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%.
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%.
出处
《机器人》
EI
CSCD
北大核心
2012年第6期660-667,共8页
Robot
基金
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)
关键词
移动机器人
地形分类
振动
支持向量机
mobile robot
terrain classification
vibration
support vector machine
