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Parameter Analysis Method for Terrain Classification of the Legged Robots  

Ko, Kwang-Jin (Department of Mechanical Engineering, Hanyang Univ.)
Kim, Ki-Sung (Department of Mechanical Engineering, Hanyang Univ.)
Kim, Wan-Soo (Department of Mechanical Engineering, Hanyang Univ.)
Han, Chang-Soo (Department of Mechanical Engineering, Hanyang Univ.)
Publication Information
Journal of the Korean Society for Precision Engineering / v.28, no.1, 2011 , pp. 56-62 More about this Journal
Abstract
Terrain recognition ability is crucial to the performance of legged robots in an outdoor environment. For instance, a robot will not easily walk and it will tumble or deviate from its path if there is no information on whether the walking surface is flat, rugged, tough, and slippery. In this study, the ground surface recognition ability of robots is discussed, and to enable walking robots to recognize the surface state and changes, a central moment method was used. The values of the sensor signals (load cell) of robots while walking were detected in the supported section and were analyzed according to signal variance, skewness, and kurtosis. Based on the results of such analysis, the surface state was detected and classified.
Keywords
Terrain Classification; Legged Robot; Central Moment; Uneven Terrain;
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1 Brooks, C. and Iagnemma, K., "Vibration-based terrain classification for planetary rovers," IEEE Transactions on Robotics, Vol. 21, No. 6, pp. 1185-1191, 2005.   DOI
2 DuPont, E. M., Moore, C. A., Collins, E. G. Jr. and Coyle, E., "Frequency response method for terrain classification in autonomous ground vehicles," Autonomous Robots, Vol. 24, No. 4, pp. 337-347, 2008.   DOI   ScienceOn
3 DuPont, E. M., Roberts, R. G. and Moore, C. A., "Speed independent terrain classification," Proceedings of the 38th Southeastern Symposium on System Theory, pp. 240-244, 2006.
4 Hirose, S., "A study of design and control of a quadruped walking vehicle," Int'l Journal of Robotics Research, Vol. 3, No. 2, pp. 113-133, 1984.   DOI   ScienceOn
5 Lewis, M. and Bekey, G., "Gait adaptation in a quadruped robot," Autonomous Robots, Vol. 12, No. 3, pp. 301-312, 2002.   DOI   ScienceOn
6 Kurazume, R., Yoneda, K. and Hirose, S., "Feedforward and feedback dynamic trot gait control for quadruped walking vehicle," Autonomous Robots, Vol. 12, No. 2, pp. 157-172, 2002.   DOI   ScienceOn
7 Cham, J., Karpick, J. and Cutkosky, M., "Stride period adaptation for a biomimetic running hexapod," Int'l Journal of Robotics Research, Vol. 23, No. 2, pp. 141-153, 2004.   DOI   ScienceOn
8 Larson, A. C., Voyles, R. M., Bae, J. and Godzdanker, R., "Evolving Gaits for Increased Selectivity in Terrain Classification," Proc. of the IEEE/RSJ Int'l Conf. on Intelligent Robots and Systems, pp. 3691-3696, 2007.
9 Iagnemma, K., Shibly, H. and Dubowsky, S., "Online terrain parameter estimation for planetary rovers," Proc. of the IEEE Int'l Conf. on Robotics and Automation, Vol. 3, pp. 142-3147, 2002.
10 Manduchi, R., Castano, A., Talukder, A. and Matthies, L., "Obstacle detection and terrain classification for autonomous off-road navigation," Robotics and Automation, Vol. 18, No. 1, pp. 81-102, 2005.
11 Castano, R., Manduchi, R. and Fox, J., "Classification experiments on real-world textures," Workshop on Empirical Evaluation in Computer Vision, pp. 1-6, 2001.
12 Vandapel, N., Huber, D., Kapuria, A. and Hebert, M., "Natural terrain classification using 3-d ladar data," IEEE International Conf. on Robotics and Automation, pp. 5117-5122, 2004.
13 Hebert, M. and Vandapel, N., "Terrain classification techniques from ladar data for autonomous navigation," Collaborative Technology Alliances Conference, 2003.