Paper Info
Title
Integrating learning into a hierarchical vehicle control system
Abstract
The National Institute of Standards and Technology's (NIST) Intelligent Systems Division (ISD) is a participant in the Defense Advanced Research Projects Agency (DARPA) LAGR (Learning Applied to Ground Robots) Program. The NIST team's objective for the LAGR Program is to embed learning algorithms into the modules that make up the 4D/RCS (Four Dimensional/Real-Time Control System), the standard reference model architecture that ISD has applied to many intelligent systems. This enables the vehicle to learn to navigate in complex, off-road terrain. 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. This paper describes the 4D/RCS structure, its application to the LAGR program, and the learning and mobility control methods used by the NIST team's vehicle. Results are shown from the series of tests conducted by an independent evaluation team, and the performance of one of the learning algorithms is evaluated.
Year
Venue
Keywords
2007
Integrated Computer-Aided Engineering
integrating learning,hierarchical vehicle control system,defense advanced research projects,mobility control method,control system,embed learning algorithm,intelligent systems division,ground robots,rcs structure,independent evaluation team,lagr program,nist team
Field
DocType
Volume
Architecture,Tree traversal,Software engineering,Reference model,Intelligent decision support system,Computer science,Terrain,NIST,Artificial intelligence,Control system,Robot
Journal
14
Issue
ISSN
Citations 
2
1069-2509
5
PageRank 
References 
Authors
0.77
5
6
Name
Order
Citations
PageRank
James Albus110820.41
Roger Bostelman216732.16
Tsai Hong313714.46
Tommy Chang4588.34
Will Shackleford5345.50
Michael Shneier625752.18