实现下一代机器人系统的边缘计算架构

Achilleas Santi Seisa, Gerasimos Damigos, S. Satpute, A. Koval, G. Nikolakopoulos
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引用次数: 5

摘要

边缘计算是一项很有前途的技术,可以在需要即时数据处理的技术领域提供新功能。机器和深度学习等领域的研究人员在其应用程序中广泛使用边缘和云计算,主要是因为它们提供了大量的计算和存储资源。目前,机器人技术公司也在寻求利用这些能力,随着5G网络的发展,该领域的一些现有限制可以被克服。在这种情况下,重要的是要知道如何利用新兴的边缘架构,目前存在哪些类型的边缘架构和平台,以及哪些可以并且应该基于每个机器人应用程序使用。一般来说,Edge平台可以以不同的方式实现和使用,特别是因为有几个提供商提供或多或少相同的服务集,但存在一些本质上的差异。因此,本研究为那些从事下一代机器人系统开发工作的人提供了这些讨论,并将有助于了解每种边缘计算架构的优缺点,以便为每种应用明智地选择正确的架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge Computing Architectures for Enabling the Realisation of the Next Generation Robotic Systems
Edge Computing is a promising technology to provide new capabilities in technological fields that require instantaneous data processing. Researchers in areas such as machine and deep learning use extensively edge and cloud computing for their applications, mainly due to the significant computational and storage resources that they provide. Currently, Robotics is seeking to take advantage of these capabilities as well, and with the development of 5G networks, some existing limitations in the field can be overcome. In this context, it is important to know how to utilize the emerging edge architectures, what types of edge architectures and platforms exist today and which of them can and should be used based on each robotic application. In general, Edge platforms can be implemented and used differently, especially since there are several providers offering more or less the same set of services with some essential differences. Thus, this study addresses these discussions for those who work in the development of the next generation robotic systems and will help to understand the advantages and disadvantages of each edge computing architecture in order to choose wisely the right one for each application.
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