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Study of Neural-Kinematics Architectures for Model-Less Calibration of Industrial Robots.

Authors :
Tiboni, Monica
Legnani, Giovanni
Pellegrini, Nicola
Source :
Journal of Robotics & Mechatronics; Feb2021, Vol. 33 Issue 1, p158-171, 14p
Publication Year :
2021

Abstract

Modeless industrial robot calibration plays an important role in the increasing employment of robots in industry. This approach allows to develop a procedure able to compensate the pose errors without complex parametric model. The paper presents a study aimed at comparing neural-kinematic (N-K) architectures for a modeless non-parametric robotic calibration. A multilayer perceptron feed-forward neural network, trained in a supervised manner with the back-propagation learning technique, is coupled in different modes with the ideal kinematic model of the robot. A comparative performance analysis of different neural-kinematic architectures was executed on a two degrees of freedom SCARA manipulator, for direct and inverse kinematics. Afterward the optimal schemes have been identified and further tested on a three degrees of freedom full SCARA robot and on a Stewart platform. The analysis on simulated data shows that the accuracy of the robot pose can be improved by an order of magnitude after compensation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09153942
Volume :
33
Issue :
1
Database :
Complementary Index
Journal :
Journal of Robotics & Mechatronics
Publication Type :
Academic Journal
Accession number :
148819676
Full Text :
https://doi.org/10.20965/jrm.2021.p0158