Error prediction and real-time compensation technology for five-degree-of-freedom hybrid robot

Abstract

The invention discloses an error prediction and real-time compensation technology for a five-degree-of-freedom hybrid robot based on a neural network. The technology comprises the following steps of (1) pose error decomposition on the hybrid robot, (2) pose error measurement and prediction of a parallel mechanism, (3) series swivel error prediction, (4) joint error compensation of the hybrid robot, (5) compensation neural network training and (6) constructing of a joint error compensator. According to the technology, prediction and compensation are implemented by directly utilizing error measurement data of the robot, the robot tail end pose errors caused by robot geometric errors and non-geometric factors such as gaps, friction, temperature and gravity can be compensated at the same time, the technology belongs to a comprehensive error compensation method, and the compensation effect is obviously superior to that of a traditional error compensation method based on geometric error identification.

Description

technical field [0001] The invention relates to robot calibration technology, in particular to a neural network-based error prediction and real-time compensation technology for a five-degree-of-freedom hybrid robot. Background technique [0002] The five-degree-of-freedom hybrid robot built by a three-degree-of-freedom parallel mechanism and a two-degree-of-freedom serial rotor is a new type of five-axis linkage processing equipment. Due to its fast speed, high precision, good dynamic characteristics, and large working space Such characteristics have been gradually applied to high-precision modern manufacturing fields such as drilling, milling, and polishing, which also put forward higher requirements for the robot's pose accuracy. [0003] Calibration is an effective way to improve the accuracy of hybrid robots. Traditional kinematics calibration models the errors of kinematic parameters, combines error measurement data and parameter identification algorithms to identify t...

AI Technical Summary

Problems solved by technology

[0004] In error compensation, the simplest compensation method is to directly modify the kinematic model of the robot. However, this method can only compensate the error of the robot kinematic parameters, and the geometric parameters of the robot after compensation no longer satisfy the Pipper principle, and the analytical inverse cannot be obtained. solution, it is difficult to apply

At present, indirect compensation methods such as pseudo-target method and reverse Jacobian iteration method are mainly used. These methods mostly use recursive and iterative calculation methods to ensure compensation accuracy. Due to the limitation of calculation efficiency, it is difficult to achieve real-time compensation.

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