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Strategies for Feet Massage Robot to Position the Pelma Acupoints with Model Predictive and Real-time Optimization
Wei YZ(魏英姿); Gu KF(谷侃锋); Cui XJ(崔旭晶); Hao, Changzhong; Wang HG(王洪光); Chang Y(常勇)
Department装备制造技术研究室
Source PublicationINTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
ISSN1598-6446
2016
Volume14Issue:2Pages:628-636
Indexed BySCI ; EI
EI Accession number20161702279193
WOS IDWOS:000373035200028
Contribution Rank2
Funding OrganizationChinese Academy of Sciences [CCI20130327-Y2A6070] ; National Natural Science Foundation of China [61203163] ; Natural Science Foundation of Liaoning Province [2014020083]
KeywordFeet Massage Robot Knowledge Consultation Least-squares Method Reinforcement Learning Robot Positioning
AbstractIt is one of critical factors for a massage robot to find and position the acupuncture point precisely in order to cure the diseases. Based on large amount of sample data offline, Chinese medical empirical knowledge is also introduced to build the prediction model. The massagist prescription and the robot mechanism devise are both considered for robot positioning. Least squares method is of simplicity, easy to use and high efficiency. Its real-time calculation is very effective, too. A modeling method for robot positioning is proposed based on least squares. Knowledge consultation is set for the calculation of acupoint position. The robot needs to get the feature points of a foot to be massaged. The foot contour sampling data are divided into piecewise curve fitting. Q-learning is adopted to optimize the robot positioning for they are model free. CMAC (Cerebellar Model Articulation Controller) cerebellum model is incorporated into the function approximation of Q learning. The learning system is rewarded by referring to the strengths of instrumental signal. By the direct representation, the model of human pelma acupoint is expressed with the vector variables and formal computer language. Through prediction model's calculation, the robot will work out the rough position of acupuncture point. Meanwhile, Q learning does the online adjustment for accurate location. These strategies provide for the robot to automatically search and position the pelma acupoint with little real-time computation and storage. The idea of this paper also prompts a research cue for the development of Chinese medical standardization.
Language英语
WOS KeywordMANIPULATOR ; ASSISTANCE
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/18501
Collection智能产线与系统研究室
Corresponding AuthorWei YZ(魏英姿)
Affiliation1.School of Information Science and Engineering, Shenyang Ligong University, No.6 Nanping middle Road, Block of Hunnan, Shenyang, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, No.114 Nanta Street, Shenyang, China
Recommended Citation
GB/T 7714
Wei YZ,Gu KF,Cui XJ,et al. Strategies for Feet Massage Robot to Position the Pelma Acupoints with Model Predictive and Real-time Optimization[J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS,2016,14(2):628-636.
APA Wei YZ,Gu KF,Cui XJ,Hao, Changzhong,Wang HG,&Chang Y.(2016).Strategies for Feet Massage Robot to Position the Pelma Acupoints with Model Predictive and Real-time Optimization.INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS,14(2),628-636.
MLA Wei YZ,et al."Strategies for Feet Massage Robot to Position the Pelma Acupoints with Model Predictive and Real-time Optimization".INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 14.2(2016):628-636.
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