DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Deok Hwa | ko |
dc.contributor.author | Park, Gyeongmoon | ko |
dc.contributor.author | Yoo, Yong Ho | ko |
dc.contributor.author | Ryu, Si Jung | ko |
dc.contributor.author | Jeong, Inbae | ko |
dc.contributor.author | Kim, Jong-Hwan | ko |
dc.date.accessioned | 2018-01-22T02:06:28Z | - |
dc.date.available | 2018-01-22T02:06:28Z | - |
dc.date.created | 2017-11-28 | - |
dc.date.created | 2017-11-28 | - |
dc.date.issued | 2017-10 | - |
dc.identifier.citation | ANNUAL REVIEWS IN CONTROL, v.44, pp.9 - 18 | - |
dc.identifier.issn | 1367-5788 | - |
dc.identifier.uri | http://hdl.handle.net/10203/237199 | - |
dc.description.abstract | In 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.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | PROBABILISTIC ROADMAPS | - |
dc.title | Realization of task intelligence for service robots in an unstructured environment | - |
dc.type | Article | - |
dc.identifier.wosid | 000416184700002 | - |
dc.identifier.scopusid | 2-s2.0-85030655904 | - |
dc.type.rims | ART | - |
dc.citation.volume | 44 | - |
dc.citation.beginningpage | 9 | - |
dc.citation.endingpage | 18 | - |
dc.citation.publicationname | ANNUAL REVIEWS IN CONTROL | - |
dc.identifier.doi | 10.1016/j.arcontrol.2017.09.013 | - |
dc.contributor.localauthor | Kim, Jong-Hwan | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Review | - |
dc.subject.keywordAuthor | Task intelligence | - |
dc.subject.keywordAuthor | Episodic memory | - |
dc.subject.keywordAuthor | Deep ART network | - |
dc.subject.keywordAuthor | Motion planning | - |
dc.subject.keywordAuthor | Task planner | - |
dc.subject.keywordPlus | PROBABILISTIC ROADMAPS | - |
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