Anomaly detection methods in autonomous robotic missions

Chirayil Nandakumar, S., Mitchell, D., Erden, M. S., Flynn, D. and Lim, T. (2024) Anomaly detection methods in autonomous robotic missions. Sensors, 24(4), 1330. (doi: 10.3390/s24041330) (PMID:38400491) (PMCID:PMC10892279)

Abstract

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Mitchell, Mr Daniel and Flynn, Professor David
• Authors: Chirayil Nandakumar, S., Mitchell, D., Erden, M. S., Flynn, D., and Lim, T.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Copyright Holders: Copyright © 2024 by the authors
• First Published: First published in Sensors 24(4):1330
• Publisher Policy: Reproduced under a Creative Commons licence