Research On Robot Control System Based On A Stereo Visual Model Irrespective Of Depth

Robot visual servoing is a technology which utilizes vision sensor and computer to realize the vision of human. It picks up information from the collected image, and deals with it, and then understands it. The result processed is used for robot control. Since robot visual servoing has high flexibility and accuracy, also it has the advantage of robust to the calibration error; it becomes one of the hotspots in the field of robot in short time. It is successfully applied in the field of industry, ocean exploring, and so on. In this paper, on the basis of giving a tutorial introduction to the development of robot visual servoing control system nowadays, the main work is summarized as follows:During positioning to static object, aimed to wrong matching of Harris corner point, a rapid RANSAC algorithm is proposed in this paper. During tracking to dynamic object, in order to improve robust and accuracy of tracking under the complex background, quantum computation and genetic algorithm (QCGA) and particle filter algorithm are combined to improve the degeneracy problem of particle filter effectively. Considering the depth estimation problem for the eye-in-hand vision servoing system, a model for binocular visual serving control is presented in this paper. There is no depth information in this model. It avoids measuring and estimating the depth of the object point, which can improve the control performance. By analyzing the error of the model presented in this paper, we educe the factors influencing on the depth of feature point.Based on the model, for the positioning target of the eye-in-hand visual servoing, we design two controllers. Firstly, under the condition of the known internal and external parameters of camera, GMC (generic model control) is introduced to confirm the output trajectory of the system and then an optimization control problem is solved to design the camera translational and rotational velocity. The positioning control is realized without the homography matrix calculating and knowledge of geometrical three-dimensional model of the object. By selecting proper parameters to adjust transient performance index of the system, the time of the output following the anticipated trajectory is adjusted.With the uncertainties existing in vision and robot system, we useγ-passivity and L2 performance rule to deal with the disturbance attenuation and design the controller of the visual servoing. The sufficient and necessary condition of global stability of the system is derived which is irrelevant to the internal and external parameters of the camera. According to the lemma, we know that the system has the gain less than or equal to L2 , that is the system has L2 performance, which is robust to the uncertainties existing in vision and robot system, especially to the internal parameters of the camera.Based on the hardware and operation environment of the robot MOTOMAN-UP6, an experiment operation platform is established and the programs of image sampling and processing, robot motion and control, controller algorithm are developed. An experiment is carried to verify the visual servoing model irrespective of depth. The analysis of the error shows that the model is validity and practicability.