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Open AccessArticle

A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision

by 1, 1,2 and 1,2,*
1
School of Mechanic and Automotive Engineering, University of Ulsan, Daehak-ro 93, Nam-gu, Ulsan 44610, Korea
2
Abeosystem Co, LTD, Daehak-ro 93, Nam-gu, Ulsan 44610, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(9), 1940; https://doi.org/10.3390/app9091940
Received: 16 April 2019 / Revised: 4 May 2019 / Accepted: 9 May 2019 / Published: 11 May 2019
It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply the robot system to vehicle assembly lines that need small position errors. In this paper, we have studied a method to reduce the absolute position error of robots using machine vision and neural network. The position/orientation of robot tool-end is compensated using a vision-based approach combined with a neural network, where a novel indirect calibration approach is presented in order to gather information for training the neural network. In the simulation, the proposed compensation algorithm was found to reduce the positional error to 98%. On average, the absolute position error was 0.029 mm. The application of the proposed algorithm in the actual robot experiment reduced the error to 50.3%, averaging 1.79 mm. View Full-Text
Keywords: error compensation; hand-eye calibration; absolute accuracy; neural network error compensation; hand-eye calibration; absolute accuracy; neural network
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Cao, C.-T.; Do, V.-P.; Lee, B.-R. A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision. Appl. Sci. 2019, 9, 1940.

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