Isabel Pietrangeli
Development of IT Architecture for Generative AI-Driven Assembly Assistive Technologies.
Rel. Alessandro Simeone, Yuchen Fan. Politecnico di Torino, Master of science program in Engineering And Management, 2025
Abstract
This thesis discusses the creation of a system intended to assist neurodivergent workers in manual assembly tasks within the framework of Industry 5.0, specifically in the manufacturing sector. The main objective is to develop an intelligent assistance system that can identify errors in real time and offer tailored advice to operators so they can quickly fix their errors and drastically cut down on the time between error detection and resolution. The system focuses on five distinct forms of neurodivergence: attention deficits, logical reasoning difficulties, dyslexia, memory problems, and dyscalculia. Cameras and a collaborative robot (cobot) that can identify mistakes made during assembly are used to keep an eye on operators.
In response, specific modules offer customized instructions according to the operator's condition: light projections with detailed instructions for attention-related difficulties, automatic component delivery for memory problems, visual flowcharts for logical difficulties, LED indicators for dyscalculia, and audio feedback for dyslexia
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