Use of Deep Learning for Characterization of Microfluidic Soft Sensors

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Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are common especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating the magnitude and the location of a contact pressure simultaneously. The proposed approach in this letter is not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-04
Language
English
Article Type
Article
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.3, no.2, pp.873 - 880

ISSN
2377-3766
DOI
10.1109/LRA.2018.2792684
URI
http://hdl.handle.net/10203/240587
Appears in Collection
CS-Journal Papers(저널논문)
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