Present-day robots are, to some extent, able to deal with high complexity and variability of the real-world environment. Their cognitive capabilities can be further enhanced, if they physically interact and explore the real-world objects. For this, the need for efficient tactile sensors is growing day after day in such a way are becoming more and more part of daily life devices especially in robotic applications for manipulation and safe interaction with the environment. In this thesis, we highlight the importance of touch sensing in humans and robots. Inspired by the biological systems, in the the first part, we merge between neuromorphic engineering and CMOS technology where the former is a eld of science that replicates what is biologically (neurons of the nervous system) inside humans into the circuit level. We explain the operation and then characterize different sensor circuits through simulation and experiment to propose finally new prototypes based on the achieved results. In the second part, we present a machine learning technique for detecting the direction and orientation of a sliding tip over a complete skin patch of the iCub robot. Through learning and online testing, the algorithm classies different trajectories across the skin patch. Through this part, we show the results of the considered algorithm with a future perspective to extend the work.

Event Driven Tactile Sensors for Artificial Devices

ABOU KHALIL, ALI
2020-03-27

Abstract

Present-day robots are, to some extent, able to deal with high complexity and variability of the real-world environment. Their cognitive capabilities can be further enhanced, if they physically interact and explore the real-world objects. For this, the need for efficient tactile sensors is growing day after day in such a way are becoming more and more part of daily life devices especially in robotic applications for manipulation and safe interaction with the environment. In this thesis, we highlight the importance of touch sensing in humans and robots. Inspired by the biological systems, in the the first part, we merge between neuromorphic engineering and CMOS technology where the former is a eld of science that replicates what is biologically (neurons of the nervous system) inside humans into the circuit level. We explain the operation and then characterize different sensor circuits through simulation and experiment to propose finally new prototypes based on the achieved results. In the second part, we present a machine learning technique for detecting the direction and orientation of a sliding tip over a complete skin patch of the iCub robot. Through learning and online testing, the algorithm classies different trajectories across the skin patch. Through this part, we show the results of the considered algorithm with a future perspective to extend the work.
Transducers, Neuromorphic engineering, MOSFET, Machine Learning
File in questo prodotto:
File Dimensione Formato  
phdunige_4168375.pdf

accesso aperto

Descrizione: This thesis focuses on event driven tactile sensor circuits characterization
Tipologia: Tesi di dottorato
Dimensione 15.13 MB
Formato Adobe PDF
15.13 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1001986
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact