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dc.contributor.authorRam, Ashwin
dc.contributor.authorArkin, Ronald C.
dc.contributor.authorBoone, Gary Noel
dc.contributor.authorPearce, Michael
dc.date.accessioned2008-06-04T16:27:46Z
dc.date.available2008-06-04T16:27:46Z
dc.date.issued1994
dc.identifier.urihttp://hdl.handle.net/1853/22236
dc.description.abstractThis 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.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectFloating point geneen_US
dc.subjectGA-ROBOTen_US
dc.subjectReactive controlen_US
dc.subjectRobot navigationen_US
dc.subjectRobotic coordinationen_US
dc.titleUsing Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigationen_US
dc.typePaperen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing


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  • Mobile Robot Laboratory Publications [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.
  • Mobile Robot Laboratory [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.

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