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Yingzi Wei,Kanfeng Gu,Xujing Cui,Changzhong Hao,Hongguang Wang,Yong Chang 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.2
It is one of critical factors for a massage robot to find and position the acupuncture point precisely inorder to cure the diseases. Based on large amount of sample data offline, Chinese medical empirical knowledgeis also introduced to build the prediction model. The massagist prescription and the robot mechanism devise areboth 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 onleast squares. Knowledge consultation is set for the calculation of acupoint position. The robot needs to get thefeature points of a foot to be massaged. The foot contour sampling data are divided into piecewise curve fitting. Qlearningis adopted to optimize the robot positioning for they are model free. CMAC (Cerebellar Model ArticulationController) cerebellum model is incorporated into the function approximation of Q learning. The learning systemis rewarded by referring to the strengths of instrumental signal. By the direct representation, the model of humanpelma acupoint is expressed with the vector variables and formal computer language. Through prediction model’scalculation, the robot will work out the rough position of acupuncture point. Meanwhile, Q learning does the onlineadjustment for accurate location. These strategies provide for the robot to automatically search and position thepelma acupoint with little real-time computation and storage. The idea of this paper also prompts a research cue forthe development of Chinese medical standardization.
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