(Georgia Institute of Technology, 1994)
Ram, Ashwin; Arkin, Ronald C.; Boone, Gary Noel; Pearce, Michael
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.
(Georgia Institute of Technology, 1992)
Arkin, Ronald C.; Clark, Russell J.; Ram, Ashwin
We describe a reactive robotic control system which incorporates aspects of machine learning to improve the system's ability to successfully navigate in unfamiliar environments. This system overcomes limitations of completely reactive systems by exercising on-line performance enhancement without the need for high level planning. The results of extensive simulation studies using the learning enhanced reactive controller are presented.
(Georgia Institute of Technology, 1992)
Arkin, Ronald C.; Pearce, Michael; Ram, Ashwin
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.
(Georgia Institute of Technology, 1992)
Ram, Ashwin; Arkin, Ronald C.; Clark, Russell J.; Moorman, Kenneth
This article presents a new line of research investigating on-line learning mechanisms for autonomous intelligent agents. We discuss a case-based method for dynamic selection and modification of behavior assemblages for a navigational system. The case-based reasoning module is designed as an addition to a traditional reactive control system, and provides more flexible performance in novel environments without extensive high-level reasoning that would otherwise slow the system down. The method is implemented in the ACBARR (A Case-BAsed Reactive Robotic) system, and evaluated through empirical simulation of the system on several different environments, including "box canyon" environments known to be problematic for reactive control systems in general.