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Intelligent Control and Tactical Behavior Development: A Long Term NIST Partnership With the Army
Published
Author(s)
James S. Albus, Anthony J. Barbera
Abstract
For more than 15 years, the Intelligent Systems Division of the National Institute of Standards and Technology (NIST) has worked closely with the Army Research Laboratory (ARL) on architectures and technology development for intelligent control of unmanned vehicle systems. During the early 1990 s, NIST developed techniques for retrotraverse of trajectories recorded using teleoperation. In the late 1990 s NIST worked with ARL and the German Ministry of Defense to develop the 4-dimensional Real-time Control System (4D/RCS) reference model architecture for the ARL Demo III program. 4D/RCS consists of a multi-layered multi-resolutional hierarchy of computational nodes (i.e., organizational units) each containing elements of sensory processing (SP), world modeling (WM), value judgment (VJ), and behavior generation (BG). At the lower echelons, these nodes generate goal-seeking reactive behavior. At higher echelons, they enable goal-defining deliberative behavior. Throughout the hierarchy, interaction between SP, WM, VJ, and BG give rise to perception, cognition, imagination, reasoning, planning, and control. In 2002-3, NIST worked with ARL to conduct a series of engineering tests to demonstrate that autonomous mobility technology developed under the Demo III program satisfied the requirements for Technology Readiness Level 6 (i.e., a prototype demonstration in a relevant environment.) More recently, NIST has worked with ARL to develop an engineering methodology and software development environment for endowing autonomous ground and air vehicles with the capability for tactical behaviors.
Proceedings Title
Robotics and Remote System for Hazardus Environments |11th| |ANS
Albus, J.
and Barbera, A.
(2006),
Intelligent Control and Tactical Behavior Development: A Long Term NIST Partnership With the Army, Robotics and Remote System for Hazardus Environments |11th| |ANS, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824590
(Accessed December 11, 2024)