Reserch On UP6 Robot Control Based On Binocular Visual Servoing

Robot visual servoing is one of the most active and challenging topics in robotics, and is also involved in many diverse research fields, such as control theory, robotics, computer vision, digital image acquisition and processing, embedded systems, network communications, scientific computing and so on. Currently speaking, the research of visual servoing could be divided into two major categories: Position-Based Visual Servoing(PBVS) and Image-Based Visual Servoing(IBVS). PBVS calculates the relative position and orientation, which are known as the 3D modeling of the object according to the known camera internal and external parameters. However, it needs perfect target geometric model and largely depends on accurate camera calibration. Comparing with PBVS, the IBVS is object model free, and robust to camera modeling and hand-eye calibration errors.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:First of all, a camera calibration method of binocular visual system is proposed in this paper, the method finishes the two separate camera calibration using the function of the Open Source Computer Vision Library at first, and then calculates the conversion relationship between the relative pose of the two cameras using the camera external parameters.Secondly, in the feature points matching tasks, a bidirectional matching algorithm based RANSAC algorithm is proposed in this paper to improve the image matching accuracy of the SIFT algorithm, and also a matching algorithm based on corner detection function of OpenCV is introduced, in which we extracts the feature points of the target object using strong Harris corner detection algorithm and matching them using location constraint. Thirdly, 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.Finally, a switching control method is proposed to decouple the rotational and translational motion control of the robot end-effector and avoid the inherent drawbacks of the origin model in practical application. The whole switching control method consists of three steps: adjusting posture; positioning; precision positioning (posture and position). The method is implemented and validated on a MOTOMAN-UP6 based eye-in-hand platform and the experimental comparison shows that it can enhance the system stability and have a better positioning trajectory.