A zero-point calibration method for a two-degree-of-freedom high-speed parallel robot

Abstract

The invention discloses a zero-point calibration method for a two-degree-of-freedom high-speed parallel robot. A correction term is introduced into a zero-point identification model based on a traditional Kalman filtering method, and an improved L-curve method is established for the recursive calculation process of the model. In addition, combined with the covariance matrix characteristics of the estimation error in the recursive process of the traditional Kalman filter method, an adaptive optimization method of the correction parameters is established, which effectively improves the robustness, accuracy and identification efficiency of the robot zero point identification process. .

Description

technical field [0001] The invention relates to a zero-point calibration method for a two-degree-of-freedom high-speed parallel robot. Background technique [0002] The zero point of the high-speed parallel robot determines the initial pose of the system, and the accuracy of the zero point directly determines the accuracy of its end moving platform during the movement process. Usually, the high-speed parallel robot will perform zero point calibration before leaving the factory. However, in the actual use process, because the high-speed parallel robot often performs operations such as quick sorting or packing, the robot is prone to motion collision, loose active joints or other control failures, resulting in zero point. of loss. Therefore, for the high-speed parallel robot, it is necessary to periodically calibrate the zero point error of the robot. [0003] The zero point error calibration of high-speed parallel robots usually needs to go through four steps, namely error m...

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Problems solved by technology

[0004] However, in the process of identifying the zero-point error of an actual high-speed parallel robot, the process noise and measurement noise characteristics of the system are difficult to obtain accurately due to factors such as changes in the working environment, fluctuations in the characteristics of measuring instruments, fluctuations in the servo adjustment system, and errors in the reducer. It is necessary to continue to manually try and make up the relevant parameters until the zero error results identified by the Kalman filter method converge to a certain threshold position

This greatly restricts the efficiency of the zero point calibration process and reduces the degree of automation of the calibration process

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