{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T14:39:22Z","timestamp":1782830362008,"version":"3.54.5"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030014230","type":"print"},{"value":"9783030014247","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01424-7_75","type":"book-chapter","created":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T17:07:37Z","timestamp":1538413657000},"page":"771-780","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Terrain Classification with Crawling Robot Using Long Short-Term Memory Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4075-116X","authenticated-orcid":false,"given":"Rudolf J.","family":"Szadkowski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0466-275X","authenticated-orcid":false,"given":"Jan","family":"Drchal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6193-0792","authenticated-orcid":false,"given":"Jan","family":"Faigl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"key":"75_CR1","doi-asserted-by":"crossref","unstructured":"Bartoszyk, S., Kasprzak, P., Belter, D.: Terrain-aware motion planning for a walking robot. In: 2017 11th International Workshop on Robot Motion and Control (RoMoCo), pp. 29\u201334 (2017)","DOI":"10.1109\/RoMoCo.2017.8003889"},{"key":"75_CR2","unstructured":"Best, G., Moghadam, P., Kottege, N., Kleeman, L.: Terrain classification using a hexapod robot. In: Australasian Conference on Robotics and Automation (2013)"},{"issue":"2","key":"75_CR3","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1018054314350","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"issue":"6","key":"75_CR4","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/s00422-006-0129-x","volume":"95","author":"A Frigon","year":"2006","unstructured":"Frigon, A., Rossignol, S.: Experiments and models of sensorimotor interactions during locomotion. Biol. Cybern. 95(6), 607 (2006)","journal-title":"Biol. Cybern."},{"key":"75_CR5","unstructured":"Gers, F.: Long short-term memory in recurrent neural networks. Unpublished Ph.D. dissertation, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Lausanne, Switzerland (2001)"},{"issue":"8","key":"75_CR6","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"Sepp Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Computation"},{"issue":"6","key":"75_CR7","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1002\/rob.21422","volume":"29","author":"MW McDaniel","year":"2012","unstructured":"McDaniel, M.W., Nishihata, T., Brooks, C.A., Salesses, P., Iagnemma, K.: Terrain classification and identification of tree stems using ground based lidar. J. Field Robot. 29(6), 891\u2013910 (2012)","journal-title":"J. Field Robot."},{"key":"75_CR8","first-page":"179","volume":"1422","author":"J Mrva","year":"2015","unstructured":"Mrva, J., Faigl, J.: Feature extraction for terrain classification with crawling robots. Inf. Technol. Appl. Theory 1422, 179\u2013185 (2015)","journal-title":"Inf. Technol. Appl. Theory"},{"key":"75_CR9","doi-asserted-by":"crossref","unstructured":"Mrva, J., Faigl, J.: Tactile sensing with servo drives feedback only for blind hexapod walking robot. In: 10th International Workshop on Robot Motion and Control (RoMoCo), pp. 240\u2013245 (2015)","DOI":"10.1109\/RoMoCo.2015.7219742"},{"issue":"2","key":"75_CR10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1002\/rob.20113","volume":"23","author":"L Ojeda","year":"2006","unstructured":"Ojeda, L., Borenstein, J., Witus, G., Karlsen, R.: Terrain characterization and classification with a mobile robot. J. Field Robot. 23(2), 103\u2013122 (2006)","journal-title":"J. Field Robot."},{"issue":"2","key":"75_CR11","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1109\/LRA.2016.2525040","volume":"1","author":"K Otsu","year":"2016","unstructured":"Otsu, K., Ono, M., Fuchs, T.J., Baldwin, I., Kubota, T.: Autonomous terrain classification with co- and self-training approach. IEEE Robot. Autom. Lett. 1(2), 814\u2013819 (2016)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"75_CR12","doi-asserted-by":"crossref","unstructured":"Otte, S., Weiss, C., Scherer, T., Zell, A.: Recurrent neural networks for fast and robust vibration-based ground classification on mobile robots. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5603\u20135608 (2016)","DOI":"10.1109\/ICRA.2016.7487778"},{"key":"75_CR13","doi-asserted-by":"crossref","unstructured":"Rebula, J.R., Neuhaus, P.D., Bonnlander, B.V., Johnson, M.J., Pratt, J.E.: A controller for the littledog quadruped walking on rough terrain. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1467\u20131473 (2007)","DOI":"10.1109\/ROBOT.2007.363191"},{"key":"75_CR14","unstructured":"Sasaki, Y., et al.: The truth of the F-measure. Teach. Tutor. Mater 1(5), 1\u20135 (2007)"},{"issue":"7","key":"75_CR15","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TPAMI.2006.134","volume":"28","author":"D Tao","year":"2006","unstructured":"Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1088\u20131099 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"75_CR16","unstructured":"Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw. Mach. Learn. 4(2), 26\u201331 (2012)"},{"issue":"12","key":"75_CR17","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1152\/jn.01124.2011","volume":"107","author":"TI T\u00f3th","year":"2012","unstructured":"T\u00f3th, T.I., Knops, S., Daun-Gruhn, S.: A neuromechanical model explaining forward and backward stepping in the stick insect. J. Neurophysiol. 107(12), 3267\u20133280 (2012)","journal-title":"J. Neurophysiol."},{"key":"75_CR18","doi-asserted-by":"crossref","unstructured":"Walas, K., Kanoulas, D., Kryczka, P.: Terrain classification and locomotion parameters adaptation for humanoid robots using force\/torque sensing. In: IEEE-RAS 16th International Conference on Humanoid Robots, pp. 133\u2013140 (2016)","DOI":"10.1109\/HUMANOIDS.2016.7803265"},{"key":"75_CR19","doi-asserted-by":"crossref","unstructured":"Walas, K., Nowicki, M.: Terrain classification using laser range finder. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5003\u20135009 (2014)","DOI":"10.1109\/IROS.2014.6943273"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01424-7_75","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:35:22Z","timestamp":1773048922000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01424-7_75"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014230","9783030014247"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01424-7_75","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"27 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"360","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":"139","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":"28","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":"39% - 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":"4","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)"}},{"value":"In addition there are 41 full poster papers and 11 short poster papers included in the proceedings","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}