Anomaly Detection Using Machine Learning for Robotics Environments on 5G Networks

Mikel Dean Oses, Aitor Domec Paz, Santiago Figueroa-Lorenzo, Saioa Arrizabalaga, Ricardo Rodriguez-Jorge

This work underscores the importance of developing and refining machine learning (ML) methods to meet the specific demands of anomaly detection in 5G-powered environments. It addresses key challenges, including the deployment of robotics within industrial settings that require robust low-latency communication and high data throughput. The proposed architecture thus delves into innovative ML-driven approaches that not only optimize anomaly detection but also maintain high performance under the constraints and requirements imposed by 5G-enabled industrial applications. Our experiments demonstrate the effectiveness of these techniques in accurately identifying anomalies while minimizing false positives. The practical implications of integrating anomaly detection into robotics processes are discussed, with potential applications in autonomous driving, warehouse automation, and remote inspection. Finally, this research contributes to the development of robust robotic systems in real-world environments.