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To err is robotic, to tolerate immunological: fault detection in multirobot systems

2015-02-02Journal article Open access
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dc.contributor.authorTarapore, Danesh
dc.contributor.authorLima, Pedro U
dc.contributor.authorCarneiro, Jorge
dc.contributor.authorChristensen, Anders Lyhne
dc.date.accessioned2016-04-26T15:53:48Z
dc.date.available2016-04-26T15:53:48Z
dc.date.issued2015-02-02
dc.description.abstractFault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.pt_PT
dc.identifier.citationTarapore, D., Lima, P. U., Carneiro, J., Christensen, A. L. (2015). To err is robotic, to tolerate immunological: fault detection in multirobot systems. Bioinspir. Biomim., 10(1), 16014.pt_PT
dc.identifier.doi10.1088/1748-3190/10/1/016014pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.7/584
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIOP Publishingpt_PT
dc.relationFrom Bio-Inspired to Institutional-Inspired Collective Robotics
dc.relation.publisherversionhttp://iopscience.iop.org/article/10.1088/1748-3190/10/1/016014/meta;jsessionid=CB8C92D732FD383872B8CC2C13879819.c3.iopscience.cld.iop.orgpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAdaptive Immunitypt_PT
dc.subjectAlgorithmspt_PT
dc.subjectAnimalspt_PT
dc.subjectBiomimeticspt_PT
dc.subjectComputer Simulationpt_PT
dc.subjectEquipment Failure Analysispt_PT
dc.subjectHumanspt_PT
dc.subjectRoboticspt_PT
dc.subjectEquipment Failurept_PT
dc.subjectModels, Immunologicalpt_PT
dc.titleTo err is robotic, to tolerate immunological: fault detection in multirobot systemspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleFrom Bio-Inspired to Institutional-Inspired Collective Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEEA-CRO%2F104658%2F2008/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PEst-OE%2FEEI%2FLA0009%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FEEI-AUT%2F0329%2F2013/PT
oaire.citation.endPage43pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleBioinspiration & Biomimeticspt_PT
oaire.citation.volume10pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isProjectOfPublication77d1f5d3-a821-4cbb-9798-5166478ea7eb
relation.isProjectOfPublicationa730b5b9-5bda-4214-a738-c7098498c7c1
relation.isProjectOfPublication6caac087-87ac-48ca-8941-a926e2d24078
relation.isProjectOfPublication.latestForDiscovery77d1f5d3-a821-4cbb-9798-5166478ea7eb

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