TerraMax: Team Oshkosh Urban Robot

  • Yi-Liang Chen
  • Venkataraman Sundareswaran
  • Craig Anderson
  • Alberto Broggi
  • Paolo Grisleri
  • Pier Paolo Porta
  • Paolo Zani
  • John Beck
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 56)

Abstract

Team Oshkosh, comprised of Oshkosh Corporation, Teledyne Scientific and Imaging Company, VisLab of the University of Parma, Ibeo Automotive Sensor GmbH, and Auburn University, participated in the DARPA Urban Challenge and was one of the eleven teams selected to compete in the final event. Through development, testing, and participation in the official events, we have experimented and demonstrated autonomous truck operations in (controlled) urban streets of California, Wisconsin, and Michigan under various climate and traffic conditions. In these experiments TerraMaxTM, a modified Medium Tactical Vehicle Replacement (MTVR) truck by Oshkosh Corporation, negotiated urban roads, intersections, and parking lots, and interacted with manned and unmanned traffic while observing traffic rules. We have accumulated valuable experience and lessons on autonomous truck operations in urban environments, particularly in the aspects of vehicle control, perception, mission planning, and autonomous behaviors which will have an impact on the further development of large-footprint autonomous ground vehicles for the military.

In this article, we describe the vehicle, the overall system architecture, the sensors and sensor processing, the mission planning system, and the autonomous behavioral controls implemented on TerraMaxTM. We discuss the performance of some notable autonomous behaviors of TerraMax and our experience in implementing these behaviors, and present results of the Urban Challenge National Qualification Event (NQE) tests and the Urban Challenge Final Event (UCFE). We conclude with a discussion of lessons learned from all of the above experience in working with a large robotic truck.

