Neural network based terramechanics modeling and estimation for deformable terrains

Abstract

In this work, a neural network based terramechanics model and terrain estimator is presented with an outlook for optimal control applications such as model predictive control. Recognizing the limitations of the state-of-the-art terramechanics models in terms of operating conditions, computational cost, and continuous differentiability for gradient-based optimization, an exE;fficient and twice continuously differentiable terramechanics model is developed using neural networks for dynamic operations on deformable terrains. It is demonstrated that the neural network terramechanics model is able to predict the lateral tire forces accurately and effixE;ciently compared to the Soil Contact Model as a state-of-the-art model.

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Document Details

Document Type
Technical Report
Publication Date
Sep 17, 2019
Accession Number
AD1080945

Entities

People

  • James Dallas
  • Michael P. Cole
  • Paramsothy Jayakumar
  • Tulga Ersal

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayesian Networks
  • Collision Avoidance
  • Computational Complexity
  • Computational Science
  • Engineering
  • Estimators
  • Friction
  • Ground Vehicles
  • Kalman Filters
  • Mechanical Engineering
  • Model Predictive Control
  • Navigation
  • Neural Networks
  • Signal Processing
  • Simulations

Readers

  • Computational Modeling and Simulation
  • Operations Research
  • Pavement Materials Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks