We explore and characterize the use of Ethernet and particularly, Time Sensitive Networking (TSN), in robotics.

Original source: "Time-Sensitive Networking for robotics". Peer written by Carlos San Vicente Gutiérrez, Lander Usategui San Juan, Irati Zamalloa Ugarte and Víctor Mayoral Vilches.

The field of robotics is growing rapidly. New areas such as professional, consumer or industrial robotics are demanding more flexible technologies and a set of standardized policies that facilitate the process of designing, manufacturing and configuring a robot for potentially more than one specific application.

One of the main problems in robotics, as it happens in other industries, is that there is no such thing as a standard communication protocol, but a variety of them. Choosing a communication protocol is not straightforward: the list is large, and each protocol has evolved to meet the needs of a particular application area.

Typically, each protocol has been customized for specific applications and, as a result, multiple communication protocols and buses are used to meet different requirements within those more complex use cases.

In fact, many industrial protocols have common technological baselines, but customize upper abstraction layers to meet different requirements for real-time Ethernet solutions. In the case of real-time communication protocols, the links and physical layers are commonly modified to achieve a better performance. This leads to hardware incompatibility problems, making communications between devices cumbersome.

A common solution is the use of gateways (or bridges), which add cost, complexity and produce a loss in performance. Having a unique standard protocol would improve the interoperability between robots and facilitate the robotic component integration, which is still one of the main hurdles in the robot building process.

Robotic peripheral manufacturers suffer especially from these problems because they need to support several protocols, further increasing the integration time and costs.

Time-Sensitive Networking (TSN) is a set of standards defined by the Time-Sensitive Networking task group of the IEEE 802.1 working group designed to make Ethernet more deterministic.

Most of the existing real-time Ethernet solutions were created for low data volume applications such as distributed motion control. These solutions are usually very limited in bandwidth and cannot reach the Ethernet bandwidth capabilities.

Now, robotics is increasingly integrating  Artificial Intelligence (AI),computer vision or predictive maintenance and there is a growing need of sensors and actuators streaming high bandwidth data in real-time. The information provided is often integrated in the control system or needs to be monitored in real time.

The typical solution is to use a specific bus for real-time control and a separate one for higher bandwidth communications. But as more and more high bandwidth traffic is generated, the control process of having two separated communications is inefficient.

Adding real-time capabilities to Ethernet, TSN provides a common communication channel for high bandwidth traffic and real-time control traffic. TSN will also improve the access to the robot components which is especially interesting for predictive maintenance, re-configurability or adaptability.

In the article ( Time-Sensitive Networking for robotics), we explore and characterize the use of Ethernet and particularly, Time Sensitive Networking (TSN), in robotics.

First we introduce an overview of the industrial communications available in the context of robotics and the corresponding related work. Then, we present an analysis of TSN standards and the Ethernet timing model and discuss the experimental results obtained while evaluating the presented hypotheses.

Particularly, we validate the results presented by Bruckner et. al. in "A new Solution for Industrial Communication". In this work, the authors extend Robert et. al.'s "Minimum cycle time analysis of Ethernet-based real-time protocols", where   Jürgen Jasperneite analytic method to estimate input and output cycle times for Ethernet technologies was used.

Bruckner et.al. extended the analysis to a solution based on TSN and OPC-UA. And their conclusions  show how a TSN based technology using 1 Gbit Ethernet and a frame aggregation approach can outperform other hard real-time industrial protocols.

To validate those results experimentally in the context of robotics, we selected a typical robotic use case with mixed-critical traffic: a modular robotic arm with a high bandwidth sensor attached at the end.

We compared the delays experienced by the queuing delay in Ethernet switches for standard Ethernet, against the delays when using a TSN Time-Aware shaper. In this work we have presented an experimental setup to show the suitability of TSN for real-time robotic applications.

The results showed the indeterminacy of Ethernet and how these problems can limit the scalability and performance in real-time robotic applications such as the exemplary modular robotic arm.

When the TSN Time-Aware shaper was used, the results showed that the time sensitive traffic was perfectly isolated from lower priority traffic, maintaining low latency and jitter even in the presence of high bandwidth background traffic.

These results suggest that it is possible to develop hard real-time motion control systems mixed with high bandwidth sensors, such as LIDARs (Light Detection and Ranging o Laser Imaging Detection and Ranging) and high resolution cameras.

Based on the presented results, we claim that Ethernet with TSN standards will become the de facto standard for communications on layers 1 and 2, in robotics.

We argue that, within robotics, many of the existing real-time industrial solutions will slowly be replaced by TSN.

For higher layers, we foresee a contending landscape where the integration of TSN in different middleware solutions focused on interoperability such as OPC-UA and DDS promise to deliver a bottom-up real-time communication solution.

To know more, find the extended research paper here: Time-Sensitive Networking for robotics.