Industrial robot-oriented reinforcement learning reward value calculation method

Abstract

The invention discloses an industrial robot-oriented reinforcement learning reward value calculation method. The method comprises the following steps of S1, carrying out initialization calculation on state parameters of an industrial robot; s2, calculating a pose reward value of an end execution mechanism of the industrial robot; s3, calculating a collision reward value of the industrial robot; s4, calculating an exploration reward value of the industrial robot; s5, calculating a target reward value; according to the method, the target nearby area and the non-target nearby area are divided, so that the tail end of the industrial robot can be quickly close to the target position in the early stage and can be adjusted to be in a proper posture while being close to the target position in the later stage, and the exploration process of the industrial robot is accelerated; various state information (positions, postures, collision and the like) of the industrial robot are comprehensively considered, and the problem that postures of a motion track finally planned by the industrial robot cannot meet actual production requirements is solved.

Description

technical field [0001] The invention belongs to the technical field of industrial robots, and in particular relates to a reinforcement learning reward value calculation method for industrial robots. Background technique [0002] Industrial robots are multi-joint manipulators or multi-degree-of-freedom machine devices for industrial fields. They have been widely used in important industries such as automobile manufacturing, electrical industry, and metal products. However, before the industrial robot is put into actual production, it is necessary to plan the motion trajectory planning of the industrial robot through manual teaching programming or manual offline programming. The biggest problem with this manual planning method is that it cannot automatically adjust the motion of the robot according to the change of the task. [0003] The emergence of reinforcement learning provides a new method for intelligent motion trajectory planning of industrial robots. Through the "exp...

AI Technical Summary

Problems solved by technology

The biggest problem with this manual planning method is that it is impossible to automatically adjust the movement of the robot according to the change of the job task.

[0004] However, due to the high dimensionality of the state and behavior of industrial robots, and the collision between the robot and obstacles needs to be considered in the motion trajectory planning

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