A Comparison of Visual Servoing from Features Velocity and Acceleration Interaction Models

Abstract

Visual Servoing has been widely investigated in the last decades as it provides a powerful strategy for robot control. Thanks to the direct feed-back from a set of sensors, it allows to reduce the impact of some modeling errors and to perform tasks even in uncertain environments. The commonly exploited approach in this field is to use a model that expresses the rate of change of a set of features as a function of sensor twist. These schemes are commonly used to obtain a velocity command, which needs to be tracked by a low-level controller. Another approach that can be exploited consists in going one step further and to consider an acceleration model for the features. This strategy allows also to obtain a natural and direct link with the dynamic model of the controlled system. This study aims at comparing the use of velocity and acceleration-based models in feed-back linearization for Visual Servoing. We consider the case of a redundant manipulator and discuss what this implies for both control techniques. By means of simulations, we show that controllers based on features acceleration give better results than those based on velocity in presence of noisy feedback signals.