In-Network Velocity Control of Industrial Robot Arms


Sándor Laki and Csaba Györgyi, ELTE Eötvös Loránd University, Budapest, Hungary; József Pető, Budapest University of Technology and Economics, Budapest, Hungary; Péter Vörös, ELTE Eötvös Loránd University, Budapest, Hungary; Géza Szabó, Ericsson Research, Budapest, Hungary


In-network computing has emerged as a new computational paradigm made possible with the advent of programmable data planes. The benefits of moving computations traditionally performed by servers to the network have recently been demonstrated through different applications. In this paper, we argue that programmable data planes could be a key technology enabler of cloud and edge-cloud robotics, and in general could revitalize industrial networking. We propose an in-network approach for real-time robot control that separates delay sensitive tasks from high-level control processes. The proposed system offloads real-time velocity control of robot arms to P4-enabled programmable data planes and only keeps the high-level control and planning at the industrial controller. This separation allows the deployment of industrial control in non-real-time environments like virtual machines and service containers running in a remote cloud or an edgecomputing infrastructure. In addition, we also demonstrate that our method can smoothly control 100s of robot arms with a single P4-switch, enables fast reroute between trajectories, solves the precise synchronization of multiple robots by design and supports the plug-and-play deployment of new robot devices in the industrial system, reducing both operational and management costs.

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {276978,
author = {S{\'a}ndor Laki and Csaba Gy{\"o}rgyi and J{\'o}zsef Pet{\H o} and P{\'e}ter V{\"o}r{\"o}s and G{\'e}za Szab{\'o}},
title = {{In-Network} Velocity Control of Industrial Robot Arms},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {995--1009},
url = {},
publisher = {USENIX Association},
month = apr

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