Competitive learning in autonomous LEGO(R) robots

Artificial neural network (ANN) algorithms provide powerful techniques for the construction of computing systems which are capable of adapting to non-stationary or incompletely specified operating environments. Unsupervised learning is of particular interest in such situations. This paper reports the results of empirical studies into the characteristics of standard and soft competitive learning algorithms and the embedding of the algorithm in a simple microcontroller based system for the operation of an autonomous mobile robot. Standard plastic LEGO parts are used for the structural components of these robots with the neural algorithms providing the control of the system from an on-board MC68HC11 microcontroller. The inputs to the network are provided by various analog sensors mounted on the robot chassis, while the outputs control the operation of the drive motors. Results of tests conducted on the algorithms demonstrate that soft competitive learning provides more robust performance than winner-take-all learning and that this algorithm can be realized in a simple microcontroller system if approximations are made to the theoretical computations. These approximations involve replacing the exponential function with a look-up table of modest size