Abstract
With the rise of soft robotics, a wide range of soft sensorised skins have been proposed and developed. One particular group, fluidic electronic skins, have been shown to be promising but, so far, have been limited to small scales. Here, we present a large-scale (35,440 mm2) sensory skin that uses an embedded channel system filled with saline and which is aimed to work as a prosthetic liner. Mechanical forces on the skin translate into channel deformations which can be measured by changes in impedance through external electrodes. We developed a novel fabrication process based on methods commonly used in prosthodontics and jewellery making to overcome challenges related to the larger size of the skin. We tested two machine learning techniques, i.e., artificial neural networks and random forests, to learn the mapping of the impedance changes to the location of the physical interaction. The results provide new insights on how to improve the design, in particular, in how to improve the channel structure to increase sensory performance.
Original language | English |
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Title of host publication | 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 734-739 |
Number of pages | 6 |
ISBN (Electronic) | 9798350381818 |
ISBN (Print) | 9798350381825 |
DOIs | |
Publication status | Published - 13 May 2024 |
Event | Robosoft 2024: 7th IEEE-RAS International Conference on Soft Robotics - Hard Rock Hotel, San Diego, United States Duration: 14 Apr 2024 → 17 Apr 2024 |
Publication series
Name | IEEE International Conference on Soft Robotics (RoboSoft) |
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Publisher | IEEE |
ISSN (Print) | 769-4526 |
ISSN (Electronic) | 2769-4534 |
Conference
Conference | Robosoft 2024 |
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Abbreviated title | ROBOSOFT 2024 |
Country/Territory | United States |
City | San Diego |
Period | 14/04/24 → 17/04/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.