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A Neural Network-Based Identification Method for Dynamic Parameters of a 6-axis Robot

A technology of dynamic parameters and neural network, which is applied in the field of dynamic parameter identification of series industrial robots and 6-axis robots, can solve problems such as difficult control of industrial robots and low accuracy of dynamic parameter estimation, and achieve high-speed search for optimal solutions. , avoid local optimum, suppress the effect of random interference

Active Publication Date: 2022-01-25
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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  • A Neural Network-Based Identification Method for Dynamic Parameters of a 6-axis Robot
  • A Neural Network-Based Identification Method for Dynamic Parameters of a 6-axis Robot
  • A Neural Network-Based Identification Method for Dynamic Parameters of a 6-axis Robot

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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] In the first step of dynamic modeling, the D-H link coordinate system of the 6-axis robot is established, and its dynamic equation is con...

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Abstract

The invention discloses a method for identifying dynamic parameters of a 6-axis robot based on a neural network. The steps are as follows: Step 1: Robot dynamics modeling and linearization; Step 2: Excitation trajectory optimization, using artificial immune algorithm to optimize the excitation trajectory; Step 3: Experimental sampling, let the robot follow the excitation trajectory, and obtain multiple groups The observation matrix and joint moments are used as experimental data. The fourth step: data processing, using the three standard deviation criterion and the median average filtering method to preprocess the data collected in the experiment to reduce the impact of data noise. The fifth step: Kinetic parameters estimation, using neural network to estimate the kinetic parameters. Step 6: Parameter verification, let the robot follow an executable trajectory different from the excitation trajectory, sample the experimental data again, predict the theoretical joint torque according to the identified dynamic parameters, and use the torque residual root to evaluate the identified dynamic parameters reliability.

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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Patent Type & Authority Patents(China)
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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