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

TL;DR

Optimus-Q tackles the problem of safe, real-time environmental monitoring in nuclear power plants by combining autonomous robotics, adaptive learning, federated training, and quantum-secured communications. The approach uses infrared gas sensing to detect $CO_2$, $CO$, and $CH_4$, decentralized model learning via FedAvg, and QKD-based secure channels to protect data and model exchanges. Key contributions include a detailed state machine for Optimus-Q, a locally trained model framework with global federation, and an evaluation showing improved contamination detection and privacy-preserving collaboration across plants. The work demonstrates a pathway toward robust, secure, and scalable monitoring systems in hazardous environments with potential for broader application in critical infrastructure.

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.

Figures (4)

• Figure 1: The system architecture describes the key components of the Optimus-Q approach, focusing on the global server, local server, and monitoring systems within the nuclear power plant. It includes essential elements such as robots, charging stations, and contamination areas, while also detailing the integration of quantum channels and quantum key distribution. Numbers here represent the life-cycle of Optimus-Q.
• Figure 2: The figure depicts the bounding box within which the Optimus-Q robot will operate to identify contamination zones.
• Figure 3: The picture outlines the various roles and responsibilities of Optimus-Q, highlighting its preset attributes.
• Figure 4: The picture outlines the various roles and responsibilities of Optimus-Q, highlighting its preset attributes.