{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T14:07:58Z","timestamp":1768745278912,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T00:00:00Z","timestamp":1551830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"USTC-GLI Cooperative Program","award":["ES2100100127"],"award-info":[{"award-number":["ES2100100127"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.<\/jats:p>","DOI":"10.3390\/s19051137","type":"journal-article","created":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T10:52:22Z","timestamp":1551955942000},"page":"1137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers"],"prefix":"10.3390","volume":"19","author":[{"given":"Mingliang","family":"Mei","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Fujian Polytechnic of Information Technology, Fuzhou 350003, China"}]},{"given":"Ji","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230027, China"}]},{"given":"Yuling","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China"}]},{"given":"Zerui","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230027, China"}]},{"given":"Xiaochuan","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Technology, De Montfort University, Leicester LE1 9BH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7583-0944","authenticated-orcid":false,"given":"Wenjun","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230027, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,6]]},"reference":[{"key":"ref_1","unstructured":"Lozano-Perez, T. (2012). Autonomous Robot Vehicles, Springer Science & Business Media."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-S\u00e1nchez, J., Tavera-Mosqueda, S., Silva-Ortigoza, R., Hern\u00e1ndez-Guzm\u00e1n, V., Sandoval-Guti\u00e9rrez, J., Marcelino-Aranda, M., Taud, H., and Marciano-Melchor, M. (2018). Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers. Sensors, 18.","DOI":"10.3390\/s18124316"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1002\/rob.21761","article-title":"Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges","volume":"35","author":"Gonzalez","year":"2018","journal-title":"J. Field Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1002\/rob.20292","article-title":"Terrain adaptive navigation for planetary rovers","volume":"26","author":"Helmick","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1177\/0278364904047392","article-title":"Traction control of wheeled robotic vehicles in rough terrain with application to planetary rovers","volume":"23","author":"Iagnemma","year":"2004","journal-title":"Int. J. Robot. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/TII.2013.2290067","article-title":"Energy management and driving strategy for in-wheel motor electric ground vehicles with terrain profile preview","volume":"10","author":"Chen","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Manjanna, S., and Dudek, G. (2015, January 26\u201330). Autonomous gait selection for energy efficient walking. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139917"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, X., Dou, L., Su, Z., and Liu, N. (2018). Study of the Navigation Method for a Snake Robot Based on the Kinematics Model with MEMS IMU. Sensors, 18.","DOI":"10.3390\/s18030879"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Luo, K., Ma, C., Liu, Q., and Jin, B. (2018). Superpixel Segmentation Based Synthetic Classifications with Clear Boundary Information for a Legged Robot. Sensors, 18.","DOI":"10.3390\/s18092808"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Khan, Y.N., Komma, P., and Zell, A. (2011, January 6\u201313). High resolution visual terrain classification for outdoor robots. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130362"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Filitchkin, P., and Byl, K. (2012, January 7\u201312). Feature-based terrain classification for littledog. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6386042"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/TCYB.2014.2368353","article-title":"Terrain classification from body-mounted cameras during human locomotion","volume":"45","author":"Anantrasirichai","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ymssp.2013.03.002","article-title":"Acoustic surface perception from naturally occurring step sounds of a dexterous hexapod robot","volume":"40","author":"Ozkul","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Christie, J., and Kottege, N. (2016, January 16\u201321). Acoustics based terrain classification for legged robots. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487543"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1177\/0278364917727062","article-title":"Deep spatiotemporal models for robust proprioceptive terrain classification","volume":"36","author":"Valada","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1109\/LRA.2016.2524073","article-title":"Integrated Ground Reaction Force Sensing and Terrain Classification for Small Legged Robots","volume":"1","author":"Wu","year":"2016","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10846-014-0067-0","article-title":"Terrain classification and negotiation with a walking robot","volume":"78","author":"Walas","year":"2015","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1016\/j.robot.2014.07.006","article-title":"The effect of motor action and different sensory modalities on terrain classification in a quadruped robot running with multiple gaits","volume":"62","author":"Hoffmann","year":"2014","journal-title":"Robot. Auton. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Weiss, C., Tamimi, H., and Zell, A. (2008, January 22\u201326). A combination of vision- and vibration-based terrain classification. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650678"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Otte, S., Weiss, C., Scherer, T., and Zell, A. (2016, January 6\u201320). Recurrent Neural Networks for fast and robust vibration-based ground classification on mobile robots. Proceedings of the IEEE International Conference on Robotics and Automation, Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487778"},{"key":"ref_21","unstructured":"Bermudez, F.L.G., Julian, R.C., Haldane, D.W., Abbeel, P., and Fearing, R.S. (2012, January 7\u201312). Performance analysis and terrain classification for a legged robot over rough terrain. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Algarve, Portugal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Libby, J., and Stentz, A.J. (2012, January 14\u201318). Using sound to classify vehicle-terrain interactions in outdoor environments. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, St. Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6225357"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hoepflinger, M.A., Remy, C.D., Hutter, M., Spinello, L., and Siegwart, R. (2010, January 3\u20138). Haptic terrain classification for legged robots. