Behavioral Programming with Hierarchy and Parallelism in the DARPA Urban Challenge and RoboCup

  • Jesse G. Hurdus
  • Dennis W. Hong
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 35)


Research in mobile robotics, unmanned systems, and autonomous man-portable vehicles has grown rapidly over the last decade. This push has taken the problems of robot cognition and behavioral control out of the lab and into the field. In such situations, completing complex, sophisticated tasks in a dynamic, partially observable and unpredictable environment is necessary. The use of a Hierarchical State Machine (HSM) for the construction, organization, and selection of behaviors can give a robot the ability to exhibit contextual intelligence. Such ability is important for maintaining situational awareness while pursuing important goals, sub-goals, and sub-sub goals. Using the approach presented in this paper, an assemblage of behaviors is activated with the possibility of competing behaviors being selected. Competing behaviors are then combined using known mechanisms to produce the appropriate emergent behavior. By combining hierarchy with parallelism we present an approach to behavior design that balances complexity and scalability with the practical demands of developing behavioral systems for use in the real-world. The effectiveness of merging our hierarchical arbitration scheme with parallel fusion mechanisms has been verified in two very important landmark challenges, the DARPA Urban Challenge autonomous vehicle race and the International RoboCup robot soccer competition.


Action Selection Hybrid Architecture DARPA Urban Challenge RoboCup 


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  1. 1.
    R. C. Arkin, E. M. Riseman, and A. Hansen, AuRA: an architecture for vision-based robot navigation, Proceedings of the DARPA Image Understanding Workshop, pp. 414–417, Los Angeles, CA, 1987.Google Scholar
  2. 2.
    R. Murphy and A. Mali, Lessons learned in integrating sensing into autonomous mobile robot architectures, Journal of Experimental and Theoretical Artificial Intelligence special issue on Software Architectures for Hardware Agents, 9(2), 191–209, 1997.Google Scholar
  3. 3.
    E. Gat, Three-layer architectures, in Artificial Intelligence and Mobile Robots, .D. Kortenkamp, R. Bonasson, and R. Murphy, editors. Cambridge, MA: MIT Press, 1998.Google Scholar
  4. 4.
    R. Simmons, R. Goodwin, K. Haigh, S. Koenig, and J. O’Sullivan, A layered architecture for office delivery robots, Proceedings Autonomous Agents 97, Marina del Rey, CA: ACM pp. 245–252, 1997.Google Scholar
  5. 5.
    K. Konolige and K. Myers, The saphira architecture for autonomous mobile robots, in Artificial Intelligence and Mobile Robots, D. Kortenkamp, R. Bonasson, and R. Murphy, editors. Cambridge, MA: MIT Press, 1998.Google Scholar
  6. 6.
    A. Bacha et al., Odin: Team VictorTango’s Entry in the DARPA Urban Challenge, Journal of Field Robotics, 25(8), 467’492, 2008.Google Scholar
  7. 7.
    S. Thrun, M. Montemerlo, et al., Stanley: the robot that won the DARPA Grand Challenge: research articles, Journal of Field Robotics, 23(9), 661–692, September 2006.CrossRefGoogle Scholar
  8. 8.
    C. Urmson, et al., A robust approach to high-speed navigation for unrehearsed desert terrain, Journal of Field Robotics, 23(8), 467, August 2006.MATHCrossRefGoogle Scholar
  9. 9.
    J. S. Albus, Outline for a theory of intelligence, IEEE Transactions On Systems, Man, and Cybernetics, 21(3), May/June 1991.Google Scholar
  10. 10.
    H. A. Simon, The New Science of Management Decision. New York: Harper and Row, 1960.Google Scholar
  11. 11.
    P. Maes, How to do the right thing, Technical Report NE-43-836, Cambridge, MA: AI Laboratory, MIT, 1989.Google Scholar
  12. 12.
    D. Mackenzie, R. Arkin, and J. Cameron, Specification and execution of multiagent missions, Autonomous Robots, 4(1), 29–52, 1997.Google Scholar
  13. 13.
    A. Saffiotti, The uses of fuzzy logic in autonomous robot navigation: a catalogue raisonne, Technical Report 2.1, IRIDA, Universite Libre de Bruxelles, 50 av. F. Roosevelt, CP 194/6, B-1050 Brussels, Belgium, 1997.Google Scholar
  14. 14.
    P. Pirjanian, Behavior coordination mechanisms – state-of-the-art,” Technical Report IRIS-99-375, Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA, 1999.Google Scholar
  15. 15.
    R. A. Brooks, A robust layered control system for a mobile robot, IEEE Journal of Robotics and Automation, 2(1), 14–23, 1986.Google Scholar
  16. 16.
    O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, The International Journal of Robotics Research, 5(1), 90–98, 1986.CrossRefMathSciNetGoogle Scholar
  17. 17.
    R. C. Arkin, Motor schema based navigation for a mobile robot: an approach to programming by behavior, in IEEE International Conference on Robotics and Automation, pp. 264–271, 1987.Google Scholar
  18. 18.
    J. Rosenblatt, DAMN: a distributed architecture for mobile navigation, in AAAI Spring Symposium on Lessons Learned from Implemented Software Architectures for Physical Agents, Menlo Park, CA: AAAI Press, 1995.Google Scholar
  19. 19.
    J. Yen and N. Pfluger, A fuzzy logic based extension to Payton and Rosenblatt’s command fusion method for mobile robot navigation, IEEE Transactions on Systems, Man, and Cybernetics, 25(6), 971–978, 1995.CrossRefGoogle Scholar
  20. 20.
    J. J. Bryson, Hierarchy and sequence vs. full parallelism in reactive action selection architectures, in From Animals to Animats 6 (SAB00), pp. 147–156. Cambridge, MA: MIT Press, 2000.Google Scholar
  21. 21.
    M. Minsky. The Society of Mind. New York, NY: Simon and Schuster, 1985.Google Scholar
  22. 22.
    B. Argall, B. Browning, and M. Veloso, Learning to select state machines using expert advice on an autonomous robot, in IEEE International Conference on Robotics and Automation, pp. 2124–2129, 2007.Google Scholar
  23. 23.
    K. Muecke and D. W. Hong, The synergistic combination of research, education, and international robot competitions through the development of a humanoid robot, 32nd ASME Mechanisms and Robotics Conference, New York City, NY, August 2008.Google Scholar
  24. 24.
    K. Muecke and D. W. Hong, DARwIn’s evolution: development of a humanoid robot, IEEE International Conference on Intelligent Robotics and Systems, October 29–November 2, 2007.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.TORC Technologies, LLCBlacksburgUSA
  2. 2.Director of the Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical EngineeringVirginia TechBlacksburgUSA

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