A Modular Neural Network linking Hyper RBF and AVITE models for reaching moving objects

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 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.