  Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for
off-road autonomy applications. When turning at high speeds, these robots tend
to undergo significant skidding and slipping. In this work, using Gaussian
Process Regression (GPR) and Sigma-Point Transforms, we estimate the non-linear
effects of tire-terrain interaction on robot velocities in a probabilistic
fashion. Using the mean estimates from GPR, we propose a data-driven dynamic
motion model that is more accurate at predicting future robot poses than
conventional kinematic motion models. By efficiently solving a convex
optimization problem based on the history of past robot motion, the GPR
augmented motion model generalizes to previously unseen terrain conditions. The
output distribution from the proposed motion model can be used for local motion
planning approaches, such as stochastic model predictive control, leveraging
model uncertainty to make safe decisions. We validate our work on a benchmark
real-world multi-terrain SSWMR dataset. Our results show that the model
generalizes to three different terrains while significantly reducing errors in
linear and angular motion predictions. As shown in the attached video, we
perform a separate set of experiments on a physical robot to demonstrate the
robustness of the proposed algorithm.
