Learning in a hierarchical control system: 4D/RCS in the DARPA LAGR program

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 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.

[1]  Kevin M. Passino,et al.  Stable Adaptive Control and Estimation for Nonlinear Systems , 2001 .

[2]  James S. Albus,et al.  Engineering of Mind: An Introduction to the Science of Intelligent Systems , 2001 .

[3]  Illah R. Nourbakhsh,et al.  Appearance-based place recognition for topological localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[5]  James S. Albus,et al.  4D/RCS Version 2.0: A Reference Model Architecture for Unmanned Vehicle Systems , 2002 .

[6]  Avinash C. Kak,et al.  Vision for Mobile Robot Navigation: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  R. A. Brooks,et al.  Intelligence without Representation , 1991, Artif. Intell..

[8]  John L. Michaloski,et al.  The Neutral Message Language: A Model and Method for Message Passing in Heterogeneous Environments , 2000 .

[9]  Clifford A. Shaffer,et al.  Real-time algorithms and data structures for underwater mapping , 1990, IEEE Trans. Syst. Man Cybern..

[10]  Marilyn N. Abrams,et al.  Concealment and Obstacle Detection for Autonomous Driving | NIST , 1999 .

[11]  Xueyin Lin,et al.  Color image segmentation using modified HSI system for road following , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[12]  Carla H. Lagorio,et al.  Psychology , 1929, Nature.

[13]  Illah R. Nourbakhsh,et al.  Appearance-Based Obstacle Detection with Monocular Color Vision , 2000, AAAI/IAAI.

[14]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[15]  Shuzhi Sam Ge,et al.  Stable adaptive control and estimation for nonlinear systems - neural and fuzzy approximator techniques: J. T. Spooner, M. Maggiore, R. Ordóñez, K. M. Passino, John Wiley & Sons, Inc., New York, 2002, ISBN: 0-471-41546-4 , 2003, Autom..

[16]  Anthony Stentz,et al.  Learning Predictions of the Load-Bearing Surface for Autonomous Rough-Terrain Navigation in Vegetation , 2003, FSR.

[17]  Peter Dayan,et al.  A Neural Substrate of Prediction and Reward , 1997, Science.

[18]  A. Shirkhodaie,et al.  Traversable Terrain Modeling and Performance Measurement of Mobile Robots , 2004 .

[19]  J. Lowrie,et al.  Autonomous road following , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[20]  Tommy Chang,et al.  Color model-based real-time learning for road following , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[21]  Edward Tunstel,et al.  Enhancing fuzzy robot navigation systems by mimicking human visual perception of natural terrain traversability , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[22]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[23]  James S. Albus,et al.  Learning traversability models for autonomous mobile vehicles , 2008, Auton. Robots.

[24]  Frederick M. Proctor,et al.  JAVA-Based Tools for Development and Diagnosis of Real-Time Control Systems , 1998 .

[25]  Tim Halliday,et al.  Causes and effects , 1983 .

[26]  M. Domjan.  The principles of learning and behavior , 1982 .

[27]  Bo Zhang,et al.  Color-based road detection in urban traffic scenes , 2004, IEEE Transactions on Intelligent Transportation Systems.

[28]  Matti Pietikäinen,et al.  Accurate color discrimination with classification based on feature distributions , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[29]  Marilyn N. Abrams,et al.  Concealment and Obstacle Detection for Autonomous Driving , 1999 .

[30]  Kevin M. Passino,et al.  Biomimicry for Optimization, Control and Automation , 2004, IEEE Transactions on Automatic Control.

[31]  James S. Albus,et al.  4DRCS :: a reference model architecture for unmanned vehicle systems version 2.0 , 2002 .