{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:05:24Z","timestamp":1766156724864,"version":"3.48.0"},"reference-count":10,"publisher":"Fuji Technology Press Ltd.","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JRM","J. Robot. Mechatron."],"published-print":{"date-parts":[[2025,12,20]]},"abstract":"<jats:p>Most of the developed mobile robots are equipped with LiDAR sensors and use SLAM for localization to move autonomously. \t \tMoreover, when operating a mobile robot using SLAM, changing the route requires specialized knowledge, and not everyone can operate it. \t \tMobile robots are expected to patrol a predetermined route within a closed indoor space, such as security guards or transporting goods. \t \tIn such cases, autonomous traveling is possible if the robot can recognize the route it is traveling without creating a map of the environment, as with SLAM. \t \tIn this study, we developed an autonomous control system that uses camera images mounted on a mobile robot to calculate a straight path based on the distinct recognition of floor and wall surfaces using instance segmentation with deep learning. \t \tAccording to the result that the recognition area of the floor surface expanded in the direction of the branching in the branched path, it was determined that the branching was possible. \t \tIn addition, when traveling along a path with a wall in front of it, a problem occurred because the target path could not be generated owing to the loss of the floor surface in the recognition results. \t \tTherefore, we recognized the wall surface using point-cloud processing and generated a target path. \t \tThe proposed system allows a mobile robot to autonomously patrol a simple route, such as a corridor, by simply specifying the patrol path, such as straight ahead or turning left or right. \t \tAutonomous running experiments were conducted on a mobile robot in a corridor, including a junction point, to verify the effectiveness of the proposed system. \t \tThis method allows autonomous route patrolling by a mobile robot indoors, without requiring specialized knowledge, such as SLAM, and can also be used to change routes.<\/jats:p>","DOI":"10.20965\/jrm.2025.p1293","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:02:07Z","timestamp":1766156527000},"page":"1293-1303","source":"Crossref","is-referenced-by-count":0,"title":["Autonomous Method for a Mobile Robot in a Corridor Using Only a Depth Camera that Recognizes the Floor and Wall"],"prefix":"10.20965","volume":"37","author":[{"given":"Tomoki","family":"Sugimoto","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Science and Engineering, Saga University, 1 Honjo, Saga, Saga 840-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naohiro","family":"Sugita","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Science and Engineering, Saga University, 1 Honjo, Saga, Saga 840-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuya","family":"Sato","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Science and Engineering, Saga University, 1 Honjo, Saga, Saga 840-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2025,12,20]]},"reference":[{"key":"key-10.20965\/jrm.2025.p1293-1","unstructured":"S. Nahavandi, R. Alizadehsani, D. Nahavandi, S. Mohamed, N. Mohajer, M. Rokonuzzaman, and I. Hossain, \u201cA Comprehensive Review on Autonomous Navigation,\u201d arXiv preprint, arXiv:2212.1280, 2022. https:\/\/doi.org\/10.48550\/arXiv.2212.12808"},{"key":"key-10.20965\/jrm.2025.p1293-2","doi-asserted-by":"crossref","unstructured":"L. Sun, R. P. Singh, and F. Kanehiro, \u201cVisual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments,\u201d J. Robot. Mechatron., Vol.33, No.6, pp. 1385-1397, 