{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129688"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129688","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Traversability prediction and navigation for unstructured environments","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Valverde Gasparino, Mateus"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chowdhary, Girish","Hoiem, Derek","Driggs-Campbell, Katherine","Becker, Marcelo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-16","date_published":"2025-04-16","updated_at":"2026-07-22T22:25:05Z","subjects":["Autonomous Navigation","Traversability","Deep Learning","Perception","Robotics"],"languages":["en","eng"],"rights":["Copyright 2025 Mateus Valverde Gasparino"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129688","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish","Hoiem, Derek","Driggs-Campbell, Katherine","Becker, Marcelo"]},{"key":"dc:creator","label":"Author","values":["Valverde Gasparino, Mateus"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-16","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Autonomous Navigation","Traversability","Deep Learning","Perception","Robotics"]}]},{"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 2025 Mateus Valverde Gasparino"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129688"]}]},{"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 2027-05-01","The student, Mateus Valverde Gasparino, accepted the attached license on 2025-04-14 at 17:08.","The student, Mateus Valverde Gasparino, submitted this Dissertation for approval on 2025-04-14 at 17:21.","This Dissertation was approved for publication on 2025-04-16 at 10:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21773 on 2025-10-19 at 19:52:52","Accurate and robust navigation in unstructured environments is a critical challenge for autonomous robots. Currently, methods rely on predetermined paths, heuristics-based obstacle avoidance, or supervised learning by handcrafted labels. With the goal of improving navigation and control of autonomous agents in unstructured environments, this dissertation presents a series of works that leverage sensor fusion, machine learning, and predictive modeling techniques to provide better decision making and accurate control for robotic systems. The proposed methods enable robots to navigate semi-structured and unstructured environments efficiently while avoiding navigational failures. By learning traversability parameters and incorporating them into navigation systems, we demonstrate significant improvements in autonomous performance. The first contribution, CropNav, addresses the challenges of navigating under agricultural canopies where GNSS signals are unreliable. By autonomously switching between sensing modalities such as LiDAR-based row-following and waypoint tracking, CropNav enables seamless navigation inside and outside crop rows. Additionally, an online optimization approach is introduced to learn traction coefficients and recover from navigation failures. This system extends autonomous navigation time with reduced human intervention, achieving up to 750~m per intervention compared to GNSS-based navigation. Subsequent works explore the integration of deep learning with model predictive control (MPC) for enhanced performance while preserving stability. LBMPC introduces a dual-timescale adaptation mechanism for real-time uncertainty estimation using deep neural networks. WayFAST and WayFASTER build on these ideas by employing self-supervised learning to predict traversable paths in unstructured environments using RGB and depth data. These methods demonstrate improved data efficiency and the ability to handle challenging terrains such as snow or tall grass. Finally, this dissertation introduces TRAIL and ZEST, two novel approaches addressing limitations in generalization and adaptability. TRAIL learns traversability representations invariant to robot embodiment, enabling deployment across diverse platforms with improved safety through uncertainty prediction. ZEST leverages large multimodal large language models (LLMs) for zero-shot traversability prediction, generating global traversability maps directly from sensory inputs. Together, these contributions advance the state-of-the-art in autonomous navigation by enabling robust and risk-aware systems capable of operating in diverse and challenging environments."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Traversability prediction and navigation for unstructured environments"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish","Hoiem, Derek","Driggs-Campbell, Katherine","Becker, Marcelo"],"dc:creator":["Valverde Gasparino, Mateus"],"dc:date":["2025-04-16","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Mateus Valverde Gasparino, accepted the attached license on 2025-04-14 at 17:08.","The student, Mateus Valverde Gasparino, submitted this Dissertation for approval on 2025-04-14 at 17:21.","This Dissertation was approved for publication on 2025-04-16 at 10:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21773 on 2025-10-19 at 19:52:52","Accurate and robust navigation in unstructured environments is a critical challenge for autonomous robots. Currently, methods rely on predetermined paths, heuristics-based obstacle avoidance, or supervised learning by handcrafted labels. With the goal of improving navigation and control of autonomous agents in unstructured environments, this dissertation presents a series of works that leverage sensor fusion, machine learning, and predictive modeling techniques to provide better decision making and accurate control for robotic systems. The proposed methods enable robots to navigate semi-structured and unstructured environments efficiently while avoiding navigational failures. By learning traversability parameters and incorporating them into navigation systems, we demonstrate significant improvements in autonomous performance. The first contribution, CropNav, addresses the challenges of navigating under agricultural canopies where GNSS signals are unreliable. By autonomously switching between sensing modalities such as LiDAR-based row-following and waypoint tracking, CropNav enables seamless navigation inside and outside crop rows. Additionally, an online optimization approach is introduced to learn traction coefficients and recover from navigation failures. This system extends autonomous navigation time with reduced human intervention, achieving up to 750~m per intervention compared to GNSS-based navigation. Subsequent works explore the integration of deep learning with model predictive control (MPC) for enhanced performance while preserving stability. LBMPC introduces a dual-timescale adaptation mechanism for real-time uncertainty estimation using deep neural networks. WayFAST and WayFASTER build on these ideas by employing self-supervised learning to predict traversable paths in unstructured environments using RGB and depth data. These methods demonstrate improved data efficiency and the ability to handle challenging terrains such as snow or tall grass. Finally, this dissertation introduces TRAIL and ZEST, two novel approaches addressing limitations in generalization and adaptability. TRAIL learns traversability representations invariant to robot embodiment, enabling deployment across diverse platforms with improved safety through uncertainty prediction. ZEST leverages large multimodal large language models (LLMs) for zero-shot traversability prediction, generating global traversability maps directly from sensory inputs. Together, these contributions advance the state-of-the-art in autonomous navigation by enabling robust and risk-aware systems capable of operating in diverse and challenging environments."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129688"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Mateus Valverde Gasparino"],"dc:subject":["Autonomous Navigation","Traversability","Deep Learning","Perception","Robotics"],"dc:title":["Traversability prediction and navigation for unstructured environments"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}