{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T06:07:10Z","timestamp":1762409230131,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,9,27]],"date-time":"2016-09-27T00:00:00Z","timestamp":1474934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Terrain perception greatly enhances the performance of robots, providing them with essential information on the nature of terrain being traversed. Several living beings in nature offer interesting inspirations which adopt different gait patterns according to nature of terrain. In this paper, we present a novel terrain perception system for our bioinspired robot, Scorpio, to classify the terrain based on visual features and autonomously choose appropriate locomotion mode. Our Scorpio robot is capable of crawling and rolling locomotion modes, mimicking Cebrenus Rechenburgi, a member of the huntsman spider family. Our terrain perception system uses Speeded Up Robust Feature (SURF) description method along with color information. Feature extraction is followed by Bag of Word method (BoW) and Support Vector Machine (SVM) for terrain classification. Experiments were conducted with our Scorpio robot to establish the efficacy and validity of the proposed approach. In our experiments, we achieved a recognition accuracy of over 90% across four terrain types namely grass, gravel, wooden deck, and concrete.<\/jats:p>","DOI":"10.3390\/robotics5040019","type":"journal-article","created":{"date-parts":[[2016,9,27]],"date-time":"2016-09-27T10:14:11Z","timestamp":1474971251000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Terrain Perception in a Shape Shifting Rolling-Crawling Robot"],"prefix":"10.3390","volume":"5","author":[{"given":"Fuchida","family":"Masataka","sequence":"first","affiliation":[{"name":"Department of Advanced Multidisciplinary Engineering, Tokyo Denki University, Tokyo 120-8551, Japan"}]},{"given":"Rajesh","family":"Mohan","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0710-6409","authenticated-orcid":false,"given":"Ning","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore"}]},{"given":"Akio","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics, Tokyo Denki University, Tokyo 120-8551, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4010-570X","authenticated-orcid":false,"given":"Thejus","family":"Pathmakumar","sequence":"additional","affiliation":[{"name":"SUTD-JTC I"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Weiss, C., Frohlich, H., and Zell, A. (2006, January 9\u201315). Vibration-based terrain classification using support vector machines. Proceedings of the 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.","DOI":"10.1109\/IROS.2006.282076"},{"key":"ref_2","unstructured":"Best, G., Moghadam, P., Kottege, N., and Kleeman, L. (2013, January 2\u20134). Terrain classification using a hexapod robot. Proceedings of the Australasian Conference on Robotics and Automation, Sydney, Australia."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Takizawa, H., Yamaguchi, S., Aoyagi, M., Ezaki, N., and Mizuno, S. (2012, January 16\u201318). Kinect cane: An assistive system for the visually impaired based on three-dimensional object recognition. Proceedings of the 2012 IEEE\/SICE International Symposium on System Integration (SII), Fukuoka, Japan.","DOI":"10.1109\/SII.2012.6426936"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zenker, S., Aksoy, E.E., Goldschmidt, D., Worgotter, F., and Manoonpong, P. (2013, January 9\u201312). Visual terrain classification for selecting energy efficient gaits of a hexapod robot. Proceedings of the 2013 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Wollongong, Australia.","DOI":"10.1109\/AIM.2013.6584154"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lu, L., Ordonez, C., Collins, E.G., and DuPont, E.M. (2009, January 10\u201315). Terrain surface classification for autonomous ground vehicles using a 2D laser stripe-based structured light sensor. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, St. Louis, MO, USA.","DOI":"10.1109\/IROS.2009.5354799"},{"key":"ref_6","unstructured":"Ascari, L., Ziegenmeyer, M., Corradi, P., Ga\u00dfmann, B., Z\u00f6llner, M., Dillmann, R., and Dario, P. (2006, January 28\u201330). Can statistics help walking robots in assessing terrain roughness? Platform description and preliminary considerations. Proceedings of the 9th ESA Workshop on Advanced Space Technologies for Robotics and Automation ASTRA 2006, ESTEC, Noordwijk, The Netherlands."