Model Predictive Control for Constrained Image-Based Visual Servoing in Uncalibrated Environments

This paper presents a novel approach for image-based visual servoing (IBVS) of a robotic system by considering the constraints in the case when the camera intrinsic and extrinsic parameters are uncalibrated and the position parameters of the features in 3-D space are unknown. Based on the model predictive control method, the robotic system's input and output constraints, such as visibility constraints and actuators limitations, can be explicitly taken into account. Most of the constrained IBVS controllers use the traditional image Jacobian matrix, the proposed IBVS scheme is developed by using the depth-independent interaction matrix. The unknown parameters can appear linearly in the prediction model and they can be estimated by the identification algorithm effectively. In addition, the model predictive control determines the optimal control input and updates the estimated parameters together with the prediction model. The proposed approach can simultaneously handle system constraints, unknown camera parameters and depth parameters. Both the visual positioning and tracking tasks can be achieved desired performances. Simulation results based on a 2-DOF planar robot manipulator for both the eye-in-hand and eye-to-hand camera configurations are used to demonstrate the effectiveness of the proposed method.