Realization of task intelligence for service robots in an unstructured environment

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dc.contributor.authorKim, Deok Hwako
dc.contributor.authorPark, Gyeongmoonko
dc.contributor.authorYoo, Yong Hoko
dc.contributor.authorRyu, Si Jungko
dc.contributor.authorJeong, Inbaeko
dc.contributor.authorKim, Jong-Hwanko
dc.date.accessioned2018-01-22T02:06:28Z-
dc.date.available2018-01-22T02:06:28Z-
dc.date.created2017-11-28-
dc.date.created2017-11-28-
dc.date.issued2017-10-
dc.identifier.citationANNUAL REVIEWS IN CONTROL, v.44, pp.9 - 18-
dc.identifier.issn1367-5788-
dc.identifier.urihttp://hdl.handle.net/10203/237199-
dc.description.abstractIn order to perform various tasks using a robot in a real environment, it is necessary to learn the tasks based on recognition, to be able to derive a task sequence suitable for the situation, and to be able to generate a behavior adaptively. To deal with this issue, this paper proposes a system for realizing task intelligence having a memory module motivated by human episodic memory, and a task planning module to resolve the current situation. In addition, this paper proposes a technique that can modify demonstrated trajectories according to current robot states and recognized target positions in order to perform the determined task sequence, as well as a technique that can generate the modified trajectory without collisions with surrounding obstacles. The effectiveness and applicability of the task intelligence are demonstrated through experiments with Mybot, a humanoid robot developed in the Robot Intelligence Technology Laboratory at KAIST.-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectPROBABILISTIC ROADMAPS-
dc.titleRealization of task intelligence for service robots in an unstructured environment-
dc.typeArticle-
dc.identifier.wosid000416184700002-
dc.identifier.scopusid2-s2.0-85030655904-
dc.type.rimsART-
dc.citation.volume44-
dc.citation.beginningpage9-
dc.citation.endingpage18-
dc.citation.publicationnameANNUAL REVIEWS IN CONTROL-
dc.identifier.doi10.1016/j.arcontrol.2017.09.013-
dc.contributor.localauthorKim, Jong-Hwan-
dc.description.isOpenAccessN-
dc.type.journalArticleReview-
dc.subject.keywordAuthorTask intelligence-
dc.subject.keywordAuthorEpisodic memory-
dc.subject.keywordAuthorDeep ART network-
dc.subject.keywordAuthorMotion planning-
dc.subject.keywordAuthorTask planner-
dc.subject.keywordPlusPROBABILISTIC ROADMAPS-
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