Journal article
3D-printed soft sensors for adaptive sensing with online and offline tunable-stiffness
- Abstract:
- The stiffness of a soft robot with structural cavities can be regulated by controlling the pressure of a fluid to render predictable changes in mechanical properties. When the soft robot interacts with the environment, the mediating fluid can also be considered an inherent information pathway for sensing. This approach to using structural tuning to improve the efficacy of a sensing task with specific states has not yet been well studied. A tunable stiffness soft sensor also renders task-relevant contact dynamics in soft robotic manipulation tasks. This paper proposes a type of adaptive soft sensor that can be directly 3D printed and controlled using pneumatic pressure. The tunability of such a sensor helps to adjust the sensing characteristics to better capture specific tactile features, demonstrated by detecting texture with different frequencies. We present the design, modelling, Finite Element Simulation, and experimental characterisation of a single unit of such a tunable stiffness sensor. How the sensing characteristics are affected by adjusting its stiffness is studied in depth. In additional to the tunability, the results show such type of adaptive sensors exhibit good sensitivity (up to 2.6 [KPa/N]), high sensor repeatability (average std < 0.008 [KPa/N]), low hysteresis (< 6%), and good manufacturing repeatability (average std = 0.0662[KPa/N]).
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Preview, Accepted manuscript, pdf, 3.8MB, Terms of use)
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- Publisher copy:
- 10.1089/soro.2021.0074
Authors
- Publisher:
- Mary Ann Liebert
- Journal:
- Soft Robotics More from this journal
- Publication date:
- 2022-03-21
- Acceptance date:
- 2021-12-03
- DOI:
- EISSN:
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2169-5180
- ISSN:
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2169-5172
- Language:
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English
- Keywords:
- Pubs id:
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1222433
- Local pid:
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pubs:1222433
- Deposit date:
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2021-12-07
Terms of use
- Copyright holder:
- Mary Ann Liebert, Inc.
- Copyright date:
- 2022
- Rights statement:
- © 2022, Mary Ann Liebert, Inc., publishers.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Mary Ann Liebert at: https://doi.org/10.1089/soro.2021.0074
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