Abstract
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.
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References
Brooks, C. and Iagnemma, K. (2005). Vibration-based terrain classification for planetary exploration rovers. IEEE Transactions on Robotics, 21(6):1185–1191.
Crisman, J. and Thorpe, C. (1993). SCARF: a color vision system that tracks roads and intersections. IEEE Transactions on Robotics and Automation, 9(1):49–58.
DARPA (2004). Darpa grand challenge rulebook. On the Web at http://www.darpa.mil/grandchallenge05/Rules_8oct04.pdf.
Davies, B. and Lienhart, R. (2006). Using CART to segment road images. In Proceedings SPIE Multimedia Content Analysis, Management, and Retrieval, San Jose, CA.
Dickmanns, E. (2002). Vision for ground vehicles: history and prospects. International Journal of Vehicle Autonomous Systems, 1(1):1–44.
Dickmanns, E., Behringer, R., Dickmanns, D., Hildebrandt, T., Maurer, M., Schiehlen, J., and Thomanek, F. (1994). The seeing passenger car VaMoRs-P. In Proceedings of the International Symposium on Intelligent Vehicles, Paris, France.
Dima, C. and Hebert, M. (2005). Active learning for outdoor obstacle detection. In Thrun, S., Sukhatme, G., Schaal, S., and Brock, O., editors, Proceedings of the Robotics Science and Systems Conference, Cambridge, MA.
Duda, R. and Hart, P. (1973). Pattern classification and scene analysis. Wiley, New York.
Ettinger, S., Nechyba, M., Ifju, P., and Waszak, M. (2003). Vision-guided flight stability and control for micro air vehicles. Advanced Robotics, 17:617–640.
Farrell, J. and Barth, M. (1999). The Global Positioning System. McGraw-Hill.
Gat, E. (1998). Three-layered architectures. In Kortenkamp, D., Bonasso, R., and Murphy, R., editors, AI-based Mobile Robots: Case Studies of Successful Robot Systems, pages 195–210. MIT Press, Cambridge, MA.
Gillespie, T. (1992). Fundamentals of Vehicle Dynamics. SAE Publications, Warrendale, PA.
Happold, M., Ollis, M., and Johnson, N. (2006). Enhancing supervised terrain classification with predictive unsupervised learning. In Sukhatme, G., Schaal, S., Burgard, W., and Fox, D., editors, Proceedings of the Robotics Science and Systems Conference, Philadelphia, PA.
Hebert, M., Thorpe, C., and Stentz, A. (1997). Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon University. Kluwer Academic Publishers.
Iagnemma, K. and Dubowsky, S. (2004). Mobile Robots in Rough Terrain: Estimation, Motion Planning, and Control with application to Planetary Rovers. Springer Tracts in Advanced Robotics (STAR) Series, Berlin, Germany.
Julier, S. and Uhlmann, J. (1997). A new extension of the Kalman filter to nonlinear systems. In International Symposium on Aerospace/Defense Sensing, Simulate and Controls, Orlando, FL.
Kelly, A. and Stentz, A. (1998). Rough terrain autonomous mobility, part 1: A theoretical analysis of requirements. Autonomous Robots, 5:129–161.
Ko, N. and Simmons, R. (1998). The lane-curvature method for local obstacle avoidance. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Victoria, Canada.
Pomerleau, D. and Jochem, T. (1996). Rapidly adapting machine vision for automated vehicle steering. IEEE Expert, 11(2):19–27.
Pomerleau, D. A. (1991). Rapidly adapting neural networks for autonomous navigation. In Lippmann, R. P., Moody, J. E., and Touretzky, D. S., editors, Advances in Neural Information Processing Systems 3, pages 429–435, San Mateo. Morgan Kaufmann.
Pomerleau, D. A. (1993). Knowledge-based training of artificial neural networks for autonomous robot driving. In Connell, J. H. and Mahadevan, S., editors, Robot Learning, pages 19–43. Kluwer Academic Publishers.
Simmons, R. and Apfelbaum, D. (1998). A task description language for robot control. In Proceedings of the Conference on Intelligent Robots and Systems (IROS), Victoria, CA.
van der Merwe, R. (2004). Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. PhD thesis, OGI School of Science & Engineering.
van der Merwe, R. and Wan, E. (2004). Sigma-point kalman filters for integrated navigation. In Proceedings of the 60th Annual Meeting of The Institute of Navigation (ION), Dayton, OH.
Wellington, C., Courville, A., and Stentz, A. (2005). Interacting markov random fields for simultaneous terrain modeling and obstacle detection. In Thrun, S., Sukhatme, G., Schaal, S., and Brock, O., editors, Proceedings of the Robotics Science and Systems Conference, Cambridge, MA.
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Thrun, S. et al. (2007). Stanley: The Robot That Won the DARPA Grand Challenge. In: Buehler, M., Iagnemma, K., Singh, S. (eds) The 2005 DARPA Grand Challenge. Springer Tracts in Advanced Robotics, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73429-1_1
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DOI: https://doi.org/10.1007/978-3-540-73429-1_1
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