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산업용 로봇 원격제어를 위한 CNN기반 손 제스처 인식 방법

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dc.contributor.author전세윤-
dc.contributor.author김은수-
dc.contributor.author박범용-
dc.date.accessioned2021-05-10T07:40:18Z-
dc.date.available2021-05-10T07:40:18Z-
dc.date.created2021-05-10-
dc.date.issued2021-02-
dc.identifier.issn1975-5066-
dc.identifier.urihttps://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/19310-
dc.description.abstractThis paper introduces a teleoperation control system of an industrial robot based on hand gestures using the convolutional neural network (CNN). The proposed system employs the gesture data obtained from an EMG sensor and considers a CNN-based deep learning method. Using the proposed CNN model, we develop a real-time teleoperation control system for the industrial robot. Finally, it is confirmed that the proposed system is reliable in real system since it can be applied to the teleoperation control of a real industrial robot.-
dc.language한국어-
dc.language.isoko-
dc.publisher대한임베디드공학회-
dc.title산업용 로봇 원격제어를 위한 CNN기반 손 제스처 인식 방법-
dc.title.alternativeCNN-based Hand Gesture Recognition Method for Teleoperation Control of Industrial Robot-
dc.typeArticle-
dc.contributor.affiliatedAuthor전세윤-
dc.contributor.affiliatedAuthor김은수-
dc.contributor.affiliatedAuthor박범용-
dc.identifier.doi10.14372/IEMEK.2021.16.2.65-
dc.identifier.bibliographicCitation대한임베디드공학회논문지, v.16, no.2, pp.65 - 72-
dc.relation.isPartOf대한임베디드공학회논문지-
dc.citation.title대한임베디드공학회논문지-
dc.citation.volume16-
dc.citation.number2-
dc.citation.startPage65-
dc.citation.endPage72-
dc.type.rimsART-
dc.identifier.kciidART002710893-
dc.description.journalClass2-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorIndustrial robot-
dc.subject.keywordAuthorROS-
dc.subject.keywordAuthorEMG-
dc.subject.keywordAuthorTeleoperation-
dc.subject.keywordAuthorCNN-
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