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混合视觉检测系统的快速全局校准技术

Rapid Global Calibration Technology for Hybrid Visual Inspection System.

作者信息

Liu Tao, Yin Shibin, Guo Yin, Zhu Jigui

机构信息

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

出版信息

Sensors (Basel). 2017 Jun 19;17(6):1440. doi: 10.3390/s17061440.

Abstract

Vision-based methods for product quality inspection are playing an increasingly important role in modern industries for their good performance and high efficiency. A hybrid visual inspection system, which consists of an industrial robot with a flexible sensor and several stationary sensors, has been widely applied in mass production, especially in automobile manufacturing. In this paper, a rapid global calibration method for the hybrid visual inspection system is proposed. Global calibration of a flexible sensor is performed first based on the robot kinematic. Then, with the aid of the calibrated flexible sensor, stationary sensors are calibrated globally one by one based on homography. Only a standard sphere and an auxiliary target with a 2D planar pattern are applied during the system global calibration, and the calibration process can be easily re-performed during the system's periodical maintenance. An error compensation method is proposed for the hybrid inspection system, and the final accuracy of the hybrid system is evaluated with the deviation and correlation coefficient between the measured results of the hybrid system and Coordinate Measuring Machine (CMM). An accuracy verification experiment shows that deviation of over 95% of featured points are less than ±0.3 mm, and the correlation coefficients of over 85% of points are larger than 0.7.

摘要

基于视觉的产品质量检测方法因其良好的性能和高效率在现代工业中发挥着越来越重要的作用。一种由带有柔性传感器的工业机器人和多个固定传感器组成的混合视觉检测系统已广泛应用于大规模生产中,尤其是在汽车制造领域。本文提出了一种用于混合视觉检测系统的快速全局校准方法。首先基于机器人运动学对柔性传感器进行全局校准。然后,借助已校准的柔性传感器,基于单应性对固定传感器逐一进行全局校准。在系统全局校准过程中仅使用一个标准球体和一个带有二维平面图案的辅助靶标,并且在校准过程可以在系统定期维护期间轻松重新执行。针对混合检测系统提出了一种误差补偿方法,并通过混合系统与坐标测量机(CMM)测量结果之间的偏差和相关系数来评估混合系统的最终精度。精度验证实验表明,超过95%的特征点偏差小于±0.3毫米,超过85%的点的相关系数大于0.7。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7d9/5492792/7c560b89ec64/sensors-17-01440-g001.jpg

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