The Learning Of Reactive Control Parameters Through Genetic Algorithms

摘要

This paper explores the application of genetic algorithms to the learning of local robot navigation behaviors for reactive control systems. Our approach is to train a reactive control system in various types of environments, thus creating a set 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. Findings from computer simulations of robot navigation through various types of environments are presented. I. Introduction A common robot task is to navigate through an environment to a goal position, without hitting any obstacles that may be present. Navigation through a cluttered environment is an extremely complex and underconstrained task. Apart from the computational constraints placed on the design of a navigation system, it is desirable that the system be robust enough to navigate through a large number ...