Predictive control of nonlinear visual servoing systems using image moments

Abstract

The visual servoing architectures with an eye-in-hand configuration, composed of a 6-d.o.f. manipulator robot and a video camera, are categorised as high-coupled and nonlinear systems. For this reason, in order to have a stable and convergent behaviour, advanced techniques like predictive control are needed. These types of techniques can solve the potential problems that can appear in classical (image-based visual servoing) while different type of constraints for visual servoing system can be fulfilled easier. Three different visual predictive control architectures based on image moments are proposed. The model for the image moments predictor used in the first architecture is an extension of a local model point features predictor. The second predictive approach uses an image moment-based predictor to describe the future behaviour of the system over a certain camera velocity scenario directly in the image moments space. In order to have a faster convergence and in the same time a desired behaviour of the servoing system, a reference trajectory is employed. This represents the main contribution of the last predictive control technique proposed in this study. A Matlab simulator was developed and all the visual servoing architectures described in the study were implemented. The simulation results showed that all the proposed visual predictive control approaches are stable and convergent when dealing with visual servoing tasks, but the best performances were assigned to the reference trajectory-based image moments architecture.