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ARS Home » Southeast Area » Auburn, Alabama » Soil Dynamics Research » Research » Publications at this Location » Publication #335586

Research Project: Enhancing Production and Ecosystem Services of Horticultural and Agricultural Systems in the Southeastern United States

Location: Soil Dynamics Research

Title: Parameterization of norfolk sandy loam properties for stochastic modeling of light in-wheel motor UGV

item Salama, Mostafa - University Of Alabama
item Way, Thomas - Tom
item Vantsevich, Vladimir - University Of Alabama

Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 9/12/2016
Publication Date: 9/12/2016
Citation: Salama, M., Way, T.R., Vantsevich, V. 2016. Parameterization of norfolk sandy loam properties for stochastic modeling of light in-wheel motor UGV. In: Proceedings of the 8th Americas Regional Conference of the International Society for Terrain-Vehicle Systems, September 12-14, 2016, Troy, Michigan. p.12.

Interpretive Summary: Two important aspects of agricultural tractors equipped with pneumatic tires and other self-propelled off-road vehicles equipped with tires are (a) the tractive performance of the vehicle on soil and (b) the amount of soil compaction caused by the vehicle tires. Traction is important in ensuring the vehicle moves across the soil surface. Minimizing soil compaction is usually desirable in agriculture, to promote root growth of plants, and ensure there is sufficient void space in soil, to provide good water-holding capacity of the soil. A small four-wheel vehicle weighing 40 kg (90 lb) and equipped with pneumatic tires, was operated on a loose sandy loam soil. Each of the four wheels was powered by its own electric motor. This paper presents an experimental and a computational approach for developing a stochastic model describing soil physical properties. These results are expected to be useful in improving the tractive performance of vehicles equipped with pneumatic tires, operating on soil.

Technical Abstract: To accurately develop a mathematical model for an In-Wheel Motor Unmanned Ground Vehicle (IWM UGV) on soft terrain, parameterization of terrain properties is essential to stochastically model tire-terrain interaction for each wheel independently. Operating in off-road conditions requires paying close attention to tire-terrain interaction as it strongly affects the IWM UGV energy efficiency. Developing a mathematical model for the IWM UGV on soft terrain is a complex task, as a result of the uncertainty behavior such as stochastic terrain properties, terrain irregularities, and the multi-pass effect. Sources of uncertainties include spatial variability of soil properties and changes in the soil moisture content. In addition to variability of soil properties, terrain surface irregularities have an influence on vehicle performance because of changes in contact forces between tires and terrain. Thus it is important to incorporate terrain surface irregularities with stochastic terrain property modeling. This can be achieved empirically by using Bekker’s technique which relates tire sinkage and normal pressure distribution at the contact patch and the Janosi and Hanamoto approach for calculating shear stress at the contact patch, to calculate stochastic wheel rolling resistance for each wheel. A small four-wheel IWM UGV weighing 40 kg (90 lb) and equipped with pneumatic tires, was operated on a loose sandy loam soil. Attention was payed to consider the pneumatic tires of this lightweight IWM UGV moving on off-road terrain (sandy loam soil). The multi-pass effect was taken into account, to appropriately model terrain properties for both front and rear wheels. Based on terramechanics equations, there are six main terrain parameters which directly affect calculations of the normal and shear stresses and on the wheel rolling resistance produced at the tire-terrain contact patch. All six terrain parameters were modeled stochastically by obtaining those values experimentally using soil cone penetrometer, Cohron sheargraph, soil bulk density, soil moisture content, and rut depth measurement methods for both trafficked and untrafficked terrain. Uncertainties for those parameters were then represented as uncorrelated random variables with normal distributions using the Monte Carlo method.