Tactile sensing has become crucial in robotic applications such as teleoperation, as it gives information about the object properties that cannot be perceived by other senses. In fact, it is essential that robots are equipped with advanced touch sensing in order to be aware of their surroundings and give a feedback to an operator. Such sensing system are made of sensors and an elaboration unit that acquires tactile signals and process the data, retrieving information such as texture, hardness, and shape. In this paper, we propose a novel tactile sensing system made of flexible, high sensitive and high spatial resolution piezoelectric polyvinylidene fluoride‐trifluoroethylene P(VDF-TrFE) sensors, and a low power and low cost Interface Electronics (IE) that can acquire data from 32 channels simultaneously with a sampling frequency of 2kSamples/s. We validate the system acquiring data from three different objects to classify their hardness using an artificial neural networks of one hidden layer with approximately 89% accuracy. The signal processing and the classifier will be hosted by the IE in the next future.

A Novel Tactile Sensing System for Robotic Tactile Perception of Object Properties

Amin Y.;Gianoglio C.;Valle M.
2023

Abstract

Tactile sensing has become crucial in robotic applications such as teleoperation, as it gives information about the object properties that cannot be perceived by other senses. In fact, it is essential that robots are equipped with advanced touch sensing in order to be aware of their surroundings and give a feedback to an operator. Such sensing system are made of sensors and an elaboration unit that acquires tactile signals and process the data, retrieving information such as texture, hardness, and shape. In this paper, we propose a novel tactile sensing system made of flexible, high sensitive and high spatial resolution piezoelectric polyvinylidene fluoride‐trifluoroethylene P(VDF-TrFE) sensors, and a low power and low cost Interface Electronics (IE) that can acquire data from 32 channels simultaneously with a sampling frequency of 2kSamples/s. We validate the system acquiring data from three different objects to classify their hardness using an artificial neural networks of one hidden layer with approximately 89% accuracy. The signal processing and the classifier will be hosted by the IE in the next future.
978-3-031-08135-4
978-3-031-08136-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1098039
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