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

TY - GEN

T1 - Towards fault diagnosis in robot swarms

T2 - 18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017

AU - O’Keeffe, James

AU - Tarapore, Danesh

AU - Millard, Alan G.

AU - Timmis, Jon

N1 - © 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

PY - 2017/7/20

Y1 - 2017/7/20

N2 - 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.

AB - 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.

KW - Behaviour characterisation

KW - Fault diagnosis

KW - Feature vector

KW - Swarm robotics

UR - http://www.scopus.com/inward/record.url?scp=85026787845&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-64107-2_31

DO - 10.1007/978-3-319-64107-2_31

M3 - Conference contribution

AN - SCOPUS:85026787845

SN - 9783319641065

VL - 10454 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 393

EP - 407

BT - Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings

PB - Springer

Y2 - 19 July 2017 through 21 July 2017

ER -