A survey of modern exogenous fault detection and diagnosis methods for swarm robotics

Article


Graham Miller, O. and Gandhi, V. 2020. A survey of modern exogenous fault detection and diagnosis methods for swarm robotics. Journal of King Saud University – Engineering Science. 33 (1), pp. 43-53. https://doi.org/10.1016/j.jksues.2019.12.005
TypeArticle
TitleA survey of modern exogenous fault detection and diagnosis methods for swarm robotics
AuthorsGraham Miller, O. and Gandhi, V.
Abstract

Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robust-ness being viewed as an inherent property of them. However, this has been shown to not always be thecase. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importancefor ensuring the continued operation and success of the swarm. This paper provides an overview of recentwork in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areaswhere research is concentrated: immune system, data modelling, and blockchain-based fault detectionmethods and local-sensing based fault diagnosis methods. Each of these areas have significant advan-tages and disadvantages which are explored in detail. Though the work presented here represents a sig-nificant advancement in the field, there are still large areas that require further research. Specifically,further research is required in testing these methods on real robotic swarms, fault diagnosis methods,and integrating fault detection, diagnosis and recovery methods in order to create robust swarms thatcan be used for non-trivial tasks.

LanguageEnglish
PublisherElsevier
JournalJournal of King Saud University – Engineering Science
ISSN1018-3639
Publication dates
Online14 Dec 2019
Print01 Jan 2020
Publication process dates
Deposited14 Jan 2020
Submitted16 Apr 2019
Accepted08 Dec 2019
Output statusPublished
Publisher's version
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Copyright Statement

© 2019 The Authors.
Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Digital Object Identifier (DOI)https://doi.org/10.1016/j.jksues.2019.12.005
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