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.

To make the controller implementation easier and to enhance the system robustness and control performance in the presence of the camera parameter uncertainties, it is very desired to develop vision-based control approaches without any offline or online camera calibration. In this article, we propose a new calibration-free image-based trajectory tracking control scheme for nonholonomic mobile robots with a truly uncalibrated fixed camera. By developing a novel camera-parameter-independent kinematic model, both offline and online camera calibration can be avoided in the proposed scheme, and any knowledge of the camera is not needed in the controller design. The proposed trajectory tracking control scheme can guarantee exponential convergence of the image position and velocity tracking errors. To illustrate the performance of the proposed scheme, experimental results are provided in this article. Note to Practitioners-This article was motivated by the vision-based motion control problem of mobile robots in uncalibrated environments. Existing vision-based motion control approaches for nonholonomic mobile robots generally depend on offline precise/coarse or online numerical/adaptive calibration of the camera intrinsic and extrinsic parameters and require precise or coarse knowledge of the camera in their implementation. This article presents a novel calibration-free image-based trajectory tracking control scheme, which can be implemented easily in real environments without any offline or online calibration of the camera parameters and can be used to efficiently control the motion of nonholonomic mobile robots with an arbitrarily placed and truly unknown overhead camera. Experimental results show that the proposed scheme can achieve satisfactory trajectory tracking control performance despite the lack of any knowledge about the camera intrinsic and extrinsic parameters and the presence of unknown camera lens distortions, and hence, can provide a simple but efficient solution to the vision-based motion control problem of nonholonomic mobile robots.

Although many vision-based control methods have been proposed for nonholonomic mobile robots, in their implementation, it is usually necessary to calibrate the camera intrinsic and/or extrinsic parameters using offline/online parameter estimation algorithms or online adaptation laws. To avoid the tediousness of camera calibration and to make the system performance highly robust to camera parameter uncertainties, in this article, we propose novel image-based pose stabilization control approaches for nonholonomic mobile robots with a truly uncalibrated overhead fixed camera. In the proposed approaches, only image position information of three feature points from an overhead camera is used for controller design, while information from other sensors (such as wheel encoders) is not required. Furthermore, either offline or online camera calibration is not necessary, and no knowledge about the camera intrinsic and extrinsic parameters is needed, which also can greatly simplify the controller implementation. Simulation and experimental results are given to demonstrate the feasibility and effectiveness of the proposed purely image-based pose stabilization approaches.