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.

Read the paper:

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

by Ashwin Ram, Ron Arkin, Gary Boone, Michael Pearce

Adaptive Behavior, 2(3):277-305, 1994
www.cc.gatech.edu/faculty/ashwin/papers/er-94-01.pdf
Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: