Vision feedback control method based on wavelet nerve network

Abstract

The invention relates to a visual feedback control method based on a wavelet neural network, relates to a robot visual feedback control method, and belongs to the field of artificial intelligence. Aiming at the problem that the traditional robot visual feedback control method needs to calibrate the camera model, robot model, hand-eye relationship, etc., the present invention proposes, designs and implements a visual feedback control method based on wavelet neural network. In the present invention, the camera (the eyes of the robot) is aimed at the target object in the workplace, and the image is taken, and the computer performs image feature extraction and feeds back to the input end of the system, and compares it with the expected value. If the two are not equal, the expected value is obtained through the wavelet neural network. The value of the key angle of the robot, and then the robot controller controls the movement of the robot (camera), and repeats this process until the difference between the image features is zero. This method uses the wavelet neural network to avoid the calibration requirements for each part of the robot system, and uses the feature space transformation to obtain image features suitable for different target objects.

Description

technical field [0001] The invention relates to a robot visual feedback control method and belongs to the field of artificial intelligence. Background technique [0002] In recent years, efforts have been made to make robots have human-like visual capabilities. The current common practice is to use various off-the-shelf imaging devices (such as CCD cameras). Obtain environmental information through imaging equipment, process the information and feed it back to the input end of the system to guide and coordinate the robot to perform various operations. Similar research has important practical value in fields such as industry, agriculture, service industry, national defense and aerospace. However, traditional methods require that the camera model, robot model, and hand-eye relationship are known or partially known, which requires a lot of time-consuming and laborious calibration work. In order to avoid or simplify the calibration process of the system, researchers have prop...

AI Technical Summary

Problems solved by technology

The advantage of this method is that no system calibration is required, but the disadvantage is that each iteration needs to find the inverse matrix of the Jacobian matrix

For complex robot systems, the image Jacobian matrix has a high dimension, so it is very difficult to calculate the Jacobian inverse matrix online. In addition, when the Jacobian matrix is ​​singular, the absence of the inverse matrix will cause control failure

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