Paper Info
Title
Learning in a hierarchical control system: 4D/RCS in the DARPA LAGR program
Abstract
The Defense Applied Research Projects Agency (DARPA) Learning Applied to Ground Vehicles (LAGR) program aims to develop algorithms for autonomous vehicle navigation that learn how to operate in complex terrain. Over many years, the National Institute of Standards and Technology (NIST) has developed a reference model control system architecture called 4D/RCS that has been applied to many kinds of robot control, including autonomous vehicle control. For the LAGR program, NIST has embedded learning into a 4D/RCS controller to enable the small robot used in the program to learn to navigate through a range of terrain types. The vehicle learns in several ways. These include learning by example, learning by experience, and learning how to optimize traversal. Learning takes place in the sensory processing, world modeling, and behavior generation parts of the control system. The 4D/RCS architecture is explained in the paper, its application to LAGR is described, and the learning algorithms are discussed. Results are shown of the performance of the NIST control system on independently-conducted tests. Further work on the system and its learning capabilities is discussed. (c) 2007 Wiley Periodicals, Inc.
Year
DOI
Venue
2006
10.1002/rob.20162
JOURNAL OF FIELD ROBOTICS
Keywords
Field
DocType
inertial navigation,global positioning system,control systems,reference model,embedding,terrain,algorithms,robots,robotics,architecture,control system,hierarchies,robot control
Robot learning,Hierarchical control system,Computer vision,Robot control,Control theory,Simulation,NIST,DARPA LAGR Program,Artificial intelligence,Engineering,Robot,Mobile robot
Journal
Volume
Issue
ISSN
23
11-12
1556-4959
Citations 
PageRank 
References 
11
1.70
12
Authors
6
Name
Order
Citations
PageRank
Albus, J.116022.48
Roger Bostelman216732.16
Tommy Chang3588.34
Tsai Hong413714.46
William P. Shackleford5112.03
Michael Shneier625752.18