Abstract
In this paper we present NeatSkin, a novel artificial skin sensor based on electrical impedance tomography. The key feature is a discrete network of fluidic channels which is used to infer the location of touch. Change in resistance of the conductive fluid within these channels during deformation is used to construct sensitivity maps. We present a method to simulate touch using this unique network-based, low output dimensionality approach. The efficacy is demonstrated by fabricating a NeatSkin sensor. This paves the way for the development of more complex channel networks and a higher resolution soft skin sensor with potential applications in soft robotics, wearable devices and safe human-robot interaction.
| Original language | English |
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| Number of pages | 6 |
| Publication status | Published - 9 Apr 2020 |
| Event | Robosoft 2020: IEEE International Conference on Soft Robotics - New Haven, United States Duration: 6 Apr 2020 → 9 Apr 2020 http://robosoft2020.org/ |
Conference
| Conference | Robosoft 2020 |
|---|---|
| Abbreviated title | Robosoft2020 |
| Country/Territory | United States |
| City | New Haven |
| Period | 6/04/20 → 9/04/20 |
| Internet address |
Keywords
- skin sensor
- machine learning
- soft robot
- impedance tomography