35,689 research outputs found

    A computer architecture for intelligent machines

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    The Theory of Intelligent Machines proposes a hierarchical organization for the functions of an autonomous robot based on the Principle of Increasing Precision With Decreasing Intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed in recent years. A computer architecture that implements the lower two levels of the intelligent machine is presented. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Details of Execution Level controllers for motion and vision systems are addressed, as well as the Petri net transducer software used to implement Coordination Level functions. Extensions to UNIX and VxWorks operating systems which enable the development of a heterogeneous, distributed application are described. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems

    A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control

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    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-03413-3_26In this work is presented a general architecture for a multi physical agent network system based on the coordination and the behaviour management. The system is organised in a hierarchical structure where are distinguished the individual agent actions and the collective ones linked to the whole agent network. Individual actions are also organised in a hybrid layered system that take advantages from reactive and deliberative control. Sensing system is involved as well in the behaviour architecture improving the information acquisition performance.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-02, under coordinated project High Integrity Partitioned Embedded Systems (Hi-PartES): TIN2011-28567-C03-03, and under the collaborative research project supported by the European Union MultiPARTES Project: FP7-ICT 287702. 2011-14.Muñoz Alcobendas, M.; Munera Sánchez, E.; Blanes Noguera, F.; Simó Ten, JE. (2013). A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control. En ROBOT2013: First Iberian Robotics Conference: Advances in Robotics, Vol. 1. Springer. 363-380. https://doi.org/10.1007/978-3-319-03413-3_26S363380Aragues, R.: Consistent data association in multi-robot systems with limited communications. Robotics: Science and Systems, 97–104 (2010)Aragues, R., Cortes, J., Sagues, C.: Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Transactions on Robotics (2012)Arkin, R.C.: Motor schema based mobile robot navigation. The International Journal of Robotics Research 8(4), 92–112 (1989)Asama, H., Habib, M.K., Endo, I., Ozaki, K., Matsumoto, A., Ishida, Y.: Functional distribution among multiple mobile robots in an autonomous and decentralized robot system. In: Proceedings of the 1991 IEEE International Conference on Robotics and Automation. IEEE (1991)Benet, G., Blanes, F., Martínez, M., Simó, J.: A multisensor robot distributed architecture. In: IFAC Conference INCOM 1998 (1998)Brooks, R.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2(1), 14–23 (1986)Canas, J.M., Matellán, V.: Dynamic schema hierarchies for an autonomous robot. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 903–912. Springer, Heidelberg (2002)Choset, H., Nagatani, K.: Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Transactions on Robotics and Automation 17(2), 125–137 (2001)Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: Efficient position estimation for mobile robots. American Association for Artificial Intelligence, 343–349 (1999)Hu, J., Xie, L., Xu, J.: Vision-based multi-agent cooperative target search. In: Control Automation Robotics & Vision (ICARCV), pp. 895–900 (2012)Huq, R., Mann, G.K.I., Gosine, R.G.: Behavior-modulation technique in mobile robotics using fuzzy discrete event system. IEEE Transactions on Robotics 22(5), 903–916 (2006)Jayasiri, A., Mann, G., Gosine, R.G.: Mobile robot behavior coordination using supervisory control of fuzzy discrete event systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 690–695 (2009)Jayasiri, A., Mann, G.K.I., Gosine, R.G.: Behavior coordination of mobile robotics using supervisory control of fuzzy discrete event systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(5), 1224–1238 (2011)Koenig, N., Howard, A.: Gazebo-3d multiple robot simulator with dynamics. Technical report (2006)Lin, F., Ying, H.: Modeling and control of fuzzy discrete event systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 32(4), 408–415 (2002)Madden, J.D.: Multi-robot system based on model of wolf hunting behavior to emulate wolf and elk interactions. In: 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO, pp. 1043–1050 (2010)Mataric, M.J.: Interaction and intelligent behavior. Technical report, DTIC Document (1994)Olivera, V.M., Molina, J.M., Sommaruga, L., et al.: Fuzzy cooperation of autonomous robots. In: Fourth International System on Intelligent Robotics Systems, Lisboa, Portugal (1996)McGann, C., Py, F., Rajan, K., Thomas, H., Henthorn, R., McEwen, R.: A deliberative architecture for auv control. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 1049–1054. IEEE (2008)Munera, E., Muñoz, M., Simó, J., Blanes, F.: Humanoid Robot Self-Location In SPL League. In: Comité Español de Automática (CEA), XXXIII Jornadas de Automatica, 797–804 (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)Qiu, D.: Supervisory control of fuzzy discrete event systems: a formal approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35(1), 72–88 (2005)Aladebaran Robotics. NAO Software Documentation 1.12. Technical report (2012)St-Pierre, M., Gingras, D.: Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system. In: 2004 IEEE Intelligent Vehicles Symposium, pp. 831–835 (2004)Stoytchev, A., Arkin, R.C.: Combining deliberation, reactivity, and motivation in the context of a behavior-based robot architecture. In: Proceedings of the 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 290–295 (2001)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

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

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    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

    Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

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    The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

    Robotic Wireless Sensor Networks

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    In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

    Development of a Semi-Autonomous Robotic System to Assist Children with Autism in Developing Visual Perspective Taking Skills

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    Robot-assisted therapy has been successfully used to help children with Autism Spectrum Condition (ASC) develop their social skills, but very often with the robot being fully controlled remotely by an adult operator. Although this method is reliable and allows the operator to conduct a therapy session in a customised child-centred manner, it increases the cognitive workload on the human operator since it requires them to divide their attention between the robot and the child to ensure that the robot is responding appropriately to the child's behaviour. In addition, a remote-controlled robot is not aware of the information regarding the interaction with children (e.g., body gesture and head pose, proximity etc) and consequently it does not have the ability to shape live HRIs. Further to this, a remote-controlled robot typically does not have the capacity to record this information and additional effort is required to analyse the interaction data. For these reasons, using a remote-controlled robot in robot-assisted therapy may be unsustainable for long-term interactions. To lighten the cognitive burden on the human operator and to provide a consistent therapeutic experience, it is essential to create some degrees of autonomy and enable the robot to perform some autonomous behaviours during interactions with children. Our previous research with the Kaspar robot either implemented a fully autonomous scenario involving pairs of children, which then lacked the often important input of the supervising adult, or, in most of our research, has used a remote control in the hand of the adult or the children to operate the robot. Alternatively, this paper provides an overview of the design and implementation of a robotic system called Sense- Think-Act which converts the remote-controlled scenarios of our humanoid robot into a semi-autonomous social agent with the capacity to play games autonomously (under human supervision) with children in the real-world school settings. The developed system has been implemented on the humanoid robot Kaspar and evaluated in a trial with four children with ASC at a local specialist secondary school in the UK where the data of 11 Child-Robot Interactions (CRIs) was collected. The results from this trial demonstrated that the system was successful in providing the robot with appropriate control signals to operate in a semi-autonomous manner without any latency, which supports autonomous CRIs, suggesting that the proposed architecture appears to have promising potential in supporting CRIs for real-world applications.Peer reviewe
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