The Learning of Reactive Control Parameters Through Genetic Algorithms

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Pearce, Michael
Ram, Ashwin
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http://hdl.handle.net/1853/22447
Abstract
This paper explores the application of genetic algorithms to the learning of local robot navigation behaviors for reactive control systems. Our approach is to train a reactive control system in various types of environments, thus creating a set of "ecological niches" that can be used in similar environments. The use of genetic algorithms as an unsupervised learning method for a reactive control architecture greatly reduces the effort required to configure a navigation system. Findings from computer simulations of robot navigation through various types of environments are presented.
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1992
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