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Front. Robot. AI | doi: 10.3389/frobt.2018.00131

Adaptive Online Fault Diagnosis in Autonomous Robot Swarms

  • 1University of York, United Kingdom
  • 2University of Southampton, United Kingdom

Previous work has shown that robot swarms are not always tolerant to the failure of individual robots, particularly those that have only partially failed and continue to contribute to collective behaviours. A case has been made for an active approach to fault tolerance in swarm robotic systems, whereby the swarm can identify and resolve faults that occur during operation. Existing approaches to active fault tolerance in swarms have so far omitted fault diagnosis, however we propose that diagnosis is a feature of active fault tolerance that is necessary if swarms are to obtain long-term autonomy. This paper presents a novel method for fault diagnosis that attempts to imitate some of the observed functions of natural immune system. The results of our simulated experiments show that our system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined.

Keywords: swarm robotics, Fault diagnosis, Adaptive, Autonomous, unsupervised learning

Received: 30 Jul 2018; Accepted: 08 Nov 2018.

Edited by:

Savvas Loizou, Cyprus University of Technology, Cyprus

Reviewed by:

Hussein Abbass, University of New South Wales Canberra, Australia
Gabriele Valentini, Arizona State University, United States  

Copyright: © 2018 O'Keeffe, Tarapore, Millard and Timmis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. James O'Keeffe, University of York, York, United Kingdom, jhok500@york.ac.uk