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Learning the forward kinematics behavior of a hybrid robot employing artificial neural networks

Published online by Cambridge University Press:  14 October 2011

Rongjie Kang ,
Hélène Chanal ,
Thomas Bonnemains ,
Sylvain Pateloup ,
David T. Branson III  and
Pascal Ray
Rongjie Kang*
Affiliation:
Laboratoire de Mécanique et Ingénieries, Institut Français de Mécanique Avancée, Rue Roche Genes 27, 63175 Aubiere, France
Hélène Chanal
Affiliation:
Laboratoire de Mécanique et Ingénieries, Institut Français de Mécanique Avancée, Rue Roche Genes 27, 63175 Aubiere, France
Thomas Bonnemains
Affiliation:
Laboratoire de Mécanique et Ingénieries, Institut Français de Mécanique Avancée, Rue Roche Genes 27, 63175 Aubiere, France
Sylvain Pateloup
Affiliation:
Laboratoire de Mécanique et Ingénieries, Institut Français de Mécanique Avancée, Rue Roche Genes 27, 63175 Aubiere, France
David T. Branson III
Affiliation:
Department of Advanced Robotics, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
Pascal Ray
Affiliation:
Laboratoire de Mécanique et Ingénieries, Institut Français de Mécanique Avancée, Rue Roche Genes 27, 63175 Aubiere, France
*
*Corresponding author. E-mails: kangrongjie@vip.163.com
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Summary

Hybrid robots, composed of a parallel platform and serial wrist, achieve a compromise of stiffness and dexterity. Thus, they are well suited for applications such as aircraft component machining and automotive assembly, where high accuracy and large workspace movements are required. However, their forward kinematics can be highly coupled and be nonlinear. To reduce the time required to define the forward kinematics of a robot with parallel–serial structure, this paper introduces the use of neural networks. Two radial basis function networks are trained to learn the parallel and serial kinematics separately, and then integrated into a complete model. The error of this network model is analyzed and identified by a particle swarm optimization algorithm. Simulation and experiment results are obtained from the hybrid robot, Exechon, which shows that the developed kinematic model is able to produce accurate position and orientation estimates of the end-effector. The computation time of the neural network model is greatly reduced when compared to the time achieved by the numerical model.

Keywords

Hybrid robotForward kinematicsNeural networkParticle swarm optimization

Information

Type
Articles
Information
Robotica , Volume 30 , Issue 5 , September 2012 , pp. 847 - 855
Copyright
Copyright © Cambridge University Press 2011

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