HighlightsOutlines method to accurately calculate the RMS of terrain profiles sampled at low resolution.Validated against ground truth data.Treats profiles as self-affine fractals (rather than self-similar) and considers both the fractal dimension and cutoff length.Greatly improves on previous work in this area that considered only fractal dimension of self-similar profiles.

    AbstractDriver comfort on rough terrain is an important factor in the off-road performance of wheeled and tracked ground vehicles. The roughness of a terrain has typically been quantified by the U.S. Army as the root-mean-square elevation deviation (RMS) of the terrain profile. Although RMS is an important input into many mobility calculations, it is not scale invariant, making it difficult to estimate RMS from low resolution terrain profiles. Fractal parameters are another measure of roughness that are scale invariant, making them a convenient proxy for RMS. While previous work found an empirical relationship between fractal dimension and RMS, this work will show that, by including the cutoff length, an analytic relationship between fractal properties and RMS can be employed. The relationship has no free parameters and agrees very well with experimental data - thus providing a powerful predictive tool for future analyses and a reliable way to calculate surface roughness from low-resolution terrain data in a way that is scale invariant. In addition, we show that this method applies to both man-made ride courses and natural terrain profiles.


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    Title :

    Calculating fractal parameters from low-resolution terrain profiles



    Published in:

    Publication date :

    2017-03-12


    Size :

    6 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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