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一种基于特征线重合的快速坐标变换方法在工业机器人标定中的应用

A Rapid Coordinate Transformation Method Applied in Industrial Robot Calibration Based on Characteristic Line Coincidence.

作者信息

Liu Bailing, Zhang Fumin, Qu Xinghua, Shi Xiaojia

机构信息

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

出版信息

Sensors (Basel). 2016 Feb 18;16(2):239. doi: 10.3390/s16020239.

Abstract

Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration.

摘要

坐标变换在工业测量中起着不可或缺的作用,包括摄影测量、大地测量、激光三维测量和机器人技术。广泛应用的坐标变换方法通常基于求解点云方程。尽管精度很高,但由于使用了病态矩阵,这可能导致无解。本文提出了一种新颖的坐标变换方法,该方法不是基于方程求解,而是基于几何变换。我们构造特征线来表示坐标系。根据空间几何关系,通过一系列旋转和平移使特征线扫描重合。利用矩阵变换理论可以得到变换矩阵。设计实验将所提出的方法与其他方法进行比较。结果表明,所提出的方法具有同样高的精度,但操作更方便、灵活。还提出了一种多传感器组合测量系统,通过机器人运动学参数的标定来提高机器人的位置精度。实验验证表明,采用所提出的方法和机器人标定后,机器人操作臂的位置精度提高了45.8%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72d5/4801615/638af60f79dd/sensors-16-00239-g001.jpg

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