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

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

February 16, 2020
October 6, 2020
February 20, 2021
modeless calibration, industrial robot, neural-kinematic model, SCARA robot, Stewart platform
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 Mar. 01, 2021