  Terramechanics plays a critical role in the areas of ground vehicles and
ground mobile robots since understanding and estimating the variables
influencing the vehicle-terrain interaction may mean the success or the failure
of an entire mission. This research applies state-of-the-art algorithms in deep
learning to two key problems: estimating wheel slip and classifying the terrain
being traversed by a ground robot. Three data sets collected by ground robotic
platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot
Fitorobot) are employed in order to compare the performance of traditional
machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer
Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural
Networks (CNNs). This work also shows the impact that certain tuning parameters
and the network architecture (MLP, DNN and CNN) play on the performance of
those methods. This paper also contributes a deep discussion with the lessons
learned in the implementation of DNNs and CNNs and how these methods can be
extended to solve other problems.
