3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing
The human tactile system is composed of multi-functional mechanoreceptors distributed in an optimized manner. Having the ability to design and optimize multi-modal soft sensory systems can further enhance the capabilities of current soft robotic systems. This work presents a complete framework for t...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/13/9/1540 |
| _version_ | 1797484636509569024 |
|---|---|
| author | David Hardman Thomas George Thuruthel Antonia Georgopoulou Frank Clemens Fumiya Iida |
| author_facet | David Hardman Thomas George Thuruthel Antonia Georgopoulou Frank Clemens Fumiya Iida |
| author_sort | David Hardman |
| collection | DOAJ |
| description | The human tactile system is composed of multi-functional mechanoreceptors distributed in an optimized manner. Having the ability to design and optimize multi-modal soft sensory systems can further enhance the capabilities of current soft robotic systems. This work presents a complete framework for the fabrication of soft sensory fiber networks for contact localization, using pellet-based 3D printing of piezoresistive elastomers to manufacture flexible sensory networks with precise and repeatable performances. Given a desirable soft sensor property, our methodology can design and fabricate optimized sensor morphologies without human intervention. Extensive simulation and experimental studies are performed on two printed networks, comparing a baseline network to one optimized via an existing information theory based approach. Machine learning is used for contact localization based on the sensor responses. The sensor responses match simulations with tunable performances and good localization accuracy, even in the presence of damage and nonlinear material properties. The potential of the networks to function as capacitive sensors is also demonstrated. |
| first_indexed | 2024-03-09T23:07:16Z |
| format | Article |
| id | doaj.art-d68bed0b35eb4068a1750677a0468ed9 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T23:07:16Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-d68bed0b35eb4068a1750677a0468ed92023-11-23T17:51:15ZengMDPI AGMicromachines2072-666X2022-09-01139154010.3390/mi130915403D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile SensingDavid Hardman0Thomas George Thuruthel1Antonia Georgopoulou2Frank Clemens3Fumiya Iida4Bio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKBio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKDepartment of Functional Materials, Empa-Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Duebendorf, SwitzerlandDepartment of Functional Materials, Empa-Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Duebendorf, SwitzerlandBio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKThe human tactile system is composed of multi-functional mechanoreceptors distributed in an optimized manner. Having the ability to design and optimize multi-modal soft sensory systems can further enhance the capabilities of current soft robotic systems. This work presents a complete framework for the fabrication of soft sensory fiber networks for contact localization, using pellet-based 3D printing of piezoresistive elastomers to manufacture flexible sensory networks with precise and repeatable performances. Given a desirable soft sensor property, our methodology can design and fabricate optimized sensor morphologies without human intervention. Extensive simulation and experimental studies are performed on two printed networks, comparing a baseline network to one optimized via an existing information theory based approach. Machine learning is used for contact localization based on the sensor responses. The sensor responses match simulations with tunable performances and good localization accuracy, even in the presence of damage and nonlinear material properties. The potential of the networks to function as capacitive sensors is also demonstrated.https://www.mdpi.com/2072-666X/13/9/1540soft robotic sensors3D printingmachine learning |
| spellingShingle | David Hardman Thomas George Thuruthel Antonia Georgopoulou Frank Clemens Fumiya Iida 3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing Micromachines soft robotic sensors 3D printing machine learning |
| title | 3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing |
| title_full | 3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing |
| title_fullStr | 3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing |
| title_full_unstemmed | 3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing |
| title_short | 3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing |
| title_sort | 3d printable soft sensory fiber networks for robust and complex tactile sensing |
| topic | soft robotic sensors 3D printing machine learning |
| url | https://www.mdpi.com/2072-666X/13/9/1540 |
| work_keys_str_mv | AT davidhardman 3dprintablesoftsensoryfibernetworksforrobustandcomplextactilesensing AT thomasgeorgethuruthel 3dprintablesoftsensoryfibernetworksforrobustandcomplextactilesensing AT antoniageorgopoulou 3dprintablesoftsensoryfibernetworksforrobustandcomplextactilesensing AT frankclemens 3dprintablesoftsensoryfibernetworksforrobustandcomplextactilesensing AT fumiyaiida 3dprintablesoftsensoryfibernetworksforrobustandcomplextactilesensing |