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.

翻译：连接机器人是推动向更智能、更强大的6G移动蜂窝网络过渡的主要用例之一。用高可靠、高吞吐量、低延迟的5G/6G无线接口替代有线连接，实现了机器人系统的移动性，并将用于机器人感知与控制的计算密集型人工智能模型卸载到位于网络边缘的服务器。向边缘人工智能即服务的过渡简化了机器人系统的现场维护，降低了工业环境中的运营成本，同时支持灵活的人工智能模型生命周期管理以及机器人功能随时间的无缝升级。在本文中，我们提出了一个基于5G/6G O-RAN的端到端测试平台，该平台集成了面向连接工业机器人应用的边缘人工智能即服务。其目标是基于开放技术和接口设计并部署一个通用的实验平台，并通过一个支持边缘人工智能即服务的自主焊接场景进行演示。在此场景中，该测试平台用于研究机器人感知中不同数据采集、边缘处理和实时流媒体方法之间的权衡，同时支持新兴范式，如语义通信和目标导向通信。