{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:39:09Z","timestamp":1762609149752,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031246661"},{"type":"electronic","value":"9783031246678"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-24667-8_44","type":"book-chapter","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T09:03:44Z","timestamp":1675155824000},"page":"496-508","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["NRTIRL Based NN-RRT* Path Planner in\u00a0Human-Robot Interaction Environment"],"prefix":"10.1007","author":[{"given":"Yao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqi","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyu","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzheng","family":"Chi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lining","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"44_CR1","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1086\/200975","volume":"9","author":"ET Hall","year":"1968","unstructured":"Hall, E.T.: Proxemics. Curr. Anthropol. 9, 83\u2013108 (1968)","journal-title":"Curr. Anthropol."},{"key":"44_CR2","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s12369-017-0448-1","volume":"10","author":"N P\u00e9rez-Higueras","year":"2018","unstructured":"P\u00e9rez-Higueras, N., Caballero, F., Merino, L.: Teaching robot navigation behaviors to optimal RRT planners. Int. J. Soc. Robot. 10, 235\u2013249 (2018)","journal-title":"Int. J. Soc. Robot."},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Chi, W., Kono, H., Tamura, Y., Yamashita, A., Meng, Q.H.: A human-friendly robot navigation algorithm using the risk-RRT approach. In: 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 227\u2013232 (2016)","DOI":"10.1109\/RCAR.2016.7784030"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Ratsamee, P., Mae, Y., Ohara, K., Takubo, T., Arai, T.: Modified social force model with face pose for human collision avoidance. In: 2012 7th ACM\/IEEE International Conference on Human-Robot Interaction (HRI), pp. 215\u2013216 (2012)","DOI":"10.1145\/2157689.2157762"},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TASE.2017.2731371","volume":"14","author":"XT Truong","year":"2017","unstructured":"Truong, X.T., Ngo, T.D.: Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion model. IEEE Trans. Autom. Sci. Eng. 14, 1\u201318 (2017)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Hoang, V.B., Nguyen, V.H., Nguyen, L.A., Quang, T.D., Truong, X.T.: Social constraints-based socially aware navigation framework for mobile service robots. In: 2020 7th NAFOSTED Conference on Information and Computer Science (NICS), pp. 84\u201389 (2020)","DOI":"10.1109\/NICS51282.2020.9335878"},{"key":"44_CR7","unstructured":"Chen, Y., Lou, Y.: A unified multiple-motion-mode framework for socially compliant navigation in dense crowds. IEEE Trans. Autom. Sci. Eng., 1\u201313 (2021)"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y.F., Liu, M., Everett, M., How, J.P.: Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 285\u2013292 (2017)","DOI":"10.1109\/ICRA.2017.7989037"},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Li, G., Wu, Y., Wei, W.: Guided dynamic window approach to collision avoidance in troublesome scenarios. In: 2008 7th World Congress on Intelligent Control and Automation, pp. 5759\u20135763 (2008)","DOI":"10.1109\/WCICA.2008.4592807"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Rob. Auton. Syst. 57(5), 469\u2013483 (2009)","DOI":"10.1016\/j.robot.2008.10.024"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Silver, D., Bagnell, J.A., Stentz, A.: Learning from demonstration for autonomous navigation in complex unstructured terrain. Int. J. Rob. Res. 29, 1569\u20131592 (2010)","DOI":"10.1177\/0278364910369715"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Kollmitz, M., Koller, T., Boedecker, J., Burgard, W.: Learning human-aware robot navigation from physical interaction via inverse reinforcement learning. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11025\u201311031 (2020)","DOI":"10.1109\/IROS45743.2020.9340865"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Ratliff, N.D., Silver, D., Bagnell, J.A.: Learning to search: Functional gradient techniques for imitation learning. Auton. Rob. 27, 25\u201353 (2009)","DOI":"10.1007\/s10514-009-9121-3"},{"key":"44_CR14","unstructured":"Brys, T., Harutyunyan, A., Suay, H.B., Chernova, S., Taylor, M.E., Now\u00e9, A.: Reinforcement learning from demonstration through shaping. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 3352\u20133358 (2015)"},{"key":"44_CR15","unstructured":"Ng, A.Y., Russell, S.J.: Algorithms for inverse reinforcement learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 663\u2013670 (2000)"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Imani, M., Braga-Neto, U.: Control of gene regulatory networks using Bayesian inverse reinforcement learning. IEEE\/ACM Trans. Comput. Biol. Bioinf. 16, 1250\u20131261 (2018)","DOI":"10.1109\/TCBB.2018.2830357"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Konar, A., Baghi, B.H., Dudek, G.: Learning goal conditioned socially compliant navigation from demonstration using risk-based features. IEEE Rob. Autom. Lett. 6, 651\u2013658 (2021)","DOI":"10.1109\/LRA.2020.3048657"},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Higueras, N., Ram\u00f3n-Vigo, R., Caballero, F., Merino, L.: Robot local navigation with learned social cost functions. In: 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), pp,618\u2013625 (2014)","DOI":"10.5220\/0005120806180625"},{"key":"44_CR19","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Higueras, N., Caballero, F., Merino, L.: Learning robot navigation behaviors by demonstration using a RRT* planner. In: International Conference on Social Robotics, pp. 1\u201310 (2016)","DOI":"10.1007\/978-3-319-47437-3_1"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Ramon-Vigo, R., Perez-Higueras, N., Caballero, F., Merino, L.: Analyzing the relevance of features for a social navigation task. In: Robot 2015: Second Iberian Robotics Conference, pp. 235\u2013246 (2016)","DOI":"10.1007\/978-3-319-27149-1_19"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, X., Su, H., Zhao, Y., Ge, S.S.: The opening workspace control strategy of a novel manipulator-driven emission source microscopy system. ISA Trans. (2022)","DOI":"10.1016\/j.isatra.2022.09.002"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Ding, Z., Chi, W., Wang, J., Chen, G., Sun, L.: PRTIRL based socially adaptive path planning for mobile robots. Int. J. Soc. Rob. (2022)","DOI":"10.1007\/s12369-022-00924-8"},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Rob. Res. 30, 846\u2013894 (2011)","DOI":"10.1177\/0278364911406761"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Kumar, V., Likhachev, M.: Search-based path planning with homotopy class constraints. In: Symposium on Combinatorial Search, pp. 1230\u20131237 (2010)","DOI":"10.1609\/aaai.v24i1.7735"}],"container-title":["Lecture Notes in Computer Science","Social Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24667-8_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T09:33:08Z","timestamp":1728811988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24667-8_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031246661","9783031246678"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24667-8_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Social Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Florence","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"socrob2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icsr2022.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"111","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}