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TRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response

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dc.contributor.authorKruijff-Korbayová, Ivana
dc.contributor.authorColas, Francis
dc.contributor.authorGianni, Mario
dc.contributor.authorPirri, Fiora
dc.contributor.authorGreeff, Joachim
dc.contributor.authorHindriks, Koen
dc.contributor.authorNeerincx, Mark
dc.contributor.authorÖgren, Petter
dc.contributor.authorSvoboda, Tomáš
dc.contributor.authorWorst, Rainer
dc.date.accessioned2018-01-08T09:17:45Z
dc.date.available2018-01-08T09:17:45Z
dc.date.issued2015
dc.description.abstractThis paper describes the project TRADR: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response. Experience shows that any incident serious enough to require robot involvement will most likely involve a sequence of sorties over several hours, days and even months. TRADR focuses on the challenges that thus arise for the persistence of environment models, multi-robot action models, and human-robot teaming, in order to allow incremental capability improvement over the duration of a mission. TRADR applies a user centric design approach to disaster response robotics, with use cases involving the response to a medium to large scale industrial accident by teams consisting of human rescuers and several robots (both ground and airborne). This paper describes the fundamentals of the project: the motivation, objectives and approach in contrast to related work.
dc.identifier.pissn1610-1987
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/11452
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 29, No. 2
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectDisaster response robotics
dc.subjectPersistent environment models
dc.subjectPersistent human-robot teaming
dc.subjectPersistent multi-robot action models
dc.subjectPersistent multi-robot collaboration models
dc.subjectUser-centric design
dc.titleTRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response
dc.typeText/Journal Article
gi.citation.endPage201
gi.citation.startPage193

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Künstliche Intelligenz 29(2) - Mai 2015