Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method (CROSBI ID 733429)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Baressi Šegota, Sandi ; Mrzljak, Vedran ; Prpić-Oršić, Jasna ; Car, Zlatan
engleski
Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method
The goal of the paper is estimating the normalized friction torque of a joint in an industrial robotic manipulator. For this purpose a source data, given as a figure, is digitized using a tool WebPlotDigitizer in order to obtain numeric data. The numeric data is the used within the machine learning algorithm genetic programming (GP), which performs the symbolic regression in order to obtain the equation that regresses the dataset in question. The obtained model shows a coefficient of determination equal to 0.87, which indicates that the model in question may be used for the wide approximation of the normalized friction torque using the torque load, operating temperature and joint velocity as inputs.
Genetic programming ; Friction torque prediction ; Industrial robotic manipulator friction ; Mechine learning ; Symbolic regression
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Podaci o prilogu
5-8.
2023.
objavljeno
Podaci o matičnoj publikaciji
XVI International Conference for Young Researchers "Technical Sciences. Industrial Management 2023" - PROCEEDINGS
Angelov, Cyril
Sofija: Scientific technical union of mechanical engineering Industry - 4.0
2535-0196
2535-020X
Podaci o skupu
XVI International Conference for Young Researchers "Technical Sciences. Industrial Management 2023"
poster
08.03.2023-11.03.2023
Borovets, Bugarska