Abstract:

Proposed in this paper is an improved image moment-based visual servoing method with learning features for planar target,which helps to overcome the singularity of interaction matrix for classical invariant moment features.In the investigation,first,based on the TRS ( 2D translation,2D rotation and scale transformation) -invariant properties of invariant moment features,the nonlinear support vector machine regression algorithm is used to reveal and model the relationship between a set of specific invariant moment features and the rotational angles around the X-axis and Y-axis of the camera.Then,the estimators of regression models,namely the learning features,are used to control the rotational motions around X-axis and Y-axis.The interaction matrix of learning features possess total decoupling and linear properties and has no singularity for any shape of planar objects.Finally,in combination with the normalized centre of gravity features,the normalized area feature and the object orientation feature,a visual servoing controller is designed to conduct the 6-DOF motion control of a camera.Simulated results show that the proposed method is effective.

Key words: visual servoing, image moment, nonlinear support vector machine regression algorithm, interaction matrix