{"created":"2025-01-19T01:29:49.398175+00:00","updated":"2025-01-19T11:14:46.094972+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230205","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230205","title":["深度画像を用いたロボットナビゲーションにおけるDeep Q-Networkの複数環境同時学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"61574ede-653b-4997-b44d-4e5527f65c0d"},"_deposit":{"id":"230205","pid":{"type":"depid","value":"230205","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深度画像を用いたロボットナビゲーションにおけるDeep Q-Networkの複数環境同時学習","author_link":["619356","619355"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深度画像を用いたロボットナビゲーションにおけるDeep Q-Networkの複数環境同時学習"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東海大"},{"subitem_text_value":"東海大"}]},"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/230205/files/IPSJ-Z85-5W-06.pdf","label":"IPSJ-Z85-5W-06.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-5W-06.pdf","filesize":[{"value":"1.3 MB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"5a95314a-3285-4c55-89b1-70713c36caaf","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"押鐘, 悠貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"尾関, 智子"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"深層強化学習を用いたロボットナビゲーションの多くは，入力として2D-LiDARを用いている．本研究は，深度画像を入力としたナビゲーションの精度向上を目指す．シミュレータはGazebo，ロボットはJetson Nano Mouseを用い，通信制御方法としてROSを用いる．学習には，Deep Q-Networkを用い，任意の始点から終点までの自律走行を学習させる．その際，複数のロボットを用意し，異なる障害物を有した異なる環境を同時に学習させることで，汎化性能の向上を図る．推論時はロボットの自己位置推定のみに2D-LiDARを用い，障害物回避と走路計画にシミュレーション学習させた学習済みモデルを用いる．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"840","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"839","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230205,"links":{}}