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Sensors 2017, 17(8), 1844; doi:10.3390/s17081844

Slippage Detection with Piezoresistive Tactile Sensors

1
Research Unit of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, Rome 00128, Italy
2
The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa 56127, Italy
*
Author to whom correspondence should be addressed.
Received: 21 June 2017 / Revised: 1 August 2017 / Accepted: 7 August 2017 / Published: 10 August 2017
(This article belongs to the Special Issue Tactile Sensors and Sensing)
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Abstract

One of the crucial actions to be performed during a grasping task is to avoid slippage. The human hand can rapidly correct applied forces and prevent a grasped object from falling, thanks to its advanced tactile sensing. The same capability is hard to reproduce in artificial systems, such as robotic or prosthetic hands, where sensory motor coordination for force and slippage control is very limited. In this paper, a novel algorithm for slippage detection is presented. Based on fast, easy-to-perform processing, the proposed algorithm generates an ON/OFF signal relating to the presence/absence of slippage. The method can be applied either on the raw output of a force sensor or to its calibrated force signal, and yields comparable results if applied to both normal or tangential components. A biomimetic fingertip that integrates piezoresistive MEMS sensors was employed for evaluating the method performance. Each sensor had four units, thus providing 16 mono-axial signals for the analysis. A mechatronic platform was used to produce relative movement between the finger and the test surfaces (tactile stimuli). Three surfaces with submillimetric periods were adopted for the method evaluation, and 10 experimental trials were performed per each surface. Results are illustrated in terms of slippage events detection and of latency between the slippage itself and its onset. View Full-Text
Keywords: slippage; tactile; sensor; algorithm; prosthetics; robotics slippage; tactile; sensor; algorithm; prosthetics; robotics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Romeo, R.A.; Oddo, C.M.; Carrozza, M.C.; Guglielmelli, E.; Zollo, L. Slippage Detection with Piezoresistive Tactile Sensors. Sensors 2017, 17, 1844.

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