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Development, experimental, and numerical characterisation of novel flexible strain sensors for soft robotics applications

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posted on 2025-02-20, 09:15 authored by Sylvester Ndidiamaka Nnadi, Ivor Ajadalu, Amir Rahmani, Aliyu Aliyu, Khaled Elgeneidy, Allahyar Montazeri, Behnaz SohaniBehnaz Sohani
Medical and agricultural robots that interact with living tissue or pick fruit require tactile and flexible sensors to minimise or eliminate damage. Until recently, research has focused on the development of robots made of rigid materials, such as metal or plastic. Due to their complex configuration, poor spatial adaptability and low flexibility, rigid robots are not fully applicable in some special environments such as limb rehabilitation, fragile objects gripping, human–machine interaction, and locomotion. All these should be done in an accurate and safe manner for them to be useful. However, the design and manufacture of soft robot parts that interact with living tissue or fragile objects is not as straightforward. Given that hyper-elasticity and conductivity are involved, conventional (subtractive) manufacturing can result in wasted materials (which are expensive), incompatible parts due to different physical properties, and high costs. In this work, additive manufacturing (3D printing) is used to produce a conductive, composite flexible sensor. Its electrical response was tested based on various physical conditions. Finite element analysis (FEA) was used to characterise its deformation and stress behaviour for optimisation to achieve functionality and durability. Also, a nonlinear regression model was developed for the sensor’s performance.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Robotics

Volume

13

Issue

7

Publisher

MDPI

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Acceptance date

2024-07-08

Publication date

2024-07-11

Copyright date

2024

eISSN

2218-6581

Language

  • en

Depositor

Dr Behnaz Sohani. Deposit date: 12 July 2024

Article number

103

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