Industrial mechanical arm visual control method based on depth convolution neural network

Abstract

The invention discloses an industrial mechanical arm visual control method based on a depth convolution neural network, and the method comprises the steps: 1), carrying out the collection and preprocessing of the visual information of a target object; 2), carrying out the training and adjustment of the depth convolution neural network; 3), verifying and storing the model. Through combining with the depth convolution neural network, the method extracts the ideal grabbing positions of target objects with different postures, enlarges the applicable range of a system, solves a problem that a conventional visual control method is poor in recognition of a specific target object, effectively simplifies the use difficulty of an industrial mechanical arm, provides a new method for the control of the industrial mechanical arm, and is good in expansibility.

Description

technical field [0001] The invention relates to the field of industrial manipulators, in particular to a visual control method for industrial manipulators based on a deep convolutional neural network. Background technique [0002] In industrial production, industrial robotic arms can replace manpower for simple and repetitive tasks, such as: picking, assembling, welding, packaging, beating, cutting, grinding, dragging and other production operations. Especially in dangerous and harsh working environments, robot technology is used to reduce potential safety risks. Research on robot technology is an important way to realize intelligent manufacturing and reduce production costs of enterprises. [0003] "Motion planning" and "task determination" are two key technologies for industrial robotic arms. "Motion planning" can be divided into two parts: path planning and trajectory generation. The purpose of path planning is to find a series of non-interfering path points for the in...

AI Technical Summary

Problems solved by technology

But the problem is that these sensors are not very accurate and cannot be used in industrial production in the short term

[0009] 3. Voice teaching: using predefined voice commands to manipulate the movement of industrial robots, the problem is that the operability accuracy is poor, and fine work cannot be performed well

[0011] The disadvantages of the above four ways of using industrial robots are that industrial robots need to operate according to predefined programs, require professionals to maintain, and are highly complex when applied to new tasks.

Difficulties in deployment and implementation have largely limited the growth of the robotics industry

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