Design of a Robot System for Surveillance and Anomaly Detection in Industrial Environments

This paper introduces a flexible and scalable system designed to streamline the deployment of Boston Dynamics' SPOT robot for industrial monitoring through advanced script analysis, editing, and creation. The system allows operators to easily modify SPOT's native scripts or generate new ones, enabling the robot to execute specialized tasks such as anomaly detection, gauge recognition, and real-time surveillance. A key contribution of this research is the dynamic adaptation of SPOT's behavior to changing conditions, where tasks are reallocated in real-time, ensuring continuous and reliable monitoring in complex industrial environments. The system also integrates AI-driven data processing to an-alyze critical industrial data collected from SPOT's multimodal sensors. By leveraging deep learning algorithms, the system identifies potential anomalies, monitors equipment status, and provides environmental surveillance insights, significantly improving operational safety and efficiency. This real-time data analysis allows for early detection of abnormalities, enabling proactive intervention and minimizing operational risks. Extensive field testing in real-world industrial settings demonstrated the system's robustness and effectiveness in handling dynamic monitoring tasks. The results validate the system's ability to enhance the safety and efficiency of industrial operations through flexible script management and AI-based data processing. By bridging the gap between commercial robots and specialized industrial applications, this system offers a promising solution for the future of autonomous industrial monitoring, ensuring safer and more efficient operations.