Frequency response method for terrain classification in autonomous ground vehicles
- 504 Downloads
Many autonomous ground vehicle (AGV) missions, such as those related to agricultural applications, search and rescue, or reconnaissance and surveillance, require the vehicle to operate in difficult outdoor terrains such as sand, mud, or snow. To ensure the safety and performance of AGVs on these terrains, a terrain-dependent driving and control system can be implemented. A key first step in implementing this system is autonomous terrain classification. It has recently been shown that the magnitude of the spatial frequency response of the terrain is an effective terrain signature. Furthermore, since the spatial frequency response is mapped by an AGV’s vibration transfer function to the frequency response of the vibration measurements, the magnitude of the latter frequency responses also serve as a terrain signature. Hence, this paper focuses on terrain classification using vibration measurements. Classification is performed using a probabilistic neural network, which can be implemented online at relatively high computational speeds. The algorithm is applied experimentally to both an ATRV-Jr and an eXperimental Unmanned Vehicle (XUV) at multiple speeds. The experimental results show the efficacy of the proposed approach.
KeywordsAutonomous ground vehicles Probabilistic neural network
Unable to display preview. Download preview PDF.
- Allen, J. (2002). Four-wheeler’s bible. St. Paul: MotorBooks. Google Scholar
- Angelova, A., Matthies, L., Helmick, D., & Perona, P. (2007). Fast terrain classification using variable-length representation for autonomous navigation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1–8). Google Scholar
- Bradley, D., Thayer, S., Stentz, A., & Rander, P. (2004). Vegetation detection for mobile robot navigation (Technical Report CMU-RI-TR-04-12). Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, February 2004. Google Scholar
- Brooks, C., Iagnemma, K., & Dubowsky, S. (2002). Vibration-based terrain analysis for mobile robots. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 3142–3147), Barcelona, Spain, May 2002. Google Scholar
- Collins, E. G., Jr., & Coyle, E. (2008, to appear). Vibration-based terrain classification using surface profile input frequency responses. In International conference on robotics and automation. Available at http://www.eng.fsu.edu/ciscor/publications.htm.
- Delong, B. (2000). 4-wheel freedom: the art of off-road driving. Boulder: Paladin. Google Scholar
- DuPont, E. M., Roberts, R. G., Moore, C. A., Selekwa, M. F., & Collins, E. G., Jr. (2005). Online terrain classification for mobile robots. In Proceedings of the ASME international mechanical engineering congress and exposition conference, Orlando, FL, November 2005. Google Scholar
- DuPont, E. M., Roberts, R. G., & Moore, C. A. (2006). Speed independent terrain classification. In Proceedings of the 38th southeastern symposium on system theory, Cookeville, TN, March 2006. Google Scholar
- Iagnemma, K., & Dubowsky, S. (2002). Terrain estimation for high speed, rough-terrain autonomous vehicle navigation. In Proceedings of the SPIE conference on unmanned ground vehicle technology (pp. 256–266), Orlando, FL, May 2002. Google Scholar
- Iagnemma, K., Shibly, H., & Dubowsky, S. (2002). Terrain parameter estimation for planetary rovers. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 3142–3147), Washington, DC, May 2002. Google Scholar
- Lu, L., Ordonez, C., Collins, E. G., Jr., & DuPont, E. M. (2008, submitted for publication). Terrain classification for autonomous ground vehicles using 2-D laser stripe-based structured light sensors. In International conference on robotics and automation. Available at http://www.eng.fsu.edu/ciscor/publications.htm.
- Masters, T. (1993). Practical neural network recipes in C++. New York: Academic Press. Google Scholar
- Murthy, V. K. (1966). Nonparametric estimation of multivariate densities with applications. In P. R. Krishnaiah (Ed.), Multivariate analysis (pp. 43–56). New York: Academic Press. Google Scholar
- Sadhukhan, D. (2004). Autonomous ground vehicle terrain classification using internal sensors. Master’s thesis, Departent of Mechanical Engineering, Florida State University, Tallahasee, FL. Google Scholar
- Sadhukan, D., & Moore, C. (2003). Online terrain estimation using internal sensors. In Proceedings of the Florida conference on recent advances in robotics, Boca Raton, FL, May 2003. Google Scholar
- Specht, D. F. (1988). Probabilistic neural networks for classification, mapping, or associative memory. In Proceedings IEEE international conference on neural networks (pp. 525–532), San Diego, CA. Google Scholar
- Sukarrieh, S. (2000). Low cost high integrity aided inertial navigation systems for autonomous land vehicles. PhD thesis, University of Sydney, Sydney, Australia. Google Scholar
- Tsoukalas, L. H., & Uhrig, R. E. (1997). Fuzzy and neural approaches in engineering. New York: Willey. ISBN 0-471-16003-2. Google Scholar
- Vandapel, N., Huber, D. F., Kapuria, A., & Herbet, M. (2004). Natural terrain classification using 3-d ladar data. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 5117–5122), New Orleans, LA, April 2004. Google Scholar
- Vanderwerp, D. (2005). What does terrain response do? http://www.caranddriver.com/features/9026/what-does-terrain-response-do.html.
- von Scheidt, J., Wunderlich, R., & Fellenberg, B. (1999). Random road surfaces and vehicle vibration. In L. Arkeryd, J. Bergh, P. Brenner, & R. Pettersson (Eds.), Progress in industrial mathematics at ECMI 98 (pp. 352–359). Stuttgart: Teubner. Google Scholar
- Washburne, T. P., Specht, D. F., & Drake, R. M. (1993). Identification of unknown categories with probabilistic neural networks. In Proceedings of the IEEE international conference on neural networks (pp. 434–437). Google Scholar
- Wong, J. Y. (2001). Theory of ground vehicles (3rd ed.). New York: Wiley. Google Scholar