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A Novel Kinematic Parameters Identification Method for Articulated Arm Coordinate Measuring Machines Using Repeatability and Scaling Factor

机译:基于重复性和比例因子的关节臂坐标测量机运动学参数辨识新方法

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摘要

Kinematic parameters identification and compensation are effective ways to improve the accuracy of articulated arm coordinate measuring machines (AACMMs) and robotic arms without increasing the cost of hardware. Generally, kinematic parameters identification methods based on standard references are relatively high in accuracy but time-consuming and not suitable for industrial sites, while kinematic parameters identification methods based on repeatability are flexible and easy to implement but lack reliability in accuracy. A novel kinematic parameters identification method for AACMMs using repeatability and scaling factor is proposed in this paper, which combines the advantages of methods based on both standard references and repeatability. Through theoretical analysis and numerical simulations, we found that the commonly used single-point-repeatability-based identification method has problems in identifying the length parameters, which is due to that high repeatability cannot guarantee the accuracy of the kinematic parameters and the measurement accuracy of the AACMM. Further analysis showed that the error of the length parameters is determined by a scaling factor which can be used to remove the error of length parameters. Therefore, a two-step novel kinematic parameters identification method for the AACMMs using repeatability and scaling factor was proposed to get accurate parameters with convenient operation. Experimental studies showed the effectiveness of the proposed identification method, which indicated that 93% more error in spatial length can be decreased comparing to the traditional method of repeatability-based identification.
机译:运动学参数识别和补偿是提高关节臂坐标测量机(AACMM)和机器人手臂精度的有效方法,而不会增加硬件成本。通常,基于标准参考的运动学参数识别方法精度较高,但是比较耗时,不适合工业现场;而基于可重复性的运动学参数识别方法比较灵活,易于实现,但是缺乏准确性。提出了一种基于重复性和比例因子的AACMM运动参数识别方法,该方法结合了基于标准参考和重复性的方法的优点。通过理论分析和数值模拟,我们发现常用的基于单点重复性的识别方法在识别长度参数方面存在问题,这是由于高重复性不能保证运动学参数的准确性和测量精度。 AACMM。进一步的分析表明,长度参数的误差由比例因子确定,该比例因子可用于消除长度参数的误差。因此,提出了一种采用可重复性和比例因子的两步新颖的AACMM运动参数识别方法,以得到准确的参数,操作方便。实验研究表明,所提出的识别方法是有效的,这表明与传统的基于重复性的识别方法相比,可以将空间长度的错误减少93%以上。

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