Most existing formation control approaches are based on the assumption that the global/relative position and/or velocity measurements of mobile robots are directly available. To extend the application domain and to improve the formation control performance, it is extremely necessary to avoid the use of position and velocity measurements in the design of formation controllers. In this paper, we propose new leader-following formation tracking control schemes for nonholonomic mobile robots with onboard perspective cameras, without using both position and velocity measurements. To address the unavailability issue of position measurements, the leader–follower kinematics model in the image space is developed, which can facilitate the complete elimination of measurement/estimation of the position information. Furthermore, feedback information from the perspective camera of the follower robot is used to design adaptive observers to estimate the leader linear velocity for feedforward compensation, which can handle the absence of velocity measurements such that the proposed schemes can be applied to control formations of mobile robots without mutual communication abilities. By using the Lyapunov stability theory, a rigorous stability analysis based on the nonlinear formation dynamics is provided to show that the global stability of the combined observer–controller closed-loop system can be guaranteed. Both simulation and experimental results are also given to demonstrate the performance of the proposed formation tracking control schemes.

The task-space tracking control of robots without the exact knowledge of kinematics and dynamics has been studied before with the assumption that the joint velocities are available for controller designs. However, the velocity measurements can be contaminated by noises, thereby resulting in poor system performance, or even leading to instability problems. Therefore, in this paper, we propose a new tracking controller for robots in the task space without the use of both task-space and joint-space-velocity measurements, under the condition that both the robot kinematics and dynamics are unknown. To overcome these incapacities without the velocity measurements, we introduce the well-known sliding-observer-design techniques to estimate the joint velocities for the purpose of our controller design. Our main concern, i.e., the stability analysis of our controller design incorporated with the siding observer, is presented with the help of Lyapunov-analysis methodology and the sliding-patch concept. Simulation results are presented to show the performance of our controller-observer designs.

In vision-based robotic manipulation, it is usually required that the object should present a desired shape or be viewed at a specific angle to facilitate subsequent operations such as object crawling and component inspection. To implement this kind of visual servoing tasks, image features that describe shape information of objects need to be designed. This paper proposes methods based on the general contour of an object. The Bezier curve and the nonuniform rational B-spline curve are, respectively, used to fit the contour of the object. Based on contour curve parameters, image contour features are designed. Visual measurements of the image features are used to provide feedback on the shape information of the object to the eye-in-hand visual servoing system. Based on the visual feedback, the motion of the robot is controlled to make the image features converge to the desired values. In this way, it is implemented that the object presents the desired shape or is viewed at the desired angle. Moreover, by using adaptive laws to estimate curve parameters online, the proposed method does not require a priori knowledge of the 3-D position of the object. The stability of the proposed controller is analyzed by Lyapunov theory. This method has substantially expanded the application domain of not only the image feature-based visual servoing, but also the B-spline curves. The feasibility of the proposed method is validated by experiments.

In this paper, we consider the position control problem of nonholonomic mobile robots, i.e., the regulation of mobile robots to a desired position specified by the image position of a feature point onboard the mobile robot, by using visual feedback information from an overhead fixed camera. Many vision-based control approaches have been proposed for motion control of nonholonomic mobile robots, however, they usually assume that exact or approximate knowledge of full/partial intrinsic and/or extrinsic parameters of the camera can be available, and large or even very small errors in these parameters can deteriorate the control performance, reduce the control accuracy, and even affect the system stability. Hence, it is interesting and highly desirable to develop vision-based controllers without depending on the camera parameters, from both control performance and controller implementation perspectives. With this in mind, we propose novel image-based position control schemes for nonholonomic mobile robots completely without knowing the camera parameters. In the proposed schemes, neither accurate nor approximate knowledge about the camera intrinsic and extrinsic paramet

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.