Dr. Domenico Famularo

This seminar explores the use of autonomous vehicles to enhance logistic operations in manufacturing systems. The primary concept involves the group arrangement of autonomous robots/vehicles into a finite set of platoons to accomplish specific tasks within the manufacturing system. Three key aspects are addressed: task scheduling, routing decisions, and command input computations. A new distributed multi-layer architecture is introduced, exploiting three methodologies: timed colored Petri nets (TCPN), deep reinforcement learning, and model predictive control. 

TCPN are used to formally model the manufacturing system, enabling the derivation of an optimal scheduling task that aligns with the required jobs and available vehicles. Run-time routing decisions are made using a distributed reinforcement learning algorithm that leverages information from the vehicle sensor module. The distributed model predictive control algorithm is designed using a set-theoretic approach, with most computations performed offline.

Biography:

Dr. Domenico Famularo received the laurea degree in computer engineering from the University of Calabria, Italy, in 1991 and the Ph.D in computational mechanics from the University of Rome, Italy, in 1996. From 1991 to 2000 he was a Research Associate at the University of Calabria. In 1997 he was a visiting Scholar Research at the University of New Mexico (USA) and in 1999 he covered the same position at the University of Southern California (USA). He was a Researcher at ICAR-CNR and since 2005 he is an Associate Professor at the University of Calabria. His current research interests include control under constraints, control reconfiguration for fault tolerant systems and networked control systems.

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