Efficient Generation of Accurate Mobility Maps Using Machine Learning Algorithms

Abstract

U.S Armys mission is to develop, integrate, and sustain the right technology solutions for all manned and unmanned ground vehicles, and mobility is a key requirement for all ground vehicles. Mobility focuses on ground vehicles capabilities that enable them to be deployable worldwide, operationally mobile in all environments, and protected from symmetrical and asymmetrical threats. In order for military ground vehicles to operate in any combat zone, the planners require a mobility map that gives the maximum predicted speeds on these off-road terrains. In the past, empirical and semi-empirical techniques [1-2] were used to predict vehicle mobility on off-road terrains such as the NATO Reference Mobility Model (NRMM). Because of its empirical nature, the NRMM method cannot be extrapolated to new vehicle designs containing advanced technologies, nor can it be applied to lightweight robotic vehicles.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 17, 2019
Accession Number
AD1070858

Entities

People

  • Dave Mechergui
  • Paramsothy Jayakumar

Organizations

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

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Automata Theory
  • Computational Science
  • Computer Science
  • Data Sets
  • Dimensionality Reduction
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Physical Properties
  • Recognition
  • Simulations
  • Supervised Machine Learning
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • Autonomy