Integration of Mobile Robot Navigation on a Control Kernel Middleware based system

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-07593-8_55This paper introduces how a mobile robot can perform navigation tasks by taking the advantages of implementing a control kernel middleware (CKM) based system. Smart resources are also included into the topology of the system for improving the distribution of computational load of the needed tasks. The CKM and the smart resources are both highly recon gurable, even on execution time, and they also implement.lt detection mechanisms and QoS policies. By combining of these capabilities, the system can be dinamically adapted to the requirements of its tasks. Furthermore, this solution is suitable for most type of robots, including those which are provided of a low computational power because of the distribution of load, the bene ts of exploiting the smart resources capabilities, and the dynamic performance of the system.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-002-02.Munera Sánchez, E.; Muñoz Alcobendas, M.; Posadas-Yagüe, J.; Poza-Lujan, J.; Blanes Noguera, F. (2014). Integration of Mobile Robot Navigation on a Control Kernel Middleware based system. En Distributed Computing and Artificial Intelligence, 11th International Conference. Springer Advances in Intelligent Systems and Computing Volume 290. 477-484. https://doi.org/10.1007/978-3-319-07593-8_55S477484Rock (Robot Constrution Toolkit), http://www.rock-robotics.org/Albertos, P., Crespo, A., Simó, J.: Control kernel: A key concept in embedded control systems. In: 4th IFAC Symposium on Mechatronic Systems (2006)Bruyninckx, H., Soetens, P., Koninckx, B.: The Real-Time Motion Control Core of the Orocos Project. In: IEEE International Conference on Robotics and Automation, pp. 2766–2771 (2003)De Souza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 237–267 (2002)Fitzpatrick, P., Metta, G., Natale, L.: Towards long-lived robot genes. Robotics and Autonomous Systems (2008)Mohamed, N., Al-Jaroodi, J., Jawhar, I.: Middleware for robotics: A survey. In: 2008 IEEE Conference on Robotics, Automation and Mechatronics, pp. 736–742. IEEE (2008)Montemerlo, M., Roy, N., Thrun, S.: Perspectives on standardization in mobile robot programming: The carnegie mellon navigation (carmen) toolkit. In: Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 3, pp. 2436–2441. IEEE (2003)Muñoz, M., Munera, E., Blanes, J.F., Simo, J.E., Benet, G.: Event driven middleware for distributed system control. XXXIV Jornadas de Automatica, 8 (2013)Muñoz, M., Munera, E., Blanes, J.F., Simó, J.E.: A hierarchical hybrid architecture for mission-oriented robot control. In: Armada, M.A., Sanfeliu, A., Ferre, M. (eds.) First Iberian Robotics Conference of ROBOT 2013. AISC, vol. 252, pp. 363–380. Springer, Heidelberg (2014)Sánchez, E.M., Alcobendas, M.M., Noguera, J.F.B., Gilabert, G.B., Ten, J.E.S.: A reliability-based particle filter for humanoid robot self-localization in RoboCup Standard Platform League. Sensors (Basel, Switzerland) 13(11), 14954–14983 (2013)Poza-Luján, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E.: Relationship between Quality of Control and Quality of Service in Mobile Robot Navigation. In: Omatu, S., De Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 557–564. Springer, Heidelberg (2012)Proetzsch, M., Luksch, T., Berns, K.: Development of complex robotic systems using the behavior-based control architecture iB2C. Robotics and Autonomous Systems 58(1), 46–67 (2010)Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: An open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3 (2009)Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation-mobile robot navigation with uncertainty in dynamic environments. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation, vol. 1, pp. 35–40. IEEE (1999)Nicolau, V., Muñoz, M., Simó, J.: KertrolBot Platform: SiDiReLi: Distributed System with Limited Resources. Technical report, Institute of Control Systems and Industrial Computing - Polytechnic University of Valencia, Valencia, Spain (2011

A Software Suite for the Control and the Monitoring of Adaptive Robotic Ecologies

