Fabrizia Auletta
Papers
3
Total Citations
39
H-Index
3
About
Fabrizia Auletta is a researcher at the forefront of multi-agent systems and autonomous robotics, with a particular focus on bio-inspired control strategies and safe human-machine interaction. Her most impactful work centers on the problem of "herding" stochastic autonomous agents—a challenge that draws from animal behavior to develop local control rules enabling a small group of herder agents to collect and contain large, non-cooperative target ensembles within a desired region. This research, published in 2022 and garnering 19 citations, demonstrates robust strategies for online target selection, offering scalable solutions for applications in swarm robotics and crowd management. In parallel, Auletta has made significant contributions to industrial automation, co-authoring a 2023 paper (17 citations) that employs deep convolutional neural networks for contamination detection in food and medical packaging. By integrating safe machine-environment interaction, her work addresses critical quality assurance challenges, reducing reliance on tedious manual inspection. Her earlier foundational work (2020) further explores these herding dynamics, laying the groundwork for her current innovations. Auletta’s research elegantly bridges theoretical control theory with practical, real-world applications, establishing her as a rising voice in autonomous systems and intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
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