%0 Journal Article 
%A Pedreño Molina, Juan Luis
%A Molina Vilaplana, Javier
%A López Coronado, Juan
%T A Modular Neural Network linking Hyper RBF and AVITE models for reaching moving objects
%D 2005
%U http://hdl.handle.net/10317/1477
%X 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 neural architecture biologically inspired has
been proposed and implemented. The goal of this solution is to combine
robustness and capability mapping trajectories from two biologically
inspired neural networks: HypRBF and AVITE. The Hyper Basis Radial
Function (HypRBF) neural model solves the inverse kinematic in
redundant robotic systems, while the Adaptive Vector Integration to
End-Point (AVITE) visuo-motor neural model quickly mapping 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.
%K Teoría de la Señal y las Comunicaciones
%K Bilogical Model
%K Visuo-motor robotic system
%K Reaching
%K Learning inverse Kinematics
%~ GOEDOC, SUB GOETTINGEN