Traversability prediction and navigation for unstructured environments

Valverde Gasparino, Mateus

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https://hdl.handle.net/2142/129688 Copy

Description

Title

Traversability prediction and navigation for unstructured environments

Author(s)

Valverde Gasparino, Mateus

Issue Date

2025-04-16

Director of Research (if dissertation) or Advisor (if thesis)

Chowdhary, Girish

Doctoral Committee Chair(s)

Chowdhary, Girish

Committee Member(s)

• Hoiem, Derek
• Driggs-Campbell, Katherine
• Becker, Marcelo

Department of Study

Siebel School Comp & Data Sci

Discipline

Computer Science

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Autonomous Navigation
• Traversability
• Deep Learning
• Perception
• Robotics

Abstract

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.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129688

Copyright and License Information

Copyright 2025 Mateus Valverde Gasparino

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