Experimental Study of Lightweight Tracked Vehicle Performance on Dry Granular Materials

Abstract

This paper presents an experimental study of the performance of a single track device driving on dry, granular soils. A single-track test rig is used to empirically investigate track motion under controlled track slip and loading conditions on three natural dry granular materials:a dry sandy material with 100 um average grain size, a dry sandy material with 400 um average grain size, and a coarse gravel with 1 cm average particle size. Test conditions can be designed to replicate typical field scenarios for lightweight robots, while key operational parameters such as drawbar force, torque, and sinkage are measured. This test rig enables imposition of velocities, or application of loads, to interchangeable running gears within a confined soil bin of dimensions 1.5 m long, 0.7 m wide, and 0.4 m deep. The tested single track device has an effective contact area measuring approximately 25 cm x 10 cm and it is tested under three vertical loads 125 N, 155 N, and 190 N. Slip is varied within -50% and +50% during travel over the three soils. The track utilizes a flexible rubber belt equipped with 0.5 cm tall grousers. Experimental measurements are compared against well-established semi-empirical models, to assess the predictive accuracy of these models .

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Document Details

Document Type
Technical Report
Publication Date
Sep 12, 2013
Accession Number
ADA586558

Entities

People

  • C. Senatore
  • K. Iagnemma
  • P. Jayakumar

Organizations

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

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Experimental Data
  • Flexible Couplings
  • Friction
  • Grain Size
  • Granular Materials
  • Ground Vehicles
  • Internal Friction
  • Lightweight
  • Manufacturing
  • Materials
  • Measurement
  • Particle Size
  • Particles
  • Pressure Distribution
  • Shear Stresses
  • Tracked Vehicles
  • Vehicles

Readers

  • Computational Modeling and Simulation
  • Geotechnical Engineering.
  • Logistics and Supply Chain Management.

Technology Areas

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