Publication type: Conferences

Distributed Control Systems (DCSs) for emerging industrial control applications impose new communication requirements that cannot be satisfied by current Industrial Ethernet protocols. As a result, industry is pushing the Time-Sensitive Networking (TSN) standards as the de-facto Ethernet-based link layer to fulfill these requirements. Adequate roadmaps are needed to support a smooth transition from Industrial-Ethernet-based legacy systems to TSN-based ones. In this context some works propose mechanisms to migrate, i.e. map, route and schedule, legacy traffic to TSN. However none of them considers traffic including streams with spatial redundancy requirements and, thus, they cannot be used to migrate legacy highly-reliable DCSs. The present work extends a previous toolchain to migrate, for the fist time, legacy critical traffic that includes spatially redundant streams. Particularly, since redundancy is costly, this work proposes and compares two routing methods that consider one redundant stream per traffic.

Factory Automation Best Paper Award 

DOI: https://doi.org/10.1109/ETFA52439.2022.9921650

The Time-Sensitive Networking (TSN) standards provide a toolbox of features to be utilized in various application domains.The core TSN features include deterministic zero-jitter and low-latency data transmission and transmitting traffic with various levels of time-criticality on the same network. To achieve a deterministic transmission, the TSN standards define a time-aware shaper that coordinates transmission of Time-Triggered (TT) traffic. In this paper, we tackle the challenge of scheduling the TT traffic and we propose a heuristic algorithm, called HERMES. Unlike the existing scheduling solutions, HERMES results in a significantly faster algorithm run-time and a high number of schedulable networks. HERMES can be configured in two modes of zero or relaxed reception jitter while using multiple TT queues to improve the schedulability. We compare HERMES with a constraint programming (CP)-based solution and we show that HERMES performs better than the CP-based solution if multiple TT queues are used, both with respect to algorithm run-time and schedulability of the networks.

DOI: . https://doi.org/10.1145/3534879.3534906

This paper proposes a fast method to robustly perform Visual Graph SLAM in underwater environments. Since Graph SLAM is not resilient to wrong loop detections, the key of our proposal is the Visual Loop Detector, which operates in two steps. First, a lightweight Siamese Neural Network performs a fast check to discard non loop closing image pairs. Second, a RANSAC based algorithm exhaustively analyzes the remaining image pairs and filters out those that do not close a loop. The accepted image pairs are then introduced as new graph constraints that will be used during the graph optimization. By executing RANSAC only on a previously filtered set of images, the gain in speed is considerable. The experimental results, which evaluate each component separately as well as the whole Visual Graph SLAM system, show the validity of our proposal both in terms of quality of the detected loops, error of the resulting trajectory and execution time.

This paper presents an end-to-end Neural Network (NN) to estimate the overlap between two scenes observed by an underwater robot endowed with a bottom-looking camera. This information is extremely valuable to perform visual loop detection in Simultaneous Localization and Mapping (SLAM). Contrarily to other existing approaches, this study does not depend on handcrafted features or similarity metrics, but jointly optimizes the image description and the loop detection by means of a Siamese NN architecture.

Twelve different configurations have been experimentally tested using large balanced datasets synthetically generated from real data. These experiments demonstrate the ability of our proposal to properly estimate the overlap with precisions, recalls and fall-outs close to 95%, 98% and 5% respectively and execution times close to 0.7 ms per loop in a standard laptop computer. The source code of this proposal is publicly available.

This paper proposes a method to efficiently map the legacy Ethernet-based traffic into Time Sensitive Networking (TSN) traffic classes considering different traffic characteristics. Traffic mapping is one of the essential steps for industries to gradually move towards TSN, which in turn significantly mitigates the management complexity of industrial communication systems. In this paper, we first identify the legacy Ethernet traffic characteristics and properties. Based on the legacy traffic characteristics we presented a mapping methodology to map them into different TSN traffic classes. We implemented the mapping method as a tool, named Legacy Ethernet-based Traffic Mapping Tool or LETRA, together with a TSN traffic scheduling and performed a set of evaluations on different synthetic networks. The results show that the proposed mapping method obtains up to 90% improvement in the schedulability ratio of the traffic compared to an intuitive mapping method on a multi-switch network architecture.

Critical Adaptive Distributed Embedded Systems (CADES) must carry out a set of functionalities while fulfilling their associated real-time and dependability requirements. Moreover, they must be able to reconfigure themselves in a bounded time as the operational context changes. Finding a proper configuration can be non-trivial and time-consuming. Several studies have proposed Deep Reinforcement Learning (DRL) approaches to solve combinatorial optimization problems. In this paper, we explore the application of such approaches to CADES by solving a simple tasks allocation problem using DRL and comparing the results with three popular heuristics. The results show that DRL beats two of them and gets very close to the third, while requiring significantly less time to generate a solution.