Journal of South China University of Technology(Natural Science) >

2023 , Vol. 51 >Issue 12: 1 - 8

DOI: https://doi.org/10.12141/j.issn.1000-565X.230027

Mechanical Engineering

Research on Optimized Teaching Strategy and BPNN-DMPs Trajectory Learning Model of Massage Robot

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  • School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
翟敬梅（1967-），女，博士，教授，主要从事机电装备信息化处理与人工智能研究。E-mail： mejmzhai@scut.edu.cn

Received date: 2023-01-20

  Online published: 2023-06-21

Supported by

the Natural Science Foundation of Guangdong Province(2023A1515010682)

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Abstract

For the individual difference of faces which are the operation object of massage robot, the dynamic motion primitives (DMPs) model was used to generalize the posture trajectory and force trajectory. Firstly, in order to improve the learning accuracy of DMPs, the study proposed an optimized teaching strategy. Based on the Mediapipe feature points in the massage area, the similarity between the operating objects was calculated to optimize the learning objects. Secondly, Gaussian mixture regression (GMR) was introduced, and the algorithm integrated multiple massage information to enhance learning ability. Finally, a back-propagation neural network (BPNN) model was constructed to fit the forced term of DMPs algorithm, which fundamentally changes the limitations of the original model. The experiment shows that the average errors of position and attitude of BPNN-DMPs model are reduced by 44.1% and 54.5%, 44.1% and 54.5%, 29.7% and 46.4% respectively, compared with DMPs, MDMPs and SADMPs algorithms without increasing the running time. Gaussian mixture regression can integrate multiple trajectory patterns and the implementation effect of the optimized teaching strategy is significant. Compared with the non-optimized object, the average errors of the position and posture of the face experiment are reduced by 52.3% and 70.2%, and the standard deviation is reduced by 46.3% and 71.1%. The average errors of position, posture and force in the back experiment decrease by 27.7%, 66.7% and 24.1%, and the standard deviation decreases by 25.7%, 54.4% and 44.1%.

Key words： massage robot; dynamic motion primitives; optimized teaching strategy; feature matching; Gaussian mixture re-gression; back-propagation neural network

Cite this article

ZHAI Jingmei, LU Dongwei . Research on Optimized Teaching Strategy and BPNN-DMPs Trajectory Learning Model of Massage Robot[J]. Journal of South China University of Technology(Natural Science), 2023 , 51(12) : 1 -8 . DOI: 10.12141/j.issn.1000-565X.230027

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