Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel
Soft sensing technologies provide a novel alternative for state estimation in wearables and robotic systems. They allow one to capture intrinsic state parameters in a highly conformable manner. However, due to the nonlinearities in the materials that make up a soft sensor, it is difficult to develop...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-10-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202300152 |
| _version_ | 1797654585880346624 |
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| author | Sojiro Sugiura David Hardman Thomas George Thuruthel Yasuhisa Hasegawa Fumiya Iida |
| author_facet | Sojiro Sugiura David Hardman Thomas George Thuruthel Yasuhisa Hasegawa Fumiya Iida |
| author_sort | Sojiro Sugiura |
| collection | DOAJ |
| description | Soft sensing technologies provide a novel alternative for state estimation in wearables and robotic systems. They allow one to capture intrinsic state parameters in a highly conformable manner. However, due to the nonlinearities in the materials that make up a soft sensor, it is difficult to develop accurate models of these systems. Consequently, design of these soft sensors is largely user defined or based on trial and error. Since these sensors conform and take the shape of the sensing body, these issues are further exacerbated when they are installed. Herein, a framework for the automated design optimization of soft sensors using closed‐loop 3D printing of a recyclable hydrogel‐based sensing material is presented. The framework allows direct printing of the sensor on the sensing body using visual feedback, evaluates the sensor performance, and iteratively improves the sensor design. Following preliminary investigations into the material and morphology parameters, this is demonstrated through the optimization of a sensorized glove which can be matched to specific tasks and individual hand shapes. The glove's sensors are tuned to respond only to particular hand poses, including distinguishing between two similar tennis racket grip techniques. |
| first_indexed | 2024-03-11T17:01:38Z |
| format | Article |
| id | doaj.art-c32eeb43e3624c57ab0a08205bccbc01 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-03-11T17:01:38Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-c32eeb43e3624c57ab0a08205bccbc012023-10-20T07:43:39ZengWileyAdvanced Intelligent Systems2640-45672023-10-01510n/an/a10.1002/aisy.202300152Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive HydrogelSojiro Sugiura0David Hardman1Thomas George Thuruthel2Yasuhisa Hasegawa3Fumiya Iida4Intelligent Robotics and Biomechatronics Laboratory Nagoya University Nagoya 464‐8603 JapanBio-Inspired Robotics Laboratory University of Cambridge Cambridge CB2 1PZ UKBio-Inspired Robotics Laboratory University of Cambridge Cambridge CB2 1PZ UKIntelligent Robotics and Biomechatronics Laboratory Nagoya University Nagoya 464‐8603 JapanBio-Inspired Robotics Laboratory University of Cambridge Cambridge CB2 1PZ UKSoft sensing technologies provide a novel alternative for state estimation in wearables and robotic systems. They allow one to capture intrinsic state parameters in a highly conformable manner. However, due to the nonlinearities in the materials that make up a soft sensor, it is difficult to develop accurate models of these systems. Consequently, design of these soft sensors is largely user defined or based on trial and error. Since these sensors conform and take the shape of the sensing body, these issues are further exacerbated when they are installed. Herein, a framework for the automated design optimization of soft sensors using closed‐loop 3D printing of a recyclable hydrogel‐based sensing material is presented. The framework allows direct printing of the sensor on the sensing body using visual feedback, evaluates the sensor performance, and iteratively improves the sensor design. Following preliminary investigations into the material and morphology parameters, this is demonstrated through the optimization of a sensorized glove which can be matched to specific tasks and individual hand shapes. The glove's sensors are tuned to respond only to particular hand poses, including distinguishing between two similar tennis racket grip techniques.https://doi.org/10.1002/aisy.202300152closed-loop 3D printingoptimizationsoft sensingwearable sensors |
| spellingShingle | Sojiro Sugiura David Hardman Thomas George Thuruthel Yasuhisa Hasegawa Fumiya Iida Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel Advanced Intelligent Systems closed-loop 3D printing optimization soft sensing wearable sensors |
| title | Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel |
| title_full | Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel |
| title_fullStr | Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel |
| title_full_unstemmed | Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel |
| title_short | Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel |
| title_sort | closed loop optimization of soft sensor morphology using 3d printing of electrically conductive hydrogel |
| topic | closed-loop 3D printing optimization soft sensing wearable sensors |
| url | https://doi.org/10.1002/aisy.202300152 |
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