Investigation of Terrain Analysis and Classification Methods for Ground Vehicles

Abstract

Unmanned ground vehicles (UGVs) must rapidly and robustly characterize the nature of the terrain they are traversing, to improve autonomous mobility. This research program has focused on the development of a framework for self-supervised terrain classification, which allows a UGV to automatically learn the properties of terrain without human guidance. Work has also focused on novel applications of the self-supervised terrain learning approach, including urban/semi-urban driving on road networks. Finally, research has led to the development of novel sensing techniques for analyzing robot-terrain interaction mechanics at the micro scale.

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Document Details

Document Type
Technical Report
Publication Date
Aug 27, 2012
Accession Number
ADA577237

Entities

People

  • Karl Iagnemma

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Ground and Sea Platforms
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Autonomous Navigation
  • Autonomous Vehicles
  • Computational Science
  • Data Mining
  • Detectors
  • Information Science
  • Jet Propulsion
  • Kernel Functions
  • Machine Learning
  • Measurement
  • Mechanical Properties
  • Mechanics
  • Remote Sensing
  • Strain Gages
  • Supervised Machine Learning
  • Unmanned Ground Vehicles
  • Unmanned Vehicles

Readers

  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy