This paper explores the application of ge- netic algorithms to the learning of local robot nav- igation behaviors for reactive control systems. Our approach is to train a reactive control system in vari- ous types of environments, thus creating a set of "eco- logical niches" that can be used in similar environ- ments. The use of genetic algorithms as an unsuper- vised learning method for a reactive control architec- ture greatly reduces the effort required to configure a navigation system. Findings from computer simu- lations of robot navigation through various types of environments are presented.
['Michael Pearce', 'Ronald C. Arkin', 'Ashwin Ram']
The Learning Of Reactive Control Parameters Through Genetic Algorithms