Optimus-Q: Utilizing Federated Learning in Adaptive Robots for Intelligent Nuclear Power Plant Operations through Quantum Cryptography

2025-11-19

AI Summary

To address the insufficient real-time responsiveness, security, and cross-site collaboration capabilities in nuclear power plant environmental monitoring, this paper proposes an intelligent monitoring framework integrating autonomous mobile robots, federated learning (FL), and quantum key distribution (QKD). Robots equipped with infrared sensors and adaptive path-planning modules enable high-coverage, low-radiation-exposure on-site inspections. FL facilitates privacy-preserving, “data-available-but-invisible” collaborative modeling across multiple nuclear sites, enhancing generalization in pollution prediction. QKD ensures end-to-end encrypted transmission of both raw monitoring data and model updates. Experimental evaluation in simulated and real nuclear environments demonstrates a 23.6% improvement in pollution identification accuracy and a 41.2% reduction in emergency response latency, while satisfying GDPR-level privacy compliance and achieving security strength equivalent to AES-256+. This work establishes the first tripartite coupling paradigm of FL–QKD–robotics for nuclear safety monitoring.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Quantum Machine Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The integration of advanced robotics in nuclear power plants (NPPs) presents a transformative opportunity to enhance safety, efficiency, and environmental monitoring in high-stakes environments. Our paper introduces the Optimus-Q robot, a sophisticated system designed to autonomously monitor air quality and detect contamination while leveraging adaptive learning techniques and secure quantum communication. Equipped with advanced infrared sensors, the Optimus-Q robot continuously streams real-time environmental data to predict hazardous gas emissions, including carbon dioxide (CO$_2$), carbon monoxide (CO), and methane (CH$_4$). Utilizing a federated learning approach, the robot collaborates with other systems across various NPPs to improve its predictive capabilities without compromising data privacy. Additionally, the implementation of Quantum Key Distribution (QKD) ensures secure data transmission, safeguarding sensitive operational information. Our methodology combines systematic navigation patterns with machine learning algorithms to facilitate efficient coverage of designated areas, thereby optimizing contamination monitoring processes. Through simulations and real-world experiments, we demonstrate the effectiveness of the Optimus-Q robot in enhancing operational safety and responsiveness in nuclear facilities. This research underscores the potential of integrating robotics, machine learning, and quantum technologies to revolutionize monitoring systems in hazardous environments.

Problem

• Autonomous air quality monitoring in nuclear power plants
• Secure data transmission using quantum cryptography techniques
• Collaborative learning across facilities without compromising data privacy

Innovation

• Federated learning enables collaborative prediction without data sharing
• Quantum cryptography secures sensitive operational data transmission
• Adaptive robots autonomously monitor contamination using infrared sensors