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Improving the measurement accuracy of distance and positioning for capacitive proximity detection in human-robot interaction

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Abstract

Capacitive proximity sensors have been widely used in human-robot interaction, but suffer from the problem of the imperfect measurement accuracy in the nonlinear detection range. Meanwhile, sensor calibration makes the measurement tedious, time consuming, and is difficult to be used in 3D positioning. In this paper, we use a hand as the sensing target and propose a new distance estimation and positioning method, which combines with capacitive sensing configurations of a sensor array and machine learning. Fourteen sensing configurations are designed to generate 14 capacitive sensors to detect the target, and the capacitances are used as the input vector of the machine learning models. We compare the four machine learning algorithms (i.e. fitting method, support vector regression (SVR), radial basis function (RBF) network and BP neural network (BPNN)) for hand distance estimation. The experimental results show the RBF offers the best performance that the root mean squared error (RMSE), mean absolute error (MAE) and mean relative error (MRE) are 0.59 cm, 0.45 cm and 5.32%. Due to the lack of a proper 3D positioning device to verify the target positions, all tests for positioning are grounded on the simulation data using COMSOL, which can mimic the real world applications with high accuracy. The testing results are compared regarding the use of a Y-shaped sensor arrangement, and show that our proposed method performs better than the method with a Y-shaped sensor arrangement, the accuracy is improved by more than 98.8% and the BPNN model achieves the highest accuracy.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under grant 61902252, the Natural Science Funding of Guangdong Province No. 2018 A030310509.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Anhui University, No. 111 Jiulong Road, Hefei, 230601, Anhui Province, China

    Yong Ye

  2. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Yuting Liu & Jiahao Deng

  3. Institute of High Energy Physics Chinese Academy of Sciences, Beijing, 100049, China

    Yuting Liu

  4. Shenzhen Nuoan Environmental and Safety Inc., Shenzhen, 518055, China

    Weihan Yin

  5. School of Intelligent Manufacturing Engineering, Ma’anshan University, No. 8 Huangchi Road, Dangtu, Ma’anshan City, 243100, Anhui Province, China

    Xiaofeng Zhu

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  1. Yong Ye
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  2. Yuting Liu
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  3. Weihan Yin
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  4. Jiahao Deng
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  5. Xiaofeng Zhu
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Correspondence to Yong Ye.

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Ye, Y., Liu, Y., Yin, W. et al. Improving the measurement accuracy of distance and positioning for capacitive proximity detection in human-robot interaction. Microsyst Technol 27, 4305–4317 (2021). https://doi.org/10.1007/s00542-021-05223-2

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  • DOI: https://doi.org/10.1007/s00542-021-05223-2

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