{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepterramechanics-terrain-classification-and","title":"DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning","arxiv_id":"1806.07379","date":"2018-06-12","proceeding":null,"authors":["Ramon Gonzalez","Karl Iagnemma"],"abstract":"Terramechanics plays a critical role in the areas of ground vehicles and\nground mobile robots since understanding and estimating the variables\ninfluencing the vehicle-terrain interaction may mean the success or the failure\nof an entire mission. This research applies state-of-the-art algorithms in deep\nlearning to two key problems: estimating wheel slip and classifying the terrain\nbeing traversed by a ground robot. Three data sets collected by ground robotic\nplatforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot\nFitorobot) are employed in order to compare the performance of traditional\nmachine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer\nPerceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural\nNetworks (CNNs). This work also shows the impact that certain tuning parameters\nand the network architecture (MLP, DNN and CNN) play on the performance of\nthose methods. This paper also contributes a deep discussion with the lessons\nlearned in the implementation of DNNs and CNNs and how these methods can be\nextended to solve other problems.","url_abs":"http://arxiv.org/abs/1806.07379v1","url_pdf":"http://arxiv.org/pdf/1806.07379v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepterramechanics-terrain-classification-and","repo_url":"https://github.com/ntseng450/DeepTerra","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}