Industrial robot space grid precision compensation method based on neural network

Abstract

The invention discloses an industrial robot space grid precision compensation method based on a neural network, and belongs to the technical field of calibration of industrial robots. By using the characteristic of high repeated positioning precision of industrial robots, training the back propagation (BP) neural network of particle swarm optimization to simulate the inherent law of the robots positioned under the same load and at different environment temperatures and combining the robot space grid precision compensation method, a random target positioning point in a robot enveloping space range is subjected to precision compensation, and the absolute positioning precision of the random target positioning point is improved. The measurement workload can be effectively reduced by determining the maximum step length of the divided grids for the industrial robots of different types, and the industrial robots are quickly put into application. Positive solution and inverse solution of robot kinematics are not required, the calculation process is simple and quick, and online compensation can be realized. The absolute positioning precision of the robots is improved, and the calibrated industrial robots can adapt to wide application occasions.

Description

technical field [0001] The invention relates to a robot positioning accuracy compensation method, in particular to a spatial three-dimensional grid accuracy compensation method for industrial robots based on a particle swarm optimization neural network, and belongs to the technical field of industrial robot 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 origin...

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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