Abstract: 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.
Abstract: In this paper, we describe a Mixed-Initiative Operational
Model (MIOM) which directly intervenes on the state of the
functionalities embedded into a robot for Urban Search&Rescue
(USAR) domain applications. MIOM extends the reasoning
capabilities of the vehicle, i.e. mapping, path planning, visual
perception and trajectory tracking, with operator knowledge.
Especially in USAR scenarios, this coupled initiative has the main
advantage of enhancing the overall performance of a rescue mission.
In-field experiments with rescue responders have been carried out to
evaluate the effectiveness of this operational model.