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

Ashwin Ram

Georgia Institute of Technology

Gary Boone

Georgia Institute of Technology

Ronald Arkin

Georgia Institute of Technology

Michael Pearce

Georgia Institute of Technology

This article 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.

Key Words: reactive control • genetic algorithms • robot navigation • machine learning

Adaptive Behavior, Vol. 2, No. 3, 277-305 (1994)
DOI: 10.1177/105971239400200303
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