Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles

TL;DR

This paper introduces a multifractal terrain generation method using the 3D Weierstrass-Mandelbrot function to create diverse off-road terrains, analyzing how fractal roughness impacts autonomous vehicle traversal performance.

Contribution

The study presents a novel multifractal terrain generation technique and systematically evaluates its effect on autonomous ground vehicle traversal difficulty.

Findings

Higher fractal dimensions lead to more high-roughness areas.

Success rates decrease as fractal dimension increases.

Vehicle stability metrics worsen with increased terrain roughness.

Abstract

We present a multifractal artificial terrain generation method that uses the 3D Weierstrass-Mandelbrot function to control roughness. By varying the fractal dimension used in terrain generation across three different values, we generate 60 unique off-road terrains. We use gradient maps to categorize the roughness of each terrain, consisting of low-, semi-, and high-roughness areas. To test how the fractal dimension affects the difficulty of vehicle traversals, we measure the success rates, vertical accelerations, pitch and roll rates, and traversal times of an autonomous ground vehicle traversing 20 randomized straight-line paths in each terrain. As we increase the fractal dimension from 2.3 to 2.45 and from 2.45 to 2.6, we find that the median area of low-roughness terrain decreases 13.8% and 7.16%, the median area of semi-rough terrain increases 11.7% and 5.63%, and the median area of high-roughness terrain increases 1.54% and 3.33%, all respectively. We find that the median success rate of the vehicle decreases 22.5% and 25% as the fractal dimension increases from 2.3 to 2.45 and from 2.45 to 2.6, respectively. Successful traversal results show that the median root-mean-squared vertical accelerations, median root-mean-squared pitch and roll rates, and median traversal times all increase with the fractal dimension.