| KEYWORD |
Parameters Optimization of a Feeding Robot Using Machine Learning
Thesis in external company
keywords COMPUTER VISION, DEEP NEURAL NETWORKS, FACTORY AUTOMATION, OPTIMIZATION ALGORITHMS, SMART ROBOTS
Reference persons ANDREA CALIMERA, ENRICO MACII, VALENTINO PELUSO
External reference persons GIAN LUCA DADONE, ALBERTO DALMASSO
Research Groups DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA
Thesis type INDUSTRIAL R&D
Description This thesis project aims to optimize the operational parameters of the SupataŽ Smart Feeder by E.P.F., a robotic system used for pick-and-place tasks. The SupataŽ Smart Feeder features a vibrating table for object singularization and a robotic arm designed to handle objects of various sizes, shapes, and weights.
The expected outcome of this work is the development of an optimization algorithm that tunes the feeder's operational parameters to minimize cycle time, thereby maximizing the number of objects processed per second. The operational parameters under optimization include the frequency, direction, velocity. and intensity of the vibrations imparted to the table. The algorithm will output the set of optimal parameters that minimize the number of vibrations needed for part singularization, tailored to the type of object being processed.
Required skills Proficiency in C and Python programming. Metaheuristic optimization and algorithms. Familiarity with machine learning and deep leaning is a plus.
Notes - The thesis project is sponsored by E.P.F. Elettrotecnica S.r.l (www.epf.it), settled in Carrų (Cuneo, Italy).
- The work will be partially carried out in the company headquarters.
Deadline 28/02/2025
PROPONI LA TUA CANDIDATURA