The National Institute of Standards and Technology (NIST) Intelligent Control of Mobility Systems (ICMS) Program provides architectures and interface standards, performance test methods and data, and infrastructure technology needed by the U.S. manufacturing industry and government agencies in developing and applying intelligent control technology to mobility systems to reduce cost, improve safety, and save lives. The ICMS Program is made up of several areas including: defense, transportation, and industry projects, among others. Each of these projects provides unique capabilities that foster technology transfer across mobility projects and to outside government, industry and academia for use on a variety of applications. A common theme among these projects is autonomy and the Four Dimensional (3D + time)/Real-time Control System (4D/RCS) standard control architecture for intelligent systems that has been applied to these projects.
This chapter will briefly describe recent project advances within the ICMS Program including: goals, background accomplishments, current capabilities, and technology transfer that has or is planned to occur. Several projects within the ICMS Program have developed the 4D/RCS into a modular architecture for intelligent mobility systems, including: an Army Research Laboratory (ARL) Project currently studying onroad autonomous vehicle control, a Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) Project studying learning within the 4D/RCS architecture with road following application, and an Intelligent Systems Ontology project that develops the description of intelligent vehicle behaviors. Within the standards and performance measurements area of the ICMS program, a Transportation Project is studying components of intelligent mobility systems that are finding their way into commercial crash warning systems (CWS). In addition, the ALFUS (Autonomy Levels For Unmanned Systems) project determines the needs for metrics and standard definitions for autonomy levels of unmanned systems. And a JAUS (Joint Architecture for Unmanned Systems) project is working to set a standard for interoperability between components of unmanned robotic vehicle systems. Testbeds and frameworks underway at NIST include the PRIDE (Prediction in Dynamic Environments) framework to provide probabilistic predictions of a moving object's future position to an autonomous vehicle's planning system, as well as the USARSim/MOAST (Urban Search and Rescue Simulation/Mobility Open Architecture Simulation and Tools) framework that is being developed to provide a comprehensive set of open source tools for the development and evaluation of autonomous agent systems. A NIST Industrial Autonomous Vehicles (IAV) Project provides technology transfer from the defense and transportation projects directly to industry through collaborations with automated guided vehicles manufacturers by researching 4D/RCS control applications to automated guided vehicles inside facilities. These projects are each briefly described in this Chapter followed by Conclusions and continuing work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Albus, J.S., Huang, H.-M., Messina, E., Murphy, K., Juberts, M., Lacaze, A., Balakirsky, S., Shneier, M.O., Hong, T., Scott, H., Horst, J., Proctor, F., Shackleford, W., Szabo, S., and Finkelstein, R., 4D/RCS Version 2.0: A Reference Model Architecture for Unmanned Vehicle Systems, NIST, Gaithersburg, MD, NISTIR 6912, 2002
Balakirsky, S., Messina, E., Albus, J.S., Architecting a Simulation and Development Environment for Multi-Robot Teams, Proceedings of the International Workshop on Multi Robot Systems, 2002
Balakirsky, S.B., Chang, T., Hong, T.H., Messina, E., Shneier, M.O., A Hierarchical World Model for an Autonomous Scout Vehicle, Proceedings of the SPIE 16th Annual International Symp. on Aerospace/Defense Sensing, Simulation, and Controls, Orlando, FL, April 1–5, 2002
Albus, J.S., Juberts, M., Szabo, S., RCS: A Reference Model Architecture for Intelligent Vehicle and Highway Systems, Proceedings of the 25th Silver Jubilee International Symposium on Automotive Technology and Automation, Florence, Italy, June 1–5, 1992
Bostelman, R.V., Jacoff, A., Dagalakis, N.G., Albus, J.S., RCS-Based RoboCrane Integration, Proceedings of the International Conference on Intelligent Systems: A Semiotic Perspective, Gaithersburg, MD, October 20–23, 1996
Madhavan, R., Messina, E., and Albus, J. (Editors), Low-Level Autonomous Mobility Implementation part of Chapter 3: Behavior Generation in the book, Intelligent Vehicle Systems: A 4D/RCS Approach, 2007
Jackel, Larry, LAGR Mission, http://www.darpa.mil/ipto/programs/lagr/index.htm, DARPA Information Processing Technology Office
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, Special Issue on Learning in Unstructured Environments, 23(11/12): 975–1003.]
Konolige, K., SRI Stereo Engine, http://www.ai.sri.com/~konolige/svs/
Tan, C., Hong, T., Shneier, M., and Chang, T., Color Model-Based Real-Time Learning for Road Following, in Proceedings of the IEEE Intelligent Transportation Systems Conference (Submitted) Toronto, Canada, 2006
Shneier, M., Chang, T., Hong, T., Shackleford, W., Bostelman, R., and Albus, J. S. Learning traversability models for autonomous mobile vehicles. Autonomous Robots 24, 1, January 2008, 69–86.
