A simulator using classifier systems with neural networks for autonomous robot navigation

This paper presents a simulator that was developed to assist in the process of implementing high-level autonomous robot navigation algorithms and in the related experimentations. Classifier systems are designed here, using neural networks as classifiers, to perform autonomous navigation. We propose a powerful simulator using classes and objects to be easily updated and extended. The simulator carries a class composed of methods for differential wheels steering, for detecting collisions and for sensor readings. Another class allows the specification of geometric shaped objects, which can also be detected as obstacles in the environment. In addition, operators are available to deal with credit assignment, genetic algorithms, and inference of the classifiers. By designing and constructing the simulator, we create conditions to explore the potentialities of neural networks as classifiers.