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.pss.2013.06.001","article-title":"Multi-resolution digital terrain models and their potential for Mars landing site assessments","volume":"85","author":"Kim","year":"2013","journal-title":"Planet. Space Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Filitchkin, P., and Byl, K. (2012, January 7\u201312). Feature-based terrain classification for littledog. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal.","DOI":"10.1109\/IROS.2012.6386042"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Moghadam, P., and Wijesoma, W.S. (2009, January 11\u201314). Online, self-supervised vision-based terrain classification in unstructured environments. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2009, SMC 2009, San Antonio, TX, USA.","DOI":"10.1109\/ICSMC.2009.5345942"},{"key":"ref_10","unstructured":"Otte, S., Laible, S., Hanten, R., Liwicki, M., and Zell, A. (2015). Proceedings, Presses Universitaires de Louvain."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Thrun, S., Montemerlo, M., and Aron, A. (2006, January 16\u201319). Probabilistic Terrain Analysis for High-Speed Desert Driving. Proceedings of the Robotics: Science and Systems, Philadelphia, PA, USA.","DOI":"10.15607\/RSS.2006.II.021"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bajracharya, M., Tang, B., Howard, A., and Turmon, M. (2008, January 19\u201323). Learning long-range terrain classification for autonomous navigation. Proceedings of the IEEE International Conference on Robotics and Automation, 2008, ICRA 2008, Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543828"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1177\/0278364906065386","article-title":"Crawling and jumping by a deformable robot","volume":"25","author":"Sugiyama","year":"2006","journal-title":"Int. J. Robot. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/TMECH.2013.2253615","article-title":"Quattroped: A Leg\u2014Wheel Transformable Robot","volume":"19","author":"Chen","year":"2014","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1126\/science.1138353","article-title":"From swimming to walking with a salamander robot driven by a spinal cord model","volume":"315","author":"Ijspeert","year":"2007","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40638-014-0023-2","article-title":"Terrain perception for a reconfigurable biomimetic robot using monocular vision","volume":"1","author":"Sinha","year":"2014","journal-title":"Robot. Biomim."},{"key":"ref_17","unstructured":"Tan, N., Mohan, R.E., and Elangovan, K. (2016). Advances in Reconfigurable Mechanisms and Robots II, Springer."},{"key":"ref_18","unstructured":"Kapilavai, A., Mohan, R.E., and Tan, N. (2015, January 27\u201330). Bioinspired design: A case study of reconfigurable crawling-rolling robot. Proceedings of the DS80-2 20th International Conference on Engineering Design (ICED 15) Vol. 2: Design Theory and Research Methodology Design Processes, Milan, Italy."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-up robust features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_20","first-page":"143","article-title":"A comparison of sift, pca-sift and surf","volume":"3","author":"Juan","year":"2009","journal-title":"Int. J. Image Process. (IJIP)"},{"key":"ref_21","unstructured":"Csurka, G., Dance, C., Fan, L., Willamowski, J., and Bray, C. (2004, January 11\u201314). Visual categorization with bags of keypoints. Proceedings of the ECCV International Workshop on Statistical Learning in Computer Vision, Prague, Czech Republic."},{"key":"ref_22","unstructured":"Weston, J., and Watkins, C. (1998). Multi-Class Support Vector Machines, University of London. Technical Report CSD-TR-98-04."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tuytelaars, T. (2010, January 13\u201318). Dense interest points. Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539911"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bentley, J.L. (1990, January 7\u20139). K-d trees for semidynamic point sets. Proceedings of the Sixth Annual Symposium on Computational Geometry, Berkley, CA, USA.","DOI":"10.1145\/98524.98564"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/5\/4\/19\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:31:53Z","timestamp":1760211113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/5\/4\/19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,27]]},"references-count":24,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["robotics5040019"],"URL":"https:\/\/doi.org\/10.3390\/robotics5040019","relation":{},"ISSN":["2218-6581"],"issn-type":[{"type":"electronic","value":"2218-6581"}],"subject":[],"published":{"date-parts":[[2016,9,27]]}}}