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.