Deep reinforcement learning-based behavior control method of yellow-peach stone digging robot

Abstract

The invention belongs to the technical fields of computer application and artificial intelligence, and relates to a deep reinforcement learning-based behavior control strategy of a yellow-peach stonedigging robot. For the problem that in traditional mechanical control methods, behavior control on yellow-peach stone digging robots is difficult to effectively carry out, the invention provides a deep reinforcement learning-based method carrying out behavior control on the yellow-peach stone digging robot with a visual function, and aims to improve working performance thereof. According to the method, perception ability of deep learning and decision-making ability of reinforcement learning are exerted, the robot is enabled to utilize deep learning to identify peach stone status, and then a single chip microcomputer is guided through a method of reinforcement learning to control motors to dig out a peach stone to finally complete a stone digging task. The method has advantages for utilizing machinery to replace manpower labor of stone digging tasks.

Description

technical field [0001] The invention belongs to the technical field of computer application and artificial intelligence, and relates to a behavior control method of a yellow peach core digging robot based on deep reinforcement learning. Background technique [0002] With the development of society and technological progress, the emergence of social problems such as labor shortage and rising labor prices have greatly promoted the research, application and popularization of industrial robots. In recent years, the research and application of industrial robots has always been one of the hot spots of scientific research and social concern. However, due to technical factors such as high dexterity, high stability, and high environmental tolerance required by industrial robots, the development and application of industrial robots have been plagued. The early industrial robots were the product of mechatronics, and as the market's requirements for the performance of industrial robots...

AI Technical Summary

Problems solved by technology

Due to the high hygiene requirements of food production, workers must wear work clothes, work boots, hats and masks that cover the whole body. The production season of canned yellow peaches is concentrated in July and August, and the weather is extremely hot, which makes the workers suffer from psychological and physical stress. The test made it very difficult for the cannery to recruit people

In addition, the quality of manual core digging has advantages and disadvantages depending on individual differences, and the quality of core digging cannot be fully guaranteed.

For the robot used for core digging, due to the different shapes of the peach cores of yellow peaches, it is difficult to use traditional mechanical control methods to control the behavior strategy of core digging.

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