Slip Prediction Using Visual Information

This paper considers prediction of slip from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering a particular terrain can be very useful for better planning and avoiding terrains with large slip. The proposed method is based on learning from experience and consists of terrain type recognition and nonlinear regression modeling. After learning, slip prediction is done remotely using only the visual information as input. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The slip prediction error is about 20% of the step size.

[1]  Hugh F. Durrant-Whyte,et al.  Estimation of track-soil interactions for autonomous tracked vehicles , 1997, Proceedings of International Conference on Robotics and Automation.

[2]  Larry H. Matthies,et al.  Visual odometry on the Mars Exploration Rovers , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[3]  Larry H. Matthies,et al.  Error modeling in stereo navigation , 1986, IEEE J. Robotics Autom..

[4]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

[5]  Clark F. Olson,et al.  Stereo ego-motion improvements for robust rover navigation , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[6]  I. Jefferson,et al.  Soil mechanics in engineering practice , 1997 .

[7]  Shane Farritor,et al.  Physics-based planning for planetary exploration , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[8]  Mahmoud Tarokh,et al.  Kinematic modeling of a high mobility Mars rover , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[9]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  K Davis,et al.  Localization and Physical Property Experiments Conducted by Opportunity at Meridiani Planum , 2004, Science.

[11]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[12]  Christopher G. Atkeson,et al.  Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.

[13]  Rover Team.  Characterization of the Martian surface deposits by the Mars Pathfinder rover, Sojourner. Rover Team. , 1997, Science.

[14]  Yang Cheng,et al.  Path following using visual odometry for a Mars rover in high-slip environments , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[15]  Philippe Bidaud,et al.  Modeling robot-soil interaction for planetary rover motion control , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[16]  Vijay Kumar,et al.  Modeling Of Frictional Contacts For Dynamic Simulation , 1997 .

[17]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[18]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[19]  A. R. Jumikis.  Introduction to Terrain-Vehicle Systems , 1970 .

[20]  S. Dubowsky,et al.  Planning and Control Algorithms for Enhanced Rough-Terrain Rover Mobility , 2001 .

[21]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[22]  Pietro Perona,et al.  Learning to predict slip for ground robots , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[23]  Randel A. Lindemann,et al.  Mars Exploration Rover mobility assembly design, test and performance , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[24]  MalikJitendra,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001 .

[25]  Stergios I. Roumeliotis,et al.  Slip compensation for a Mars rover , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Roberto Manduchi,et al.  Terrain perception for DEMO III , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).