Towards fault diagnosis in robot swarms:An online behaviour characterisation approach

O’Keeffe, James, Tarapore, Danesh orcid.org/0000-0002-3226-6861, Millard, Alan G. orcid.org/0000-0002-4424-5953 et al. (1 more author) (2017) Towards fault diagnosis in robot swarms:An online behaviour characterisation approach. In: Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings. 18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017, 19-21 Jul 2017 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , GBR , pp. 393-407.

Abstract

Although robustness has been cited as an inherent advantage of swarm robotics systems, it has been shown that this is not always the case. Fault diagnosis will be critical for future swarm robotics systems if they are to retain their advantages (robustness, flexibility and scalability). In this paper, existing work on fault detection is used as a foundation to propose a novel approach for fault diagnosis in swarms based on a behavioural feature vector approach. Initial results show that behavioural feature vectors can be used to reliably diagnose common electro-mechanical fault types in most cases tested.

Metadata

Item Type:	Proceedings Paper
Authors/Creators:	O’Keeffe, James Tarapore, Danesh https://orcid.org/0000-0002-3226-6861 Millard, Alan G. https://orcid.org/0000-0002-4424-5953 Timmis, Jon https://orcid.org/0000-0003-1055-0471
Copyright, Publisher and Additional Information:	© Springer International Publishing AG 2017. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details
Keywords:	Behaviour characterisation,Fault diagnosis,Feature vector,Swarm robotics
Dates:	Published: 20 July 2017 Accepted: 25 March 2017
Institution:	The University of York
Academic Units:	The University of York > Faculty of Sciences (York) > Electronic Engineering (York)
Depositing User:	Pure (York)
Date Deposited:	18 Aug 2017 15:45
Last Modified:	05 Jan 2025 00:43
Published Version:	https://doi.org/10.1007/978-3-319-64107-2_31
Status:	Published
Publisher:	Springer
Series Name:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Identification Number:	10.1007/978-3-319-64107-2_31
Related URLs:	http://www.scopus.com/inward/record.url?...
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:120341

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