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References

  1. Arkin, R.C.: Behavior-based robotics. MIT Press, Cambridge (1998)Google Scholar
  2. Bertozzi, M., Bombini, L., Broggi, A., Cerri, P., Grisleri, P., Zani, P.: GOLD: a complete framework for developing artificial vision applications for intelligent vehicles. IEEE Intelligent Systems 23(1), 69–71 (2008)CrossRefGoogle Scholar
  3. Bertozzi, M., Broggi, A.: GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Transactions on Image Processing 1(7), 62–81 (1998)CrossRefGoogle Scholar
  4. Bertozzi, M., Broggi, A., Cellario, M., Fascioli, A., Lombardi, P., Porta, M.: Artificial vision in road vehicles. Proc. of the IEEE - Special issue on Technology and Tools for Visual Perception 90(7), 1258–1271 (2002)Google Scholar
  5. Bertozzi, M., Broggi, A., Fascioli, A.: VisLab and the evolution of vision-based UGVs. IEEE Computer 39(12), 31–38 (2006)Google Scholar
  6. Bertozzi, M., Broggi, A., Medici, P., Porta, P.P., Sjögren, A.: Stereo vision-based start-inhibit for heavy goods vehicles. In: Proc. IVS 2006, pp. 350–355 (2006)Google Scholar
  7. Braid, D., Broggi, A., Schmiedel, G.: The TerraMax autonomous vehicle. J. of Field Robotics 23(9), 655–835 (2006)CrossRefGoogle Scholar
  8. Broggi, A., Bertozzi, B., Fascioli, A., Conte, G.: Automatic vehicle guidance: the experience of the ARGO vehicle. World Scientific, Singapore (1999)MATHGoogle Scholar
  9. Broggi, A., Medici, P., Porta, P.P.: StereoBox: a robust and efficient solution for automotive short range obstacle detection. EURASIP Journal on Embedded Systems - Special Issue on Embedded Systems for Intelligent Vehicles (June 2007) ISSN 1687-3955Google Scholar
  10. Caraffi, C., Cattani, S., Grisleri, P.: Off-road path and obstacle detection using decision networks and stereo. IEEE Trans. on Intelligent Transportation Systems 8(4), 607–618 (2007)CrossRefGoogle Scholar
  11. Cassandras, C., Lafortune, S.: Introduction to Discrete Event Systems, 2nd edn. Kluwer, Dordrecht (1999)MATHGoogle Scholar
  12. Chen, Y.-L., Lin, F.: Modeling of discrete event systems using finite state machines with parameters. In: Proc. 9th IEEE Int. Conf. on Control Applications, September 2000, pp. 941–946 (2000)Google Scholar
  13. Chen, Y.-L., Lin, F.: Safety control of discrete event systems using finite state machines with parameters. In: Proc. 2001 American Control Conf. (ACC), June 2001, pp. 975–980 (2001)Google Scholar
  14. Chen, Y.-L., Lin, F.: An optimal effective controller for discrete event systems. In: Proc. 40th IEEE Conf. on Decision and Control (CDC), December 2001, pp. 4092–4097 (2001)Google Scholar
  15. Chung, S.-L., Lafortune, S., Lin, F.: Limited lookahead policies in supervisory control of discrete event systems. IEEE Trans. on Automatic Control 37(12), 1921–1935 (1992)MATHCrossRefMathSciNetGoogle Scholar
  16. Cormen, T., Leiserson, C., Rivest, R.: Introduction to Algorithms. MIT Press, Cambridge (1990)MATHGoogle Scholar
  17. Horst, J., Barbera, A.: Trajectory generation for an on-road autonomous vehicle. In: Proc. SPIE: Unmanned Systems Technology VIII. vol. 6230 (2006)Google Scholar
  18. Hwang, Y.K., Meirans, L., Drotning, W.D.: Motion planning for robotic spray cleaning with environmentally safe solvents. In: Proc. IEEE Intl. Workshop on Advanced Robotics, Tsukuba, Japan (November 1993)Google Scholar
  19. Kaempchen, N., Bühler, M., Dietmayer, K.: Feature-level fusion for free-form object tracking using laserscanner and video. In: Proc. 2005 IEEE Intelligent Vehicles Symposium, Las Vegas (2005)Google Scholar
  20. Labayrade, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereo vision on non flat road geometry through V-disparity representation. In: Proc. IEEE Intell. Veh. Symp., vol. II, pp. 646–651 (2002)Google Scholar
  21. Lee, K., Lee, J.: Generic obstacle detection on roads by dynamic programming for remapped stereo images to an overhead view. In: Proc. ICNSC 2004, vol. 2, pp. 897–902 (2004)Google Scholar
  22. Pin, F.G., Vasseur, H.A.: Autonomous trajectory generation for mobile robots with non-holonomic and steering angle constraints. In: Proc. IEEE Intl. Workshop on Intelligent Motion Control, August 1990, pp. 295–299 (1990)Google Scholar
  23. Ramadge, P.J., Wonham, W.M.: Supervisory control of a class of discrete event processes. SIAM J. Control and Optimization 25(1), 206–230 (1987)MATHCrossRefMathSciNetGoogle Scholar
  24. Schoenberg, I.J.: Cardinal interpolation and spline functions. Journal of Approximation theory 2, 167–206 (1969)MATHCrossRefMathSciNetGoogle Scholar
  25. Sundareswaran, V., Johnson, C., Braid, D.: Implications of lessons learned from experience with large truck autonomous ground vehicles. In: Proc. AUVSI 2006 (2006)Google Scholar
  26. Team Oshkosh DARPA Urban Challenge Technical Report. Oshkosh Corp. (April 2007), http://www.darpa.mil/grandchallenge/TechPapers/Team_Oshkosh.pdf
  27. Wender, S., Weiss, T., Dietmayer, K., Fuerstenberg, K.: Object classification exploiting high level maps of intersections. In: Proc. 10th Intl. Conf. on Advanced Microsystems for Automotive Applications (AAMA 2006), Berlin, Germany (April 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yi-Liang Chen
    • 1
  • Venkataraman Sundareswaran
    • 1
  • Craig Anderson
    • 1
  • Alberto Broggi
    • 2
  • Paolo Grisleri
    • 2
  • Pier Paolo Porta
    • 2
  • Paolo Zani
    • 2
  • John Beck
    • 3
  1. 1.Teledyne Scientific & ImagingThousand Oaks
  2. 2.VisLab - University of ParmaParmaItaly
  3. 3.Oshkosh CorporationOshkosh

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