The change of industrial automation towards a highly interconnected architecture is still ongoing. Time-sensitive networking (TSN) is an essential element for this transition. This paper focuses on TSN end stations, mainly answering how to implement a software-based worst-case execution time (WCET) analyzable buffer management. The two-level segregated fit (TLSF) algorithm was chosen, implemented, and evaluated based on an asymptotic complexity analysis of dynamic storage allocation algorithms. The implemented and improved TLSF algorithm showed similar timing behavior as calculated when executed on a time-predictable platform. The paper concludes by outlining further research.

Buffer Management for TSN-Enabled End Stations

Factory automation is evolving from a hierarchical structure towards an interconnected architecture in which systems at different levels can communicate seamlessly with each other. This requires an appropriate replacement of proprietary fieldbus systems in the automation sector. A promising solution to implement cross-plane end-to-end real-time capable communication between computing nodes is proposed in literature. The usage of Open Platform Communication Unified Architecture (OPC UA) as the information model with the Publish-Subscribe (PubSub) communication mechanism in combination with Time Sensitive Networking (TSN) is presented to fulfill the requirements for message transmission. In contrast to the state of the art of configuring fieldbus systems, the configuration of such a real-time communication node has to be done manually. This process is complex, as deep knowledge in different domains is required. In this work we elaborate the requirements for such a communication node and present the resulting implementation challenges, which can be used as the basis for a configuration tool. 

Requirements and Challenges in the Configuration of a Real-Time Node for OPC UA Publish-Subscribe Communication

This paper deals with the integration of OPC UA over Wireless / Wired (Hybrid) Networks with Time Sensitive Networking (TSN) capabilities. We propose a HW/SW device architecture, based on the SHARP platform, to enable the integration of OPC UA and Hybrid TSN. The integration includes both the OPC UA Client/Server and PubSub mechanism. Based on the device architecture, we built a HW testbed that integrated Hybrid TSN and OPC UA to assess its performance. The results demonstrate that the network can provide seamless application interoperability and that can satisfy the traffic needs in a typical industrial setup. However, the paper also highlights that there is still significant research to be done to achieve deep integration between OPC UA and Hybrid TSN. 

On the integration of OPC UA over Wired – Wireless Time Sensitive Networking

OPC UA is an industry-standard machine-to-machine communication protocol in the Industrial Internet of Things. It relies on time-sensitive networking to meet the real-time requirements of various applications. Time-sensitive networking is implemented through various queueing disciplines (qdiscs), including Time Aware Priority, Multiqueue Priority, Earliest TxTime First, and Credit-Based Shaper. Despite their significance, prior studies on these qdiscs have been limited to a few. They have often been confined to point-to-point network topologies using proprietary software or specialized hardware. This study builds upon existing research by evaluating all these qdiscs in point-to-point and bridged topologies using open-source software on commercial off-the-shelf hardware. We first identify the optimal configuration for each qdisc and then compare their jitter, latency, and reliability through experiments. Our results show that open-source OPC UA on commercial off-the-shelf hardware can effectively meet the stringent real-time requirements of many industrial applications and provide a foundation for future research and practical deployments.

Real-Time Performance of OPC UA

—OPC UA PubSub is a recent addition to the OPC UA industry standard used for machine-type communication in the Industrial Internet of Things. It can be combined with Time Sensitive Networking to offer deterministic latency and bounded jitter. In this work, we model the network interactions between Credit-Based Shaper (CBS) and PubSub using Network Calculus. We derive worst-case delay bounds for various numbers of hops, payload sizes, and competing flows. The derived bounds are confirmed using deployments on commercial-of-the-shelf hardware and open-source solutions relying on a low-latency Linux kernel. Based on the results, we observe that the derived bounds are satisfied in all scenarios of seven hops with a round-trip-time (RTT) varying between 1 to 2.5ms for respective payload sizes of 59 to 1481B. Similarly, for jitter we observe fluctuations of ± 250 µ s . Complementing the CBS results, we evaluate time-aware shaping, where we observe lower RTTs for average payload sizes of around 800µs. Our contributions show that OPC UA PubSub applications can co-exist with TSN.

Assessment of OPC UA PubSub at Scale Using TSN Infrastructure and Network Calculus

Industrial automation is changing towards a flat and highly interconnected architecture, with requirements for end-to-end real-time enabled machine-to-machine communications. Technologies such as time-sensitive networking (TSN) and OPC Unified Architecture (OPC UA) publish-subscribe provide the necessary features. While TSN is well explored, OPC UA's execution time behavior remains unknown. This article presents findings made while extending the open62541 OPC UA Pub Sub stack with the 802.1q VLAN tag to enable IEEE 802.1Qbv time-aware scheduling. The results include end-to-end timing measures and worst-case execution time analyses considering various payloads. Time-predictable T-CREST platforms host the publisher and subscriber, and a TSN network handles message transmission. The paper concludes by outlining further research focusing on dynamic memory access, buffer management, and the inclusion of non-priority access to the Ethernet port.

Timing Analysis of TSN-Enabled OPC UA PubSub

Robust and efficient learning remains a challenging problem in robotics, in particular with complex visual inputs. Inspired by human attention mechanism, with which we quickly process complex visual scenes and react to changes in the environment, we think that embedding auxiliary information about focus point into robot learning would enhance efficiency and robustness of the learning process. In this paper, we propose a novel approach to model and emulate the human attention with an approximate prediction model. We then leverage this output and feed it as a structured auxiliary feature map into downstream learning tasks. We validate this idea by learning a prediction model from human-gaze recordings of manual driving in the real world. We test our approach on two learning tasks - object detection and imitation learning. Our experiments demonstrate that the inclusion of predicted human attention leads to improved robustness of the trained models to out-of-distribution samples and faster learning in low-data regime settings. Our work highlights the potential of incorporating structured auxiliary information in representation learning for robotics and opens up new avenues for research in this direction. All code and data are available online.

pdf/enhancing-robot-learning-through-learned-human-attention-1p1lv0p4y9.pdf

Enhancing Robot Learning through Learned Human-Attention Feature Maps

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Timing Analysis of TSN-Enabled OPC UA PubSub

Enhancing Robot Learning through Learned Human-Attention Feature Maps