国产自拍 Engineer Earns U.S. Patent for Human-AI Manufacturing System
The 鈥淎daptive Cyber Manufacturing Through Online Human-AI Partnerships鈥 system dynamically adjusts tasks based on worker performance, creating a safer, more efficient and flexible model for human-AI collaboration while helping retain advanced manufacturing capabilities domestically.
Technology Snapshot: A new patent titled, 鈥淎daptive Cyber Manufacturing (ACM) Through Online Human-AI Partnerships鈥 awarded to Mehrdad Nojoumian, Ph.D., associate professor in the College of Engineering and Computer Science at Florida Atlantic University, enables workers to operate manufacturing equipment remotely using sensors, cameras and real-time communication platforms, allowing humans and AI-driven systems to collaborate seamlessly on complex tasks from off-site locations.
Designed for extended, real-world use, the system dynamically adapts tasks and workloads based on individual performance and experience, while predictive models minimize delays in human-machine interactions. This approach supports a more flexible, distributed manufacturing model, helping organizations retain advanced capabilities domestically while accessing a broader global workforce. By combining AI, machine learning and adaptive remote control, ACM creates a safer, more efficient and inclusive workplace 鈥 paving the way for human-robot collaboration while keeping high-value manufacturing jobs in industrial countries without sacrificing productivity and cost.
Researchers from Florida Atlantic University have developed an innovative remote manufacturing system that allows workers to operate factory tasks from anywhere, addressing a critical gap in traditional automation. Mehrdad Nojoumian, Ph.D., an associate professor in the Department of Electrical Engineering and Computer Science within 国产自拍鈥檚 College of Engineering and Computer Science, has been awarded a patent from the United States Patent and Trademark Office for this breakthrough technology titled, 鈥淎daptive Cyber Manufacturing (ACM) Through Online Human-AI Partnerships.鈥 聽
ACM represents a new paradigm in which human workers and artificial intelligence-driven systems collaborate remotely through real-time, online partnerships to perform complex manufacturing tasks. Nojoumian鈥檚 system will enable factory workers to operate facilities remotely through sensors, cameras and an online platform, similar to a video conference on Zoom or Google Meet, enabling seamless collaboration with robotic systems from any location.
Designed for extended, real-world use, the system dynamically adapts tasks and workloads based on individual user performance and experience level, including support for less-experienced workers. It incorporates predictive models to reduce delays in human-machine interaction, helping ensure smoother, more responsive operation in remote manufacturing environments.
This approach supports a more flexible and distributed manufacturing model while maintaining performance and reliability over long working hours. It also introduces a pathway for organizations to retain advanced manufacturing capabilities domestically while expanding access to a broader, global workforce.
鈥淎dvancing human-AI collaboration in manufacturing is essential to building systems that are responsive to real-world conditions,鈥 said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. 鈥淭he future of manufacturing will be driven by intelligent systems that adapt to people 鈥 enhancing performance while maintaining human oversight in complex decision-making.鈥
Nojoumian鈥檚 work presents a new model for real-time integration of human input with AI-driven systems, targeting industries where precision, adaptability and oversight are critical.
The research has been tested through simulations and partial implementations, with full experiments involving human subjects planned for the near future. ACM could pave the way for a new era of human-robot collaboration in manufacturing, where high-value jobs remain in industrial countries without sacrificing productivity or efficiency.
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