Autonomous Cooking with Digital Twin Methodology
It addresses the challenge of real-time simulation for autonomous processes like cooking, making it more accessible, though it appears incremental as it applies existing Digital Twin concepts to a new domain.
This work tackled the problem of enabling autonomous cooking by introducing a Digital Twin methodology that combines physics-based simulations with data-driven system identification, achieving low errors and faster-than-real-time simulations on device level without cloud or high-performance computing.
This work introduces the concept of an autonomous cooking process based on Digital Twin method- ology. It proposes a hybrid approach of physics-based full order simulations followed by a data-driven system identification process with low errors. It makes faster-than-real-time simulations of Digital Twins feasible on a device level, without the need for cloud or high-performance computing. The concept is universally applicable to various physical processes.