An approach to mobile robot self-training
Vladimir A. Golovko, O. Ignatiuk, V. Sauta · 2002
The unsupervised learning of the autonomous mobile robot is one of the actual research topics. It permits the artificial system to interact successfully with their environment and to avoid obstacles. This paper presents an intelligent control architecture which integrates self-training methods and is available to operate in complex, unknown environment in order to achieve the target. Our approach is based on the reactive obstacle avoidance. The intelligent model integrates different neural networks and permits the robot to perform online learning. The results of experiments are discussed.