Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Technologies

DOI: 10.3390/technologies14020108

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

Institutions: Centro de Estudios e Investigaciones Técnicas de Gipuzkoa, Universidad de Navarra

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930