770 research outputs found
Cooperative Behavior In ModSAF
Report on research into mechanisms for cooperative behaviors in computer generated forces
Engineering framework for service-oriented automation systems
Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201
Recent Advances in Multi Robot Systems
To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems
Second Workshop on Modelling of Objects, Components and Agents
This report contains the proceedings of the workshop Modelling of Objects, Components, and Agents (MOCA'02), August 26-27, 2002.The workshop is organized by the 'Coloured Petri Net' Group at the University of Aarhus, Denmark and the 'Theoretical Foundations of Computer Science' Group at the University of Hamburg, Germany. The homepage of the workshop is: http://www.daimi.au.dk/CPnets/workshop02
Architecting centralized coordination of soccer robots based on principle solution
This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 2015, available online:http://www.tandfonline.com/10.1080/01691864.2015.1017534Coordination strategy is a relevant topic in multi-robot systems, and robot soccer offers a suitable domain to conduct research in multi-robot coordination. Team strategy collects and uses environmental information to derive optimal team reactions, through cooperation among individual soccer robots. This paper presents a diagrammatic approach to architecting the coordination strategy of robot soccer teams by means of a principle solution. The proposed model focuses on robot soccer leagues that possess a central decision-making system, involving the dynamic selection of the roles and behaviors of the robot soccer players. The work sets out from the conceptual design phase, facilitating cross-domain development efforts, where different layers must be interconnected and coordinated to perform multiple tasks. The principle solution allows for intuitive design and the modeling of team strategies in a highly complex robot soccer environment with changing game conditions. Furthermore, such an approach enables systematic realization of collaborative behaviors among the teammates.This work was partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-01/02. Jose G. Guarnizo was supported by a scholarship from the Administrative Department of Science, Technology and Innovation COLCIENCIAS, Colombia.Guarnizo Marín, JG.; Mellado Arteche, M.; Low, CY.; Blanes Noguera, F. (2015). Architecting centralized coordination of soccer robots based on principle solution. Advanced Robotics. 29(15):989-1004. https://doi.org/10.1080/01691864.2015.1017534S98910042915Farinelli, A., Iocchi, L., & Nardi, D. (2004). Multirobot Systems: A Classification Focused on Coordination. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34(5), 2015-2028. doi:10.1109/tsmcb.2004.832155Tews, A., & Wyeth, G. (2000). MAPS: a system for multi-agent coordination. Advanced Robotics, 14(1), 37-50. doi:10.1163/156855300741429Stulp, F., Utz, H., Isik, M., & Mayer, G. (2010). Implicit Coordination with Shared Belief: A Heterogeneous Robot Soccer Team Case Study. Advanced Robotics, 24(7), 1017-1036. doi:10.1163/016918610x496964Guarnizo, J. G., Mellado, M., Low, C. Y., & Aziz, N. (2013). Strategy Model for Multi-Robot Coordination in Robotic Soccer. Applied Mechanics and Materials, 393, 592-597. doi:10.4028/www.scientific.net/amm.393.592Riley, P., & Veloso, M. (2002). Recognizing Probabilistic Opponent Movement Models. Lecture Notes in Computer Science, 453-458. doi:10.1007/3-540-45603-1_59Ros, R., Arcos, J. L., Lopez de Mantaras, R., & Veloso, M. (2009). A case-based approach for coordinated action selection in robot soccer. Artificial Intelligence, 173(9-10), 1014-1039. doi:10.1016/j.artint.2009.02.004Atkinson, J., & Rojas, D. (2009). On-the-fly generation of multi-robot team formation strategies based on game conditions. Expert Systems with Applications, 36(3), 6082-6090. doi:10.1016/j.eswa.2008.07.039Costelha, H., & Lima, P. (2012). Robot task plan representation by Petri nets: modelling, identification, analysis and execution. Autonomous Robots, 33(4), 337-360. doi:10.1007/s10514-012-9288-xAbreu, P. H., Silva, D. C., Almeida, F., & Mendes-Moreira, J. (2014). Improving a simulated soccer team’s performance through a Memory-Based Collaborative Filtering approach. Applied Soft Computing, 23, 180-193. doi:10.1016/j.asoc.2014.06.021Duan, Y., Liu, Q., & Xu, X. (2007). Application of reinforcement learning in robot soccer. Engineering Applications of Artificial Intelligence, 20(7), 936-950. doi:10.1016/j.engappai.2007.01.003Hwang, K.-S., Jiang, W.-C., Yu, H.-H., & Li, S.-Y. (2011). Cooperative Reinforcement Learning Based on Zero-Sum Games. Mobile Robots - Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training. doi:10.5772/26620Gausemeier, J., Dumitrescu, R., Kahl, S., & Nordsiek, D. (2011). Integrative development of product and production system for mechatronic products. Robotics and Computer-Integrated Manufacturing, 27(4), 772-778. doi:10.1016/j.rcim.2011.02.005Klančar, G., Zupančič, B., & Karba, R. (2007). Modelling and simulation of a group of mobile robots. Simulation Modelling Practice and Theory, 15(6), 647-658. doi:10.1016/j.simpat.2007.02.002Gausemeier, J., Frank, U., Donoth, J., & Kahl, S. (2009). Specification technique for the description of self-optimizing mechatronic systems. Research in Engineering Design, 20(4), 201-223. doi:10.1007/s00163-008-0058-
An approach to task coordination for hyperflexible robotic workcells
2014 - 2015The manufacturing industry is very diverse and covers a wide range of specific processes ranging from extracting minerals to assembly of very complex products such as planes or computers, with all intermediate processing steps in a long chain of industrial suppliers and customers. It is well know that the introduction of robots in manufacturing industries has many advantages. Basically, in relation to human labor, robots work to a constant level of quality. For example, waste, scrap and rework are minimized. Furthermore they can work in areas that are hazardous or unpleasant to humans. Robots are advantageous where strength is required, and in many applications they are also faster than humans. Also, in relation to special-purpose dedicated equipment, robots are more easily reprogrammed to cope with new products or changes in the design of existing ones.
