Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client s requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client s requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.This work has been supported by the Spanish Science and Innovation Ministry MICINN under the CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and the Programme for Research and Development PAID of the Polytechnic University of Valencia: UPV-PAID-FPI-2013. The responsibility for the content remains with the authors.Munera Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors. 15(8):18080-18101. https://doi.org/10.3390/s150818080S1808018101158Gupta, R. A., & Mo-Yuen Chow. (2010). Networked Control System: Overview and Research Trends. IEEE Transactions on Industrial Electronics, 57(7), 2527-2535. doi:10.1109/tie.2009.2035462Morales, R., Badesa, F. J., García-Aracil, N., Perez-Vidal, C., & Sabater, J. M. (2012). Distributed Smart Device for Monitoring, Control and Management of Electric Loads in Domotic Environments. Sensors, 12(5), 5212-5224. doi:10.3390/s120505212Zhang, Z. (2012). Microsoft Kinect Sensor and Its Effect. IEEE Multimedia, 19(2), 4-10. doi:10.1109/mmul.2012.24Gonzalez-Jorge, H., Riveiro, B., Vazquez-Fernandez, E., Martínez-Sánchez, J., & Arias, P. (2013). 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Integrating Smart Resources in ROS-based systems to distribute services

[EN] Mobile robots execute complexes tasks that involve the management of several embedded sensors and actuators. Therefore, in many cases, a robot is characterized as an intelligent distributed system formed with a central unit, which manages the on-board embedded devices and distributes the tasks execution. Embedded devices are also evolving to more complex systems. These systems are developed not only for executing simple tasks but also for offering some advanced mechanisms. Thus, complex data processing, adaptive execution, or fault-tolerance routines are some common system features. The Smart Resource topology has been developed in order to manage these embedded systems. This topology offers high-level routines that rely on a certain physical hardware execution. Therefore, Smart Resources are defined as distributed services providers, which operates within some context and quality requirements. Provided services can adapt its execution in order accomplish the set requirements and maximize the system performance. How to improve the versatility of the Smart Resources by making their services compatibles with the Robot Operating System (ROS) is addressed along this work. This solution integrates all the execution mechanisms provided by ROS with the service distribution, adaptive execution, and fault-tolerance routines offered by the Smart Resources. This integration is tested through a set of experiments using the Turtlebot robot platform and a simulated version of it. In both approaches ROS mechanisms are used to access the Smart Resource Services. Finally, obtained results are used to characterize the performance of this proposal.Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and PAID (Polytechnic University of Valencia): UPV-PAID-FPI-2013.Munera-Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2017). Integrating Smart Resources in ROS-based systems to distribute services. Advances in Distributed Computing and Artificial Intelligence Journal. 6(1):13-19. https://doi.org/10.14201/ADCAIJ2017611319S13196

Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing

Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies

Arquitectura distribuida para el control autónomo de drones en interior

[Resumen] Actualmente los drones son uno de los sistemas de control más complejos. Este control va desde el control de la estabilidad del propio dron, hasta el control automático de la navegación de dicho dron en entornos complejos. En el caso de drones que deben navegar en interior los retos tecnológicos son específicos. En el presente artículo se muestra la arquitectura de control inteligente de un dron orientado a la navegación en entornos interiores. La seguridad es el eje principal del diseño del sistema. Esto hace que el principal reto de la arquitectura sea la interconexión segura entre los componentes y la definición de los diferentes métodos de navegación basándose en la seguridad. El dron debe disponer de diversos modos de navegación: manual, reactivo, deliberativo e inteligente. Para la navegación en interior es necesario conocer la posición del dron en todo momento, por ello el sistema debe disponer de un modo de localización similar al GPS, pero que proporcione una precisión mucho mayor. Para los modos deliberativo e inteligente, el sistema debe disponer de un mapa del entorno, así como de un sistema de control que envíe al dron las órdenes de navegación correspondientes. El sistema diseñado se está implementando en el marco del proyecto europeo H2020 AiRT (Arts indoor RPAS Technology transfer). El resultado es una propuesta de arquitectura suficientemente versátil para ser empleadas en sistemas similares y servir como base de diseño para futuras implementaciones

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

[EN] Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.This research was funded by the Spanish Science and Innovation Ministry grant number MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN2017-86520-C3-1-R.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2020). Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities. Sensors. 20(1):1-18. https://doi.org/10.3390/s20010112S118201Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. 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Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments

[EN] This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.This work is partially supported by the TIN2009-13839-C03-01, TIN2011-27652-C03-01, 547CSD2007-00022, COST Action IC0801, FP7-294931 and the FPI grant AP2013-01276 548 awarded to Jaime-Andres Rincon.Rincón Arango, JA.; Poza Luján, JL.; Julian Inglada, VJ.; Posadas Yagüe, JL.; Carrascosa Casamayor, C. (2016). Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments. PLoS ONE. 11(2):1-27. https://doi.org/10.1371/journal.pone.0149665S127112Luck, M., & Aylett, R. (2000). Applying artificial intelligence to virtual reality: Intelligent virtual environments. Applied Artificial Intelligence, 14(1), 3-32. doi:10.1080/088395100117142Barella A, Ricci A, Boissier O, Carrascosa C. MAM5: Multi-Agent Model For Intelligent Virtual Environments. 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Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-