A four-point measurement model for evaluating the pose of industrial robot and its influence factor analysis
Article publication date: 15 May 2017
This paper aims to propose a four-point measurement model for directly measuring the pose (i.e. position and orientation) of industrial robot and reducing its calculating error and measurement uncertainty.
A four-point measurement model is proposed for directly measuring poses of industrial robots. First, this model consists of a position measurement model and an orientation model gotten by the position of spherically mounted reflector (SMR). Second, an influence factor analysis, simulated by Monte Carlo simulation, is performed to investigate the influence of certain factors on the accuracy and uncertainty. Third, comparisons with the common method are carried out to verify the advantage of this model. Finally, a test is carried out for evaluating the repeatability of five poses of an industrial robot.
In this paper, results show that the proposed model is better than the three-SMRs model in measurement accuracy, measurement uncertainty and computational efficiency. Moreover, both measurement accuracy and measurement uncertainty can be improved by using the proposed influence laws of its key parameters on the proposed model.
The proposed model can measure poses of industrial robots directly, accurately and effectively. Additionally, influence laws of key factors on the accuracy and uncertainty of the proposed model are given to provide some guidelines for improving the performance of the proposed model.
This work was supported by the demonstration project (2016GFW015) of science and technology service industry in Sichuan Province, Sichuan Science and Technology Support Program (2014GZ0119), Programs (K855, K856) in Laboratory of Ultra precision Manufacturing technology CAEP.
Yang, C., Wang, J., Mi, L., Liu, X., Xia, Y., Li, Y., Ma, S. and Teng, Q. (2017), "A four-point measurement model for evaluating the pose of industrial robot and its influence factor analysis", Industrial Robot, Vol. 44 No. 3, pp. 343-352. https://doi.org/10.1108/IR-08-2016-0208
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