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Six-axes robot kinetic parameter identification method based on neural network

A technology of dynamic parameters and neural network, which is applied in the field of dynamic parameter identification of serial industrial robots and 6-axis robots, can solve problems such as low accuracy of dynamic parameter estimation and difficult control of industrial robots, and achieve suppression of random interference and accurate dynamics The effect of learning parameters and high fault tolerance

Active Publication Date: 2019-05-21
ZHEJIANG UNIV
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  • Application Information

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Problems solved by technology

At present, most of the robot dynamic parameter identification uses genetic algorithm to optimize the excitation trajectory, and uses the least square method to iteratively estimate the dynamic parameters. The estimation accuracy of the dynamic parameters is not high, which makes it difficult for industrial robots to control accurately.

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  • Six-axes robot kinetic parameter identification method based on neural network
  • Six-axes robot kinetic parameter identification method based on neural network
  • Six-axes robot kinetic parameter identification method based on neural network

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Embodiment Construction

[0042] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings of the description.

[0043] Select a suitable 6-axis robot, and obtain the robot's D-H parameters and other information.

[0044] D-H parameters

[0045] alpha1=pi / 2; a1=160; d1=0; theta1=q1; init_theta1=0

[0046] alpha2=0; a2=575; d2=0; theta2=q2; init_theta2=pi / 2

[0047] alpha3=pi / 2; a3=130; d3=644; theta3=q3; init_theta3=0

[0048] alpha4=-pi / 2; a4=0; d4=0; theta4=q4; init_theta4=0

[0049] alpha5=pi / 2; a5=0; d5=0; theta5=q5; init_theta5=-pi / 2

[0050] alpha6=0; a6=0; d6=109.5; theta6=q6; init_theta6=pi / 2

[0051] The present invention adopts following steps:

[0052] A method for identifying dynamic parameters of a 6-axis robot based on a neural network mainly includes the following steps:

[0053] The first step of dynamic modeling is to establish the D-H link coordinate system of the 6-axis robot, and construct its dynamic equations...

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Abstract

The invention discloses a six-axes robot kinetic parameter identification method based on a neural network. The six-axes robot kinetic parameter identification method comprises the following steps that firstly, robot kinetic modeling and linearization are conducted; secondly, motivation trajectory optimization is conducted, and specifically a motivation trajectory is optimized through an artificial immune algorithm; thirdly, experiment sampling is conducted, specifically a robot moves along the motivation trajectory, and multiple sets of observation matrices and joint torque are obtained as experiment data; fourthly, data processing is conducted, the data collected in an experiment are preprocessed through a three standard deviation norm and a median average filter method, and the influence brought by data noise is lowered; fifthly, kinetic parameter estimation is conducted, and kinetic parameters are estimated through the neural network; and sixthly, parameter verification is conducted, the robot follows an executable trajectory different from the motivation trajectory, experiment data are sampled again, theoretical joint torque is predicted according to kinetic parameters obtained by identification, and reliability of the identified kinetic parameters is evaluated with the torque residual root.

Description

technical field [0001] The invention relates to a method for identifying dynamic parameters of a robot, in particular to a method for identifying dynamic parameters of a 6-axis robot based on a neural network, and relates to serial industrial robots widely used in industrial production, logistics and transportation, etc., accurate The dynamic parameters of the robot are the basis for high-precision control of the robot. Background technique [0002] Robot dynamics is the basis for discussing robot control, so dynamic parameter identification is a very critical technology in robot control. Industrial robots need high-precision dynamic parameters to achieve high-precision control and smooth motion trajectories. At present, genetic algorithm is mostly used in the identification of robot dynamics transmission parameters to optimize the excitation trajectory, and the least square method is used to iteratively estimate dynamic parameters. The estimation accuracy of dynamic paramet...

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Application Information

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IPC IPC(8): B25J9/16B25J17/00
Inventor 泮求亮林旭军王进陆国栋
Owner ZHEJIANG UNIV
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