Accession Number:

AD1092809

Title:

An Active Learning Framework for Constructing High-fidelity Mobility Maps

Descriptive Note:

Technical Report,01 Jan 2019,02 Mar 2020

Corporate Author:

ARMY TANK-AUTOMOTIVE RESEARCH AND DEVELOPMENT COMMAND WARREN MI WARREN United States

Report Date:

2020-03-02

Pagination or Media Count:

11.0

Abstract:

A mobility map, which provides maximum achievable speed on a given terrain, is essential for path planning of autonomous ground vehicles in off-road settings. While physics-based simulations play a central role in creating next-generation, high-fidelity mobility maps, they are cumbersome and expensive. For instance, a typical simulation can take weeks to run on a supercomputer and each map requires thousands of such simulations. Recent work at the U.S. Army CCDC Ground Vehicle Systems Center has shown that trained machine learning classifiers can greatly improve the efficiency of this process. However, deciding which simulations to run in order to train the classifier efficiently is still an open problem. According to PAC learning theory, data that can be separated by a classifier is expected to require O1e randomly selected points simulations to train the classifier with error less than . In this paper, building on existing algorithms, we introduce an active learning paradigm that substantially reduces the number of simulations needed to train a machine learning classifier without sacrificing accuracy. Experimental results suggest that our sampling algorithm can train a neural network, with higher accuracy, using less than half the number of simulations when compared to random sampling

Subject Categories:

  • Land and Riverine Navigation and Guidance
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE