{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:21:38Z","timestamp":1784737298253,"version":"3.55.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,5,23]],"date-time":"2021-05-23T00:00:00Z","timestamp":1621728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,23]],"date-time":"2021-05-23T00:00:00Z","timestamp":1621728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J of Soc Robotics"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>To achieve robot navigation in crowded environments having high densities of moving people, it is insufficient to simply consider humans as moving obstacles and avoid collisions with them. That is, the impact of an approaching robot on human movements must be considered as well. Moreover, various navigation methods have been tested in their own environments in the literature, which made them difficult to compare with one another. Thus, we propose an autonomous robot navigation method in densely crowded environments for data-based predictions of robot-human interactions, together with a reproducible experimental test under controlled conditions. Based on localized positional relationships with humans, this method extracts multiple alternative paths, which can implement either following or avoidance, and selects an optimal path based on time efficiency. Each path is selected using neural networks, and the various paths are evaluated by predicting the position after a given amount of time has elapsed. These positions are then used to calculate the time required to reach a certain target position to ensure that the optimal path can be determined. We trained the predictor using simulated data and conducted experiments using an actual mobile robot in an environment where humans were walking around. Using our proposed method, collisions were avoided more effectively than when conventional navigation methods were used, and navigation was achieved with good time efficiency, resulting in an overall reduction in interference with humans. Thus, the proposed method enables an effective navigation in a densely crowded environment, while collecting human-interaction experience for further improvement of its performance in the future.<\/jats:p>","DOI":"10.1007\/s12369-021-00791-9","type":"journal-article","created":{"date-parts":[[2021,5,23]],"date-time":"2021-05-23T13:02:27Z","timestamp":1621774947000},"page":"373-387","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Robot Navigation Based on Predicting of Human Interaction and its Reproducible Evaluation in a Densely Crowded Environment"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0189-0060","authenticated-orcid":false,"given":"Yuichi","family":"Kobayashi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Sugimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuhito","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Shimomura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco J.","family":"Arjonilla Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chyon Hae","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidenori","family":"Yabushita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takahiro","family":"Toda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,23]]},"reference":[{"key":"791_CR1","doi-asserted-by":"crossref","unstructured":"Triebel\u00a0R, Arras\u00a0K, Alami\u00a0R, et\u00a0al (2016) SPENCER: a socially aware service robot for passenger guidance and help in busy airports. In: David\u00a0SW, Timothy\u00a0DB (eds) Field and service robotics, vol. 11. Springer, Cham, pp 607\u2013622","DOI":"10.1007\/978-3-319-27702-8_40"},{"key":"791_CR2","doi-asserted-by":"crossref","unstructured":"Jafari\u00a0OH, Mitzel\u00a0D, Leibe\u00a0B (2014) Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras. In: Proceedings of 2014 IEEE international conference on robotics and automation. IEEE, pp 5636\u20135643","DOI":"10.1109\/ICRA.2014.6907688"},{"key":"791_CR3","doi-asserted-by":"crossref","unstructured":"Durrant\u00a0WH, Bailey\u00a0T (2006) Simultaneous localization and mapping (SLAM): part I, the essential algorithms. In: Bram V (ed) IEEE robots and automation magazine, vol. 13. IEEE, pp 99-110","DOI":"10.1109\/MRA.2006.1638022"},{"key":"791_CR4","unstructured":"Berg\u00a0V, Lin\u00a0M, Manocha\u00a0D (2008) Reciprocal velocity obstacles for real-time multi-agent navigation. Robotics and automation. In: Proceedings of 2008 IEEE international conference on robotics and automation. IEEE, pp 1928\u20131935"},{"key":"791_CR5","doi-asserted-by":"crossref","unstructured":"Kim\u00a0S, Guy\u00a0SJ, Liu\u00a0W, et\u00a0al (2013) Predicting pedestrian trajectories using velocity-space reasoning. In: Emilio\u00a0F, Tomas\u00a0LP, et\u00a0al (eds) Algorithmic foundations of robotics X, vol. 86. Springer, Berlin, Heidelberg, pp 609\u2013623","DOI":"10.1007\/978-3-642-36279-8_37"},{"key":"791_CR6","doi-asserted-by":"crossref","unstructured":"Trautman\u00a0P, Krause\u00a0A (2010) Unfreezing the robot: navigation in dense, interacting crowds. In: Proceedings of 2010 IEEE\/RSJ international conference on intelligent robots and systems. IEEE, pp 797\u2013803","DOI":"10.1109\/IROS.2010.5654369"},{"key":"791_CR7","doi-asserted-by":"crossref","unstructured":"Mehta\u00a0G, Ferrer\u00a0G, Olson\u00a0E (2016) Autonomous navigation in dynamic social environments using multi-policy decision making. In: Proceedings of 2016 IEEE\/RSJ international conference on intelligent robots and systems. IEEE, pp 1190\u20131197","DOI":"10.1109\/IROS.2016.7759200"},{"key":"791_CR8","doi-asserted-by":"crossref","unstructured":"Cunningham\u00a0AG, Galceran\u00a0E, Eustice\u00a0RM, et\u00a0al. (2015) MPDM: multipolicy decision-making in dynamic, uncertain environments for autonomous driving. In: Proceeding of 2015 IEEE international conference robots and automation. IEEE, pp 1670\u20131677","DOI":"10.1109\/ICRA.2015.7139412"},{"key":"791_CR9","doi-asserted-by":"crossref","unstructured":"Tamura Y, Terada Y, Yamashita A, Asama H (2013) Modelling behaviour patterns of pedestrians for mobile robot trajectory generation. Int J Adv Robotic Syst 10(8): 1\u201311","DOI":"10.5772\/56668"},{"key":"791_CR10","doi-asserted-by":"crossref","unstructured":"Abbeel\u00a0P, Andrew\u00a0YN (2004) Apprenticeship learning via inverse reinforcement learning. In: Proceedings of 21st international conference on machine learning. ACM, pp 1\u20138","DOI":"10.1145\/1015330.1015430"},{"issue":"11","key":"791_CR11","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1177\/0278364915619772","volume":"35","author":"H Kretzschmar","year":"2016","unstructured":"Kretzschmar H, Spies M, Sprunk C, Burgard W (2016) Socially compliant mobile robot navigation via inverse reinforcement learning. Int J Robot Res 35(11):1289\u20131307","journal-title":"Int J Robot Res"},{"key":"791_CR12","doi-asserted-by":"crossref","unstructured":"Mnih\u00a0V, Kavukcuoglu\u00a0K, Silver\u00a0D, et\u00a0al (2015) Human-level control throughdeep reinforcement learning. Nature 518(7540): 529\u2013533","DOI":"10.1038\/nature14236"},{"key":"791_CR13","doi-asserted-by":"crossref","unstructured":"Chen\u00a0Y, Liu\u00a0M, Everett\u00a0M, et\u00a0al (2017) Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. In: Proceedings of 2017 IEEE international conference on robotics and automation. IEEE, pp 285\u2013292","DOI":"10.1109\/ICRA.2017.7989037"},{"key":"791_CR14","doi-asserted-by":"crossref","unstructured":"Trautman P, et\u00a0al (2015) Robot navigation in dense human crowds: statistical models and experimental studies of human-robot cooperation. Int J Robot Res 34(3): 335\u2013356 (John\u00a0MH (ed))","DOI":"10.1177\/0278364914557874"},{"key":"791_CR15","doi-asserted-by":"crossref","unstructured":"Luo Y, Cai P, Bera A, Hsu D, Lee WS, Manocha D (2018) PORCA: modeling and planning for autonomous driving among many pedestrians. IEEE Robot Autom Lett (RA-L) 3(4): 3418\u20133425","DOI":"10.1109\/LRA.2018.2852793"},{"key":"791_CR16","doi-asserted-by":"crossref","unstructured":"Bando\u00a0M, Hasebe\u00a0K, Nakayama\u00a0A, et al (1994) Structure stability of congestion in traffic dynamics. Jpn J Indus Appl Math 11(2): 203\u2013223 (Yamaguchi M et al (eds)), Springer-Verlag","DOI":"10.1007\/BF03167222"},{"key":"791_CR17","doi-asserted-by":"crossref","unstructured":"Tordeux A, Schadschneider A (2016) A stochastic optimal velocity model for pedestrian flow. In: Wyrzykowski R, Deelman E, Dongarra J, Karczewski K, Kitowski J, Wiatr K (eds) Parallel processing and applied mathematics. Lecture notes in computer science, vol 9574. Springer","DOI":"10.1007\/978-3-319-32152-3_49"},{"key":"791_CR18","unstructured":"http:\/\/wiki.ros.org\/simulator_stage"},{"key":"791_CR19","unstructured":"http:\/\/wiki.ros.org\/leg_detector"},{"key":"791_CR20","unstructured":"https:\/\/sensor.eng.shizuoka.ac.jp\/~koba\/crowdednavigation.html"},{"key":"791_CR21","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s12293-012-0076-0","volume":"4","author":"J Botzheim","year":"2012","unstructured":"Botzheim J, Toda Y, Kubota N (2012) Bacterial memetic algorithm for offline path planning of mobile robots. Mimetic Comput 4:73\u201386","journal-title":"Mimetic Comput"},{"key":"791_CR22","doi-asserted-by":"crossref","unstructured":"Cs\u00edk \u00c1, Botzheim J, Bal\u00e1zs J, Csoknyai T, Hontv\u00e1ri JL (2012) Energy and cost optimal design for the reconstruction of residential building envelopes by bacterial memetic algorithms. In: Proceedings of the 6th international conference on soft computing and intelligent systems, and the 13th international symposium on advanced intelligence systems, pp. 1226\u20131231","DOI":"10.1109\/SCIS-ISIS.2012.6505181"},{"key":"791_CR23","doi-asserted-by":"crossref","unstructured":"Morales Y, Akai N, Murase H (2018) Personal mobility vehicle autonomous navigation through pedestrian flow: a data driven approach for parameter extraction. In: 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS 2018)","DOI":"10.1109\/IROS.2018.8593902"},{"key":"791_CR24","doi-asserted-by":"publisher","unstructured":"Bera A, Randhavane T, Prinja R, Manocha D (2017) SocioSense: robot navigation amongst pedestrians with social and psychological constraints. In: 2017 IEEE\/RSJ international conference on intelligent robots and systems (IROS). Vancouver, BC, pp 7018\u20137025. https:\/\/doi.org\/10.1109\/IROS.2017.8206628","DOI":"10.1109\/IROS.2017.8206628"},{"key":"791_CR25","doi-asserted-by":"crossref","unstructured":"van den Berg J, Guy Stephen J, Ming L, Dinesh M (2011) Reciprocal n-body collision avoidance, robotics research. In: Pradalier C, Siegwart R, Hirzinger G (eds) The 14th international symposium ISRR, Springer tracts in advanced robotics, vol. 70. Springer-Verlag, pp 3\u201319","DOI":"10.1007\/978-3-642-19457-3_1"},{"key":"791_CR26","first-page":"2121","volume":"12","author":"D John","year":"2011","unstructured":"John D, Elad H, Yoram S (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"}],"container-title":["International Journal of Social Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12369-021-00791-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12369-021-00791-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12369-021-00791-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:03:53Z","timestamp":1648598633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12369-021-00791-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,23]]},"references-count":26,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["791"],"URL":"https:\/\/doi.org\/10.1007\/s12369-021-00791-9","relation":{},"ISSN":["1875-4791","1875-4805"],"issn-type":[{"value":"1875-4791","type":"print"},{"value":"1875-4805","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,23]]},"assertion":[{"value":"26 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}