Biorobotics is a new scientific technological area, that has recently evolved from the fusion of biology and robotics. It increasingly challenges robotics engineers to solve, supplement and assist scientists of biology in the quantitative demands of nature related questions. This study present 3 different biorobotics application, that share their theory to reverse engineer human behaviour, signal encoding and cognition, where the applications often maintains or even over-perform the desired standard by human needs. The author presents an industrial 4.0 platform with a multi-agent system, inspired by the human behaviour of performing a quality control task. The system successfully detected and repaired various types of defects on vehicle parts with high precision. Later, the candidate discusses a morphological characterisation strategy of a rehabilitation robotics platform approach that enabled two trans-radial amputees to successfully discriminat naturalistic textures via the MNC-based tactile feedback. As last, the author presents a novel application for collaborative robotics, that employs Fiber Bragg Grating (FBG) technology embedded into an artificial skin. The study shows, that the candidate was able to successfully localize and predict its force of the exterior mechanical interaction on a large surface of a robotic arm covered with artificial skin.

Artificial Intelligence Strategies for Visual-Tactile Perception with Robots in Industry 4.0 and Bionics

CZIMMERMANN, TAMAS
2020

Abstract

Biorobotics is a new scientific technological area, that has recently evolved from the fusion of biology and robotics. It increasingly challenges robotics engineers to solve, supplement and assist scientists of biology in the quantitative demands of nature related questions. This study present 3 different biorobotics application, that share their theory to reverse engineer human behaviour, signal encoding and cognition, where the applications often maintains or even over-perform the desired standard by human needs. The author presents an industrial 4.0 platform with a multi-agent system, inspired by the human behaviour of performing a quality control task. The system successfully detected and repaired various types of defects on vehicle parts with high precision. Later, the candidate discusses a morphological characterisation strategy of a rehabilitation robotics platform approach that enabled two trans-radial amputees to successfully discriminat naturalistic textures via the MNC-based tactile feedback. As last, the author presents a novel application for collaborative robotics, that employs Fiber Bragg Grating (FBG) technology embedded into an artificial skin. The study shows, that the candidate was able to successfully localize and predict its force of the exterior mechanical interaction on a large surface of a robotic arm covered with artificial skin.
3-giu-2020
Italiano
artificial intelligence
artificial senses
collaborative robotics
deep learning
Industry 4.0
machine vision
DARIO, PAOLO
ODDO, CALOGERO MARIA
CIUTI, GASTONE
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/216908
Il codice NBN di questa tesi è URN:NBN:IT:SSSUP-216908