In the last 30-40 years, large enterprises in high-volume markets have managed to remain competitive and maintain qualified jobs by increasing their productivity with the incremental adoption and use of advanced ICT and robotics technologies. In the 70s, robots have been introduced for the automation of a wide spectrum of tasks such as: assembly of cars, white goods, electronic devices, machining of metal and plastic parts, and handling of workpieces and objects of all kinds. Robotics has thus soon become a synonym for competitive manufacturing and a key contributing technology for strengthening the economic base of Europe . So far, the automotive and electronics industries and their supply chains are the main users of robot systems and are accounting for more than 60% of the total annual robot sales. Robotic technologies have thus mainly been driven by the needs of these high-volume market industries.
The degree of automation in the automotive industries is expected to increase in the future as robots will push the limits towards flexibility regarding faster change-over-times of different product types (through rapid programming generation schemes), capabilities to deal with tolerances (through an extensive use of sensors) and costs (by reducing customized work-cell installations and reuse of manufacturing equipment).
There are numerous new fields of applications in which robot technology is not widespread today due to its lack of flexibility and high costs involved when dealing with varying lot sizes and variable product geometries. In such cases, hyper-flexible robotic work cells can help in providing flexibility to the system and making it adaptable to the different dynamic production requirements. Hyper-flexible robotic work cells, in fact, can be composed of sets of industrial robotic manipulators that cooperate to achieve the production step that characterize the work cell; they can be programmed and re-programmed to achieve a wide class of operations and they may result versatile to perform different kind of tasks
Related key technology challenges for pursuing successful long-term industrial robot automation are introduced at three levels: basic technologies, robot components and systems integration. On a systems integration level, the main challenges lie in the development of methods and tools for instructing and synchronising the operation of a group of cooperative robots at the shop-floor. Furthermore, the development of the concept of hyper flexible manufacturing systems implies soon the availability of: consistent middleware for automation modules to seamlessly connect robots, peripheral devices and industrial IT systems without reprogramming everything (”plug-and-play”) .
In this thesis both innovative and traditional industrial robot applications will be analyzed from the point of view of task coordination. In the modeling environment, contribution of this dissertation consists in presenting a new methodology to obtain a model oriented to the control the sequencing of the activities of a robotic hyperflexible cell. First a formal model using the Colored Modified Hybrid Petri Nets (CMHPN) is presented. An algorithm is provided to obtain an automatic synthesis of the CMHPN of a robotic cell with detail attention to aircraft industry. It is important to notice that the CMHPN is used to model the cell behaviour at a high level of abstraction. It models the activities of each cell component and its coordination by a supervisory system. As more, an object oriented approach and supervisory control are proposed to implement industrial automation control systems (based on Programmable Logic Controllers) to meet the new challenges of this field capability to implement applications involving widely distributed devices and high reuse of software components. Hence a method is proposed to implement both controllers and supervisors designed by Petri Nets on Programmable Logic Controllers (PLCs) using Object Oriented Programming (OOP). Finally preliminary results about a novel cyber-physical approach to the design of automated warehouse systems is presented. [edited by author]XIV n.s
From Active Perception to Active Cooperation Fundamental Processes of Intelligent Behavior
In the ten years since we put forward the idea of active perception (Bajcsy 1985, Bajcsy 1988) we have found that cooperative processes of various kinds and at various levels are often called for. In this paper we suggest that a proper understanding of cooperative processes will lead to a foundation for intelligent behavior and demonstrate the feasibility of this approach for some of the difficult and open problems in the understanding of intelligent behaviors
Workshop on Modelling of Objects, Components, and Agents, Aarhus, Denmark, August 27-28, 2001
This booklet contains the proceedings of the workshop Modelling of Objects, Components, and Agents (MOCA'01), August 27-28, 2001. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark and the "Theoretical Foundations of Computer Science" Group at the University of Hamburg, Germany. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop01
- …