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Deep Learning-based Target Following and Obstacle Avoidance Methods in Mobile Robots

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dc.contributor.authorLee, M.C.-
dc.contributor.authorLee, M.-
dc.date.accessioned2024-01-09T04:33:59Z-
dc.date.available2024-01-09T04:33:59Z-
dc.date.issued2022-10-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/69898-
dc.description.abstractIn this study, we develop a companion robot function that can recognize people and obstacles, and track the human target while avoiding the obstacles. In implementing the JetBot tracking function, a deep learning-based object detection model is modified and utilized. A transfer learning algorithm with a pretrained mobile model is introduced for a feasible operation in the proposed framework with mobile robots.-
dc.language영어-
dc.language.isoENG-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleDeep Learning-based Target Following and Obstacle Avoidance Methods in Mobile Robots-
dc.typeArticle-
dc.identifier.doi10.1109/ICCE-Asia57006.2022.9954653-
dc.identifier.bibliographicCitation2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022-
dc.description.isOpenAccessN-
dc.identifier.scopusid2-s2.0-85143847529-
dc.citation.title2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022-
dc.type.docTypeConference Paper-
dc.subject.keywordAuthorcompanion robot-
dc.subject.keywordAuthormobile robot-
dc.subject.keywordAuthorobject avoidance-
dc.subject.keywordAuthorobject detection and classification-
dc.subject.keywordAuthorobject tracking-
dc.description.journalRegisteredClassscopus-
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