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A modular neural network linking Hyper RBF and AVITE models for reaching moving objects

Published online by Cambridge University Press:  23 August 2005

J. L. Pedreño-Molina
Affiliation:
Department of Information Technologies and Communications
J. Molina-Vilaplana
Affiliation:
Department of Systems Engineering and Automation Technical University of Cartagena. Campus Muralla del Mar,s7sol;n. 30.202 Cartagena (SPAIN)
J. López-Coronado
Affiliation:
Department of Systems Engineering and Automation Technical University of Cartagena. Campus Muralla del Mar,s7sol;n. 30.202 Cartagena (SPAIN)
P. Gorce
Affiliation:
Université du Sud Toulon-Var, LESP EA 31-62, Avenue de l'Université, 83957 La Garde (France) Corresponding author. E-mail:gorce@reniv,tln.fr

Abstract

In this paper, the problem of precision reaching applications in robotic systems for scenarios with static and non-static objects has been considered and a solution based on a modular neural architecture has been proposed and implemented. The goal of this solution is to combine robustness and capability mapping trajectories from two biologically plausible neural network sub-modules: Hyper RBF and AVITE. The Hyper Basis Radial Function (HypRBF) neural network solves the inverse kinematic in redundant robotic systems, while the Adaptive Vector Integration to End-Point (AVITE) visuo-motor neural model quickly maps the difference vector between current and desired position in both spatial (visual information) and motor coordinates (propioceptive information). The anthropomorphic behaviour of the proposed architecture for reaching and tracking tasks in presence of spatial perturbations has been validated over a real arm-head robotic platform.

Type
Research Article
Copyright
© 2005 Cambridge University Press

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