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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorKhaldi, Belkacem
dc.contributor.authorSun, Ying
dc.contributor.authorCherif, Foudil
dc.date.accessioned2019-07-21T13:05:46Z
dc.date.available2019-07-21T13:05:46Z
dc.date.issued2018-10
dc.identifier.citationHarrou, F., Khaldi, B., Sun, Y., & Cherif, F. (2018). Statistical detection of faults in swarm robots under noisy conditions. 2018 6th International Conference on Control Engineering & Information Technology (CEIT). doi:10.1109/ceit.2018.8751862
dc.identifier.doi10.1109/CEIT.2018.8751862
dc.identifier.urihttp://hdl.handle.net/10754/656138
dc.description.abstractFault detection plays an important role in supervising the operation of robotic swarm systems. If faults are not detected, they can considerably affect the performance of the robot swarm. In this paper, we present a robust fault detection mechanism against noise and uncertainties in data, by merging the multiresolution representation of data using wavelets with the sensitivity to small changes of an exponentially weighted moving average scheme. Specifically, to monitor swarm robotics systems performing a virtual viscoelastic control model for circle formation task, the proposed mechanism is applied to the uncorrelated residuals form principal component analysis model. Monitoring results using a simulation data from ARGoS simulator demonstrate that the proposed method achieves improved fault detection performances compared with the conventional approach.
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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8751862/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8751862
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.titleStatistical detection of faults in swarm robots under noisy conditions
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.contributor.departmentStatistics Program
dc.conference.date25-27 Oct. 2018
dc.conference.name2018 6th International Conference on Control Engineering & Information Technology (CEIT)
dc.conference.locationIstanbul, Turkey
dc.eprint.versionPost-print
dc.contributor.institutionLESIA Laboratory, University of Mohamed Khider, Biskra, 07000, Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberCRG4-258
refterms.dateFOA2019-07-22T12:42:49Z


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