Fully Uncalibrated Image-Based Visual Servoing of 2DOFs Planar Manipulators With a Fixed Camera.

We consider the uncalibrated vision-based control problem of robotic manipulators in this work. Though lots of approaches have been proposed to solve this problem, they usually require calibration (offline or online) of the camera parameters in the implementation, and the control performance may be largely affected by parameter estimation errors. In this work, we present new fully uncalibrated visual servoing approaches for position control of the 2DOFs planar manipulator with a fixed camera. In the proposed approaches, no camera calibration is required, and numerical optimization algorithms or adaptive laws for parameter estimation are not needed. One benefit of such features is that exponential convergence of the image position errors can be ensured regardless of the camera parameter uncertainties. Generally, existing uncalibrated approaches only can guarantee asymptotical convergence of the position errors. Moreover, different from most existing approaches which assume that the robot motion plane and the image plane are parallel, one of the proposed approaches allows the camera to be installed at a general pose. This also simplifies the controller implementation and improves the system design flexibility. Finally, simulation and experimental results are provided to illustrate the effectiveness of the presented fully uncalibrated visual servoing approaches.

Visual Servoing of Flexible-Link Manipulators by Considering Vibration Suppression Without Deformation Measurements.

Visual servoing and vibration suppression of spatial flexible-link manipulators with a fixed camera setup are addressed in this article. The singular perturbation method is adopted to decouple the dynamic equations of the flexible manipulator; hence, two subsystems that represent the rigid robot motion and flexible-link vibration are obtained, respectively. Then, for the slow subsystem related to the rigid motion, an image-based controller is designed to converge the image errors with the consideration of compensating for the errors of approximating the Jacobian matrix. For the fast subsystem corresponding to the elastic vibration, to eliminate the requirements of measuring the vibration states, an observer is designed to estimate the fast states and then a feedback controller of the fast subsystem is presented to suppress the vibration of the flexible manipulator by using the estimation values. The closed-loop stabilities of the slow and fast subsystem are both proved by employing the Lyapunov theory. Numerical simulations demonstrate the effectiveness of the proposed controller, which shows that the image errors approach zero with the vibration of the flexible manipulator damped out simultaneously.

Disturbance observer based adaptive model predictive control for uncalibrated visual servoing in constrained environments.

This paper presents an adaptive model predictive control (MPC) method based on disturbance observer (DOB) to improve the disturbance rejection performance of the image-based visual servoing (IBVS) system. The proposed control method is developed based on the depth-independent interaction matrix, which can simultaneously handle unknown camera intrinsic and extrinsic parameters, unknown depth parameters, system constraints, as well as external disturbances. The proposed control scheme includes two parts which are the feedback regulation part based on the adaptive MPC and the feedforward compensation part based on the modified DOB. Unlike the traditional DOB that is based on the fixed nominal plant model, the modified DOB here is based on the estimated plant model. The adaptive MPC controller consists of an iterative identification algorithm, which not only can provide the model parameters for both the controller and the modified DOB, but also can be used to control plant dynamics and to minimize the effects of DOB. Simulations for both the eye-in-hand and eye-to-hand camera configurations are conducted to illustrate the effectiveness of the proposed method.

Adaptive Task-Space Cooperative Tracking Control of Networked Robotic Manipulators Without Task-Space Velocity Measurements.

In this paper, the task-space cooperative tracking control problem of networked robotic manipulators without task-space velocity measurements is addressed. To overcome the problem without task-space velocity measurements, a novel task-space position observer is designed to update the estimated task-space position and to simultaneously provide the estimated task-space velocity, based on which an adaptive cooperative tracking controller without task-space velocity measurements is presented by introducing new estimated task-space reference velocity and acceleration. Furthermore, adaptive laws are provided to cope with uncertain kinematics and dynamics and rigorous stability analysis is given to show asymptotical convergence of the task-space tracking and synchronization errors in the presence of communication delays under strongly connected directed graphs. Simulation results are given to demonstrate the performance of the proposed approach.