A real-time unsupervised neural network for the control of a mobile robot

We introduce an unsupervised neural architecture for the control of a mobile robot. The mobile robot to be controlled is organized in a tricycle structure. Movement is performed by selection of angular velocities for the motors attached to the two propulsive wheels. Following an initial learning phase, the controller architecture allows movement between arbitrary points through exteroceptive or visual information. It is important to note that rather than learning explicit trajectories, the controller learns the relationship between angular velocities and the magnitude and direction of the resulting movement. This approach solves the inverse kinematic problem, so that visual information in spatial coordinates can generate the appropriate wheel angular velocities to move the mobile robot to a desired goal. The main characteristic of this architecture that distinguishes it from other neural controllers is that it does not require supervision during the training phase