Mechanical arm grabbing control method based on machine vision and depth learning

Abstract

The invention discloses a mechanical arm grabbing control method based on machine vision and depth learning. The method comprises the steps that an operation scene image under the current state of a mechanical arm is obtained, and a motion instruction vector group is generated according to a sampling mean value and an initial variance of motion instruction vectors; the motion instruction vectors are combined with operation scene pictures to obtain possibility prediction values corresponding to the motion instruction vectors; the multiple possibility prediction values corresponding to the motion instruction vectors are sequenced according to the value, and at least one optimal motion instruction vector corresponding to the maximum possibility prediction value is obtained; and the possibility prediction value of the grabbed object under the current state of the mechanical arm is compared with the possibility prediction value of the optimal motion instruction vector to determine a grabbing motion decision. The invention further discloses a mechanical arm grabbing control system based on machine vision and depth learning. The technical scheme can be applied to many mechanical arm application fields such as industrial mechanical arm sorting, feeding, service mechanical arm grabbing and the like, and an intelligent and stable grabbing effect is provided.

Description

technical field [0001] The invention belongs to the field of grasping control of a mechanical arm, and in particular relates to a grasping control method of a mechanical arm based on machine vision and deep learning. Background technique [0002] The robotic arm is the most widely used automatic mechanical device in the field of robotics. It can be seen in industrial manufacturing, medical treatment, entertainment services, military, semiconductor manufacturing, and space exploration. Although their shapes are different, they all have a common feature, that is, they can accept instructions and precisely locate a certain point in three-dimensional (or two-dimensional) space for operation. As robotic arms are more and more widely used in industrial production and life services, using machine vision, machine learning and other technologies to endow robotic arms with more functions and improve their intelligence is an important issue in the development of robotic arm technology....

AI Technical Summary

Problems solved by technology

On the other hand, there needs to be a certain degree of matching between the shape of the image entered in the prior art and the object to be grasped in real time. If there is no match, the robotic arm will not be able to succes

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