{"created":"2025-01-19T01:14:16.127469+00:00","updated":"2025-01-19T17:10:49.976272+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213387","sets":["6164:6165:7006:10727"]},"path":["10727"],"owner":"44499","recid":"213387","title":["強化学習に基づく自律移動ロボットナビゲーション用シミュレータの設計"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-18"},"_buckets":{"deposit":"ff6615ef-bce6-4b15-907e-32de17decd6f"},"_deposit":{"id":"213387","pid":{"type":"depid","value":"213387","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"強化学習に基づく自律移動ロボットナビゲーション用シミュレータの設計","author_link":["545915","545914"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"強化学習に基づく自律移動ロボットナビゲーション用シミュレータの設計"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"自律移動ロボット，強化学習，データセット，シミュレーション，歩行者移動モデル","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-10-18","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京女子大学大学院理学研究科"},{"subitem_text_value":"東京女子大学大学院理学研究科"}]},"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/213387/files/IPSJ-DPSWS2021034.pdf","label":"IPSJ-DPSWS2021034.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-18"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DPSWS2021034.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"34"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c34988d4-afe9-4360-9645-9f9acf1967d1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"田中, 碧"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"加藤, 由花"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"人・ロボット共存環境で動作する自律移動ロボットへの期待が高まっている．これらのロボットでは，安全かつ効率的な経路計画が重要な研究課題となっており，我々もこれまで，歩行者が存在する動的環境を対象に，強化学習を用いたナビゲーション手法の研究開発を進めてきた．ロボットを対象とした強化学習モデルの訓練には，高性能なシミュレータが用いられるのが一般的である．しかし，複雑な構造を持つリアルなシミュレータの開発は難しく，簡易なシミュレータで効率的に学習を進める手法の開発が望まれる．本稿では，強化学習に基づく自律移動ロボットナビゲーションを対象に，簡易なレイアウト環境を大量に用意することで，高精度な学習を実現するシミュレータを設計する．ここでは，歩行者モデルをシミュレータに組み込むことで，人と共存する環境に対応する．本稿では，シミュレータの設計結果を示す．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"229","bibliographic_titles":[{"bibliographic_title":"第29回マルチメディア通信と分散処理ワークショップ論文集"}],"bibliographicPageStart":"227","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-18","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213387,"links":{}}