Authors:
James Albus
;
Roger Bostelman
;
Tsai Hong
;
Tommy Chang
;
Will Shackleford
and
Michael Shneier
Affiliation:
National Institute of Standards and Technology, United States
Keyword(s):
LAGR, Learning, 4D/RCS, mobile robot, hierarchical control, reference model architecture.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Modeling, Simulation and Architectures
;
Robot Design, Development and Control
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
The National Institute of Standards and Technology’s (NIST) Intelligent Systems Division (ISD) is a participant the Defense Advanced Research Project Agency (DARPA) LAGR (Learning Applied to Ground Robots) Project. The NIST team’s objective for the LAGR Project is to insert learning algorithms into the modules that make up the 4D/RCS (Four Dimensional/Real-Time Control System), the standard reference model architecture to which ISD has applied to many intelligent systems. This paper describes the 4D/RCS structure, its application to the LAGR project, and the learning and mobility control methods used by the NIST team’s vehicle.