Abstract
The DARPA Urban Challenge required robotic vehicles to travel over 90km through an urban environment without human intervention and included situations such as stop intersections, traffic merges, parking, and road blocks. Team VictorTango separated the problem into three parts: base vehicle, perception, and planning. A Ford Escape outfitted with a custom drive-by-wire system and computers formed the basis for Odin. Perception used laser scanners, GPS, and a priori knowledge to identify obstacles, cars, and roads. Planning relied on a hybrid deliberative/reactive architecture to analyze the situation, select the appropriate behavior, and plan a safe path. All vehicle modules communicated using the JAUS standard. The performance of these components in the Urban Challenge is discussed and successes noted. The result of VictorTango’s work was successful completion of the Urban Challenge and a third place finish.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Avila-Garcıa, O., Hafner, E., Canamero, L.: Relating Behavior Selection Architectures to Environmental Complexity. In: Proc. Seventh Intl. Conf. on Simulation of Adaptive Behavior, MIT Press, Cambridge (2002)
Cacciola, S.J.: Fusion of Laser Range-Finding and Computer Vision Data for Traffic Detection by Autonomous Vehicles. Master’s Thesis. Virginia Tech., Blacksburg, VA (2007)
Currier, P.N.: Development of an Automotive Ground Vehicle Platform for Autonomous Urban Operations. Master’s Thesis. Virginia Tech, Blacksburg, VA (2008)
Duda, R.O., Hart, P.E.: Use of the Hough Transform to Detect Lines and Curves in Pictures. Commun. ACM 15(1), 11–15 (1972)
Eren, H., Fung, C.C., Evans, J.: Implementation of the Spline Method for Mobile Robot Path Control. In: Piuri, V., Savino, M. (eds.) Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference, vol. 2, pp. 739–744. IEEE, Venice (1999)
Fuerstenberg, K.C., Dietmayer, K.C.J., Lages, U.: Laserscanner Innovations for Detection of Obstacles and Road. In: Proceedings of 7th International Conference on Advanced Microsystems for Automotive Applications, Berlin, Germany (2003)
Fuerstenberg, K.C., Linzmeier, D.T., Dietmayer, K.C.J.: Pedestrian Recognition and Tracking of Vehicles using a Vehicle Based Multilater Laserscanner. In: Proceedings of 10th World Congress on Intelligent Transport Systems, Madrid, Spain (2003)
Hart, P.E., Nilsson, N.J., Raphael, B.: A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics SSC 4(2), 100–107 (1968)
Hurdus, J.G.: A Portable Approach to High-Level Behavioral Programming for Complex Autonomous Robot Applications. Master’s Thesis. Virginia Tech, Blacksburg, VA (2008)
Kelly, A.J.: A 3D State Space Formulation of a Navigation Kalman Filter for Autonomous Vehicles. CMU Robotics Institute Technical Report CMU-RI-TR-94-19 (1994)
Konolige, K., Myers, K.: The Saphira Architecture for Autonomous Mobile Robots. In: Kortenkamp, D., Bonasson, R., Murphy, R. (eds.) Artificial Intelligence and Mobile Robots. MIT Press, Cambridge (1998)
Lacaze, A., Moscovitz, Y., DeClaris, N., Murphy, K. (1998). Path Planning for Autonomous Vehicles Driving Over Rough Terrain. In: Proceedings of the ISIC/CIRA/ISAS Conference. Gaithersburg, MD, September 14-17 (1998)
Maes, P.: How To Do the Right Thing.Technical Report NE 43–836, AI Laboratory. MIT, Cambridge (1989)
Milliken, W.F., Milliken, D.L.: Race Car Vehicle Dynamics. SAE International, Warrendale, PA (1995)
Murphy, R.R.: Introduction to AI Robotics. MIT Press, Cambridge (2000)
Pirjanian, P.: Multiple Objective Behavior-Based Control. Robotics and Autonomous Systems 31(1), 53–60 (2000)
Pirjanian, P.: Behavior Coordination Mechanisms – State-of-the-Art. Tech Report IRIS-99-375, Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, California (1999)
Rosenblatt, J.: DAMN: A Distributed Architecture for Mobile Navigation. In: AAAI Spring Symposium on Lessons Learned from Implemented Software Architectures for Physical Agents, Stanford, CA. AAAI Press, Menlo Park (1995)
Russel, S., Norvig, P.: Artificial Intelligence – A Modern Approach. Pearson Education, Inc., Upper Saddle River (2003)
Thrun, S., Montemerlo, M., et al.: Stanley: The robot that won the DARPA Grand Challenge: Research Articles. Journal of Field Robotics 23(9), 661–692 (2006)
Urmson, C., et al.: A Robust Approach to High-Speed Navigation for Unrehearsed Desert Terrain. Journal of Field Robotics 23(8), 467 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Reinholtz, C. et al. (2009). Odin: Team VictorTango’s Entry in the DARPA Urban Challenge. In: Buehler, M., Iagnemma, K., Singh, S. (eds) The DARPA Urban Challenge. Springer Tracts in Advanced Robotics, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03991-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-03991-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03990-4
Online ISBN: 978-3-642-03991-1
eBook Packages: EngineeringEngineering (R0)