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

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. Adaptive Behavior, volume 2, issue 3, pages 277--304, 1994. 1 Introduction Navigation through a cluttered environment to a specified destination without hitting obstacles is a common, but complex and underconstrained robotic task. Apart from the computational constraints on the navigat..