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    Using Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigation

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    Date
    1994
    Author
    Ram, Ashwin
    Arkin, Ronald C.
    Boone, Gary Noel
    Pearce, Michael
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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 evolves reactive control systems in various environments, thus creating sets 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. Unlike standard genetic algorithms, our method uses a floating point gene representation. The system is fully implemented and has been evaluated through extensive computer simulations of robot navigation through various types of environments.
    URI
    http://hdl.handle.net/1853/22236
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    • Mobile Robot Laboratory [187]
    • Mobile Robot Laboratory Publications [187]

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