Learning In A Hierarchical Control System: 4D/RCS In The DARPA LAGR Program
NATIONAL INST OF STANDARDS AND TECHNOLOGY GAITHERSBURG MD
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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 4DRCS that has been applied to many kinds of robot control, including autonomous vehicle control. For the LAGR program, NIST has embedded learning into a 4DRCS 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 4DRCS 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.
- Numerical Mathematics
- Computer Programming and Software
- Surface Transportation and Equipment
- Navigation and Guidance