Method for improving robot parameter identification accuracy

Abstract

The invention discloses a method for improving robot parameter identification accuracy and belongs to the field of industrial robot control. The method comprises optimizing stimulation trajectories through a genetic algorithm; stimulate a robot to perform cyclical movement according to the optimized stimulation trajectories; sampling actual joint positions and actual torque data during moving and processing the data to obtain torque values, joint positions, joint speeds and joint accelerated speeds which are high in signal to noise ratio; establishing a dynamic linear model, and identifying all parameters related to joint dynamics through a weighted least square method. By the aid of the method, the actual trajectory Fourier series form is fit through an inverse method, and accordingly, speeds and accelerated speeds which cannot be obtained directly in experiments can be obtained conveniently, noise signals in sampling position signals and torque signals can be eliminated through an equalization method, the signal to noise ratio is improved and the robot parameter identification accuracy is improved.

Description

technical field [0001] The invention relates to a method for improving the identification accuracy of robot parameters, which belongs to the field of industrial robot control. Background technique [0002] An industrial robot is a multi-joint manipulator or a multi-degree-of-freedom machine device for the industrial field. It can perform work automatically and is a machine that realizes various functions by its own power and control capabilities. It can be directed by a human, or it can run according to a pre-programmed program. At present, industrial robot technology is developing in the direction of high speed, high precision and intelligence, so higher requirements are put forward for the control precision of industrial robots. [0003] A key factor affecting the control accuracy of a robot is the accuracy of its dynamic model, and establishing an accurate dynamic model requires the dynamic parameters of each joint of the robot, but in general, the dynamic parameters can...

AI Technical Summary

Problems solved by technology

[0004] The excitation trajectory selection and optimization steps in the existing robot parameter identification process are complex, the optimization efficiency is low, and the optimized excitation trajectory cannot meet the subsequent high-precision sampling requirements

In addition, the position information and torque information of each joint obtained through the direct measurement of the optimized excitation trajectory of the existing technology contains a lot of measurement noise, especially the noise of the torque value. If these measurement data are directly used for parameter identification, it will inevitably be due to The introduction of large noise leads to inaccurate identification, and even some parameters that affect dynamics cannot be identified, affecting the final dynamic model

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