{"id":209838,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00209838","sets":["1164:4619:10416:10532"]},"path":["10532"],"owner":"44499","recid":"209838","title":["Switch State Detection by MSRS and YOLOv4 and Automatic Switch Operation with a Robot Arm by Reinforcement Learning in Virtual Environments"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-02-25"},"_buckets":{"deposit":"6f19b988-f815-49dd-9cee-8635b80af0ea"},"_deposit":{"id":"209838","pid":{"type":"depid","value":"209838","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Switch State Detection by MSRS and YOLOv4 and Automatic Switch Operation with a Robot Arm by Reinforcement Learning in Virtual Environments","author_link":["530091","530093","530090","530095","530092","530094"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Switch State Detection by MSRS and YOLOv4 and Automatic Switch Operation with a Robot Arm by Reinforcement Learning in Virtual Environments"},{"subitem_title":"Switch State Detection by MSRS and YOLOv4 and Automatic Switch Operation with a Robot Arm by Reinforcement Learning in Virtual Environments","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セッション5-2","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-02-25","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Faculty of Science and Engineering, Waseda University"},{"subitem_text_value":"Faculty of Science and Engineering, Waseda University"},{"subitem_text_value":"Faculty of Science and Technology, Seikei University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Technology, Seikei University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/209838/files/IPSJ-CVIM21225040.pdf","label":"IPSJ-CVIM21225040.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM21225040.pdf","filesize":[{"value":"1.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"da690e33-dbb3-4e31-bc18-08e3dfb0743b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Li, Qi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jun, Ohya"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Ogata"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Li, Qi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jun, Ohya","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Ogata","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"This paper proposes a method for detecting switch states and operating the switch using arms of disaster response robots, which are expected to be widely used for handling dangerous tasks caused by disasters. To enable YOLOv4 to detect switches with various scales, the training dataset is built by a Multi-Scale Reduction Stitching (MSRS) data enhancement algorithm, which reduces two original images into multi scales and stitch them in one new image; thereby, the new image contains half, quarter and one eighth of the original scale of the switch. The YOLOV4, which is trained by the dataset built by MSRS, detects the switch state. Based on the result of the switch detection, arms of a four-leg robot (in this paper, D’Kitty) operate the switch by reinforcement learning. As a result of exploring effectiveness of different reinforcement learning algorithms for the task of operating switches, it turns out that PPO gives the best performance. In a simulated environment, the success rate for fixed positions of the switch and robot is 97%, and for small-scale random positions is higher than 90%.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper proposes a method for detecting switch states and operating the switch using arms of disaster response robots, which are expected to be widely used for handling dangerous tasks caused by disasters. To enable YOLOv4 to detect switches with various scales, the training dataset is built by a Multi-Scale Reduction Stitching (MSRS) data enhancement algorithm, which reduces two original images into multi scales and stitch them in one new image; thereby, the new image contains half, quarter and one eighth of the original scale of the switch. The YOLOV4, which is trained by the dataset built by MSRS, detects the switch state. Based on the result of the switch detection, arms of a four-leg robot (in this paper, D’Kitty) operate the switch by reinforcement learning. As a result of exploring effectiveness of different reinforcement learning algorithms for the task of operating switches, it turns out that PPO gives the best performance. In a simulated environment, the success rate for fixed positions of the switch and robot is 97%, and for small-scale random positions is higher than 90%.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-02-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"40","bibliographicVolumeNumber":"2021-CVIM-225"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:22:27.757807+00:00","created":"2025-01-19T01:11:07.334322+00:00","links":{}}