Magneto-Active Elastomer Filter for Tactile Sensing Augmentation Through Online Adaptive Stiffening
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Publication Date
2022-07Journal Title
IEEE Robotics and Automation Letters
ISSN
2377-3766
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Pages
1-1
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Costi, L., Tagliabue, A., Maiolino, P., Clemens, F., & Iida, F. (2022). Magneto-Active Elastomer Filter for Tactile Sensing Augmentation Through Online Adaptive Stiffening. IEEE Robotics and Automation Letters, 1-1. https://doi.org/10.1109/LRA.2022.3160590
Abstract
The mechanical properties of a sensor strongly affect its tactile sensing capabilities. The role of morphology and stiffness on the quality of the tactile data has already been the subject of several studies, which focus mainly on static sensor designs and design methodologies. However, static designs always come with trade-offs: considering stiffness, soft compliant sensors ensure a better contact, but at the price of mechanically filtering and altering the detected signal. Conversely, online adaptable filters can tune their characteristics, becoming softer or stiffer when needed. We propose a magneto-active elastomer filter which, when placed on top of the tactile unit, allows the sensor to change its stiffness on demand. We showcase the advantages provided by online stiffening adaptation in terms of information gained and data structure. Moreover, we illustrate how adaptive stiffening influences classification, using 9 standard machine learning algorithms, and how adaptive stiffening can increase the classification accuracy up to 34% with respect to static stiffness control.
Sponsorship
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (860108)
Identifiers
External DOI: https://doi.org/10.1109/LRA.2022.3160590
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336001
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