# Predict the Rover Mobility over Soft Terrain using Articulated Wheeled Bevameter

arXiv: https://arxiv.org/abs/2202.08495 · HTML: https://mlsanity.com/paper/2202.08495/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2022.

## Built on by (most cited first, 4 of 4)

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- [A Similarity Paradigm Through Textual Regularization Without Forgetting](https://mlsanity.com/paper/2502.14376/) (2025)
- [Generalizable Prompt Learning of CLIP: A Brief Overview](https://mlsanity.com/paper/2503.01263/) (2025)
- [Enhancing Target-unspecific Tasks through a Features Matrix](https://mlsanity.com/paper/2505.03414/) (2025)

## Abstract (arXiv)

Robot mobility is critical for mission success, especially in soft or deformable terrains, where the complex wheel-soil interaction mechanics often leads to excessive wheel slip and sinkage, causing the eventual mission failure. To improve the success rate, online mobility prediction using vision, infrared imaging, or model-based stochastic methods have been used in the literature. This paper proposes an on-board mobility prediction approach using an articulated wheeled bevameter that consists of a force-controlled arm and an instrumented bevameter (with force and vision sensors) as its end-effector. The proposed bevameter, which emulates the traditional terramechanics tests such as pressure-sinkage and shear experiments, can measure contact parameters ahead of the rover's body in real-time, and predict the slip and sinkage of supporting wheels over the probed region. Based on the predicted mobility, the rover can select a safer path in order to avoid dangerous regions such as those covered with quicksand. Compared to the literature, our proposed method can avoid the complicated terramechanics modeling and time-consuming stochastic prediction; it can also mitigate the inaccuracy issues arising in non-contact vision-based methods. We also conduct multiple experiments to validate the proposed approach.