Structural Parameter Identification of Articulated Arm Coordinate Measuring Machines

Guanbin Gao, Huaishan Zhang, Xing Wu and Yu Guo

Mathematical Problems in Engineering, 2016, vol. 2016, 1-10

Abstract:

Precise structural parameter identification of a robotic articulated arm coordinate measuring machine (AACMM) is essential for improving its measuring accuracy, particularly in robotic applications. This paper presents a constructive parameter identification approach for robotic AACMMs. We first develop a mathematical kinematic model of the AACMM based on the Denavit-Hartenberg (DH) approach established for robotic systems. This model is further calibrated and verified via the practical test data. Based on the difference between the calculated coordinates of the AACMM probe via the kinematic model and the given reference coordinates, a parameter identification approach is proposed to estimate the structural parameters in terms of the test data set. The Jacobian matrix is further analyzed to determine the solvability of the identification model. It shows that there are two coupling parameters, which can be removed in the regressor. Finally, a parameter identification algorithm taking the least-square solution of the identification model as the structural parameters by using the obtained poses data is suggested. Practical experiments based on a robotic AACMM test rig are carried out, and the results reveal the effectiveness and robustness of the proposed identification approach.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4063046

DOI: 10.1155/2016/4063046

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