Amundarain-Irizar, A. (Aiert)

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    Flexible framework to model industry 4.0 processes for virtual simulators
    (MDPI AG, 2019) Ottogalli-Fernández, K.A. (Kiara Alexandra); Amundarain-Irizar, A. (Aiert); Aguinaga-Hoyos, I. (Iker); Rosquete, D. (Daniel); Borro-Yagüez, D. (Diego)
    Virtual reality (VR)- and augmented reality (AR)-based simulations are key technologies in Industry 4.0 which allow for testing and studying of new processes before their deployment. A simulator of industrial processes needs a flexible way in which to model the activities performed by the worker and other elements involved, such as robots and machinery. This work proposes a framework to model industrial processes for VR and AR simulators. The desk method was used to review previous research and extract the most important features of current approaches. Novel features include interaction among human workers and a variety of automation systems, such as collaborative robots, a broader set of tasks (including assembly and disassembly of components), flexibility of modeling industrial processes for different domains and purposes, a clear separation of process definition and simulator, and independence of specific programming languages or technologies. Three industrial scenarios modeled with this framework are presented: an aircraft assembly scenario, a guidance tool for high-voltage cell security, and an application for the training of machine-tool usage.
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    Sensors on the move: onboard camera-based real-time traffic alerts paving the way for cooperative roads
    (MDPI, 2021) Iparraguirre-Gil, O. (Olatz); Amundarain-Irizar, A. (Aiert); Brazález-Guerra, A. (Alfonso); Borro-Yagüez, D. (Diego)
    European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.