Self-supervised neural system for reactive navigation

This paper deals with an artificial neural system for a mobile robot reactive navigation in an unknown, cluttered environment. A task of a presented system is to provide a steering angle signal letting a robot reach a goal while avoiding collisions with obstacles. Basic reactive navigation methods are briefly characterized, a special attention is paid to neural approaches. Then a qualitative description of a presented system is given. The main parts of the system are: the Fuzzy-ART classifier performing a perceptual space partitioning, and the neural associative memory, storing system's experience and superposing influences of different behaviours. Preliminary tests show that the learning by trial-and-error is efficient, as well in a case of beginning from scratch, as after some disturbances of either system's or environmental characteristics. >