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

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

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

2 Dyfyniadau(SciVal)

Crynodeb

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.

Iaith wreiddiolSaesneg
TeitlTowards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings
GolygyddionYang Gao, Saber Fallah, Yaochu Jin, Constantina Lekakou
CyhoeddwrSpringer Nature
Tudalennau393-407
Nifer y tudalennau15
ISBN (Argraffiad)9783319641065
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2017
Digwyddiad18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017 - Guildford, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 19 Gorff 201721 Gorff 2017

Cyfres gyhoeddiadau

EnwLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol10454 LNAI
ISSN (Argraffiad)0302-9743
ISSN (Electronig)1611-3349

Cynhadledd

Cynhadledd18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
DinasGuildford
Cyfnod19 Gorff 201721 Gorff 2017

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