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

• Main Authors: Lin Cheng, Wen Wang, Yixin Weng, Guang Shi, He Yang, Keqing Lu
• Format: Article
• Language: English
• Published: Hindawi Limited 2018-01-01
• Series: Mathematical Problems in Engineering
• Online Access: http://dx.doi.org/10.1155/2018/1760190

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.