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A morphologically adaptive dome-shaped tactile sensor for evaluating elastic modulus and defect depth

Published online by Cambridge University Press:  16 June 2025

Cuong Manh Bui ,
Trang Xuan Mai ,
Anh Viet Phan  and
Hiep Xuan Trinh Open the ORCID record for Hiep Xuan Trinh [Opens in a new window]
Cuong Manh Bui
Affiliation:
Faculty of Mechanical Engineering, Le Quy Don Technical University, Hanoi, Vietnam
Trang Xuan Mai
Affiliation:
School of Computing, Phenikaa University, Hanoi, Vietnam
Anh Viet Phan
Affiliation:
Institute of Information and Communication Technology, Le Quy Don Technical University, Hanoi, Vietnam
Hiep Xuan Trinh*
Affiliation:
Faculty of Mechanical Engineering, Le Quy Don Technical University, Hanoi, Vietnam
*
Corresponding author: Hiep Xuan Trinh; Email: hieptx@mta.edu.vn
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Abstract

This article introduces a dome-type soft tactile sensor that can autonomously adjust its stiffness to evaluate surface contact characteristics, including the elastic modulus, contact force, and the presence of abnormal hardness within soft materials, using a strain gauge as a single sensing element. The strain sensor element is placed at the tip of the dome to measure the deformations during contact that reflect the properties of the contacted object. Using machine learning techniques, the sensor system can accurately predict these characteristics in various materials with an error rate of less than approximately 8%. A hybrid approach that combines experimental and simulation data enables the sensor to be trained effectively, generating sufficient data for accurate predictions without extensive experiments. The high accuracy results of the machine learning models demonstrate that the sensor system can precisely calculate the elastic modulus and depth of the defect. The adaptability and precision of the proposed sensor make it ideal for applications in medical diagnostics and other fields requiring careful interaction with soft materials. Furthermore, its innovative approach can be referenced for exploiting the properties of soft materials to achieve task-specific morphology without redesigning soft sensors or soft robots.

Keywords

soft tactile sensorsmorphological computationelastic modulusdefect depth

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 7 , July 2025 , pp. 2335 - 2357
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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