Abstract
A general method for optimizing the behavior of fuzzy control systems that receive sensory information from a robot is presented. Fuzzy control systems use a set of fuzzy variables. The fuzzy membership functions that define these variables perform a kind of packing of information from the sensors. These fuzzy membership functions have an unfixed shape and a set of unfixed points that may be adjusted to obtain a good performance of the control system. Genetic algorithms are a search technique analogous to natural genetics. The DPE (dynamic parameter encoding) algorithm is a mechanism that is more adaptable for controlling the mapping from fixed-length binary genes to real values. Genetic information encoding and the implemented genetic algorithms are used to adjusted the fuzzy membership functions associated with the linguistic labels that define the fuzzy variables of a rule-based control system. The control system designed allows a mobile semiautonomous robot to avoid unexpected obstacles in a partially unknown environment.
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