Analysis of Stress Distributions Under Lightweight Wheeled Vehicles

Abstract

In recent years, the need for reliable modeling tools for lightweight robotic systems deployed on various terrains has spurred research efforts into development of vehicle terrain interaction (VTI) models. This paper presents an analysis of rigid wheels - dry sand interaction and compares experimental results with predictions from established terramechanics theory. A novel experimental setup, based on sensing elements placed on the wheel surface, allows inference of normal and tangential stress at the wheel-terrain interface. A particle image velocimetry (PIV) analysis is used to study the soil kinematics under the wheel. The analysis of stress profiles shows that stress patterns under lightweight vehicle wheels conform reasonably well to established terramechanics theory developed for heavy vehicles. For the wheel under investigation, the stress distribution had minor variation along wheel width for low slip conditions. The wheel model proposed by Wong and Reece was analyzed in light of the stress and soil kinematics measurements available. It was found that, by appropriately characterizing the model coefficients $c_1$ and $c_2$, and understanding the physical meaning of the shear modulus $k_x$, it is possible to obtain torque, drawbar force, and sinkage predictions within 11\% (full scale error) of experimental data.

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Document Details

Document Type
Technical Report
Publication Date
Oct 09, 2013
Accession Number
ADA613600

Entities

People

  • C. Senatore
  • K. Iagnemma

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Experimental Data
  • Failure Mode And Effect Analysis
  • Fluid Mechanics
  • Friction
  • Internal Friction
  • Kinematics
  • Load Cells
  • Measurement
  • Mechanics
  • Particle Image Velocimetry
  • Pressure Distribution
  • Shear Modulus
  • Shear Stresses
  • Shear Tests
  • Strain Gages
  • Stratified Fluids
  • Transducers

Fields of Study

  • Engineering

Readers

  • Logistics and Supply Chain Management.
  • Robotics and Automation.
  • Structural Dynamics.

Technology Areas

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