Little Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban Challenge
This paper describes “Little Ben,” an autonomous ground vehicle constructed by the Ben Franklin Racing Team for the 2007 DARPA Urban Challenge in under a year and for less than $250,000. The sensing, planning, navigation, and actuation systems for Little Ben were carefully designed to meet the performance demands required of an autonomous vehicle traveling in an uncertain urban environment. We incorporated an array of GPS/INS, LIDAR’s, and stereo cameras to provide timely information about the surrounding environment at the appropriate ranges. This sensor information was integrated into a dynamic map that could robustly handle GPS dropouts and errors. Our planning algorithms consisted of a high-level mission planner that used information from the provided RNDF and MDF to select routes, while the lower level planner used the latest dynamic map information to optimize a feasible trajectory to the next waypoint. The vehicle was actuated by a cost-based controller that efficiently handled steering, throttle, and braking maneuvers in both forward and reverse directions. Our software modules were integrated within a hierarchical architecture that allowed rapid development and testing of the system performance. The resulting vehicle was one of six to successfully finish the Urban Challenge.
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- Amir, Y., Danilov, C., Miskin-Amir, M., Schultz, J., Stanton, J.: The Spread toolkit: Architecture and performance. Technical Report CNDS-2004-1, Johns Hopkins University, Baltimore, MD (2004)Google Scholar
- Coulter, R.: Implementation of the pure pursuit path tracking algorithm. Technical Report CMU-RI-TR-92-01, Carnegie Mellon University, Pittsburgh, PA (1992)Google Scholar
- DARPA (2007), http://www.darpa.mil/grandchallenge/rules.asp (retrieved July 24, 2008)
- Elfes, A.: Using occupancy grids for mobile robot perception and navigation. IEEE Computer Magazine 22 (1989)Google Scholar
- Gillespie, T.D.: Fundamentals of vehicle dynamics. In: Society of Automotive Engineers, Warrendale, PA (1992)Google Scholar
- Guitton, A.: The iteratively reweighted least squares method. Stanford University Lecture Notes (2000)Google Scholar
- Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., James Diebel, P.F., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L.-E., Koelen, C., Markey, C., Rummel, C., van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A., Mahoney, P.: Stanley: The robot that won the darpa grand challenge. Journal of Field Robotics 23(9), 661–692 (2006)CrossRefGoogle Scholar
- Vernaza, P., Lee, D.D.: Robust GPS/INS-aided localization and mapping via GPS bias estimation. In: Proceedings of the 10th International Symposium on Experimental Robotics, Rio de Janeiro, Brazil (2006)Google Scholar