Skip to main content
Log in

Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

We present a new terrain classification technique both for effective, autonomous locomotion over rough, unknown terrains and for the qualitative analysis of terrains for exploration and mapping. Our approach requires a single camera with little processing of visual information. Specifically, we derived a gait bounce measure from visual servoing errors that results from vehicle-terrain interactions during normal locomotion. Characteristics of the terrain, such as roughness and compliance, manifest themselves in the spatial patterns of this signal and can be extracted using pattern classification techniques. This vision-based approach is particularly beneficial for resource-constrained robots with limited sensor capability. In this paper, we present the gait bounce derivation. We demonstrate the viability of terrain classification for legged vehicles using gait bounce with a rigorous study of more than 700 trials, obtaining an 83% accuracy on a set of laboratory terrains. We describe how terrain classification may be used for gait adaptation, particularly in relation to an efficiency metric. We also demonstrate that our technique may be generally applicable to other locomotion mechanisms such as wheels and treads.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bekker, G. 1969. Introduction to Terrain-Vehicle Systems. University of Michigan Press.

  • Cham, J., Karpick, J., and Cutkosky, M. 2004. Stride period adaptation for a biomimetic running hexapod. Int’l Journal of Robotics Research, 23(2):141–153.

    Article  Google Scholar 

  • Demir, G., Voyles, R., and Larson, A. 2004. Motion estimation with cooperatively working multiple robots. In Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, page to appear.

  • Duda, R., Hart, P., and Stork, D. 2001. Pattern Classification, 2nd edition. John Wiley and Sons, Inc.

  • Espenschied, K.S., Quinn, R.D., Beer, R.D., and Chiel, H.J. 1996. Biologically based distributed control and local reflexes improve rough terrain locomotion in a hexapod robot. Robotics and Autonomous Systems, 18(1/2):59–64.

    Article  Google Scholar 

  • Gennery, D. 1999. Traversability analysis and path planning for a planetary rover. Autonomous Robots, 6:131–146.

    Article  Google Scholar 

  • Hirose, S. 1984. A study of design and control of a quadruped walking vehicle. Int’l Journal of Robotics Research, 3:113–133.

    Google Scholar 

  • Huber, D., Denes, L., Hebert, M., Gottlieb, M., Kaminsky, B., and Metes, P. 1998. A spectro-polarimetric imager for intelligent transportation systems. In Proc. of SPIE—The International Society for Optical Engineering, vol. 3207, pp. 94–102.

    Google Scholar 

  • Iagnemma, K., Rzepniewski, A., Dubowsky, S., and Schenker, P. 2003. Control of robotic vehicles with actively articulated suspensions in rough terrain. Autonomous Robots, 14(1):5–16.

    Article  Google Scholar 

  • Iagnemma, K., Shibly, H., and Dubowsky, S. 2002. On-line terrain parameter estimation for planetary rovers. In Proc. of the IEEE Int’l Conference on Robotics and Automation, vol 3, pp. 3142–3147.

  • Kurazume, R., Byong-Won, A., Ohta, K., and Hasegawa, T. 2003. Experimental study on energy efficiency for quadruped walking vehicles. In Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, pp. 613–618.

  • Kurazume, R., Yoneda, K., and Hirose, S. 2002. Feedforward and feedback dynamic trot gait control for quadruped walking vehicle. Autonomous Robots, 12:157–172.

    Article  Google Scholar 

  • Langer, D., Rosenblatt, J., and Hebert, M. 1994. A behavior-based system for off-road navigation. IEEE Transactions on Robotics and Automation, 10(6):776–783.

    Article  Google Scholar 

  • Larson, A., Voyles, R., and Demir, G. 2004. Terrain classification through weakly-structured vehicle/terrain interaction. In Proc. of the IEEE Int’l Conference on Robotics and Automation, pp. 218–224.

  • Lenz, R. and Tsai, R., 1988. Techniques for calibration of the scale factor and image center for high accuracy 3-d machine vision metrology. IEEE Trans. Pattern Analysis and Machine Intelligence, 10(5):713–720.

    Article  Google Scholar 

  • Lewis, M. and Bekey, G. 2002. Gait adaptation in a quadruped robot. Autonomous Robots, 12:301–312.

    Article  Google Scholar 

  • Marhefka, D.W. and Orin, D.E. 1997. Gait planning for energy efficiency in walking machines. In Proc. of the IEEE Int’l Conference on Robotics and Automation, pp. 474–480.

  • Nelson, B., Papanikolopoulos, N., and Khosla. P. 1993. Visual Servoing—Real-Time Control of Robot Manipulators Based on Visual Sensory Feedback, chapter Visual servoing for robotic assembly, World Scientific Publishing Co. Pte. Ltd., pp. 129–164.

  • Seraji, H. and Howard, A. 2002. Behavior-based robot navigation on challenging terrain: A fuzzy logic approach. IEEE Transactions on Robotics and Automation, 18(3):308–321.

    Article  Google Scholar 

  • Shi, J. and Tomasi, C. 1994. Good features to track. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 21–23.

  • Simmons, R., Krotkov, E., Chrisman, L., Cozman, F., Goodwin, R., Hebert, M., Katragadda, L., Koenig, S., Krishnaswamy, G., Shinoda, Y., and Whitaker, W. 1995. Experience with rover navigation for lunar-like terrain. In Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, vol. 1, pp. 441–446.

  • Talukder, A., Manduchi, R., Castano, R., Owens, K., Matthies, L., Castano, A., and Hogg, R. 2002. Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles. In Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, pp. 708–713.

  • Voyles, R. and Larson, A. 2005. Terminatorbot: A novel robot with dual-use mechanism for locomotion and manipulation. IEEE/ASME Transactions on Mechatronics, to appear.

  • Voyles, R., Larson, A., Yesin, K., and Nelson, B. 2001. Using orthogonal visual servoing errors for classifying terrain. In Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, vol. 2, pp. 772–777.

  • Weingarten, J., Lopes, G., Buehler, M., Groff, R., and Koditschek, D. 2004. Automated gait adaptation for legged robots. In Proc. of the IEEE Int’l Conference on Robotics and Automation, pp. 2153–2158.

  • Wettergreen, D., Pangels, H., and Bares, J. 1995. Behavior-based gait execution for the Dante II walking robot. In Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, vol. 3, pp. 274–279.

  • Willson, R. and Shafer, S. 1994. What is the center of the image? Journal of the Optical Society of America, 11(11):2946–2955.

    Google Scholar 

  • Yoshida, K. and Hamano, H. 2002. Motion dynamics and control of a planetary rover with slip-based traction model. In Proc. of SPIE—The International Society for Optical Engineering, vol. 4715, pp. 275–286.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Richard M. Voyles.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Larson, A.C., Demir, G.K. & Voyles, R.M. Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction. Auton Robot 19, 41–52 (2005). https://doi.org/10.1007/s10514-005-0605-5

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-005-0605-5

Keywords

Navigation