Adaptive robotic ecologies are networks of heterogeneous robotic devices (sensors, actuators, automated appliances) pervasively embedded in everyday environments, where they learn to cooperate towards the achievement of complex tasks. While their flexibility makes them an increasingly popular way to improve a system’s reliability, scalability, robustness and autonomy, their effective realisation demands integrated control and software solutions for the specification, integration and management of their highly heterogeneous and computational constrained components. In this extended abstract we briefly illustrate the characteristic requirements dictated by robotic ecologies, discuss our experience in developing adaptive robotic ecologies, and provide an overview of the specific solutions developed as part of the EU FP7 RUBICON Project

Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

Autonomous robots are complex systems that require the interaction between numerous heterogeneous components (software and hardware). Because of the increase in complexity of robotic applications and the diverse range of hardware, robotic middleware is designed to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and the sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure across multiple research efforts, and to reduce production costs. This paper presents a literature survey and attribute-based bibliography of the current state of the art in robotic middleware design. The main aim of the survey is to assist robotic middleware researchers in evaluating the strengths and weaknesses of current approaches and their appropriateness for their applications. Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified based on middleware attributes.http://dx.doi.org/10.1155/2012/95901

MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION

Home environments are changing as more technological devices are used to improve daily life. The growing demand for high technology in our homes means that robot integration will soon arrive. Home devices are evolving in a connected paradigm in which data flows to perform efficient home task management. Heterogeneous home robots connected in a network can establish a workflow that complements their capabilities and so increases performance within a mission execution. This work addresses the definition and requirements of a robot-group mission in the home context. The proposed solution relies on a network of smart resources, which are defined as cyber-physical systems that provide high-level service execution. Firstly, control middleware architecture is introduced as the execution base for the Smart resources. Next, the Smart resource topology and its integration within a robotic platform are addressed. Services supplied by Smart resources manage their execution through a robot behavior architecture. Robot behavior execution is hierarchically organized through a mission definition that can be established as an individual or collective approach. Environment model and interaction tasks characterize the operation capabilities of each robot within a mission. Mission goal achievement in a heterogeneous group is enhanced through the complement of the interaction capabilities of each robot. To offer a clearer explanation, a full use case is presented in which two robots cooperate to execute a mission and the previously detailed steps are evaluated. Finally, some of the obtained results are discussed as conclusions and future works is introduced.Los entornos domésticos se encuentran sometidos a un proceso de cambio gracias al empleo de dispositivos tecnológicos que mejoran la calidad de vida de las personas. La creciente demanda de alta tecnología en los hogares señala una próxima incorporación de la robótica de servicio. Los dispositivos domésticos están evolucionando hacia un paradigma de conexión en el cual la información fluye