| Generally, the current welding robot systems almost belong to the 1st generation's teach and play back and few belong to the 2nd generation's off-line programming welding robot. These two types of robots cannot adapt to the changes of environment and working conditions. It is very important for welding robot to have the ability to adjust itself and autonomous plan according to some sensor information, which is important in practical production to realize autonomous welding. In order to improve the intelligent level and reliability of robots, this dissertation fixed two CCD cameras on the end-effecter of robot, which simulated the function of welder's two eyes, to observe the welding environment in a large extent. We realized the autonomous recognition and positioning of the initial welding position (IWP) and guiding the robot to IWP. We also realized the acquisition of three-dimensional (3D) coordinates of spatial weld seam using the principal of binocular vision. The research is the foundation to realize autonomous robot welding.Positioning of IWP and acquisition of 3D seam information is a procedure of visual 3D reconstruction. Binocular vision is an important method in this field. We placed two CCD cameras in a certain angel and fixed them on the end-effecter of the robot. The welding environment should be in the two cameras'common field of view, thus we can capture the work-pieces images in large welding environment. The aim of this thesis is finding and positioning the IWP in a relative large extent. And the 3D coordinates of welding seam in robot coordinates system is also calculated. Using the information, the robot is guided to the initial welding position and controlled to move along the seam path.Image recognition is the first step in 3D reconstruction. This thesis proposed an algorithm to recognize the IWP and the whole seam in relative larger welding environment. The algorithms first recognized the whole seam then IWP. The recognition of the whole seam including the procedure of pre-processing, such as the removing of effect of light, filter et al., image enhancement, edge detection and post-processing. This thesis proposed an algorithm named ARFIE (Adaptive Regional Fuzzy Image Enhancement), it defines the parameters named normalized relative fuzzy contrast as standard to part the image to different regions. The image is enhanced according to the level of region in fuzzy space. This method can enhance image of different contrast effectively. The whole procedure recognizes well the seam of work-pieces for different material and environment. The thesis also proposed a method to recognize IWP, it took the intersection point of seam and work-pieces boundaries as the initial value, and detected the corner in a window which take the initial value as the center. The method utilized both the edge and grey information, which assure the accuracy of recognition of IWP.Calibration is a bridge from 2D image to 3D spatial information. The thesis calibrated the binocular vision system, including the calibration of two cameras'intrinsic parameters, external parameters, relative relationship of the two cameras and hand-eye relationship. If the cameras were selected and placed, their intrinsic parameters are unchangeable, but the hand-eye relationship may change in the working because the collision, we adopt the online hand-eye relationship calibration algorithms.Stereo matching is a key and difficult problem in stereo vision. The thesis proposed an invariable transformation optimized image rectifying algorithms, which can rectify the image in general placement to ideal parallel placement. The algorithms calculated theory projection area of rectified images, proposed rectified relationship using information in this area. Experiments show that the algorithms reduce or avoid image distortion and the loss of image information, increase image resolution. The images have higher quality after the optimized rectification. An algorithm named CTFMIMM ( Coarse to Fine Multi-Information Matching Method)is proposed to solve the corresponding problem according to the characters of welding environment. The searching range is defined according to the special point such as IWP in welding image, and the structured edge information of processed image and grey information of original image are considered in the matching searching process, which ensured the rapid and accurate matching.The 3D information were reconstructed and transformed to the robot coordinates system. We can control the robot moving along the coordinates'data. This thesis analyzed the effect of robot error to 3D information's calculating, including the effect of TCP calibration and repeat positioning precision. Experiments showed that the TCP should re-calibration when its error is more than 1mm. The effect of placement of the vision system is also analyzed and the corresponding experimental results are given.The experimental system is introduced and the experimental results are given at last. Some types of typical planar and spatial seam are selected for the experiments. Experiments show the visual computing error is less than 0.56mm in camera coordinates system, which don't including the effect of robot moving. For guiding of IWP, the error is less than 1.1mm when the experimental conditions are satisfied. For the acquisition of 3D information, the planar distance error and height error is less than 1.2mm, 1.3mm for the planar seam, respectively. And the planar distance error and height error is less than 1.2mm, 1.6mm for the spatial seam. The program is organized in blocking model, the program related to hardware and 3D visual computing are in different model, which is convenient for the application of the algorithms in different robots and situation.The research is the technology foundation to realize autonomous welding. It has the ability to adapt to the changes of environment, which is a good method to replace work type of teach and playback, offline programming based on CAD, it is very important especially for the danger welding environments. |
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