Archive for January 1st, 1997

1 Jan

Case-Based Reactive Navigation: A Case-Based Method for On-Line Selection and Adaptation of Reactive Control Parameters in Autonomous Robotic Systems

Posted by cognitivecomputing in Robotics. Tagged: , .

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.

Read the paper:

Case-Based Reactive Navigation: A Case-Based Method for On-Line Selection and Adaptation of Reactive Control Parameters in Autonomous Robotic Systems

by Ashwin Ram, Ron Arkin, Kenneth Moorman, Russ Clark

IEEE Transactions on Systems, Man, and Cybernetics, 27B(3), 1997. Preliminary version published as Technical Report GIT-CC-92/57, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1992
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-92-57.pdf

1 Jan

Learning Adaptive Reactive Controllers

Posted by cognitivecomputing in Agents, Learning, Robotics. Tagged: , , , .

Reactive controllers has been widely used in mobile robots since they are able to achieve successful performance in real-time. However, the configuration of a reactive controller depends highly on the operating conditions of the robot and the environment; thus, a reactive controller configured for one class of environments may not perform adequately in another. This paper presents a formulation of learning adaptive reactive controllers. Adaptive reactive controllers inherit all the advantages of traditional reactive controllers, but in addition they are able to adjust themselves to the current operating conditions of the robot and the environment in order to improve task performance. Furthermore, learning adaptive reactive controllers can learn when and how to adapt the reactive controller so as to achieve effective performance under different conditions.

The paper presents an algorithm for a learning adaptive reactive controller that combines ideas from case-based reasoning and reinforcement learning to construct a mapping between the operating conditions of a controller and the appropriate controller configuration; this mapping is in turn used to adapt the controller configuration dynamically. As a case study, the algorithm is implemented in a robotic navigation system that controls a Denning MRV-III mobile robot. The system is extensively evaluated using statistical methods to verify its learning performance and to understand the relevance of different design parameters on the performance of the system.

Read the paper:

Learning Adaptive Reactive Controllers

by Juan Carlos Santamaria, Ashwin Ram

Technical Report GIT-CC-97/05, College of Computing, Georgia Institute of Technology, Atlanta, GA, January 1997
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-97-05.pdf