Monitoring a robot swarm using a data-driven fault detection approach

Handle URI:
http://hdl.handle.net/10754/625165
Title:
Monitoring a robot swarm using a data-driven fault detection approach
Authors:
Khaldi, Belkacem; Harrou, Fouzi; Cherif, Foudil; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Khaldi B, Harrou F, Cherif F, Sun Y (2017) Monitoring a robot swarm using a data-driven fault detection approach. Robotics and Autonomous Systems. Available: http://dx.doi.org/10.1016/j.robot.2017.06.002.
Publisher:
Elsevier BV
Journal:
Robotics and Autonomous Systems
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
30-Jun-2017
DOI:
10.1016/j.robot.2017.06.002
Type:
Article
ISSN:
0921-8890
Sponsors:
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The work is done in collaboration with the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider, Biskra, Algeria. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0921889017300854
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKhaldi, Belkacemen
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorCherif, Foudilen
dc.contributor.authorSun, Yingen
dc.date.accessioned2017-07-06T09:43:06Z-
dc.date.available2017-07-06T09:43:06Z-
dc.date.issued2017-06-30en
dc.identifier.citationKhaldi B, Harrou F, Cherif F, Sun Y (2017) Monitoring a robot swarm using a data-driven fault detection approach. Robotics and Autonomous Systems. Available: http://dx.doi.org/10.1016/j.robot.2017.06.002.en
dc.identifier.issn0921-8890en
dc.identifier.doi10.1016/j.robot.2017.06.002en
dc.identifier.urihttp://hdl.handle.net/10754/625165-
dc.description.abstractUsing swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).en
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The work is done in collaboration with the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider, Biskra, Algeria. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0921889017300854en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Robotics and Autonomous Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Autonomous Systems, 30 June 2017. DOI: 10.1016/j.robot.2017.06.002. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectExogenous fault detectionen
dc.subjectSwarm roboticsen
dc.subjectViscoelastic control modelen
dc.subjectData-driven approachesen
dc.subjectStatistical monitoring schemesen
dc.titleMonitoring a robot swarm using a data-driven fault detection approachen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalRobotics and Autonomous Systemsen
dc.eprint.versionPost-printen
dc.contributor.institutionLESIA Laboratory, Department of Computer Science, University of Mohamed Khider, R.P. 07000 Biskra, Algeriaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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