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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-07082023-113332


Tipo di tesi
Tesi di laurea magistrale
Autore
IOSI, MASSIMILIANO
URN
etd-07082023-113332
Titolo
Development and Characterization of a Tactile-Proximity Sensor Technology
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Prof. Ciuti, Gastone
correlatore Chiurazzi, Marcello
controrelatore Prof. Scilingo, Enzo Pasquale
Parole chiave
  • biorobotics
  • capacitive sensing
  • medical robotics
  • robotics
Data inizio appello
25/07/2023
Consultabilità
Non consultabile
Data di rilascio
25/07/2093
Riassunto
The purpose of this work is to present updates and improvements of an innovative proximity sensing technology, also compatible with contact measurements, based on capacitive principles, yet presented in the state of art [1] and also patented [2]. An optimization of sensor geometry and electrical performances was conducted with the goal of expanding the maximum range for proximity via FEM simulations, leading to three final configurations that have been developed and tested. According to the results provided in [1], the device already shows flexibility, compactness, scalability, and suitability for being employed in several application scenarios, such as enhanced collaborative robotics, human-machine interaction, and medical robotics. Sensors have been developed on flexible polyamide substrate, a dedicated mould has been designed and sensors were finally encapsuled using a silicone material exhibiting force/deformation characteristics aligned with the requirements and applicability in various fields, such as robotics, medical applications, and consumer products. A dedicated bench test setup – including conditioning electronic circuit for data collection, amplification, and digitalization – has been designed and experiments have been performed to compare the performances of the three selected sensor geometries with the previous original geometry, both for tactile and proximity sensing modalities, deriving calibration curves. Results were compared with simulation data to validate the accuracy of the analytical optimization model and further discussed in relation to the previous conducted research.
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