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

James O’Keeffe*, Danesh Tarapore, Alan G. Millard, Jon Timmis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings
Number of pages15
Volume10454 LNAI
ISBN (Print)9783319641065
Publication statusPublished - 20 Jul 2017
Event18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017 - Guildford, United Kingdom
Duration: 19 Jul 201721 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10454 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017
Country/TerritoryUnited Kingdom

Bibliographical note

© 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


  • Behaviour characterisation
  • Fault diagnosis
  • Feature vector
  • Swarm robotics

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