Technology Category
Intelligent Robots: Localization, Mapping, and NavigationKnowledge Representation and Reasoning: Computational Complexity of ReasoningApplication Domains: Mobility, Driving & Flight
Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles
🤖 AI Summary
Quantifying the impact of complex terrain on off-road autonomous driving performance remains challenging. Method: This study proposes a multi-fractal terrain generation method based on the 3D Weierstrass–Mandelbrot function, introducing fractal dimension (2.3–2.6) as a continuous, quantifiable terrain difficulty metric. Sixty discrete terrains with controllable roughness are synthesized, and gradient analysis is employed to classify regions into low-, medium-, and high-roughness categories. A comprehensive real-vehicle evaluation—measuring traversal success rate, RMS vertical acceleration, RMS pitch/roll rates, and traversal time—is conducted to establish quantitative mappings between terrain fractal features and vehicle dynamic responses. Contribution/Results: Results demonstrate strong sensitivity of off-road traversability to fractal complexity: a 0.1 increase in fractal dimension reduces low-roughness area by 20.96%, decreases traversal success rate by 47.5%, and significantly elevates RMS acceleration and attitude rates—validating fractal dimension as a robust, interpretable terrain difficulty indicator for autonomous off-road navigation.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Complex Terrain
Autonomous Off-road Vehicle
Performance Evaluation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Complex Terrain Generation
Autonomous Off-road Vehicle Testing
Ruggedness Adjustment
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