Neural network-based vision guided robotics

Abstract

An essential problem of image based visual servoing is evaluating the inverse Jacobian which, relates changes in image features to the change in robot position. Neural networks can learn to approximate the inverse feature Jacobian. In addition, neural networks have been used in dimensionality reduction of image input. We show that it is possible to use neural networks for both feature extraction using compression and for feature Jacobian approximation in the visual servoing problem. In our system, we consider the following feature extraction methods: geometric features, averaging compression, vector quantization, and principal component extraction.