Oskard, D., Hong, T., Shaffer, C., Real-time Algorithms and Data Structures for Underwater Mapping, Proceedings of the SPIE Advances in Intelligent Robotics Systems Conference, Boston, MA, November, 1988
Shackleford, W., The NML Programmer’s Guide (C++ Version) http://www.isd.mel.nist.gov/projects/rcslib/NMLcpp.html
Heyes-Jones, J., A∗ algorithm tutorial, http://us.geocities.com/jheyesjones/astar.html
Tan, C., Hong, T., Shneier, M., Chang, T., Color Model-Based Real-Time Learning for Road Following, Proceedings of the IEEE Intelligent Transportation Systems Conference, 2006
He, Y., Wang, H., Zhang, B., Color-based road detection in urban traffic scenes, IEEE Transactions on Intelligent Transportation Systems, 5(4), 309–318, 2004
Kristensen, D., Autonomous Road Following. PhD thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 2004
Lin, X., Chen, S., Color image segmentation using modified HSI system for road following, IEEE International Conference on Robotics and Automation, 1991, pp. 1998–2003
Ulrich, I., Nourbakhsh, I., Appearance-Based Obstacle Detection with Monocular Color Vision, Proceedings of the AAAI National Conference on Artificial Intelligence, 2000
Shneier, M., Bostelman, R., Albus, J.S., Shackleford, W., Chang, T., Hong, T., A Common Operator Control Unit Color Scheme for Mobile Robots, National Institute of Standards and Technology, Gaithersburg, MD, August, 2007
Huang, H.-M., The Autonomy Levels for Unmanned Systems (ALFUS) Framework–Interim Results, in Performance Metrics for Intelligent Systems (PerMIS) Workshop, Gaithersburg, Maryland, 2006
Huang, H.-M. et al., Characterizing Unmanned System Autonomy: Contextual Autonomous Capability and Level of Autonomy Analyses, in Proceedings of the SPIE Defense and Security Symposium, April, 2007
Huang, H.-M. ed., Autonomy Levels for Unmanned Systems (ALFUS) Framework, Volume I: Terminology, NIST Special Publication 1011, Gaithersburg: National Institute of Standards and Technology, 2004
The OWL Services Coalition, “OWL-S 1.0 Release,” http://www.daml.org/services/owl-s/1.0/owl-s.pdf, 2003
Schlenoff, C., Washington, R., and Barbera, T., Experiences in Developing an Intelligent Ground Vehicle (IGV) Ontology in Protege, Proceedings of the 7th International Protege Conference, Bethesda, MD, 2004
Szabo, S., Wilson, B., Application of a Crash Prevention Boundary Metric to a Road Departure Warning System. Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop, NIST, Gaithersburg, MD, August, 24–26, 2004. http://www.isd.mel.nist.gov/documents/szabo/PerMIS04.pdf
Balakirsky, S., Scrapper, C., Carpin, S., and Lewis, M., USARSim: Providing a Framework for Multi-robot Performance Evaluation, Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop, 2006
Scrapper, C., Balakirsky, S., and Messina, E., MOAST and USARSim – A Combined Framework for the Development and Testing of Autonomous Systems, SPIE 2006 Defense and Security Symposium, 2006
USARSim Homepage. http://usarsim.sourceforge.net/, 2007
MOAST Homepage. http://sourceforge.net/projects/moast/, 2007
Dickmanns, E.D., A General Dynamic Vision Architecture for UGV and UAV, Journal of Applied Intelligence, 2, 251, 1992
Schlenoff, C., Ajot, J., and Madhavan, R., PRIDE: A Framework for Performance Evaluation of Intelligent Vehicles in Dynamic, On-Road Environments, Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) 2004 Workshop, 2004
Madhavan, R. and Schlenoff, C., The Effect of Process Models on Short-term Prediction of Moving Objects for Autonomous Driving, International Journal of Control, Automation and Systems, 3, 509–523, 2005
Bishop, R., Industrial Autonomous Vehicles: Results of a Vendor Survey of Technology Needs, Bishop Consulting, February 16, 2006
Bostelman, R., Hong, T., Chang, T., Visualization of Pallets, Proceedings of SPIE Optics East 2006 Conference, Boston, MA, USA, October 1–4, 2006
Bostelman, R., Hong, T., Chang, T., Shackleford, W., Shneier, M., Unstructured Facility Navigation by Applying the NIST 4D/RCS Architecture, CITSA 06 Conference Proceedings, July 20–23, 2006
Iagnemma, K., Buehler, M., Special Issues on the DARPA Grand Challenge, Journal of Field Robotics, Volume 23, Issues 8 & 9, Pages 461–835, Aug/Sept 2006
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Albus, J. et al. (2008). Intelligent Control of Mobility Systems. In: Prokhorov, D. (eds) Computational Intelligence in Automotive Applications. Studies in Computational Intelligence, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79257-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-79257-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79256-7
Online ISBN: 978-3-540-79257-4
eBook Packages: EngineeringEngineering (R0)