Forward Kinematic Modeling of Conical-Shaped ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Forward Kinematic Modeling of Conical-Shaped Continuum Manipulators
Author(s) :
Bouyom Boutchouang, A. [Auteur]
Melingui, Achille [Auteur]
Mvogo Ahanda, J. [Auteur]
Lakhal, Othman [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Biya Motto, Frederic [Auteur]
Université de Yaoundé I [UY1]
Merzouki, Rochdi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Melingui, Achille [Auteur]
Mvogo Ahanda, J. [Auteur]
Lakhal, Othman [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Biya Motto, Frederic [Auteur]
Université de Yaoundé I [UY1]
Merzouki, Rochdi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Journal title :
Robotica
Pages :
1760-1778
Publisher :
Cambridge University Press
Publication date :
2021-10
ISSN :
0263-5747
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
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. ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
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