End-to-End O-RAN Testbed for Edge-AI-Enabled 5G/6G Connected Industrial Robotics
This work addresses the need for flexible and cost-effective AI model management in industrial robotics, though it is incremental as it builds on existing O-RAN and edge computing concepts.
The paper tackles the challenge of integrating Edge AI as a Service (E-AIaaS) into 5G/6G networks for industrial robotics by presenting an O-RAN-based end-to-end testbed, demonstrated through an autonomous welding scenario to explore trade-offs in data acquisition, edge processing, and real-time streaming.
Connected robotics is one of the principal use cases driving the transition towards more intelligent and capable 6G mobile cellular networks. Replacing wired connections with highly reliable, high-throughput, and low-latency 5G/6G radio interfaces enables robotic system mobility and the offloading of compute-intensive artificial intelligence (AI) models for robotic perception and control to servers located at the network edge. The transition towards Edge AI as a Service (E-AIaaS) simplifies on-site maintenance of robotic systems and reduces operational costs in industrial environments, while supporting flexible AI model life-cycle management and seamless upgrades of robotic functionalities over time. In this paper, we present a 5G/6G O-RAN-based end-to-end testbed that integrates E-AIaaS for connected industrial robotic applications. The objective is to design and deploy a generic experimental platform based on open technologies and interfaces, demonstrated through an E-AIaaS-enabled autonomous welding scenario. Within this scenario, the testbed is used to investigate trade-offs among different data acquisition, edge processing, and real-time streaming approaches for robotic perception, while supporting emerging paradigms such as semantic and goal-oriented communications.