2021. https:\/\/doi.org\/10.20965\/jrm.2021.p1385","DOI":"10.20965\/jrm.2021.p1385"},{"key":"key-10.20965\/jrm.2025.p1293-3","doi-asserted-by":"crossref","unstructured":"T. Shimoda, S. Koga, and K. Sato, \u201cAutonomous Motion Control of a Mobile Robot Using Marker Recognition via Deep Learning in GPS-Denied Environments,\u201d J. Robot. and Mechatron., Vol.35, No.1, pp. 136-144, 2023. https:\/\/doi.org\/10.20965\/jrm.2023.p0136","DOI":"10.20965\/jrm.2023.p0136"},{"key":"key-10.20965\/jrm.2025.p1293-4","doi-asserted-by":"crossref","unstructured":"R. Miyamoto, M. Adachi, H. Ishida, T. Watanabe, K. Matsutani, H. Komatsuzaki, S. Sakata, R. Yokota, and S. Kobayashi, \u201cVisual Navigation Based on Semantic Segmentation Using Only a Monocular Camera as an External Sensor,\u201d J. Robot. Mechatron., Vol.32, No.6, pp. 1137-1153, 2020. https:\/\/doi.org\/10.20965\/jrm.2020.p1137","DOI":"10.20965\/jrm.2020.p1137"},{"key":"key-10.20965\/jrm.2025.p1293-5","doi-asserted-by":"crossref","unstructured":"M. Adachi, K. Honda, and R. Miyamoto, \u201cTurning at Intersections Using Virtual LiDAR Signals Obtained from a Segmentation Result,\u201d J. Robot. Mechatron., Vol.35, No.2, pp. 347-361, 2023. https:\/\/doi.org\/10.20965\/jrm.2023.p0347","DOI":"10.20965\/jrm.2023.p0347"},{"key":"key-10.20965\/jrm.2025.p1293-6","doi-asserted-by":"crossref","unstructured":"M. Wada, Y. Ueda, J. Morioka, M. Adachi, and R. Miyamoto, \u201cDataset Creation for Semantic Segmentation Using Colored Point Clouds Considering Shadows on Traversable Area,\u201d J. Robot. Mechatron., Vol.35, No.6, pp. 1406-1418, 2023. https:\/\/doi.org\/10.20965\/jrm.2023.p1406","DOI":"10.20965\/jrm.2023.p1406"},{"key":"key-10.20965\/jrm.2025.p1293-7","doi-asserted-by":"crossref","unstructured":"M. Adachi, K. Honda, J. Xue, H. Sudo, Y. Ueda, Y. Yuda, M. Wada, and R. Miyamoto, \u201cPractical Implementation of Visual Navigation Based on Semantic Segmentation for Human-Centric Environments,\u201d J. Robot. Mechatron., Vol.35, No.6, pp. 1419-1434, 2023. https:\/\/doi.org\/10.20965\/jrm.2023.p1419","DOI":"10.20965\/jrm.2023.p1419"},{"key":"key-10.20965\/jrm.2025.p1293-8","doi-asserted-by":"crossref","unstructured":"Y. Ueda, M. Adachi, J. Morioka, M. Wada, and R. Miyamoto, \u201cData Augmentation for Semantic Segmentation Using a Real Image Dataset Captured Around the Tsukuba City Hall,\u201d J. Robot. Mechatron., Vol.35, No.6, pp. 1450-1459, 2023. https:\/\/doi.org\/10.20965\/jrm.2023.p1450","DOI":"10.20965\/jrm.2023.p1450"},{"key":"key-10.20965\/jrm.2025.p1293-9","doi-asserted-by":"crossref","unstructured":"D. Bolya, C. Zhou, F. Xiao and Y. J. Lee, \u201cYOLACT: Real-Time Instance Segmentation,\u201d 2019 IEEE\/CVF Int. Conf. on Computer Vision (ICCV), pp. 9157-9166, 2019.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"key-10.20965\/jrm.2025.p1293-10","doi-asserted-by":"crossref","unstructured":"M. A. Fischler and R. C. Bolles, \u201cRandom Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,\u201d Communications of the ACM, Vol.24, No.6, pp. 381-395, 1981. https:\/\/doi.org\/10.1145\/358669.358692","DOI":"10.1145\/358669.358692"}],"container-title":["Journal of Robotics and Mechatronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/hyosetsu.php?ppno=robot003700060002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:02:14Z","timestamp":1766156534000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jrm\/rb\/robot003700061293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,20]]},"references-count":10,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,12,20]]},"published-print":{"date-parts":[[2025,12,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jrm.2025.p1293","relation":{},"ISSN":["1883-8049","0915-3942"],"issn-type":[{"value":"1883-8049","type":"electronic"},{"value":"0915-3942","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12,20]]}}}