Industrial mechanical arm automatic control method based on deep reinforcement learning

A technology for reinforcement learning, industrial machinery, applied in neural learning methods, adaptive control, manipulators, etc.

Active Publication Date: 2018-05-18
HUBEI UNIV OF TECH
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  • Abstract
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for automatic control of industrial manipulators based on deep reinforcement learning. By adding a deep rein

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  • Industrial mechanical arm automatic control method based on deep reinforcement learning

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Embodiment Construction

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] Such as figure 1 Shown is a schematic flow chart of an automatic control method for industrial manipulators based on deep reinforcement learning, including the following steps:

[0044] Step 1) Build a deep reinforcement learning model

[0045] 1.1) Experience pool initialization: set the experience pool as a two-dimensional matrix with m rows and n columns, and initialize the value of each element in the two-dimensional matrix to 0, where m is the size of the sample and n is the information stored in each sample Quantity, n=2×state_dim+action_dim+1, state_dim is the dimension of the state, action_dim is the dimension of the action; at the same time, reserve space for storing reward information in the experience pool, n=2×state_dim+action_dim+1 1 in this formula is the reserved space for storing reward information;

[0046] ...

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Abstract

The invention relates to an industrial mechanical arm automatic control method based on deep reinforcement learning. The industrial mechanical arm automatic control method based on deep reinforcementlearning includes the steps: constructing a deep reinforcement learning model, constructing output interference, establishing a reward rt calculation model, constructing a simulation environment, accumulating an experience pool, training a deep reinforcement learning neural network, and controlling an industrial mechanical arm to move in reality by means of the trained deep reinforcement learningmodel. By adding into the deep reinforcement learning network, the industrial mechanical arm automatic control method based on deep reinforcement learning can solve the automatic control problem of the industrial mechanical arm in an complicated environment, so as to complete automatic control of the mechanical arm, and the operating speed and the accuracy of the industrial mechanical arm are highafter completion of training.

Description

technical field [0001] The invention belongs to the technical field of reinforcement learning algorithms, and in particular relates to an automatic control method of an industrial manipulator based on deep reinforcement learning. Background technique [0002] Compared with manpower, industrial robotic arms can complete some simple, repetitive and cumbersome operations more efficiently. While greatly improving production efficiency, it can also reduce labor costs and labor intensity, while ensuring production quality. Reduce the probability of human accidents. In some harsh environments, such as high temperature, high pressure, low temperature, low pressure, dust, flammable, explosive, etc., it is of great significance to replace manual operations with robotic arms, which can prevent manual accidents caused by negligent operations. [0003] The motion solution process of the robotic arm is to first obtain the pose information of the grasping target, then obtain its own pose ...

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Application Information

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IPC IPC(8): G05B13/04G06F17/50G06N3/08B25J9/16
CPCB25J9/16B25J9/1664G05B13/027G05B13/045G06F30/20G06N3/08
Inventor 柯丰恺周唯倜赵大兴孙国栋许万丁国龙吴震宇赵迪
Owner HUBEI UNIV OF TECH
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