# 9295 / Granular Scaling Laws For Accurate Prediction Of Wheel Mobility On Slopes In Low-Gravity Environments ## Authors Takuya Omura and Genya Ishigami {% hint style="info" %} Paper presented at ISTVS 2024 | 21st International and 12th Asia-Pacific Regional Conference of the ISTVS\ Keywords: Granular scaling laws; Wheel mobility prediction; Low gravity; Lunar regolith simulant\ {% endhint %} ## Abstract Analyzing the mobility of wheeled rovers on loose sand in low-gravity environments remains a significant challenge. Among several experimental techniques, such as parabolic flight and reduced-weight tests, granular scaling laws (GSL) have recently been proposed to predict wheel mobility under low-gravity conditions via earth-gravity tests. Although the GSL accurately predicts the wheel mobility on flat terrain in low-gravity environments, its capability to predict the wheel mobility on slopes in such environments still needs to be verified. In this study, we developed a GSL and investigated its accuracy for predicting wheel mobility on slopes in low-gravity environments. The discrete element method (DEM) was utilized to test wheel mobility at various slope angles under Earth’s gravity. Subsequently, by applying a multiple scaling function, the GSL converted the results from the Earth-gravity tests to predict the wheel mobility under lunar gravity. The GSL-based predictions were compared with DEM simulations conducted under lunar gravity conditions. The results indicated that the wheel mobility under lunar gravity predicted by the GSL closely corresponded to that calculated via the DEM. These findings indicate that the GSL can accurately predict wheeled-rover mobility on slopes in low-gravity environments. *** Full paper purchase: \ ISTVS members receive three complimentary papers per year: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://2024.istvs.org/submissions/papers/9295.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.