An approach to self-training of the mobile robot
Vladimir A. Golovko, O. Ignatiuk, Rauf Kh. Sadykhov · 2002
The unsupervised learning of an autonomous mobile robot is a real research topic. It permits an artificial system to interact successfully with its environment and to avoid obstacles. This paper presents an intelligent control architecture which integrates self-training methods and is able to operate in complex, unknown environments in order to achieve its target. Our approach is based on reactive obstacle avoidance. The intelligent model integrates different neural networks and permits on-line learning. The results of experiments are discussed.