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Scalability of Classical Terramechanics Models for Lightweight Vehicle Applications Incorporating Stochastic Modeling and Uncertainty Propagation.
| 题名： | Scalability of Classical Terramechanics Models for Lightweight Vehicle Applications Incorporating Stochastic Modeling and Uncertainty Propagation. |
| 作者： | Gorsich, D.; Jayakumar, P.; Maclennan, J.; Melanz, D.; Senatore, C. |
| 关键词： | Ground Vehicles; Mathematical Models; Robotics; Shear Tests; Soil Mechanics; Stochastic Processes; Terrain; Test Beds; Uncertainty; Wheels |
| 摘要： | This paper investigates the validity of commonly used terramechanics models for light-weight vehicle applications while accounting for experimental variability. This is accomplished by means of cascading uncertainty up to the terminal point of operations measurement. Vehicle-terrain interaction is extremely complex, and thus models and simulation methods for vehicle mobility prediction are largely based on empirical test data. Analytical methods are compared to experimental measurements of key operational parameters such as drawbar force, torque, and sinkage. Models of these operational parameters ultimately depend on a small set of empirically determined soil parameters, each with an inherent uncertainty due to test variability. The soil parameters associated with normal loads are determined by fitting the dimensionless form of Bekker's equation to the data given by the pressure-sinkage test. In a similar approach, the soil parameters associated with shear loads are determined by fitting Janosi and Hanamoto's equation to the data given by the direct shear test. An uncertainty model is used to propagate the soil parameter variability through to the wheel performance based on Wong and Reece. The commonly used analytical model is shown to be inaccurate as the envelope of model uncertainty does not lie within the experimental measures, suggesting that model improvements are required to accurately predict the performance of light-weight vehicles on deformable terrain. |
| 报告类型： | 科技报告 |
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