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基于磁场的软触觉传感器的设计优化

Design Optimisation of a Magnetic Field Based Soft Tactile Sensor.

作者信息

Boer Gregory de, Raske Nicholas, Wang Hongbo, Kow Junwai, Alazmani Ali, Ghajari Mazdak, Culmer Peter, Hewson Robert

机构信息

School of Mechanical Engineering, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.

Department of Aeronautics, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.

出版信息

Sensors (Basel). 2017 Nov 3;17(11):2539. doi: 10.3390/s17112539.

DOI:10.3390/s17112539
PMID:29099787
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5712876/
Abstract

This paper investigates the design optimisation of a magnetic field based soft tactile sensor, comprised of a magnet and Hall effect module separated by an elastomer. The aim was to minimise sensitivity of the output force with respect to the input magnetic field; this was achieved by varying the geometry and material properties. Finite element simulations determined the magnetic field and structural behaviour under load. Genetic programming produced phenomenological expressions describing these responses. Optimisation studies constrained by a measurable force and stable loading conditions were conducted; these produced Pareto sets of designs from which the optimal sensor characteristics were selected. The optimisation demonstrated a compromise between sensitivity and the measurable force, a fabricated version of the optimised sensor validated the improvements made using this methodology. The approach presented can be applied in general for optimising soft tactile sensor designs over a range of applications and sensing modes.

摘要

本文研究了一种基于磁场的软触觉传感器的设计优化，该传感器由一块磁铁和一个通过弹性体隔开的霍尔效应模块组成。目的是使输出力相对于输入磁场的灵敏度最小化；这通过改变几何形状和材料特性来实现。有限元模拟确定了负载下的磁场和结构行为。遗传编程生成了描述这些响应的现象学表达式。进行了受可测量力和稳定加载条件约束的优化研究；这些研究产生了帕累托设计集，从中选择了最佳传感器特性。优化表明在灵敏度和可测量力之间存在折衷，优化传感器的制造版本验证了使用该方法所取得的改进。所提出的方法通常可应用于在一系列应用和传感模式中优化软触觉传感器设计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/08e3697f102e/sensors-17-02539-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/fdbae161a942/sensors-17-02539-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/7193a87dfb51/sensors-17-02539-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/86352f02bc9e/sensors-17-02539-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/a317a0796ddd/sensors-17-02539-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/cfb81186278a/sensors-17-02539-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/1c1621f7e6a9/sensors-17-02539-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/7128ac4e3a01/sensors-17-02539-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/be012277a414/sensors-17-02539-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/08e3697f102e/sensors-17-02539-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/fdbae161a942/sensors-17-02539-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/a046d441fa5b/sensors-17-02539-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/c53d2903d935/sensors-17-02539-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/e3de15d56324/sensors-17-02539-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/599d779c930b/sensors-17-02539-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/7193a87dfb51/sensors-17-02539-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/86352f02bc9e/sensors-17-02539-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/a317a0796ddd/sensors-17-02539-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/cfb81186278a/sensors-17-02539-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/1c1621f7e6a9/sensors-17-02539-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/7128ac4e3a01/sensors-17-02539-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/be012277a414/sensors-17-02539-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3582/5712876/08e3697f102e/sensors-17-02539-g012.jpg

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