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End-to-End O-RAN Testbed for Edge-AI-Enabled 5G/6G Connected Industrial Robotics

Sasa Talosi, Vladimir Vincan, Srdjan Sobot, Goran Martic, Vladimir Morosev, Vukan Ninkovic, Dragisa Miskovic, Dejan Vukobratovic

Abstract

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.

End-to-End O-RAN Testbed for Edge-AI-Enabled 5G/6G Connected Industrial Robotics

Abstract

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.
Paper Structure (24 sections, 5 figures, 1 table)

This paper contains 24 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Connected Robotics Use Case: Edge-AI Empowered Autonomous Welding
  3. Use Case Description
  4. Use Case Requirements
  5. 3D Data Generation and Frame Rate
  6. Traffic Characteristics
  7. Latency and Reliability Requirements
  8. Testbed Architecture
  9. 3GPP/O-RAN 5G Network Testbed Architecture
  10. Edge-AI Empowered Robotic Testbed Architecture
  11. Testbed Deployment
  12. 3GPP/O-RAN 5G Network Testbed Deployment
  13. Infrastructure for Computing and Switching
  14. VLAN segmentation and network topology
  15. Radio and UE Setup
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Robotic workcell setup for AI-enabled autonomous welding.
  • Figure 2: End-to-end Connected Robotics O-RAN-based Testbed Architecture.
  • Figure 3: End-to-end Connected Robotics O-RAN-based Testbed Components and Deployment.
  • Figure 4: O-RAN Testbed Network Diagram
  • Figure 5: Sample performance results for DL/UL throughput under different TDD slot configurations.