Anomaly Detection for Safe Robot Operations

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Item Type:Conference or Workshop contribution (Poster)
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Abstract

Faults in robot operations are risky, particularly when robots are operating in the same environment as humans. Early detection of such faults is necessary to prevent further escalation and endangering human life. However, due to sensor noise and unforeseen faults in robots, creating a model for fault prediction is difficult. Existing supervised data-driven approaches rely on large amounts of labelled data for detecting anomalies, which is impractical in real applications. In this paper, we present an unsupervised machine learning approach for this purpose, which requires only data corresponding to the normal operation of the robot. We demonstrate how to fuse multi-modal information from robot motion sensors and evaluate the proposed framework in multiple scenarios collected from a real mobile robot.

Keywords:mobile robotics, Anomaly Detection, one-class classifier
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:46369
Deposited On:19 Nov 2021 11:13

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