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Evolutionary learning of fuzzy rules in a modified classifier system for mobile agents control

In this work we present the creation of a platform, along with an algorithm to evolve the learning of FLCs, especially aiming to the development of fuzzy controllers for mobile robot navigation. The structure has been proven on a Kephera robot. The conceptual aspects that sustain the work include topics such as Artificial Intelligence (AI), control advanced techniques, sensorial systems and mechatronics. Topics related with the control and automatic navigation of robotic systems especially with learning are approached, based on the Fuzzy Logic theory and evolutionary computing. We can say that our structure corresponds basically to a Classifier System, with appropriate modifications for the objective of generating controllers for mobile robot trajectories. The more stress is made on genetic profile than in the characteristics of the individuals and on the other, the strategy of distribution of the reinforcement is emphasized, fundamental aspects on which the work seeks to contribute.