  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.
