{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115870"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115870","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Neural network enhanced off-road skid-steer vehicle modeling with an application to path planning","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","abstract_has_math":false,"creators":["Yurkanin, Justin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Norris, William R","Ramos, Joao"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["Off-road","neural ODE","dynamics","Autonomous"],"languages":["en","eng"],"rights":["Copyright 2022 Justin Yurkanin"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115870","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William R","Ramos, Joao"]},{"key":"dc:creator","label":"Author","values":["Yurkanin, Justin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-06-28"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Off-road","neural ODE","dynamics","Autonomous"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Justin Yurkanin"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115870"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","The student, Justin Yurkanin, accepted the attached license on 2022-06-21 at 11:32.","The student, Justin Yurkanin, submitted this Thesis for approval on 2022-06-21 at 11:49.","This Thesis was approved for publication on 2022-06-28 at 08:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18082 on 2022-11-16 at 10:17:14","This thesis discusses the development and validation of 2D and 3D dynamic skid-steer vehicle models for the purpose of enabling or facilitating the future development of control, path planning, and localization algorithms off-road. The ideal dynamic model is fast, accurate, general, adaptive, and 3D. The work presented in this thesis tries to approach this ideal with different models. First, a very simple linear 2D vehicle model was trained from data as a benchmark. Following this, a more complex 2D neural network model was developed. Next, a 3D floating base dynamic vehicle model was created. This was integrated with the Bekker tire-soil model which was approximated with a neural network and used to realistically simulate terrain. Auto differentiation was leveraged to create a fully differentiable 3D simulation which enabled optimization techniques. Gradient descent was used to select soil parameters that maximized 3D model accuracy and achieved offline model adaption to unknown soil types. Finally, experiments were performed to train the 3D vehicle model as a physics based neural ODE. All vehicle models were trained and evaluated with an external data set. To demonstrate the usefulness of the 3D model, a Rapid Random Trees (RRT) algorithm was implemented in simulation to search for valid paths across a 3D terrain."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Neural network enhanced off-road skid-steer vehicle modeling with an application to path planning"]}]}],"canonical_facts":{"dc:contributor":["Norris, William R","Ramos, Joao"],"dc:creator":["Yurkanin, Justin"],"dc:date":["2022-08","2022-06-28"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","The student, Justin Yurkanin, accepted the attached license on 2022-06-21 at 11:32.","The student, Justin Yurkanin, submitted this Thesis for approval on 2022-06-21 at 11:49.","This Thesis was approved for publication on 2022-06-28 at 08:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18082 on 2022-11-16 at 10:17:14","This thesis discusses the development and validation of 2D and 3D dynamic skid-steer vehicle models for the purpose of enabling or facilitating the future development of control, path planning, and localization algorithms off-road. The ideal dynamic model is fast, accurate, general, adaptive, and 3D. The work presented in this thesis tries to approach this ideal with different models. First, a very simple linear 2D vehicle model was trained from data as a benchmark. Following this, a more complex 2D neural network model was developed. Next, a 3D floating base dynamic vehicle model was created. This was integrated with the Bekker tire-soil model which was approximated with a neural network and used to realistically simulate terrain. Auto differentiation was leveraged to create a fully differentiable 3D simulation which enabled optimization techniques. Gradient descent was used to select soil parameters that maximized 3D model accuracy and achieved offline model adaption to unknown soil types. Finally, experiments were performed to train the 3D vehicle model as a physics based neural ODE. All vehicle models were trained and evaluated with an external data set. To demonstrate the usefulness of the 3D model, a Rapid Random Trees (RRT) algorithm was implemented in simulation to search for valid paths across a 3D terrain."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115870"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Justin Yurkanin"],"dc:subject":["Off-road","neural ODE","dynamics","Autonomous"],"dc:title":["Neural network enhanced off-road skid-steer vehicle modeling with an application to path planning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}