The Multi-Joint Measuring Arm’s Error Compensation Method Based on the Pose Configurations

Xiong, Hui; Xu, You Sheng; Zong, Zhi Jian; Gao, Qun

doi:10.4028/www.scientific.net/amm.117-119.751

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…3) Compensation calculation: To improve the accuracy of the RAACMM, the compensation value (Dx i , Dy i , Dz i ) for the coordinate (x i , y i , z i ) of the probe should be calculated by the residuals. According to the relationship of cylindrical coordinate system and Cartesian coordinates, the compensation value can be calculated by equation (11)…”

Section: Residual Correction Modeling and Data Acquisition Based On Rmentioning

confidence: 99%

“…The output data of the RBFNN can be obtained through equation (10). Then, the compensation value can be calculated by equation (11).…”

Section: Data Acquisition and Training For Rbfnnmentioning

confidence: 99%

“…Xiong et al 11 analyzed the relationship between the joint angles of the RAACMMs and the single-point position accuracy in the Cartesian coordinate system and presented a method to improve the measurement accuracy in a certain range according to the relationship model. The work by Bing et al 12 improved the polynomials on the basis of the work by Xiong et al 11 and presented the periodic characteristics of key parameters affecting the accuracy of the RAACMM. However, they are all based on polynomials; thus, many dynamic factors affecting the accuracy of the RAACMM are not considered comprehensively.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is not practical for RAACMM to deal with real-time information owing to the data scanning and large memory capacity. Xiong et al 11 analyzed the relationship between the joint angles of the RAACMMs and the single-point position accuracy in the Cartesian coordinate system and presented a method to improve the measurement accuracy in a certain range according to the relationship model. The work by Bing et al 12 improved the polynomials on the basis of the work by Xiong et al 11 and presented the periodic characteristics of key parameters affecting the accuracy of the RAACMM.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Gao

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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The robotic articulated arm coordinate measuring machine (RAACMM) is a special robotic structural measuring instrument used to perform industrial field inspection. The accuracy of a RAACMM in different poses presents certain characteristics due to the influence of various dynamic factors. However, the existing error compensation model of the RAACMM cannot include dynamic factors, which imposes certain limits on improving the accuracy of the RAACMM. In this article, a residual correction method for the RAACMM based on a radial basis function neural network (RBFNN) is proposed to compensate the dynamic factors and improve the accuracy of the RAACMM. Firstly, the influence of the pose configuration of the RAACMM on the residual error of the probe is analyzed. The periodic characteristics of the residual error are obtained based on the analysis results. Secondly, a relationship model between the residual errors and the structural parameters is established in the cylindrical coordinate system. Then, a residual correction model based on the single point repeatability and RBFNN is proposed to further enhance the accuracy of the RAACMM. The probe of the RAACMM is constrained with a cone-hole gauge to acquire the single point repeatability data. The residual correction model is trained with the data of single point repeatability, and residual errors are calculated via the residual correction model. Experimental results show that the repeatability and measurement accuracy of the RAACMM are all improved after the residual correction, which validates the effectiveness of the residual correction method.

show abstract

Section: Residual Correction Modeling and Data Acquisition Based On Rmentioning

confidence: 99%

“…The output data of the RBFNN can be obtained through equation (10). Then, the compensation value can be calculated by equation (11).…”

Section: Data Acquisition and Training For Rbfnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Gao

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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Single-point Residual Correction Method for Articulated Arm Coordinate Measuring Machine Based on Pose Configuration's polynomial model

Gao

et al. 2017

2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

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Method of compensating for instrumental uncertainty in measurements using a coordinate measuring ARM

Zaporozhets,

Kataiev

2024

Sist. dosl. energ.

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Due to the influence of dynamic factors in various measurement configurations, the degree of uncertainty in measurements using a Coordinate Measuring Arm (CMA) is directly related to the measurement configuration. However, existing models for compensating CMA errors do not account dynamic factors, which impose certain limits for improving the accuracy of CMAs. To solve this issue, a method for residual error correction based on a polynomial model for single-point measurements was proposed. The influence of the CMA configuration on the residual probe error was analyzed. To enhance accuracy, a polynomial model has been developed by studying the relationship between the rotation angles of the CMA's moving elements and the probe coordinates in a cylindrical coordinate system. Experimental results demonstrate that the residual error correction method significantly compensates for instrumental uncertainty, greatly improving the accuracy of measurements using CMAs. Keywords: coordinate measuring arm, measurement error, coordinate measurements, calculation method, single-point residual correction, compensation.

show abstract

The Multi-Joint Measuring Arm’s Error Compensation Method Based on the Pose Configurations

Cited by 3 publications

References 4 publications

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Single-point Residual Correction Method for Articulated Arm Coordinate Measuring Machine Based on Pose Configuration's polynomial model

Method of compensating for instrumental uncertainty in measurements using a coordinate measuring ARM

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