基于振动采用支持向量机方法的移动机器人地形分类

doi:10.3724/SP.J.1218.2012.00660

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引用本文:
李强, 薛开, 徐贺, 潘文林, 王天龙 . 基于振动采用支持向量机方法的移动机器人地形分类[J]. 机器人, 2012, 34(6): 660-667.DOI: 10.3724/SP.J.1218.2012.00660. LI Qiang, XUE Kai, XU He, PAN Wenlin, WANG Tianlong. Vibration-based Terrain Classification for Mobile Robots Using Support Vector Machine. ROBOT, 2012, 34(6): 660-667. DOI: 10.3724/SP.J.1218.2012.00660.

摘要

为了提高移动机器人地形分类的准确率，提出基于原始数据时域幅值分析的特征提取方法，利用LIBSVM中的一对一支持向量机（SVM）程序，采用投票决策法实现分类，给出票数相同情形下的新算法．在四轮移动机器人左前轮轮臂上安装x、y、z向加速度计和z向传声器，使之在沙、碎石、草、土、沥青地面上分别以6种速度行驶，提取车轮与地面相互作用的加速度和声压信号．根据本文的算法，分别对每种速度下的5种地形进行分类，平均准确率为88.7%．

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关键词 ：移动机器人, 地形分类, 振动, 支持向量机

Abstract：

In order to enhance terrain classification accuracy of mobile robots, a feature extraction method is proposed based on time amplitude domain analysis, and a new voting decisions classification algorithm is proposed to deal with the situation of same number of votes via one-against-one support vector machine (SVM) program in LIBSVM. A four-wheeled mobile robot on which arm accelerometers in x,y,z directions and a microphone in z direction are installed in left front wheel, is used to get the acceleration and sound pressure signals of wheel-terrain interaction by traversing on sand, gravel, grass, soil and asphalt terrains with six different velocities respectively. Five kinds of terrains in each velocity are classified by the proposed algorithm, and the average classification accuracy is 88.7%.

Key words： mobile robot terrain classification vibration support vector machine

收稿日期: 2012-05-05 录用日期: 2012-10-18

TP242

基金资助:

国家自然科学基金资助项目（60775060）；黑龙江省自然科学基金资助项目（F200801）；高等学校博士学科点专项科研基金资助项目（200802171053，20102304110006）；哈尔滨市科技创新人才研究专项基金资助项目（2012RFXXG059）

通讯作者: 李强 E-mail: lq0451@126.com

作者简介: 李强（1970-），男，硕士，副教授．研究领域：移动机器人地形分类，机械系统设计及动力学分析．
薛开（1964-），男，博士，教授．研究领域：现代集成制造系统．
徐贺（1964-），男，博士，教授．研究领域：特种机器人技术，多场耦合和弱信号处理等．

链接本文:

https://robot.sia.cn/CN/10.3724/SP.J.1218.2012.00660 或 https://robot.sia.cn/CN/Y2012/V34/I6/660

[1] Vandapel N, Huber D F, Kapuria A, et al. Natural terrain classification using 3-D ladar data[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2004: 5117-5122.

[2] 朱江,王耀南,余洪山,等.未知环境下移动机器人自主感知斜坡地形方法[J].仪器仪表学报,2010,31(8):1916-1920. Zhu J, Wang Y N, Yu H S, et al. Mobile robot autonomous perceiving slope terrain under unknown environment[J]. Chinese Journal of Scientific Instrument, 2010, 31(8): 1916-1920.

[3] 徐正飞,杨汝清,翁新华.移动机器人四杆地形感知机构的设计[J].机械工程学报,2003,39(4):44-48. Xu Z F, Yang R Q, Weng X H. Design of four-link terrain perceptional mechanism for mobile robot[J]. Chinese Journal of Mechanical Engineering, 2003, 39(4): 44-48.

[4] 许宏岩,付宜利,王树国.局部地形变化检测与移动机器人的行为决策[J].控制与决策,2005,20(8):951-954. Xu H Y, Fu Y L, Wang S G. On local terrain changes detection and reactive behavior of mobile robot[J]. Control and Decision, 2005, 20(8): 951-954.

[5] Wilcox B H. Non-geometric hazard detection for a Mars microrover[C]//Proceedings of the Conference on Intelligent Robotics in Field, Factory, Service, and Space, vol.2. Washington, DC, USA: NASA, 1994: 675-684.

[6] Iagnemma K, Brooks C A, Dubowsky S. Visual, tactile, and vibration-based terrain analysis for planetary rovers[C]//IEEE Aerospace Conference. Piscataway, NJ, USA: IEEE, 2004: 841-848.

[7] Brooks C A, Iagnemma K, Dubowsky S. Vibration-based terrain analysis for mobile robots[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2005: 3415-3420.

[8] Brooks C A, Iagnemma K. Vibration-based terrain classification for planetary exploration rovers[J]. IEEE Transactions on Robotics, 2005, 21(6): 1185-1191.

[9] Sadhukhan D. Autonomous ground vehicle terrain classification using internal sensors[D]. Florida, USA: Florida State University, 2004.

[10] DuPont E M, Roberts R G, Selekwa M F, et al. Online terrain classification for mobile robots[C]//ASME International Mechanical Engineering Congress and Exposition. New York, USA: ASME, 2005: 1643-1648.

[11] Weiss C, Fröhlich H, Zell A. Vibration-based terrain classification using support vector machines[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2006: 4429-4434.

[12] Weiss C, Stark M, Zell A. SVMs for vibration-based terrain classification[C]//Autonome Mobile Systeme 2007. Berlin, Germany: Springer, 2007: 1-7.

[13] Ojeda L, Borenstein J, Witus G, et al. Terrain characterization and classification with a mobile robot[J]. Journal of Field Robotics, 2006, 23(2): 103-122.

[14] 李舜酩,李香莲.振动信号的现代分析技术与应用[M].北京:国防工业出版社,2008:2-5. Li S M, Li X L. Modern analysis techniques and application of vibration signals[M]. Beijing: National Defense Industry Press, 2008: 2-5.

[15] Vapnik V N. The nature of statistical learning theory[M]. New York, USA: Springer, 1995.

[16] 温熙森.模式识别与状态监控[M].北京:科学出版社,2007:327.Wen X S. Pattern recognition and condition monitoring[M]. Beijing: Science Press, 2007: 327.

[17] 张学工.模式识别[M].3版.北京:清华大学出版社,2010:75-77,120-122,219. Zhang X G. Pattern recognition[M]. 3rd ed. Beijing: Tsinghua University Press, 2010: 75-77,120-122,219.

[18] Cristianini N, Shawe-Taylor J. 支持向量机导论[M].李国正,王猛,曾华军,译.北京:电子工业出版社,2004:25-26. Cristianini N, Shawe-Taylor J. Introduction to support vector machines and other kernel-based learning methods[M]. Li G Z, Wang M, Zeng H J, trans. Beijing: Publishing House of Electronics Industry, 2004: 25-26.

[19] Chang C C, Lin C J. LIBSVM: A library for support vector machines[OL]. (2001) [2011-11-14]. http://www.csie.ntu.edu.tw/ ～ cjlin/libsvm.

[20] Xu H, Zhang Z Y, Alipour K, et al. Prototypes selection by multi-objective optimal design: Application to a reconfigurable robot in sandy terrain[J]. Industrial Robot, 2011, 38(6): 599-613.