Mobile Robot Control Based on 2D Visual Servoing: A New Approach Combining Neural Network With Variable Structure and Flatness Theory

Abstract

This paper focuses on the 2D visual servo-control of a mobile robot using a neural network (NN) with variable structure. The interaction matrix relating camera movement and changes in visual characteristics requires an estimation phase to determine its parameters as well as a camera calibration phase. It is common in applications related to mobile robotics that the robot model contains uncertainties generated by the sliding phenomenon. We suggest online identification, using NN to avoid this problem. The RBF NN is used to estimate the block formed by the interaction matrix and the reverse robot. Since the number of variables to be estimated is large, this can lead to the use of an excessive number of RBFs. We propose to use a single point of the scene which is sufficient to solve the problem. This problem reduction is possible thanks to flatness theory which allows to reduce the number of NN inputs from 8 inputs (4 image points) -generally used in the literature- to 2 (one image point) only. In order to further reduce the complexity of the proposed algorithm, the number of neurons for each layer and for each iteration is optimized. We use a neural network with variable structure to reach this objective. The very encouraging results obtained validate the proposed approach.