基于机器视觉的机油冷却管圆度检测与分拣研究

针对机油冷却管生产过程中，管口的圆度误差主要是人工测量导致的，且检测效率低，无法实时检测的问题，基于视觉检测提出一种机油冷却管口圆度检测与分拣方法。构建机器人SR7CL的运动学模型，利用ADAMS和MATLAB对其运动学和动力学进行仿真，验证模型的正确性，以保证抓取的准确性。以机油冷却管为抓取对象，通过自适应阈值分割，突出管口特征；利用Canny算法提取出边缘信息，通过最小二乘法检测圆度误差，并根据圆度误差对冷却管进行分拣。结果表明：所提方法提高了检测的质量和效率，解决了实时性的问题。

Research on Roundness Detection and Sorting of Oil Cooling Pipe Based on Machine Vision

In view of the problem that in the production process of the oil cooling tube,the roundness error of the nozzle is mainly measured by manual measurement,and has the problem of low detection efficiency and inability to detect in real time,a method for roundness inspection and sorting of oil cooling nozzles was proposed based on visual inspection.The kinematics model of the robot SR7CL was established,ADAMS and MATLAB were used to simulate the kinematics and dynamics to verify the correctness of the model to ensure the accuracy of grasping.The oil cooling pipe was taken as the object to be captured,through adaptive threshold segmentation to highlighted the characteristics of the nozzle;the edge information was extracted by using the Canny algorithm,the roundness error was detected by using the least square method,and the cooling pipes were sorted based on the roundness error.The results show that by using the proposed method,the quality and efficiency of detection can be improved and the problem of real-time performance can be solved.