Browse > Article

Utilizing Visual Information for Non-contact Predicting Method of Friction Coefficient  

Kim, Doo-Gyu (BK21 Mechatronics Group at Chungnam National University)
Kim, Ja-Young (BK21 Mechatronics Group at Chungnam National University)
Lee, Ji-Hong (BK21 Mechatronics Group at Chungnam National University)
Choi, Dong-Geol (Korea Advanced Institute of Science and Technology)
Kweon, In-So (Korea Advanced Institute of Science and Technology)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.47, no.4, 2010 , pp. 28-34 More about this Journal
Abstract
In this paper, we proposed an algorithm for utilizing visual information for non-contact predicting method of friction coefficient. Coefficient of friction is very important in driving on road and traversing over obstacle. Our algorithm is based on terrain classification for visual image. The proposed method, non-contacting approach, has advantage over other methods that extract material characteristic of road by sensors contacting road surface. This method is composed of learning group(experiment, grouping material) and predicting friction coefficient group(Bayesian classification prediction function). Every group include previous work of vision. Advantage of our algorithm before entering such terrain can be very useful for avoiding slippery areas. We make experiment on measurement of friction coefficient of terrain. This result is utilized real friction coefficient as prediction method. We show error between real friction coefficient and predicted friction coefficient for performance evaluation of our algorithm.
Keywords
Friction Coefficient; Mobile Robot; Path Planning; Bayesian Classification;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Paul Jansen, Wannes van der Mark, Johan C. van den Heuvel, Frans C.A. Geroen, "Colour based Off-Road Environment and Terrain Type Classification," IEEE Conference on Intelligent Transportation Systems, pp.61-66, 2005.
2 성기열, 곽동민, 이승연, 유준, "무인차량 적용을 위한 영상 기반의 지형 분류 기법" , 제어.로봇.시스템학회 논문지, vol. 15, no. 4, pp.372-378, 2009.   과학기술학회마을
3 T. Leung and J. Malik, "Representing and Recognizing the Visual Appearance of Materials Using the Three-dimensional Texton," IJCV vol. 42, no. 1, 2001.
4 M. Varma and A. Zisserman, "Classifying images of materials achieving viewpoint and illumination indepencec," European Conference on Computer Vision, pp.255-271, 2003.
5 P. felzenszwalb and D. Huttenlocher, "Efficient Graph based Image Segmentation," IJCV, vol. 59, no. 2, pp.167-181, 2004.   DOI
6 Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification 2nd ed, Wiley, New York, 2001.
7 Sethu Vijayakumar, Aaron D'Souza, and Stefan Schaal, "Incremental Online Learning in High Dimensions," Neural Computation, vol. 17, no. 12, pp.2602-2634, 2005.   DOI   ScienceOn
8 CHIA-SHANG LIU and HUEI PENG, "Road Friction Coefficient Estimation for Vehicle Path Prediction," Vehicle System Dynamics, vol. 25,pp.413-425, 1996.
9 P. Lamon, A. Krebs, M. Lauria, and S. Shooter, R. Siegwart, "Wheel torque control for a rough terrain rover," IEEE International Conference on Robotics and Automation, pp. 4682-4687, 2004.
10 F. Gustafsson, "Slip based tire-road friction estimation," Automatica: the journal of IFAC, the International Federation of Automatic Control, vol. 33, no. 6, pp.1087-1099, 1997.
11 Christopher A. Brooks and Karl Iagnemma, "Vibration-based terrain classification for planetary exploration rovers," IEEE Transactions on Robotics, vol. 21, no. 6, pp.1185-1191, 2005.
12 Anelia Angelova, Larry Matthies, Daniel Helmick and Pietro Perona, "Learning and Prediction of Slip from Visual Information," Journal of Field Robotics, vol. 24, no. 3. pp.205-231, 2007.   DOI   ScienceOn