Abstract: Aiming at the current situation that the absolute positioning accuracy of industrial robots is not high,a method of Kriging prediction of positioning error with comprehensive consideration of geometrical and non-geometrical error influencing factors was proposed. Firstly,a 3D dynamic tracking measurement system was used to measure the position errors of a set of points in the workspace of the industrial robot,and the experimental variational function was constructed from the position error; secondly,the particle swarm algorithm combined with the Kriging method was used for parameter optimization of the experimental variational function to obtain the optimal model and parameters; then,the weight coefficients and Lagrange multipliers were solved by Kriging equation combined with the optimal experimental variational function model,and the Kriging prediction equation was utilized to predict the positioning error;finally,the positioning errors were compensated and the results were veried. Experimental results show that the average absdute positioning error decreased from 1.048 9 mm to 0.178 6 mm,with an increase in accuracy of 82.97 %; the root mean square error decreased from 0.393 7 mm to 0.058 5 mm,with an increase in accuracy of 85.14 %,verifying the efficiency and practicality of this method.

Key words: Kriging, geometric error, non-geometric error, particle swarm, error prediction compensation