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.

This paper presents a novel scheme for image-based visual servoing (IBVS) of a robot manipulator by considering robot dynamics without using joint velocity measurements in the presence of constraints, uncalibrated camera intrinsic and extrinsic parameters and unknown feature position parameters. An approach to design model predictive control (MPC) method based on identification algorithm and sliding mode observer has been proposed. Based on the MPC method, the IBVS tasks can be considered as a nonlinear optimization problem while the constraints due to the visibility constraint and the torque constraint can be explicitly taken into account. By using the depth-independent interaction matrix framework, the identification algorithm can be used to update the unknown parameters and the prediction model. In addition, many existing controllers require the joint velocity measurements which can be contaminated by noises, thus resulting in the IBVS performance degradation. To overcome the problem without joint velocity measurements, the sliding mode observer is designed to estimate the joint velocities of the IBVS system. The simulation results for both eye-in-hand and eye-to-hand camera configurations are presented to verify the effectiveness of the proposed control method. INDEX TERMS Image-based visual servoing, model predictive control, constrained optimization control, depth-independent interaction matrix, sliding mode observer.

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This paper presents a novel adaptive neural network control strategy for image-based visual servoing (IBVS) of robotic manipulators with both eye-in-hand and eye-to-hand camera configurations in the presence of unknown dynamics and external disturbances. The IBVS method is combined with the adaptive neural network to construct the proposed adaptive neural network controller to solve the visual servoing control problem of robots. The adaptive neural network based IBVS controller is designed based on the depth-independent interaction matrix, which can be trained on-line to identify the visual servoing robotic system modeling errors. Moreover, the proposed method can approach the unknown nonlinear dynamics for both eye-in-hand and eye-to-hand camera configurations without requiring the robot dynamics to be linearly parameterizable, and the exact knowledge of the robot structure is not needed. On the basis of the nonlinear robot dynamics, the Lyapunov stability analysis is given to prove the asymptotical convergence of the image position and velocity errors. Simulation results for both camera configurations are provided to demonstrate the performance of the proposed adaptive neural network based approach.