2D visual servoïng of wheeled mobile robot by neural networs

Abstract

We are interested in this paper in the 2D visual servoïng for a mobile robot type Koala using radial basis function (RBF) neural network (NN). Seen that the interaction matrix, expressing the relationship between the camera motion and the consequent changes on the visual features, contains parameters to be estimated (depth) and requires a calibration phase of the camera. In more, the model of the robot can contain uncertainties engendered the movement with sliding. An online identification, using NN was proposed to overcome these problems. The RBF NN is used to estimate the block formed by the interaction matrix and the model inverts of the robot. The considered images are described by objects given by four points. Seen that the variables number of the estimated function is important, what can cause a problem of the use of an excessive number of RBFs. As remedy, we used a new approach consists in considering that a single point is sufficient to solve the problem of the 2D visual servoïng of the mobile robot.