[1]马蕊,刘华平,孙富春,等.基于触觉序列的物体分类[J].智能系统学报,2015,10(03):362-368.[doi:10.3969/j.issn.1673-4785.201408026]
 MA Rui,LIU Huaping,SUN Fuchun,et al.Object classification based on the tactile sequence[J].CAAI Transactions on Intelligent Systems,2015,10(03):362-368.[doi:10.3969/j.issn.1673-4785.201408026]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年03期
页码:
362-368
栏目:
出版日期:
2015-06-25

文章信息/Info

Title:
Object classification based on the tactile sequence
作者:
马蕊123 刘华平23 孙富春23 高蒙1
1. 石家庄铁道大学 电气与电子工程学院, 河北 石家庄 050043;
2. 清华大学 计算机科学与技术系, 北京 100084;
3. 清华大学 智能技术与系统国家重点实验室, 北京 100084
Author(s):
MA Rui123 LIU Huaping23 SUN Fuchun23 GAO Meng1
1. College of Electrical and Electronic Engineering, Shijiazhuang Railway University, Shijiazhuang 050043, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
3. State Key Laboratory of Intelligent Technology and System, Tsinghua University, Beijing 100084, China
关键词:
物体分类触觉序列线性动态系统系统包马丁距离支持向量机K-Medoid算法
Keywords:
object classificationtactile sequencelinear dynamical system (LDS)bag-of-systemMartin distancesupport vector machine (SVM)K-Medoid algorithm
分类号:
TP24
DOI:
10.3969/j.issn.1673-4785.201408026
文献标志码:
A
摘要:
通过安装触觉传感器的灵巧手对物体进行抓取,可以采集到丰富的触觉序列信息.对这些触觉信息进行分类可以显著提高机器人的环境感知和灵巧操作能力.为此,将触觉序列划分为一系列子触觉序列,使用基于线性动态系统(LDS)的方法进行特征提取.由于使用LDS提取的特征存在于非欧式空间,在对特征进行处理时,使用与欧式距离不同的马丁距离(Martin distance)作为量度来表征2个LDS特征之间的距离,并使用K-Medoid算法进行聚类.而后使用聚类得到的码书表征触觉序列,完成系统包(bag-of-system)特征模型构建,并利用支持向量机(SVM)实现高效分类.最后使用16种实验样本构建的触觉序列数据集对上述算法进行评测,获得了可观的识别效果,表明了该算法可以用于触觉序列的物体分类.
Abstract:
A large amount of information on tactile sequences can be collected by using a dexterous hand with a tactile sensor to grasp different objects. The abilities of a robot’s environmental perception and dexterous manipulation are significantly improved after these tactile sequences are classified. Therefore, tactile sequences are separated into a series of subgroups and features are extracted by using a method based on the linear dynamical system (LDS). Since the features extracted by LDS are located in the non-Euclidean space, when dealing with these features, the Martin distance which is a measurement different from Euclidean distance is applied to represent the distance between two LDS features, and the K-Medoid algorithm is used for clustering. Then, the codebook which is formed after clustering is used to represent the tactile sequence, the model of bag-of-system is formed, and the support vector machine (SVM) is used to classify these objects efficiently. Finally, a dataset based on 16 objects is used to evaluate the algorithm and the result of recognition is good, which proves this algorithm can be used in tactile sequences for object classification.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-8-20;改回日期:。
基金项目:国家“973”计划资助项目(2013CB329403);国家自然科学基金重大国际合作研究项目(61210013).
作者简介:马蕊,女,1990年生,硕士研究生,主要研究方向为机器人智能控制、微机测控技术等.刘华平,男,1976年生,副教授,主要研究方向为机器人智能控制、视觉目标跟踪.孙富春,男,1964年生,教授,博士生导师,中国人工智能学会理事、智能控制与智能管理专业委员会副主任兼秘书长.主要研究方向为模糊神经系统、变结构控制、网络控制系统和人工认知系统的信息感知和处理等.
通讯作者:马蕊. E-mail: marui519@126.com.
更新日期/Last Update: 2015-07-15