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Learning in a Hierarchical Control System: 4D/RCS in the DARPA LAGR Program

Published

Author(s)

James S. Albus, Roger V. Bostelman, Tommy Chang, Tsai H. Hong, William P. Shackleford, Michael O. Shneier

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 com-plex 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 experi-ence, and learning how to optimize traversal. Learning takes place in the sensory processing, world model-ing, 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.
Citation
Journal of Field Robotics

Keywords

4D/RCS, hierarchical control, LAGR, learning, map, mobile robot, operator control unit, robot control system, traversability

Citation

Albus, J. , Bostelman, R. , Chang, T. , Hong, T. , Shackleford, W. and Shneier, M. (2006), Learning in a Hierarchical Control System: 4D/RCS in the DARPA LAGR Program, Journal of Field Robotics, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824552 (Accessed April 16, 2024)
Created December 28, 2006, Updated October 12, 2021