Robotic Optimization of Powdered Beverages Leveraging Computer Vision and Bayesian Optimization
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ID: 282974
2024
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Abstract
The growing demand for innovative research in the food industry is driving
the adoption of robots in large-scale experimentation, as it offers increased
precision, replicability, and efficiency in product manufacturing and
evaluation. To this end, we introduce a robotic system designed to optimize
food product quality, focusing on powdered cappuccino preparation as a case
study. By leveraging optimization algorithms and computer vision, the robot
explores the parameter space to identify the ideal conditions for producing a
cappuccino with the best foam quality. The system also incorporates computer
vision-driven feedback in a closed-loop control to further improve the
beverage. Our findings demonstrate the effectiveness of robotic automation in
achieving high repeatability and extensive parameter exploration, paving the
way for more advanced and reliable food product development.
| Reference Key |
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| Authors | Emilia Szymanska; Josie Hughes |
| Journal | arXiv |
| Year | 2024 |
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