High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical Components

Soft tactile sensors are soft and sufficiently flexible for attachment to a robot's gripper to enhance human‐like sensory capabilities. However, existing tactile sensors exhibit large size and a limited force measurement range. This article presents a novel design of a new soft tactile sensor f...

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Main Authors: Buyong Lim, Jungwon Yoon
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Advanced Intelligent Systems
Subjects:
Online Access:https://doi.org/10.1002/aisy.202400275
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author Buyong Lim
Jungwon Yoon
author_facet Buyong Lim
Jungwon Yoon
author_sort Buyong Lim
collection DOAJ
description Soft tactile sensors are soft and sufficiently flexible for attachment to a robot's gripper to enhance human‐like sensory capabilities. However, existing tactile sensors exhibit large size and a limited force measurement range. This article presents a novel design of a new soft tactile sensor for a robotic gripper, incorporating a sandwich‐like multilayered structure, together with a deep learning (DL) model, which overcomes the limitations of traditional sensors. The structure consists of three distinct layers: a 15 wt% iron magnetorheological elastomer, a flexible printable circuit board layer equipped with three‐dimensional Hall sensors (TLE493D; Infineon), and permanent magnets. Additionally, a multilayer perceptron network that can classify the loading state is adopted for the DL model. This new tactile sensor is capable of performing three distinct functions simultaneously: measurement of normal forces up to 3.73 kgf, identification of the precise location of force occurrence by subdivision into intervals of 2.5 mm, and differentiation between a wide (≈8 mm) and narrow (≈2 mm) contacted surface area. This newly developed soft tactile sensor has considerable potential for improvement in the performance of robotic grippers through its high accuracy, resolution, and large measurement range, as demonstrated by experimentation with the sensor attached to a real gripper.
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spelling doaj-art-909d80e3d3514a01bb3345839904965a2025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400275High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical ComponentsBuyong Lim0Jungwon Yoon1School of Integrated Technology Gwangju Institute of Science and Technology Gwangju 61005 Republic of KoreaSchool of Integrated Technology Gwangju Institute of Science and Technology Gwangju 61005 Republic of KoreaSoft tactile sensors are soft and sufficiently flexible for attachment to a robot's gripper to enhance human‐like sensory capabilities. However, existing tactile sensors exhibit large size and a limited force measurement range. This article presents a novel design of a new soft tactile sensor for a robotic gripper, incorporating a sandwich‐like multilayered structure, together with a deep learning (DL) model, which overcomes the limitations of traditional sensors. The structure consists of three distinct layers: a 15 wt% iron magnetorheological elastomer, a flexible printable circuit board layer equipped with three‐dimensional Hall sensors (TLE493D; Infineon), and permanent magnets. Additionally, a multilayer perceptron network that can classify the loading state is adopted for the DL model. This new tactile sensor is capable of performing three distinct functions simultaneously: measurement of normal forces up to 3.73 kgf, identification of the precise location of force occurrence by subdivision into intervals of 2.5 mm, and differentiation between a wide (≈8 mm) and narrow (≈2 mm) contacted surface area. This newly developed soft tactile sensor has considerable potential for improvement in the performance of robotic grippers through its high accuracy, resolution, and large measurement range, as demonstrated by experimentation with the sensor attached to a real gripper.https://doi.org/10.1002/aisy.2024002753D Hall sensordeep learning modelforce‐measuring sensormagnetorheological elastomerrobotic gripperssoft tactile sensors
spellingShingle Buyong Lim
Jungwon Yoon
High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical Components
Advanced Intelligent Systems
3D Hall sensor
deep learning model
force‐measuring sensor
magnetorheological elastomer
robotic grippers
soft tactile sensors
title High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical Components
title_full High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical Components
title_fullStr High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical Components
title_full_unstemmed High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical Components
title_short High‐Load Capable Soft Tactile Sensors: Incorporating Magnetorheological Elastomer for Accurate Contact Detection and Classification of Asymmetric Mechanical Components
title_sort high load capable soft tactile sensors incorporating magnetorheological elastomer for accurate contact detection and classification of asymmetric mechanical components
topic 3D Hall sensor
deep learning model
force‐measuring sensor
magnetorheological elastomer
robotic grippers
soft tactile sensors
url https://doi.org/10.1002/aisy.202400275
work_keys_str_mv AT buyonglim highloadcapablesofttactilesensorsincorporatingmagnetorheologicalelastomerforaccuratecontactdetectionandclassificationofasymmetricmechanicalcomponents
AT jungwonyoon highloadcapablesofttactilesensorsincorporatingmagnetorheologicalelastomerforaccuratecontactdetectionandclassificationofasymmetricmechanicalcomponents