{"created":"2025-01-19T01:43:57.347352+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240015","sets":["581:11492:11503"]},"path":["11503"],"owner":"44499","recid":"240015","title":["自律移動ロボットを用いた人流計測のための探索経路生成"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-10-15"},"_buckets":{"deposit":"1b37f03e-4583-4e10-a2cf-1b7a979740e7"},"_deposit":{"id":"240015","pid":{"type":"depid","value":"240015","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"自律移動ロボットを用いた人流計測のための探索経路生成","author_link":["658031","658027","658026","658025","658024","658023","658022","658030","658028","658029"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"自律移動ロボットを用いた人流計測のための探索経路生成"},{"subitem_title":"Search Path Generation for Human Flow Measurement Using Autonomous Mobile Robot","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:ユビキタスコンピューティングシステム（XII）] 自律移動ロボット，人流計測，エリア探索経路生成","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2024-10-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科／名古屋大学未来社会創造機構"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering Nagoya University / Institutes of Innovation for Future Society, Nagoya University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/240015/files/IPSJ-JNL6510007.pdf","label":"IPSJ-JNL6510007.pdf"},"date":[{"dateType":"Available","dateValue":"2026-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6510007.pdf","filesize":[{"value":"4.9 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"19631457-c23d-46b4-9941-f9a0496fee68","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"下里, 浩昇"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"片山, 晋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浦野, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"米澤, 拓郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"河口, 信夫"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hironori, Shimosato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shin, Katayama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenta, Urano","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuro, Yonezawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuo, Kawaguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"人流データの収集は，交通流の改善や公共空間の人流最適化，混雑緩和による災害対策，効果的な避難計画策定，消費者行動分析の観点から重要な役割を果たしている．人流データは，監視カメラや赤外線センサなどの固定センサを使用して収集される．これら固定センサの課題として，センサが未設置のエリアの計測は困難であり，また，カメラやLiDARなどの視覚センサを用いる場合は，人やものの重なりによるオクルージョンが計測誤差を生じさせることがあげられる．本研究ではこれらの課題の解決策として，自律移動ロボットを用いた人流データの収集について考える．自律移動ロボットで人流計測するためには，自律移動ロボット搭載のセンサで人流データの抽出やエリアの探索経路の生成，複数ロボットやセンサなどとの協調などの課題が存在する．本稿では，人流データ抽出とエリア探索経路の生成に着目し，3次元LiDARから得られる点群より人流データを抽出するモジュールの開発と，エリアの計測状況や人の重なりに応じた探索経路アルゴリズムの開発を行った．3DシミュレータのGazeboを用いて人流抽出モジュールの評価をしたところ，約75%の人数計測精度で計測可能で，平均位置誤差は約27cmで抽出が可能だと分かった．またエリアの探索経路を生成するためのモジュールを実装し，人の滞留を模倣したシミュレーション環境内で，一般的な網羅探索アルゴリズムであるCoverage Path Planning（CPP）との比較結果から，提案手法がCPPよりも移動距離を短く抑え計測ができることが分かった．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The collection of pedestrian flow data plays a crucial role in improving traffic flow, optimizing public spaces, alleviating congestion for disaster management, devising effective evacuation plans, and analyzing consumer behavior. Pedestrian data is gathered using sensors such as surveillance cameras and infrared sensors. A challenge with these fixed sensors is the difficulty in measuring areas where sensors are not installed, and visual sensors like cameras may encounter measurement errors due to occlusions caused by overlapping people and objects. This study considers the use of autonomous mobile robots to collect pedestrian data as a solution to these challenges. To measure pedestrian flow using autonomous mobile robots, there are challenges such as extracting pedestrian data using sensors mounted on the robots, generating exploration paths for the areas, and coordinating with multiple robots and sensors. This paper focuses on the development of a module for extracting pedestrian data from point clouds obtained from 3D LiDAR and the development of an exploration path algorithm tailored to the measurement conditions of the area and overlapping people. Evaluation of the pedestrian extraction module using the 3D simulator Gazebo showed that it is possible to measure with about 75% accuracy in counting people, and extraction is possible with an average location error of about 27cm. Additionally, a module for generating exploration paths in the area was implemented, and simulations mimicking human pseudo-dwell showed that the proposed method can reduce travel distances compared to the common Coverage Path Planning (CPP) algorithm, achieving more efficient measurements.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1522","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1511","bibliographicIssueDates":{"bibliographicIssueDate":"2024-10-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"65"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00239895","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":240015,"updated":"2025-01-19T08:04:53.675916+00:00","links":{}}