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    The Learning of Reactive Control Parameters Through Genetic Algorithms

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    er-92-02.pdf (177.0Kb)
    Date
    1992
    Author
    Arkin, Ronald C.
    Pearce, Michael
    Ram, Ashwin
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    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.
    URI
    http://hdl.handle.net/1853/22447
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    • Mobile Robot Laboratory [187]
    • Mobile Robot Laboratory Publications [187]

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