Modular neural-visual servoing using a neural-fuzzy decision network

Abstract

Visual servoing is a growing research area. One of the key problems of feature based visual servoing is calculating the inverse Jacobian, relating change in features to change in robot position. Neural networks can learn to approximate the inverse feature Jacobian. However, the neural network approach can only approximate the feature Jacobian for a small workspace. In order to overcome this problem, we propose using a modular approach, where several networks are trained over a small area. Furthermore, we use a neural-fuzzy counterpropagation network to decide which subspace the robot is currently occupying. The neural fuzzy network provides smoother transitions between subspaces than hard switching. Preliminary results of the system's operation are also presented.