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Staff

Greta Di Vincenzo

Ph.D. candidate in Ingegneria Meccanica , 41st cycle (2025-2028)
Department of Mechanical and Aerospace Engineering (DIMEAS)

Profile

PhD

Research topic

Modelling and Optimization of Human–Robot Teaming for Safe and Ergonomic Industrial Collaboration

Tutors

Keywords

Ergonomics and human-machine interface / Ergonomia e interfaccia uomo-macchina
Mechatronics and robotics / Meccatronica e robotica
Multi-body analysis / Analisi multi-body

Biography

ggn and optimisation of safer, more ergonomic, and adaptive collaborative systems.
  • Data-driven modelling and adaptive human–robot collaboration
The quantitative information extracted from human motion analysis and workspace modelling will be exploited to develop data-driven methodologies supporting the interpretation of human behaviour and interaction dynamics during collaborative tasks. Machine learning and artificial intelligence approaches will be investigated to identify meaningful patterns, recognise interaction states, and provide quantitative information to support adaptive human–robot collaboration.
The developed methodologies will contribute to the definition of adaptive collaboration strategies, enabling robotic systems to modify their behaviour according to the characteristics of the operator, the task being performed, and the collaborative environment. Particular attention will be devoted to investigating how different interaction strategies influence safety, ergonomics, adaptability, and overall collaboration quality.
  • Simulation and experimental validation of collaborative systems
The proposed methodologies will be integrated and evaluated through simulation and experimental validation in representative industrial collaborative scenarios. Simulation environments will enable the investigation of different collaborative configurations, workspace layouts, task allocations, and interaction strategies under controlled and repeatable conditions.
Subsequently, the developed approaches will be validated through experimental activities involving collaborative robotic systems. The evaluation will focus on assessing their applicability and effectiveness in supporting safe, ergonomic, adaptive, and efficient human–robot collaboration, while identifying the factors that most significantly influence interaction quality and collaborative task performance.

The complementary educational programme, designed to support the research objectives, includes the following courses: “Robotic Devices and Systems”, “Human–AI Interaction”, and “Digital Twin Modelling and Integration for Multidisciplinary Research”.

Publications

Latest publications View all publications in Porto@Iris