Autonomous cognition and reinforcement learning for mobile robots

This paper describes a new class of autonomous intelligent systems for robot navigation application focusing on the synthesis, analysis and discussion of the learning process. Systems in this class are able to learn independently of supervision. In fact, they learn interacting with the environment while exploring it. A reinforcement learning strategy (inspired on the classical animal conditioning) and Hebb-like rule learning mechanisms support the knowledge acquisition process. The intelligent system must learn navigate the robot in an unknown environment, guiding it to targets according to a safe trajectory (without collisions). Their modular and hierarchical architecture is based on fuzzy systems and neural network techniques. The proposed approach has been validated by using a simulator and a mobile robot. In both cases, the experiments show that the autonomous intelligent system has a clear evidence of independent learning capability and exhibits a good performance during the navigation. Furthermore, this approach is compared with other system where there is no intelligent mechanisms to guide the robot.