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Magneto-Active Elastomer Filter for Tactile Sensing Augmentation Through Online Adaptive Stiffening

Accepted version
Peer-reviewed

Type

Article

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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.

Description

Keywords

Force and tactile sensing, soft sensors and actuators, biologically-inspired robots

Journal Title

IEEE Robotics and Automation Letters

Conference Name

Journal ISSN

2377-3766
2377-3766

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE)
Sponsorship
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (860108)
Engineering and Physical Sciences Research Council (EP/N029003/1)