para ofrecer una gestión más eficiente. En este entorno, robots heterogéneos conectados a la red pueden establecer un flujo de trabajo que ofreciendo nuevas soluciones y incrementando la eficiencia en la ejecución de tareas. Este trabajo aborda la definición y los requisitos necesarios para la ejecución de misiones en grupos de robots heterogéneos en entornos domésticos. La solución propuesta se apoya en una red de Smart resources, que son definidos como sistemas ciber-físicos que proporcionan servicios de alto nivel. En primer lugar, se presenta la arquitectura del middleware de control en la cual se basa la ejecución de los Smart resources. A continuación se detalla la topología de los Smart resources, así como su integración en plataformas robóticas. Los servicios proporcionados por los Smart resources gestionan su ejecución mediante una arquitectura de comportamientos para robots. La ejecución de estos comportamientos se organiza de forma jerárquica mediante la definición de una misión con un objetivo establecido de forma individual o colectiva a un grupo de robots. Dentro de una misión, las tareas de modelado e interacción con el entorno define las capacidades de operación de los robots dentro de una misión. Mediante la integración de un grupo heterogéneo de robots sus diversas capacidades son complementadas para el logro un objetivo común. A fin de caracterizar esta propuesta, los mecanismos presentados en este documento se evaluarán en detalle a lo largo de una serie experimentos en los cuales un grupo de robots heterogéneos ejecutan una misión colaborativa para alcanzar un objetivo común. Finalmente, los resultados serán discutidos a modo de conclusiones dando lugar el establecimiento de un trabajo futuro.Els entorns domèstics es troben sotmesos a un procés de canvi gràcies a l'ocupació de dispositius tecnològics que milloren la qualitat de vida de les persones. La creixent demanda d'alta tecnologia a les llars assenyala una propera incorporació de la robòtica de servei. Els dispositius domèstics estan evolucionant cap a un paradigma de connexió en el qual la informació flueix per oferir una gestió més eficient. En aquest entorn, robots heterogenis connectats a la xarxa poden establir un flux de treball que ofereix noves solucions i incrementant l'eficiència en l'execució de tasques. Aquest treball aborda la definició i els requisits necessaris per a l'execució de missions en grups de robots heterogenis en entorns domèstics. La solució proposada es recolza en una xarxa de Smart resources, que són definits com a sistemes ciber-físics que proporcionen serveis d'alt nivell. En primer lloc, es presenta l'arquitectura del middleware de control en la qual es basa l'execució dels Smart resources. A continuació es detalla la tipologia dels Smart resources, així com la seva integració en plataformes robòtiques. Els serveis proporcionats pels Smart resources gestionen la seva execució mitjançant una arquitectura de comportaments per a robots. L'execució d'aquests comportaments s'organitza de forma jeràrquica mitjançant la definició d'una missió amb un objectiu establert de forma individual o col·lectiva a un grup de robots. Dins d'una missió, les tasques de modelatge i interacció amb l'entorn defineix les capacitats d'operació dels robots dins d'una missió. Mitjançant la integració d'un grup heterogeni de robots seves diverses capacitats són complementades per a l'assoliment un objectiu comú. Per tal de caracteritzar aquesta proposta, els mecanismes presentats en aquest document s'avaluaran en detall mitjançant d'una sèrie experiments en els quals un grup de robots heterogenis executen una missió col·laborativa per aconseguir un objectiu comú. Finalment, els resultats seran discutits a manera de conclusions donant lloc a l'establiment d'un treball futur.Munera Sánchez, E. (2017). MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88404TESI

