Three-dimensional grid precision compensation method for industrial robot

Abstract

The invention discloses a three-dimensional grid precision compensation method for an industrial robot and belongs to the technical field of inverse calibration of industrial robots. In the method, by taking advantage of the characteristic that the industrial robot has relatively high reposition accuracy and establishing the relationship between theory coordinates and actual positioning coordinates by a laser tracker, the theoretical coordinates of any one point in a certain cubic grid divided in an enveloping space of the robot can be corrected by a spatial interpolation process, so that theabsolute position precision compensation of the robot at the point is accomplished. In the method, the calculation procedure is simple and fast; the absolute position precision of the robot can be obviously improved, and the robot can be suitable for a wider application range.

Description

technical field [0001] The invention relates to a robot positioning compensation method, in particular to a spatial three-dimensional grid accuracy compensation method for industrial robots, and belongs to the technical field of industrial robot inverse calibration. Background technique [0002] The accuracy of the robot is an important index reflecting the performance of the robot, which includes absolute positioning accuracy and repeat positioning accuracy. The absolute positioning accuracy error is the deviation between the actual movement of the robot and the expected movement, which is generated by the deterministic original error (such as the error of the connecting rod parameter, the clearance of the kinematic pair, etc.); the repeat positioning accuracy error is when the robot repeatedly performs the same expected movement. The degree of mutual discreteness between the actual motions is generated by random original errors (such as joint servo positioning errors, etc....

AI Technical Summary

Problems solved by technology

[0005] 1) In order to enable the trained network to achieve a certain accuracy and adapt to all points within the envelope of the robot, training the neural network requires a large number of learning samples, so the measurement workload is heavy;

[0006] 2) This method needs to convert Cartesian coordinates into joint coordinates, so it needs to find the inverse solution to the robot kinematics equation. This process requires a lot of calculation, and the effect is not ideal near the singular point;

[0007] 3) The test results show that the positioning accuracy of the robot after calibration is still not ideal

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