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, Alaska.","DOI":"10.1109\/ROBOT.2010.5509309"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6312","DOI":"10.3390\/s90806312","article-title":"A comparison of RBF neural network training algorithms for inertial sensor based terrain classification","volume":"9","author":"Kurban","year":"2009","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Khan, Y.N., Masselli, A., and Zell, A. (2012, January 14\u201318). Visual terrain classification by flying robots. Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224988"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yin, J., Yang, J., and Zhang, Q. (2017). Assessment of GF-3 polarimetric sar data for physical scattering mechanism analysis and terrain classification. Sensors, 17.","DOI":"10.3390\/s17122785"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yan, Y., Rangarajan, A., and Ranka, S. (2018, January 20\u201324). An Efficient Deep Representation Based Framework for Large-Scale Terrain Classification. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545021"},{"key":"ref_28","unstructured":"Gonzalez, R., and Iagnemma, K. (arXiv, 2018). DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning, arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lu, L., Ordonez, C., Collins, E.G., and DuPont, E.M. (2009, January 11\u201315). Terrain surface classification for autonomous ground vehicles using a 2D laser stripe-based structured light sensor. Proceedings of the 2009 IEEE\/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA.","DOI":"10.1109\/IROS.2009.5354799"},{"key":"ref_30","first-page":"804503","article-title":"Unmanned ground vehicle perception using thermal infrared cameras. Unmanned Systems Technology XIII","volume":"8045","author":"Rankin","year":"2011","journal-title":"Int. Soc. Opt. Photonics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1002\/rob.21417","article-title":"Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain","volume":"29","author":"Zhou","year":"2012","journal-title":"J. Field Robot."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1002\/rob.21422","article-title":"Terrain classification and identification of tree stems using ground-based LiDAR","volume":"29","author":"McDaniel","year":"2012","journal-title":"J. Field Robot."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TRO.2005.855994","article-title":"Vibration-based terrain classification for planetary exploration rovers","volume":"21","author":"Brooks","year":"2005","journal-title":"IEEE Trans. Robot."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, C., and Sanchez, R.V. (2015). Gearbox fault identification and classification with convolutional neural networks. Shock Vib., 2015.","DOI":"10.1155\/2015\/390134"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","article-title":"Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks","volume":"63","author":"Ince","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","article-title":"Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks","volume":"388","author":"Abdeljaber","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MIS.2017.2581327","article-title":"Two-Stage Road Terrain Identification Approach for Land Vehicles Using Feature-Based and Markov Random Field Algorithm","volume":"33","author":"Wang","year":"2018","journal-title":"IEEE Intell. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/LRA.2016.2525040","article-title":"Autonomous terrain classification with co-and self-training approach","volume":"1","author":"Otsu","year":"2016","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1109\/TSMC.2016.2531700","article-title":"Ensemble learning with weak classifiers for fast and reliable unknown terrain classification using mobile robots","volume":"47","author":"Dutta","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3938502","DOI":"10.1155\/2017\/3938502","article-title":"A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots","volume":"2017","author":"Zhao","year":"2017","journal-title":"Math. Probl. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vicente, A., Liu, J., and Yang, G.Z. (October, January 28). Surface classification based on vibration on omni-wheel mobile base. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353480"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","article-title":"Convolutional neural network based fault detection for rotating machinery","volume":"377","author":"Janssens","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Weiss, C., Fechner, N., Stark, M., and Zell, A. (2007). Comparison of Different Approaches to Vibration-Based Terrain Classification, EMCR.","DOI":"10.1007\/978-3-540-74764-2_1"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Komma, P., Weiss, C., and Zell, A. (2009, January 12\u201317). Adaptive bayesian filtering for vibration-based terrain classification. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA\u201909, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152327"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.patrec.2013.11.004","article-title":"Comparison of different approaches to visual terrain classification for outdoor mobile robots","volume":"38","author":"Zou","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jterra.2017.09.001","article-title":"Unsupervised classification of slip events for planetary exploration rovers","volume":"73","author":"Bouguelia","year":"2017","journal-title":"J. Terramech."},{"key":"ref_48","first-page":"27","article-title":"LIBSVM: a library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/THMS.2015.2453203","article-title":"Robust biometric recognition from palm depth images for gloved hands","volume":"45","author":"Nguyen","year":"2015","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1007413511361","article-title":"On the optimality of the simple Bayesian classifier under zero-one loss","volume":"29","author":"Domingos","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Breiman, L. (2017). Classification and Regression Trees, Routledge.","DOI":"10.1201\/9781315139470"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J.Comput. Syst. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_57","unstructured":"Lin, M., Chen, Q., and Yan, S. (arXiv, 2013). Network in network, arXiv."},{"key":"ref_58","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_59","unstructured":"Snoek, J., Larochelle, H., and Adams, R.P. (2012, January 3\u20138). Practical bayesian optimization of machine learning algorithms. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Liu, X. (1999, January 15\u201319). A re-examination of text categorization methods. Proceedings of the 22nd International Conference on Research and Development in Information Retrieval, Berkeley, CA, USA.","DOI":"10.1145\/312624.312647"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/5\/1137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:36:43Z","timestamp":1760186203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/5\/1137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,6]]},"references-count":60,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["s19051137"],"URL":"https:\/\/doi.org\/10.3390\/s19051137","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,6]]}}}