Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground VehiclesSource: Journal of Autonomous Vehicles and Systems:;2025:;volume( 005 ):;issue: 002::page 21003-1DOI: 10.1115/1.4067769Publisher: The American Society of Mechanical Engineers (ASME)
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 by 13.8% and 7.16%, the median area of semi-rough terrain increases by 11.7% and 5.63%, and the median area of high-roughness terrain increases by 1.54% and 3.33%, respectively. We find that the median success rate of the vehicle decreases by 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.
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contributor author | Majhor, Casey D. | |
contributor author | Bos, Jeremy P. | |
date accessioned | 2025-04-21T10:37:34Z | |
date available | 2025-04-21T10:37:34Z | |
date copyright | 2/17/2025 12:00:00 AM | |
date issued | 2025 | |
identifier issn | 2690-702X | |
identifier other | javs-23-1047.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4306575 | |
description 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 by 13.8% and 7.16%, the median area of semi-rough terrain increases by 11.7% and 5.63%, and the median area of high-roughness terrain increases by 1.54% and 3.33%, respectively. We find that the median success rate of the vehicle decreases by 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles | |
type | Journal Paper | |
journal volume | 5 | |
journal issue | 2 | |
journal title | Journal of Autonomous Vehicles and Systems | |
identifier doi | 10.1115/1.4067769 | |
journal fristpage | 21003-1 | |
journal lastpage | 21003-12 | |
page | 12 | |
tree | Journal of Autonomous Vehicles and Systems:;2025:;volume( 005 ):;issue: 002 | |
contenttype | Fulltext |