Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network. (29th May 2020)

• Record Type: Journal Article
• Authors: Gao, Guanbin; Wang, Sen; Na, Jing; Liu, Fei
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 3(2020:May/Jun.)
• Publication Date: 2020-05-29
• Subjects: Robotic articulated arm coordinate measuring machine -- RBF neural network -- single-point repeatability -- residual correction -- compensation; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420925638
• Languages: English
• ISSNs: 1729-8806

Abstract

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.