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摘要:
以SCARA机器人、机械夹爪、摄像机CCD为硬件基础，搭建了基于单目视觉的SCARA机器人自动识别和定位插件系统平台，并利用摄像机参数标定和建立的抓取系统参数化模型，将CCD摄像机获取的工件图像坐标信息转化为机器人坐标系下的抓取位置信息。本系统以Visual studio软件为开发平台，利用OpenCV视觉数据库函数进行颜色识别与定位算法开发，经测试，该视觉算法能够实现工件的颜色识别和获取工件的位置信息，并控制机器人夹爪进行目标工件的精确抓取，满足了一般工业生产中抓取工件实时性的要求。
Abstract:The system uses SCARA robot, mechanical jaw and camera CCD as the hardware foundation, building a SCARA robot automatic identification and positioning plug-in system platform based on monocular vision. The co-ordinate information of the workpiece image acquired by the CCD camera is transformed into the crawling position information in the robot coordinate system by using the parametric model of the crawler system calibration and establishment of the camera parameters. The system uses Visual Studio as the development platform, utilizing OpenCV visual database function for the secondary development of visual algorithms. The visual algorithm can realize the color recognition of the workpiece and obtain the position information of the workpiece, and control the robot jaws to grasp the target workpiece accurately, which meets the general industrial production in the grasp of real-time requirements of the workpiece.
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Key words:
- SCARA robot /
- monocular vision /
- plug-in system /
- color recognition /
- positioning /
- precision crawl
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Abstract: The machine vision is introduced into the field of the plug-in robot system as a new type of sensor, and the function of the environment visual information (color, shape and attitude of the target workpiece) is realized by machine vision to achieve fast crawling and precise positioning. This method for the realization of fully automated plug, reduces the insertion error rate, improves the efficiency of plug-in workpiece, which is of great significance. The system uses SCARA robot, mechanical jaw and camera CCD as the hardware base, building a SCARA robot automatic identification and positioning plug-in system platform based on monocular vision, which mixed with a variety of colors of the insurance piece in a circular feeding tray. Under the vibration of the disk motor, the insurance piece is sent to the linear feeder in turn, and then the CCD camera is used to obtain the image information of the insurance piece, the contour shape and coordinate information are extracted from the image and the camera parameters are calibrated and parameterized model is established. The workpiece image coordinate information is transformed into the robot coordinate system under the crawl position information. The Visual Studio software is used as the development platform, and the visual recognition and positioning algorithm is developed by using the OpenCV visual database function. The visual algorithm prepares the image of the fuse piece, the image segmentation, the color recognition, the corner detection and the center point extraction. The center point of the workpiece is determined. Finally, the coordinates of the target point are obtained by calculating the scale ratio and the conversion of the coordinates. The visual algorithm can realize the color recognition of the workpiece and obtain the position information of the workpiece, and control the robot jaws to grasp the target workpiece accurately, which meets the general industrial production in the real-time requirements of the workpiece. In the field debugging, the visual algorithm can identify the color of the workpiece, get the workpiece coordinate information, and control the robot jaws for fast target positioning and accurate crawling. The results show that the system has high positioning accuracy, fastness and stability, and can meet the high precision and high reliability requirements of automatic plug-in inserts under robot operation. It can achieve a variety of colors and multi-station fully automated plug-in operations, without manual participation, reduces the number of recycling, improves the efficiency of the plug and has the advantages of high efficiency.
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图 1 基于机器视觉的SCARA工业机器人硬件系统组成.
Figure 1. SCARA industrial robot hardware system based on machine vision.
图 7 图像分割过程. (a)原始图像. (b)自适应阈值. (c)轮廓图像. (d)图像分割.
Figure 7. Image segmentation process. (a) Original image. (b) Adaptive threshold. (c) Contour image. (d) Image segmentation.
图 10 中心点提取过程. (a)原始图像. (b)角点检测. (c)中心点提取.
Figure 10. Center point extraction process. (a) Original image. (b) Corner detection. (c) Center point extraction.
图 11 夹取工件过程. (a)原点. (b)夹起保险片. (c)插接完毕. (d)回到原点.
Figure 11. Gripping the workpiece process. (a) Origin. (b) Clip Insurance pieces. (c) Plugged in. (d) Back to the origin.
表 1 测试坐标点结果表.
Table 1. Test coordinate point results table.
角点/pixel 1 2 3 4 平均值 中心点 u 115 116 443 433 276.75 277.26 v 103 356 355 92 226.5 225.42 
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表 2 视觉定位实验分析.
Table 2. Experimental analysis of visual positioning.
序号 视觉定位位置/mm 末端实际位置/mm 颜色识别时间/ms 定位时间/ms 1 (41.39, 260.30, 100.06) (40.55, 259.43, 100.87) 20.3 133.7 2 (40.86, 258.23, 101.53) (41.02, 259.20, 100.96) 19.8 131.2 3 (41.53, 259.46, 100.56) (41.36, 260.12, 100.66) 19.4 132.6 4 (41.53, 259.46, 100.56) (41.59, 260.51, 100.42) 19.9 133.3. 5 (40.93, 260.13, 100.67) (41.06, 260.73, 100.68) 20.1 134.2
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