Neural network enhanced off-road skid-steer vehicle modeling with an application to path planning

Yurkanin, Justin

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https://hdl.handle.net/2142/115870 Copy

Description

Title

Neural network enhanced off-road skid-steer vehicle modeling with an application to path planning

Author(s)

Yurkanin, Justin

Issue Date

2022-06-28

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

• Norris, William R
• Ramos, Joao

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Autonomous
• Off-road
• neural ODE
• dynamics

Abstract

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.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Justin Yurkanin

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