An Artificial Brain System of a Maze-Like Robot

This paper presents an Artificial Brain System of a Maze-like robot, which comprises of a perception unit and decision-making unit. The perception module is based on ART1 neural network, trained to identify the signposts of the maze; decision-making units is based on behavior probability matrix P, and it uses reinforcement learning to update the action strategy. The maze in which the robot would navigate has signposts at every intersection. The signposts are 2-D symbols with noise. In the simulation tests, the robot moves randomly in the maze. By adjusting the vigilance parameter ¿ and reinforcement constant CRF, the robot will eventually pass through the maze after a learning process during the self-exploration. The simulation show that the artificial brain system can be self-organized to make sense of the signposts and successfully guide the robot through the maze.