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.
Learning In A Hierarchical Control System: 4D/RCS In The DARPA LAGR Program
2009
36 pages
Report
Keine Angabe
Englisch
Algebra, Analysis, Geometry, & Mathematical Logic , Computer Software , Computers, Control & Information Theory , Transportation , Navigation Systems , Algorithms , Control systems , Inertial navigation , Autonomous navigation , Robotics , Ground vehicles , Architecture , Hierarchies , Embedding , Learning , Terrain , Robots , Models , Computer programs , Global positioning system , Hierarchical control system , Rd/rcs reference model architecture , Autonomous vehicle navigation , Lagr(Learning applied to ground vehicles) , Robotic vehicles , Hand-tuned algorithms , Rcs(Real time control system) , Computational nodes
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