Intelligent captured image generation method based on conditional generative adversarial network

Abstract

The invention discloses an intelligent grabbed image generation method based on a conditional generative adversarial network, and the method comprises the steps: firstly constructing a virtual-real grabbed environment and a conditional generative adversarial neural network, and carrying out the loop iteration training of a grabbed quality discriminator, an image quality discriminator DPQ and a generator G through employing an existing grabbed data set. and finally, generating a depth image of specific noise by the trained generator. A high-precision mechanical structure of the robot is combined with the characteristics of high robustness of deep learning, and a data basis is provided for the robot to realize intelligent and reliable grabbing behaviors in occasions where no specific task is given or the shape of an object to be sorted is relatively complex and the environment is relatively variable.

Description

technical field [0001] The invention belongs to the fields of intelligent manufacturing and machine learning, and in particular relates to an intelligent capture image generation method based on a conditional generative confrontation network. Background technique [0002] With the development of Industry 3.0, the initial automated robots undertake repetitive, boring and low-intelligence labor, liberating human beings. The robotic arm is one of the most common robots in the industry. At present, the robotic arm is widely used in industrial environments and even in home hospitals. Grabbing and moving objects is one of the most important tasks of the robotic arm. The advantage of the robotic arm is that it can quickly complete a given task with high precision. When the position, shape and posture of the object are fixed, the reasonable setting of the robotic arm's action can efficiently complete the grasping. However, with the trend and development of Industry 4.0, the above m...

AI Technical Summary

Problems solved by technology

However, with the trend and development of Industry 4.0, the above methods have revealed the following problems: robots are not only required to be able to perform repetitive tasks, but are also expected to be able to complete complex tasks to a certain extent and have the ability to respond to environmental changes

However, this calibration process takes a lot of time, and the grasping model is limited, resulting in poor generalization ability of the data set.

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