Abstract
This paper presents a supervised learning approach to improving the autonomous mobility of wheeled robots through sensing the robot’s interaction with terrain ‘underfoot.’ Mobility characterization is cast as a hierarchical task, in which pre-immobilization detection is achieved using support vector machines in time to prevent full immobilization, and if a pre-immobilization condition is detected, the associated terrain feature affecting mobility is identified using a Hidden Markov model. These methods are implemented using a hierarchical, layered control scheme developed for the Yeti robot, a 73-kg, four-wheeled robot designed to perform autonomous medium-range missions in polar terrain. The methodology is motivated by the difficultly of visually recognizing terrain features that impact mobility in low contrast terrain. The efficacy of the approach is evaluated using data from a suite of proprioceptive sensors. Real-time implementation shows that Yeti can consistently detect pre-immobilization conditions, stop in time to avoid unrecoverable immobilization, identify the terrain feature presenting the mobility challenge, and execute an escape sequence to retreat from the condition.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Angelova, A., Matthies, L., Helmick, D., & Perona, P. (2007). Learning and prediction of slip from visual information. Journal of Field Robotics, 24(3), 205–231.
Bajracharya, M., Howard, A., Matthies, L., Tang, B., & Turmon, M. (2009). Autonomous off-road navigation with end-to-end learning for the LAGR program. Journal of Field Robotics, 26(1), 3–25.
Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1), 14–23
Brooks, C. A., & Iagnemma, K. (2005). Vibration-based terrain classification for planetary exploration rovers. IEEE Transactions on Robotics, 21(6), 1185–1191.
Chang, C., & Lin, C. (2009). Libsvm: a library for support vector machines (Technical Report). National Taiwan University, http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed Jan 2009.
Dugad, R., & Desai, U. (1996). A tutorial on hidden Markov models (Technical Report No: SPANN-96.1). Signal Processing and Artificial Neural Networks Laboratory, Dept. of Electrical Engineering, Indian Institute of Technology-Bombay, Powai, Bombay 400 076, India.
Happold, M., Ollis, M., & Johnson, N. (2006). Enhancing supervised terrain classification with predictive unsupervised learning. In Proc. of robotics: science and systems II.
Helmick, D., Angelova, A., & Matthies, L. (2009) Terrain adaptive navigation for planetary rovers. Journal of Field Robotics, 26(4), 391–410.
Howard, A., Turmon, M., Matthies, L., Tang, B., Angelova, A., & Mjolsness, E. (2006). Towards learned traversability for robot navigation: from underfoot to the far field. Journal of Field Robotics, 23(11–12), 1005–1017.
Hsu, C., Chang, C., & Lin, C. (2003) A practical guide to support vector classification (Technical Report). Department of Computer Science, National Taiwan University.
Jackel, L., Krotkov, E., Perschbacher, M., Pippine, J., & Sullivan, C. (2006). The DARPA LAGR program: goals, challenges, methodology, and phase I results. Journal of Field Robotics, 23(11–12), 945–973.
Iagnemma, K., & Ward, C. (2009) Classification-based wheel slip detection and detector fusion for mobile robots on outdoor terrain. Autonomous Robots, 26(1), 33–46.
Kim, D., Sun, J., Oh, S., Rehg, J., & Bobick, A. (2006). Traversability classification using unsupervised on-line visual learning for outdoor robot. In Proc. of IEEE international conf. on robotics and automation (ICRA) (pp. 518–525). Orlando, Florida.
Murphy, K. (1998). Hidden Markov model (HMM) toolbox for Matlab. http://people.cs.ubc.ca/~murphyk/Software/HMM/hmm.html. Accessed Jan 2009.
Ojeda, L., Borenstein, J., Witus, G., & Karlsen, R. (2006). Terrain characterization and classification with a mobile robot. Journal of Field Robotics, 26(2), 103–122.
Rabiner, L., & Juang, B. (1986). An introduction to hidden Markov models. IEEE ASSP Magazine, 4–16.
Ray, L. (2009). Estimation of terrain forces and parameters for autonomous robots, IEEE Transactions on Robotics, 25(3), 717–719.
Trautmann, E. (2009). Advanced mobility for autonomous robotic systems. Master of Science Thesis, Thayer School of Engineering, Dartmouth College.
Weiss, C., Holger Frohlich, H., & Zell, A. (2006). Vibration-based terrain classification using support vector machines. In 2006 IEEE/RSJ international conference on intelligent robots and systems (pp. 4429–4434).
Author information
Authors and Affiliations
Corresponding author
Additional information
This research is funded through the Army Research Office Contract No. W911NF-06-1-0153.
Rights and permissions
About this article
Cite this article
Trautmann, E., Ray, L. Mobility characterization for autonomous mobile robots using machine learning. Auton Robot 30, 369–383 (2011). https://doi.org/10.1007/s10514-011-9224-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-011-9224-5