Dr. Gentilini completed his Ph.D. in Mechanical Engineering at Carnegie Mellon University, Pittsburgh. He also received his Diplom in Mechanical Engineering from Universität Karlsruhe (TH), Karlsruhe, Germany and his Laurea in Mechanical Engineering from Politecnico di Torino, Torino, Italy.
His research interests are in the areas of robotics, dynamics and controls, and operations research. In particular his focus has been on robotic path planning in redundant configuration spaces, cycle time and energy consumption minimization for redundant industrial robotic systems, space robots control, exact solution and heuristics for the generalized travelling salesman problem with neighborhoods (GTSPN), mixed integer non-linear programming (MINLP), loop shaping using complex proportional integral lead compensators, and robust Bode (RBode) plots. More details can be found in the projects, pubblications, outreach, and sponsors sections.
|Office Location:||King Engineering Center, Rm 106|
|Mailing Address:||Aerospace and Mechanical Engineering Department|
|Embry-Riddle Aeronautical University|
|3700 Willow Creek Road|
|Prescott, AZ 86301-3720|
|Office Phone:||(928) 777-6626|
The project focuses on the development of Autonomous Redundant Space Robot (ARSR) Control through supervised learning techniques. An ARSR is a satellite with one or more robotic manipulators, designed to detect and rendezvous with other satellites or space targets and perform maintenance, debris removal and other similar procedures. Currently, robotic manipulators on satellites like the International Space Station (ISS) are much smaller and less massive than the satellite itself and therefore, does not significantly affect the inertia of the system. On the contrary, smaller satellites carrying robotic manipulators however pose a challenge to developing the control system for the satellite due to variable inertia. The primary challenge is to identify and model the full system dynamics for the control system. This requires simulating the dynamics of ARSRs in a micro-gravity environment.
This project is developed in cooperation with Dr. Douglas Isenberg and it is supported by two Ignite Grants.
A heuristic solution procedure for the Generalized Travelling Salesman Problem with Neighborhoods (GTSPN) is generated using an hybrid random-key Genetic Algorithm (HRKGA). The definition of the neighborhoods is extended to multi-shaped domains and mixed instances with linear, quadratic, and hybrid constraints are used to define the neighborhoods. Clustered neighborhoods are also considered introducing the concept of neighborhoodsets.
This project has been developed in cooperation with Dr. Ken Bordignon and it has been supported by three Ignite Grants and the two NASA Space Grants. Results were presented at:
In the current industrial environment there is an increasing need for the real time adaptation of plant productivity levels to conform to actual market demands. Since industrial plants are highly automated through the employment of robotic systems, we want to investigate the possibility, not only of optimizing the operation cycle time through exploitation of the redundancy in the system, but also minimizing the total energy consumption, and thus reducing the energy cost per manufactured unit.
This project has been developed in cooperation with Dr. Brian Davis and it has been supported by an ERAU Faculty Internal Reserach Grant. Results were presented at:
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3700 Willow Creek Road, Prescott, AZ 86301-3720