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Forward Kinematic Modeling of Conical-Shaped Continuum Manipulators

Published online by Cambridge University Press:  03 February 2021

A. H. Bouyom Boutchouang*
Affiliation:
Department of Electrical and Telecommunications Engineering, Ecole Nationale Supérieure Polytechnique, University of Yaounde I, Yaounde 8390, Cameroon. E-mail: achillemelingui@gmail.com
Achille Melingui
Affiliation:
Department of Electrical and Telecommunications Engineering, Ecole Nationale Supérieure Polytechnique, University of Yaounde I, Yaounde 8390, Cameroon. E-mail: achillemelingui@gmail.com
J. J. B. Mvogo Ahanda
Affiliation:
Department of Electrical and Power Engineering, University of Bamenda, Bamenda 39 Bambili, Cameroun. E-mail: josephjeanmvogo@yahoo.fr
Othman Lakhal
Affiliation:
CRIStAL Laboratory, CNRS-UMR,Villeneuve d’Ascq 59655, France. E-mails: lakhal.othman@polytech-lille.fr, rochdi.merzouki@polytech-lille.fr
Frederic Biya Motto
Affiliation:
Department of Physic’s, Faculty of Sciences, University of Yaounde I, Yaounde 8390, Cameroon. E-mail: biyamotto@yahoo.fr
Rochdi Merzouki
Affiliation:
CRIStAL Laboratory, CNRS-UMR,Villeneuve d’Ascq 59655, France. E-mails: lakhal.othman@polytech-lille.fr, rochdi.merzouki@polytech-lille.fr
*
*Corresponding author. E-mail: bouyom_hyacinthe@yahoo.fr

Summary

Forward kinematics is essential in robot control. Its resolution remains a challenge for continuum manipulators because of their inherent flexibility. Learning-based approaches allow obtaining accurate models. However, they suffer from the explosion of the learning database that wears down the manipulator during data collection. This paper proposes an approach that combines the model and learning-based approaches. The learning database is derived from analytical equations to prevent the robot from operating for long periods. The database obtained is handled using Deep Neural Networks (DNNs). The Compact Bionic Handling robot serves as an experimental platform. The comparison with existing approaches gives satisfaction.

Type
Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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