Abstract
This paper introduces a signal-recognition based approach for detecting autonomous mobile robot immobilization on outdoor terrain. The technique utilizes a support vector machine classifier to form class boundaries in a feature space composed of statistics related to inertial and (optional) wheel speed measurements. The proposed algorithm is validated using experimental data collected with an autonomous robot operating in an outdoor environment. Additionally, two detector fusion techniques are proposed to combine the outputs of multiple immobilization detectors. One technique is proposed to minimize false immobilization detections. A second technique is proposed to increase overall detection accuracy while maintaining rapid detector response. The two fusion techniques are demonstrated experimentally using the detection algorithm proposed in this work and a dynamic model-based algorithm. It is shown that the proposed techniques can be used to rapidly and robustly detect mobile robot immobilization in outdoor environments, even in the absence of absolute position information.
Similar content being viewed by others
References
Anderson, R., & Bevly, D. (2004). Estimation of slip angles using a model based estimator and GPS. In Proceedings of the 2004 American control conference (Vol. 3, pp. 2122–2127).
Angelova, A., Matthies, L., Helmick, D., Sibley, G., & Perona, P. (2006). Learning to predict slip for ground robots. In Proceedings of the IEEE international conference on robotics and automation.
Barshan, B., & Durrant-Whyte, H. (1995). Inertial navigation systems for mobile robots. IEEE Transactions on Robotics and Automation, 11(3).
Bekker, M. (1956). Theory of land locomotion. Ann Arbor: Univ. of Michigan Press.
Bekker, M. (1969). Introduction to terrain-vehicle systems. Ann Arbor: Univ. of Michigan Press.
Borenstein, J., Everett, H., & Feng, L. (1996). Where am I? Sensors and methods for mobile robot positioning. Univ. of Michigan. Available: http://www-personal.umich.edu/~johannb/shared/pos96rep.pdf.
Brooks, C., & Iagnemma, K. (2005). Vibration-based terrain classification for planetary exploration rovers. IEEE Transactions on Robotics, 21(6).
Chang, C., & Lin, C. (2001). LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 2001.
Dahlkamp, H., Kaehler, A., Stavens, D., Thrun, S., & Bradski, G. (2006). Self-supervised monocular road detection in desert terrain. In Proceedings of the robotics science and systems conference.
Dissanayake, G., Sukkarieh, S., Nebot, E., & Durrant-Whyte, H. (2001). The aiding of a low-cost strapdown measurement unit using vehicle model constraints for land vehicle applications. IEEE Transactions on Robotics and Automation, 17(5).
Flenniken, W., Wall, J., & Bevly, D. (2005). Characterization of various IMU error sources and the effect on navigation performance. In Proceedings of the Institute of Navigation GNSS conference.
Fuke, Y., & Krotkov, E. (1996). Dead reckoning for a lunar rover on uneven terrain. In Proceedings of the IEEE international conference on robotics and automation.
Ganapathiraju, A., Hamaker, J., & Picone, J. (2004). Applications of support vector machines to speech recognition. IEEE Transactions on Signal Processing, 52(8).
Geeter, J., Brussel, H., & Schutter, J. (1997). A smoothly constrained Kalman filter. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(10).
Gillespie, T. (1992). Fundamentals of vehicle dynamics. Warrendale: Society of Automotive Engineers.
GPS 16/17 Series Technical Specifications (2005). Revision A, Garmin International, Inc. Olathe, KS.
Grewal, M., & Andrews, A. (1993). Kalman filtering: theory and practice. Englewood Cliffs: Prentice-Hall.
Gustafsson, F. (1997). Slip-based tire-road friction estimation. Automatica, 33(6), 1087–1099.
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: data mining, inference, and prediction. New York: Springer.
Hofmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (2001). Global positioning system: theory and practice (5th ed.). New York: Springer.
Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2006). A practical guide to support vector classification. Available: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. Accessed 2006.
Hung, M., & Orin, D. (2001). Dynamic simulation of actively-coordinated wheeled vehicle systems on uneven terrain. In Proceedings of the 2001 IEEE international conference on robotics and automation.
Julier, S., & Durrant-Whyte, H. (2003). On the role of process models in autonomous land vehicle navigation systems. IEEE Transactions on Robotics and Automation, 19(1).
Karlsen, R.E., Witus, G. (2007). Terrain understanding for robot navigation. In Proceedings of the IEEE/RSJ conference on intelligent robots and systems.
Kelly, A. (1994). A 3D state space formulation of a navigation Kalman filter for autonomous vehicles (CMU Technical Report CMU-RI-TR-94-19).
Kumar, V., & Waldron, K. (1998). Force distribution in closed kinematic chains. IEEE Transactions on Robotics and Automation, 4(6).
Learning Applied to Ground Robots (2006). http://www.darpa.mil/ipto/Programs/lagr/vision.htm. Accessed 2006.
Manduchi, R. (2004). Learning outdoor color classification from just one training image. Proceedings of European Conference on Computer Vision (ECCV), 28(11).
Moving Averages (2006). http://www.stockcharts.com/education/IndicatorAnalysis/indic_movingAvg.html. Accessed July 2006.
Ojeda, L., Reina, G., & Borenstein, J. (2004). Experimental results from FLEXnav: an expert rule-based dead-reckoning system for Mars rovers. In Proceedings of the IEEE aerospace conference.
Ojeda, L., Cruz, D., Reina, G., & Borenstein, J. (2006). Current-based slippage detection and odometry correction for mobile robots and planetary rovers. IEEE Transactions on Robotics, 22(2).
Plagemann, C., Fox, D., & Burgard, W. (2007). Efficient failure detection on mobile robots using particle filters with Gaussian process proposals. In Proceedings of the international joint conferences on artificial intelligence.
Rasmussen, C. (2001). Laser range-, color-, and texture-based classifiers for segmenting marginal roads. In Proceedings of conference on computer vision & pattern recognition technical sketches.
Ryu, J., Rossetter, E., & Gerdes, J. (2002). Vehicle sideslip and roll parameter estimation using GPS. In Proceedings of the advanced vehicle control international symposium.
Sukkarieh, S., Nebot, E., & Durrant-Whyte, H. (1999). A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Transactions on Robotics and Automation, 15(3).
Wadda, M., Yoon, K., & Hashimoto, H. (2000). High accuracy road vehicle state estimation using extended Kalman filter. In Proceedings of 2000 IEEE intelligent transportation systems.
Ward, C., & Iagnemma, K. (2007). Model-based wheel slip detection for outdoor mobile robots. In Proceedings of the IEEE international conference on robotics and automation.
Ward, C., & Iagnemma, K. (2008). A dynamic model-based wheel slip detector for mobile robots on outdoor terrain. IEEE Transactions on Robotics, 24(4), 821–831.
Welch, G., & Bishop, G. (2001). An introduction to the Kalman filter. In Proceedings of SIGGRAPH.
Wong, J. (2001). Theory of ground vehicles (3rd ed.). New York: Wiley.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Iagnemma, K., Ward, C.C. Classification-based wheel slip detection and detector fusion for mobile robots on outdoor terrain. Auton Robot 26, 33–46 (2009). https://doi.org/10.1007/s10514-008-9105-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-008-9105-8