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| Robot Positioning Error Compensation Method Based on Deep Neural Network |
| HUA Fangfang, TIAN Wei, HU Junshan, LI Bo, PU Yuxiao |
| ( College of Mechanical and Electrical Engineering, Nanjing University of Aeromautics and Astronautics, Nanjing 210016, China ) |
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Abstract 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.
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2020, 
