Moment-Based 2.5-D Visual Servoing for Textureless Planar Part Grasping

Conventional moment-based visual servoing methods suffer from several problems in industrial applications due to the utilization of high-order image moments. In this paper, we analyze the shortcomings of the moment-based visual servoing from the viewpoint of practical industrial applications, and propose a novel moment-based two-and-a-half-dimensional visual servoing method for grasping textureless planar parts. We use hybrid visual features that combine image moments with three-dimensional (3-D) rotation in the Cartesian space to control the robot motion. Instead of applying high-order image moments, we use rotation features, which provide a decoupled interaction matrix that is full rank and with no local minimum in the control scheme. Furthermore, to estimate the relative rotation of the textureless part in real time, a new estimation method based on a cross-correlation analysis is proposed. The proposed visual servoing method provides a better motion control and 3-D trajectory of the robot arm and remains stable in the workspace. Experimental results demonstrate the effectiveness of the method.