{"_buckets": {"deposit": "2dd49252-6706-4380-91b8-3f9690f80255"}, "_deposit": {"id": "10259", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "10259"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00010259", "sets": ["503"]}, "author_link": ["31084", "31085", "31086"], "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2008-09", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "3432", "bibliographicPageStart": "3426", "bibliographic_titles": [{"bibliographic_title": "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)", "bibliographic_titleLang": "en"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "This paper proposes an optimal gait generation framework using virtual constraint and learning optimal control. In this method, firstly, we add a constraint by a virtual potential energy to prevent the robot from falling. Secondly, we execute iterative learning control (ILC) to generate an optimal feedforward input. Thirdly, we execute iterative feedback tuning (IFT) to mitigate the strength of the virtual constraint  automatically according to the progress of learning control. Consequently, it is expected to generate an optimal gait without constraint eventually. Although existing ILC frameworks require a lot of experimental data under the same initial condition, the proposed method does not need to repeat experiments under the same initial condition because the virtual constraint restricts the motion of the robot to a symmetric  trajectory. Furthermore, it does not require the precise knowledge of the plant system. Finally, some numerical simulations demonstrate the effectiveness of the proposed method.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://dx.doi.org/10.1109/IROS.2008.465086"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/12079"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "IEEE", "subitem_publisher_language": "en"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1109/IROS.2008.465086", "subitem_relation_type_select": "DOI"}}]}, "item_10_rights_12": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "Copyright © 2008 IEEE. Reprinted from IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008. IROS 2008. p.3426-3432.\u003cbr/\u003eThis material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.", "subitem_rights_language": "en"}]}, "item_10_select_15": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_10_text_14": {"attribute_name": "フォーマット", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_1615787544753": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_ab4af688f83e57aa", "subitem_version_type": "AM"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Satoh, Satoshi", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "31084", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Fujimoto, Kenji", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "31085", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Hyon, Sang-Ho", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "31086", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "iros08.pdf", "filesize": [{"value": "1.2 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 1200000.0, "url": {"label": "iros08.pdf", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/10259/files/iros08.pdf"}, "version_id": "93ab04ee-82d2-4dc1-9ff1-334c2a250e69"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "A framework for optimal gait generation via learning optimal control using virtual constraint", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "A framework for optimal gait generation via learning optimal control using virtual constraint", "subitem_title_language": "en"}]}, "item_type_id": "10", "owner": "1", "path": ["503"], "permalink_uri": "http://hdl.handle.net/2237/12079", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2009-08-25"}, "publish_date": "2009-08-25", "publish_status": "0", "recid": "10259", "relation": {}, "relation_version_is_last": true, "title": ["A framework for optimal gait generation via learning optimal control using virtual constraint"], "weko_shared_id": -1}