  In this work, a terrain estimation framework is developed for autonomous
vehicles operating on deformable terrains. Previous work in this area usually
relies on steady state tire operation, linearized classical terramechanics
models, or on computationally expensive algorithms that are not suitable for
real-time estimation. To address these shortcomings, this work develops a
reduced-order nonlinear terramechanics model as a surrogate of the Soil Contact
Model (SCM) through extending a state-of-the-art Bekker model to account for
additional dynamic effects. It is shown that this reduced-order surrogate model
is able to accurately replicate the forces predicted by the SCM while reducing
the computation cost by an order of magnitude. This surrogate model is then
utilized in a unscented Kalman filter to estimate the sinkage exponent.
Simulations suggest this parameter can be estimated within 4% of its true value
for clay and sandy loam terrains. It is also shown that utilizing this
estimated parameter can reduce the prediction errors of the future vehicle
states by orders of magnitude, which could assist with achieving more robust
model-predictive autonomous navigation strategies.
