Extension of the ALVINN-architecture for robust visual guidance of a miniature robot

Abstract

Extensions of the ALVINN architecture are introduced for a KHEPERA miniature robot to navigate visually robust in a labyrinth. The reimplementation of the ALVINN-approach demonstrates, that also in indoor-environments a complex visual robot navigation is achievable using a direct input-output-mapping with a multilayer perceptron network, which is trained by expert-cloning. With the extensions it succeeds to overcome the restrictions of the small visual field of the camera by completing the input vector with history-components, introduction of the velocity dimension and evaluation of the network's output by a dynamic neural field. This creates the prerequisites to take turns which are no longer visible in the actual image and so make use of several alternatives of actions.