It is always a human dream to create a human-like robot that can replace human labor, and more than 80% of the information that humans acquire is through vision. Therefore, in the research of intelligent robots , the research of robots with vision has become the first. The research on robot vision servo system is one of the important contents in the robot field. The research results can be applied to the problems of robot automatic obstacle avoidance, trajectory tracking and moving target tracking. From the perspective of feedback information types, robot vision servo systems can be divided into position-based visual servo-based (position-base) and image-based visual servo-system (image-base) [1]. The position-based visual servo system first estimates the position of the target object relative to the camera in the Cartesian coordinate space. The visual servo error is defined in the three-dimensional Cartesian space, and the visual or feature information is used to estimate the relative position of the end of the robot and the target. The method requires precise calibration of the vision system and the robot, and the calculation is large because of the interpretation of the image. The servo error of image-based visual servo system is directly defined in the image feature space, that is, the feature information observed by the camera is directly used for feedback. It is not necessary to estimate the three-dimensional pose, but the system needs to calculate the image Jacobian matrix online (image feature parameter change) The relation matrix between the quantity and the change of the pose of the task space) and its inverse matrix, and the image Jacobian matrix is ​​related to many parameters of real-time variation, which is a complex nonlinear process, which is theoretically difficult to analyze and Control design puts forward greater requirements [2]. The robot vision servo system is a very complex system that integrates the content of many disciplines, and the development of various disciplines is extremely unbalanced, which affects its further development. Based on this, the research of robot vision servo is currently in a state of stagnation. Most of the researches in this field in China have only carried out simulation experiments, but not on actual robot systems. In this paper, the servo system is composed of Panasonic AC servo system, pmac motion control card, dsp image processing system, industrial computer and robot. The experimental research is carried out in the specific robot system to explore the image-based robot visual servo implementation problem.
Robot vision servo system component
Robot system and research content
Figure 1 is a hardware diagram of the robot system of this experiment. The first joint of the two-degree-of-freedom plane robot is vertically fixed on the working platform. Both joints are driven by the Panasonic AC servo motor and can only be rotated in the horizontal plane. The camera fixed at the end of the second rod is a monocular ccd camera. The image acquisition and processing are completed by the dsp image processing system, and the target feature value is extracted as the visual information feedback amount. In this paper, the coordinate value of the gray center of the circular target object in the image plane is selected as the image feature [3]. This paper studies the servo implementation of image-based robotic visual servo system. The motion of the two joints of the robot is controlled according to the change of the image value of the target object so that the second end of the rod can track the stationary or moving object located on the workbench. Visual servo control is implemented for the system.
Mathematical model of robot vision system
This paper intends to conduct experimental research on the system. Establish coordinate transformation relationship between robot coordinates, camera coordinates, image coordinates, and target point coordinates. Figure 2 is a coordinate transformation diagram established by installing a camera at the end of the second rod of the robot to form an eye-in-hand configuration. FIG. 3 is a schematic diagram showing the relationship between camera system coordinates and image coordinates and the imaging principle.
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