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JRM Vol.33 No.1 pp. 158-171
doi: 10.20965/jrm.2021.p0158
(2021)

Development Report:

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

Monica Tiboni, Giovanni Legnani, and Nicola Pellegrini

Università degli Studi di Brescia
via Branze 38, Brescia 25123, Italy

Received:
February 16, 2020
Accepted:
October 6, 2020
Published:
February 20, 2021
Keywords:
modeless calibration, industrial robot, neural-kinematic model, SCARA robot, Stewart platform
Abstract
Study of Neural-Kinematics Architectures for Model-Less Calibration of Industrial Robots

Forward and inverse neural-kinematic calibration schemes

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.

Cite this article as:
Monica Tiboni, Giovanni Legnani, and Nicola Pellegrini, “Study of Neural-Kinematics Architectures for Model-Less Calibration of Industrial Robots,” J. Robot. Mechatron., Vol.33, No.1, pp. 158-171, 2021.
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Last updated on May. 18, 2021