Resilient middleware for a multi-robot team

Tese de mestrado em Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2010Actualmente, equipas de robôs móveis intervém em diversos contextos e ambientes onde a intervenção humana é perigosa ou mesmo impossível, podemos mencionar como exemplo a vigilância de espaços físicos, como zonas militares ou nucleares. Devido à crescente complexidade inserida nos seus sistemas, esses robôs ficam mais poderosos mas paradoxalmente mais susceptíveis a falhas de hardware e software. Além disso, a incerteza na comunicação wireless pode privá-los temporariamente do seu suporte de informação remoto. Este tipo de problema pode ser causado pelo alcance limitado do emissor wireless e pelas zonas de sombra criadas pelo terreno. Por todas essas razões, desenhar arquitecturas capazes de oferecer mais resiliência para controlo das aplicações, tornou-se um verdadeiro desafio. Este documento aborda um motor cooperativo e resiliente para equipas de robôs que lhes permite partilharem uma vista comum e lidar com novos eventos de uma forma fiável e resiliente. Este middleware tem como função estabelecer a guarda de uma qualquer zona física e detectar eventos inabituais como os intrusos. Neste ultimo caso, um robô tem que encontrar uma maneira de bloquear o intruso para o impedir de fugir. O sistema apoia-se em duas características chave, a primeira é uma camada de controlo baseado em dois sub-módulos de controlo, o payload e o wormhole, a segunda é uma arquitectura baseada em eventos que executam tarefas do payload. Em relação à camada de controlo, o payload pode ser complexo e acede à informação partilhada pelos robôs enquanto que o wormhole é confiável mas apenas utiliza a informação local. O payload utiliza uma estrutura de dados chamada “promessa” na qual fornece o deadline correspondente ao momento mais tarde onde deve enviar a próxima promessa. No caso de receber esta promessa depois do deadline, o wormhole considera que o payload falhou, toma o controlo e executa as tarefas criticas no lugar do payload. Os eventos são propagados às traves de uma estrutura em forma de alvor, da raiz até as folhas. Cada folha do alvor é um módulo que pode ser executado e produz eventos. A produção dos eventos no alvor pode ser assimilado a uma reacção em cadeia. Durante o ciclo dos eventos as traves do alvor não são possíveis, o que permite evitar as reacções não controladas e garantem assim a estabilidade do sistema. A juntar a essa arquitectura, propomos também neste documento alguns mecanismos de sincronizações resilientes, para manter uma vista coerente num mundo ou de navegação para dar ao robô a possibilidade de se mover no mundo e de encontrar o melhor caminho. Guardar uma vista homogénea do mundo é um ponto fundamental que pode não ser fácil em caso de uma reunião de dois grupos. Introduzimos três implementações de middleware, uma versão simulada usada para validar arquitectura e testar a sincronização dos algoritmos num ambiente multi-robô, uma versão móvel apontada para ser implementado em plataformas de hardware compostas por robôs móveis reais e finalmente uma versão de posição capaz de comunicar com robôs móveis, recolher informação e enviar ordens remotas.Nowadays, teams of mobile robots are involved in many contexts and environments where human intervention would be risky or even impossible, we can mention the surveillance of physical areas as military zones or nuclear plants. Due to increasing complexity in their embedded systems, these robots become more powerful but paradoxically more susceptible to face a hardware or software failure. What’s more, unreliability in the wireless communication could deprive them temporally of their remote information support. For all these reasons, designing architectures able to offer more resilience to the control application has become a real challenge. In this document, we present a middleware architecture for the robots to share a common view and to handle new events in a safe and resilient way. The system relies on two key features, first a control layer based on two sub-modules, the payload and the wormhole, and secondly a cycle-proof event-based architecture used to run critical tasks in the payload. Regarding the control layer, the payload could be complex and has access to information shared among robots, while the wormhole is reliable but only uses local information. The wormhole controls the timely execution of the critical tasks by the payload. In case of timing failure, the wormhole takes control and runs these tasks in place of the payload. In addition to this architecture, we propose as well in this document some resilient synchronization mechanisms to maintain a coherent view of the world when two groups of robots are merging. We introduce three implementations of the middleware, a simulation version used to validate the architecture and test the synchronization algorithms in a multi-robot environment, a mobile version aimed to be ported to hardware platforms composed by real mobile robots and finally a station version able to communicate with mobiles, collect information and send remote orders

A distributed framework for the control and cooperation of heterogeneous mobile robots in smart factories.

Doctoral Degree. University of KwaZulu-Natal, Durban.The present consumer market is driven by the mass customisation of products. Manufacturers are now challenged with the problem of not being able to capture market share and gain higher profits by producing large volumes of the same product to a mass market. Some businesses have implemented mass customisation manufacturing (MCM) techniques as a solution to this problem, where customised products are produced rapidly while keeping the costs at a mass production level. In addition to this, the arrival of the fourth industrial revolution (Industry 4.0) enables the possibility of establishing the decentralised intelligence of embedded devices to detect and respond to real-time variations in the MCM factory. One of the key pillars in the Industry 4.0, smart factory concept is Advanced Robotics. This includes cooperation and control within multiple heterogeneous robot networks, which increases flexibility in the smart factory and enables the ability to rapidly reconfigure systems to adapt to variations in consumer product demand. Another benefit in these systems is the reduction of production bottleneck conditions where robot services must be coordinated efficiently so that high levels of productivity are maintained. This study focuses on the research, design and development of a distributed framework that would aid researchers in implementing algorithms for controlling the task goals of heterogeneous mobile robots, to achieve robot cooperation and reduce bottlenecks in a production environment. The framework can be used as a toolkit by the end-user for developing advanced algorithms that can be simulated before being deployed in an actual system, thereby fast prototyping the system integration process. Keywords: Cooperation, heterogeneity, multiple mobile robots, Industry 4.0, smart factory, manufacturing, middleware, ROS, OPC, framework

Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning