Learning the Dynamics of Articulated Tracked Vehicles

In this work, we present a Bayesian non-parametric
approach to model the motion control of ATVs. The motion control
model is based on a Dirichlet Process-Gaussian Process (DP-GP)
mixture model. The DP-GP mixture model provides a flexible
representation of patterns of control manoeuvres along trajectories
of different lengths and discretizations. The model also estimates the
number of patterns, sufficient for modeling the dynamics of the ATV.

Authors:

• Mario Gianni
• Manuel A. Ruiz Garcia
• Fiora Pirri

Keywords:

• Dirichlet processes
• Gaussian processes
• robot control learning
• tracked vehicles.

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