Hybrid Predictive Control for Constrained Visual Servoing

Visual servoing provides a smart solution for robot control in unstructured environments. However, exploiting camera images for control imposes additional constraints to the system. Previously image-based predictive controllers were proposed as a remedy for this problem. Nevertheless, these methods suffered from local stability and possible numerical infeasibility. This work presents two hybrid predictive controllers that are globally stable and have minimum computation time. One of the controllers is based on hybrid visual servoing, while the other minimizes the error in both image and Cartesian space simultaneously with selected ratios. The effectiveness of the proposed methods is verified through numerous simulations.