[1]MA Rui,LIU Huaping,SUN Fuchun,et al.Object classification based on the tactile sequence[J].CAAI Transactions on Intelligent Systems,2015,10(3):362-368.[doi:10.3969/j.issn.1673-4785.201408026]
Copy

Object classification based on the tactile sequence

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 362-368 Column: 学术论文—机器感知与模式识别 Public date: 2015-06-25
Title:
Object classification based on the tactile sequence
Author(s):
MA Rui1; 2; 3;  LIU Huaping2; 3;  SUN Fuchun2; 3;  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
Keywords:
object classification; tactile sequence; linear dynamical system (LDS); bag-of-system; Martin distance; support vector machine (SVM); K-Medoid algorithm
CLC:
TP24
DOI:
10.3969/j.issn.1673-4785.201408026
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.
References:
[1] 李德毅. 网络时代人工智能研究与发展[J]. 智能系统学报, 2009, 4(1): 1-6.LI Deyi. AI research and development in the network age [J]. CAAI Transactions on Intelligent Systems, 2009, 4(1): 1-6.
[2] 解仑, 王志良, 余军. 人工心理情感模型的个人机器人[J]. 智能系统学报, 2009, 4(1): 59-66.XIE Lun, WANG Zhiliang, YU Jun. A personal robot based on an affective model of artificial psychology[J]. CAAI Transactions on Intelligent Systems, 2009, 4(1): 59-66.
[3] RUSSELL R A, WIJAYA J A. Object location and recognition using whisker sensors[C]//Australian Conference on Robotics and Automation. Brisbane, Australia, 2003: 761-768.
[4] GOLDFEDER C, CIOCARLIE M, DANG H, et al. The Columbia grasp database[C]//IEEE International Conference on Robotics and Automation. Kobe, Japan, 2009: 1710-1716.
[5] BEKIROGLU Y, LAAKSONEN J, JORGENSEN J, et al. Assessing grasp stability based on learning and haptic data[J]. IEEE Transactions on Robotics, 2011, 27(3): 616-629.
[6] BEKIROGLU Y, KRAGIC D, KYRKI V. Learning grasp stability based on tactile data and HMMs[C]//IEEE International Symposium on Robot ad Human Interactive Communication. Viareggio, Italy, 2010: 132-137.
[7] DRIMUS A, KOOTSTRA G, BILBERG A, et al. Design of a flexible tactile sensor for classification of rigid and deformable objects[J]. Robotics and Autonomous Systems, 2014, 62(1): 3-15.
[8] SOH H, SU Y Y, DEMIRIS Y. Online spatio-temporal Gaussian process experts with application to tactile classification[C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura, Portugal, 2012: 4489-4496.
[9] JIMéNEZA A, SOEMBAGIJOB A, REYNAERTSB D, et al. Featureless classification of tactile contacts in a gripper using neural networks[J]. Sensors and Actuators A: Physical, 1997, 62(1/2/3): 488-491.
[10] SCHNEIDER A, STURM J, STACHNISS C, et al. Object identification with tactile sensors using bag-of-features[C]//IEEE/RSJ International Conference on Intelligent Robot Systems. St Louis, USA, 2009: 243-248.
[11] SCHOPFER M, PARDOWITZ M, HASCHKE R, et al. Identifying relevant tactile features for object identification[M]//PRASSLER E, Z?LLNER M, BISCHOFF R. Towards Service Robots for Everyday Environments. Berlin/Heidelberg: Springer, 2012: 417-430.
[12] 萧伟, 孙富春, 刘华平. 机器人灵巧手的触觉分析与建模[J]. 机器人, 2013, 35(4): 394-401. XIAO Wei, SUN Fuchun, LIU Huaping. Tactile analysis and modeling of dexterous robotic hand[J]. Robot, 2013, 35(4): 394-401.
[13] BAYDOGAN M G, RUNGER G, TUV E. A bag-of-features framework to classify time series[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2796-2802.
[14] DORETTO G, CHIUSO A, WU Y N, et al. Dynamic textures[J]. International Journal of Computer Vision, 2003, 51(2): 91-109.
[15] RAVICHANDRAN A, CHAUDHRY R, VIDAL R. Categorizing dynamic textures using a bag of dynamical systems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(2): 342-353.
[16] De COCK K, De MOOR B. Subspace angles between ARMA models[J]. Systems & Control Letters, 2002, 46(4): 265-270.
[17] WANG W, LIU H P, YU L Z, et al. Human activity recognition using smart phone embedded sensors: a linear dynamical systems method[C]//2014 International Joint Conference on Neural Networks (IJCNN). Beijing, China, 2014: 1185-1190.
[18] 赵春晖, 陈万海, 郭春燕. 多类支持向量机方法的研究现状与分析[J]. 智能系统学报, 2007, 2(2): 11-17. ZHAO Chunhui, CHEN Wanhai, GUO Chunyan. Research and analysis of methods for multiclass support vector machines[J]. CAAI Transactions on Intelligent Systems, 2007, 2(2): 11-17.
[19] CHITTA S, STURM J, PICCOLI M, et al. Tactile sensing for mobile manipulation[J]. IEEE Transactions on Robotics, 2011, 27(3): 558-568.
Similar References:

Memo

-

Last Update: 2015-07-15

Copyright © CAAI Transactions on Intelligent Systems