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Multirobot system architecture: environment representation and protocols

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An approach to the problem of interoperability in open and heterogeneous multirobot system is presented. It is based on the paradigm of Service Oriented Architecture (SOA) and a generic representation of the environment. A robot, and generally a cognitive and intelligent device, is seen as a collection of its capabilities exposed as services. Several experimental protocols (for publishing, discovering, arranging, and executing the composite services) are proposed in order to assure the interoperability in the system. The environment representation, the description language for tasks and service interfaces definition, as well as the protocols constitute together the proposed information technology for automatic task accomplishment in an open heterogeneous multirobot system.
Rocznik
Strony
3--13
Opis fizyczny
Bibliogr. 20 poz., rys., fot.
Twórcy
autor
  • Institute of Computer Science, Polish Academy of Sciences, 21 Ordona Ave, 01-237 Warsaw, Poland, sambrosz@ipipan.waw.pl
Bibliografia
  • [1] J. Hertzberg and A. Saffiotti, “Workshop on semantic information in Robotics”, ICRA-07 Proc. 1, CD-ROM (2007).
  • [2] M.E. Jefferies and W. Yeap, “Robotics and cognitive approaches to spatial mapping”, in: Tracts in Advanced Robotics Springer, vol. 38, Springer, Berlin, 2008.
  • [3] G. Beni, “From swarm intelligence to swarm robotics”, Swarm Robotics: Lecture Notes in Computer Science 3342, 1–9 (2005).
  • [4] L. E. Parker, and F. Tang, “Building multi-robot coalitions through automated task solution synthesis”, Proc. IEEE, Special Issue on Multi-Robot Systems 94, 1289–1305 (2006).
  • [5] Y. Ha, J. Sohn, Y. Cho, and H. Yoon, “A robotic service framework supporting automated integration of ubiquitous sensors and devices”, Information Science 177, 657–679 (2007).
  • [6] PALCOM Project Home page: http://www.ist-palcom.org/.
  • [7] IBM Services Architecture Team, “Web services architecture overview: the next stage of evolution for e-business”, http://www.ibm.com/developerworks/webservices/library/w-ovr/, (2000).
  • [8] S. Thrun, „Robotic mapping: a survey”, in Exploring Artificial Intelligence in the New Millenium, eds. G. Lakemeyer and B. Nebel, Morgan Kaufmann, Massachusetts, 2002.
  • [9] J. Kuipers, “The spatial semantic hierarchy”, Artificial Intelligence 119, 191–233 (2000).
  • [10] S. Vasudevan, S. G¨achter, V. Nguyen, and R. Siegwart, “Cognitive maps for mobile robots – an object based approach”, Robotics and Autonomous Systems 55 (5), 359–371 (2007).
  • [11] D. Anguelov, R. Biswas, D. Koller., B. Limketkai, S. Sanner, and S. Thrun, “Learning hierarchical object maps of nonstationary environments with mobile robots”, Proc. 17th Annual Conf. Uncertainty AI 1, CD-ROM (2002).
  • [12] C. Galindo, A. Saffiotti, S. Coradeschi., P. Buschka, J.A. Fernandez-Madrigal, and J. Gonz´alez, “Multi-hierarchical semantic maps for mobile robotics”, Proc IEEE / RSJ Int. Conf. on Intelligent Robots and Systems 1, 3492–3497 (2005).
  • [13] C.P. Eastman, P. Teicholz, R. Sacks, and K. Liston, BIM Handbook. A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers, and Contractors, Wiley, Hoboken, New Jersey, 2008.
  • [14] C. Zieliński, “Description of command semantics of programming languages of robots”, Archives of Automatics and Telemechanics of Warsaw University of Technology 35 (1–2), 15–45 (1990), (in Polish).
  • [15] C. Zieliński, “TORBOL: an object level robot programming language”, Mechatronics 1 (4), 469–485 (1991).
  • [16] C. Zieliński, “Description of semantics of robot programming languages”, Mechatronics 2 (2), 171–198 (1992).
  • [17] S. Ambroszkiewicz, “Entish: a language for describing data processing in open distributed systems”, Fundamenta Informaticae 60 (1–4), 41–66 (2004).
  • [18] T.R. Gruber, “A translation approach to portable ontology specifications”, Knowledge Acquisition 5 (2), 99–220 (1993).
  • [19] R. Fikes and N. Nilsson, “STRIPS: a new approach to the application of theorem proving to problem solving”, Artificial Intelligence 2, 189–208 (1971).
  • [20] PDDL – Planning Domain Definition Language, Drew V. Mc-Dermott web site: http://cs-www.cs.yale.edu/homes/dvm/
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0020-0001
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