Visual Predictive Control from Distance-based and Homography-based Features

This paper deals with the issue of improved performance for image based visual servoing combining features selection and constraints handling. Observed object is described as a set of 3D points, corresponding 2D pixel points features in camera view are extracted and transformed to spherical point features through a virtual projection process; then, a set of six independent visual features combining distance-based and Homography-based Features, is determined and the related feature Jacobian is calculated. After features selection, image based visual servoing (IBVS) is formulated into a constrained optimization problem by nonlinear model predictive control (NMPC) method. Visibility constraints, 3D velocities constraints and 3D task space constraints are all considered in the optimization problem. Finally, simulations are provided and demonstrate the effectiveness and improved behavior of proposed method.