Framework of Reliability-Based Stochastic Mobility Map for Next Generation NATO Reference Mobility Model

Abstract

A framework for generation of reliability-based stochastic off-road mobility maps is developed to support the next generation NATO reference mobility model (NG-NRMM) using full stochastic knowledge of terrain properties and modern complex terramechanics modeling and simulation capabilities. The framework is for carrying out uncertainty quantification (UQ) and reliability assessment for Speed Made Good and GO/NOGO decisions for the ground vehicle based on the input variability models of the terrain elevation and soil property parameters. To generate the distribution of the slope at given point, realizations of the elevation raster are generated using the normal distribution. For the soil property parameters, such as cohesion, friction, and bulk density, the min and max values obtained from geotechnical databases for each of the soil types are used to generate the normal distribution with a 99% confidence value range. In the framework, the ranges of terramechanics input parameters that will cover the regions of interest are first identified. Within these ranges of input parameters, a dynamic kriging (DKG) surrogate model is obtained for the maximum speed of the nevada automotive test center (NATC) wheeled vehicle platform complex terramechanics model. Finally, inverse reliability analysis using Monte Carlo simulation is carried out to generate the reliability-based stochastic mobility maps for Speed Made Good and GO/NOGO decisions. It is found that the deterministic map of the region of interest has probability of only 25% to achieve the indicated speed.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 07, 2019
Source ID
10.1115/1.4041350

Entities

People

  • K K Choi
  • Matthew Funk
  • Nicholas Gaul
  • Paramsothy Jayakumar
  • Tamer M. Wasfy

Organizations

  • Esri
  • United States Army
  • United States Army Tank Automotive Research, Development and Engineering Center
  • University of Iowa

Tags

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.