Robots are increasingly being used in a variety of applications, from manufacturing and healthcare to education and customer service. However, the mobility, power, and price points of these robots often dictate that they do not have sufficient computing power on board to run modern algorithms for personalization in human-robot interaction at desired rates. This can limit the effectiveness of the interaction and limit the potential applications for these robots. 5G connectivity provides a solution to this problem by offering high data rates, bandwidth, and low latency that can facilitate robotics services. Additionally, the widespread availability of cloud computing has made it easy to access almost unlimited computing power at a low cost. Edge computing, which involves placing compute resources closer to the action, can offer even lower latency than cloud computing. In this paper, we explore the potential of combining 5G, edge, and cloud computing to provide improved personalization in human-robot interaction. We design, develop, and demonstrate a new framework, entitled NetROS-5G, to show how the performance gained by utilizing these technologies can overcome network latency and significantly enhance personalization in robotics. Our results show that the integration of 5G network slicing, edge computing, and cloud computing can collectively offer a cost-efficient and superior level of personalization in a modern human-robot interaction scenario.

翻译：机器人正越来越多地应用于制造、医疗、教育及客户服务等多个领域。然而，这些机器人的移动性、功耗和价格成本往往限制了其机载计算能力，使其无法以所需速率运行用于人机交互个性化的现代算法。这可能会限制交互的有效性，并制约该类机器人的潜在应用。5G连接通过提供高数据速率、大带宽和低延迟来支持机器人服务，从而为该问题提供了解决方案。此外，云计算的广泛普及使得以低成本获取近乎无限的计算资源变得便捷。边缘计算通过将计算资源部署至更靠近应用场景的位置，可提供比云计算更低的延迟。本文探究了结合5G、边缘计算与云计算以增强人机交互个性化的潜力。我们设计、开发并展示了一个名为NetROS-5G的新框架，以证明利用这些技术获得的性能提升如何能够克服网络延迟，并显著增强机器人个性化。研究结果表明，5G网络切片、边缘计算与云计算的集成能够以经济高效的方式，在现代人机交互场景中提供卓越的个性化水平。