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<p>Integration of robotics and AI in the food industry holds out the prospect of a higher level of accuracy, efficient processes, and better consistency of food. The research presents an autonomous robotic cooking system that includes a 7 D.O.F. robotic arm, real-time thermal modelling, and adaptive control algorithms, improving the efficiency of cooking and quality of food. The system uses its digital twin technology to simulate and manipulate cooking variables in real time so that the perfect heat distribution and preservation of nutrients are achievable. The system uses Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in its algorithm, which is based on energy-minimizing motion planning, to deliver energy savings of 50% and a 20% reduction in the task completion time compared to the standard techniques. Assessment using Gajar Ka Halwa showed effective maintenance of nutrients, minor β-carotene oxidation, due to the stable temperatures (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\pm \)</EquationSource> </InlineEquation> 1&#xa0;°C) and the proper stirring speeds (100–200 RPM). The Lyapunov stability control component of the system helps deliver consistent performance in a varied cooking environment, and its modular structure offers methods of adapting for industrial kitchens. Utilizing robotics and food science, this study is pushing the boundaries of smart cooking technology, focusing on food safety, sensory quality, and sustainability, with viable benefits for both industrial and home cooking.</p>

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Precision cooking with 7 DOF robotic arms: integrating digital twins for nutrient-conserving food preparation

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Rajpal,
  • Anirudha Rajodia,
  • Abir Chakravorty

摘要

Integration of robotics and AI in the food industry holds out the prospect of a higher level of accuracy, efficient processes, and better consistency of food. The research presents an autonomous robotic cooking system that includes a 7 D.O.F. robotic arm, real-time thermal modelling, and adaptive control algorithms, improving the efficiency of cooking and quality of food. The system uses its digital twin technology to simulate and manipulate cooking variables in real time so that the perfect heat distribution and preservation of nutrients are achievable. The system uses Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in its algorithm, which is based on energy-minimizing motion planning, to deliver energy savings of 50% and a 20% reduction in the task completion time compared to the standard techniques. Assessment using Gajar Ka Halwa showed effective maintenance of nutrients, minor β-carotene oxidation, due to the stable temperatures ( \(\pm \) 1 °C) and the proper stirring speeds (100–200 RPM). The Lyapunov stability control component of the system helps deliver consistent performance in a varied cooking environment, and its modular structure offers methods of adapting for industrial kitchens. Utilizing robotics and food science, this study is pushing the boundaries of smart cooking technology, focusing on food safety, sensory quality, and sustainability, with viable benefits for both industrial and home cooking.