Industrial robots are widely used in intelligent manufacturing industry because of their high efficiency and low cost

but their low absolute positioning accuracy limits their application in the field of high-precision manufacturing. To improve the absolute positioning accuracy of robot and solve the traditional complex error modeling problems

a robot positioning error compensation method based on deep neural network is proposed. Firstly

the Latin hypercube sampling planning is carried out in Cartesian space

and the influence rule of target attitude on error is obtained. Then

positioning error prediction model based on GPSO–DNN is established to realize the prediction and compensation of the error. Finally

to verify the correctness and superiority of the method

other error compensation models are used to compare with it. The experimental results show that the positioning error compensation method based on GPSO–DNN has the highest compensation accuracy. The positioning error is reduced from 1.529mm before compensation to 0.343mm

and the accuracy is increased by 77.57%. This method can effectively compensate the positioning error of the robot and greatly improve the positioning accuracy of the robot.