Paper
30 April 2009 Evaluation of terrain parameter estimation using a stochastic terrain model
Author Affiliations +
Abstract
Autonomous vehicles driving on off-road terrain exhibit substantial variation in mobility characteristics even when the terrain is horizontal and qualitatively homogeneous. This paper presents a simple stochastic model for characterizing observed variability in vehicle response to terrain and for representing transitions between homogeneous terrain with local variability or between heterogeneous terrain types. Such a model provides a means for more realistic evaluation of terrain parameter estimation methods through simulation. A stochastic terrain model in which friction angle and soil cohesion are represented by Gaussian random variables qualitatively represents observed variability in traction vs. slip characteristics measured experimentally. The stochastic terrain model is used to evaluate a terrain parameter estimation method in which terrain forces are first estimated independent of a terrain model, and subsequently, parameters of a terrain model, such as soil cohesion, friction angle, and stress distribution parameters are determined from estimated vehicle-terrain forces. Simulation results show drawbar pull vs. slip characteristics resulting from terrain parameter estimation are within statistical bounds established by the stochastic terrain model.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Danielle A. Dumond, Laura E. Ray, and Eric Trautmann "Evaluation of terrain parameter estimation using a stochastic terrain model", Proc. SPIE 7332, Unmanned Systems Technology XI, 73321F (30 April 2009); https://doi.org/10.1117/12.817737
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Stochastic processes

Robots

Soil science

Sensors

Filtering (signal processing)

Statistical analysis

Resistance

Back to Top