A survey of modern exogenous fault detection and diagnosis methods forswarm robotics

Graham Miller, Olivier and Gandhi, Vaibhav ORCID: https://orcid.org/0000-0003-1121-7419 (2019) A survey of modern exogenous fault detection and diagnosis methods forswarm robotics. Journal of King Saud University – Engineering Science . pp. 1-11. ISSN 1018-3639 (Accepted/In press) (doi:10.1016/j.jksues.2019.12.005)

[img] PDF - Final accepted version (with author's formatting)
Restricted to Repository staff and depositor only until 14 December 2020.
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives.

Download (933kB)


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.

Item Type: Article
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 28740
Notes on copyright: © 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/).
Useful Links:
Depositing User: Vaibhav Gandhi
Date Deposited: 14 Jan 2020 16:13
Last Modified: 24 Jan 2020 18:01
URI: https://eprints.mdx.ac.uk/id/eprint/28740

Actions (login required)

Edit Item Edit Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year