OPTIMIZED CLOUD MANUFACTURING FRAMEWORKS FOR ROBOTICS AND AUTOMATION WITH ADVANCED TASK SCHEDULING TECHNIQUES

Abstract

Background: This revolutionizes the framework of manufacturing due to the adoption of cloud computing into robotics and automation, allowing the sharing of resources, real-time management of tasks, and enhancement of productivity. The paper reviews optimized cloud manufacturing frameworks for advanced task scheduling in robotic and automation systems, indicating the benefits toward smart manufacturing.
Objectives: The paper's objectives include optimization of the task scheduling within the cloud-based framework for robotics and automation systems with the aid of improved resource utilization and better efficiency in operational work. This paper focuses on real-time management of tasks for improvement in responsiveness, scalability in the manufacturing environments.
Methods: This paper suggests a cloud architecture integrating task scheduling algorithms, cloud robotics, and automation systems. The methods apply machine learning to predict and optimize, real-time data processing, and cloud computing for resource allocation
Results: The Optimized Cloud Manufacturing Framework discussed above would benefit to enhance efficiency in task scheduling by 41%, latency improvement by 47%, and enhanced utilization of the resources by 35% in real-time coordination flows in robotics and automation workflows.
Conclusion: This cloud manufacturing framework proposed with advanced techniques of task scheduling enhances operational efficiency, reduces cost, and scales well. It provides a robust solution to integrate robotics and automation in the smart manufacturing environment.