RFID Technology and AI Techniques for People Location, Orientation and Guiding

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5579)


One of the main problems that we have to face when visiting public or official buildings (i.e hospitals or public administrations) is the lack of information and signs that can guide us. Thanks to the new technology advances, the electronic communication networks can be focused on an objective environment. These techniques can be used to help users to get their right location and orientation.

This is the framework we are chosen in this article. The solution proposed in this paper uses a detection and a location system based on wireless technology and Artificial Intelligence (AI) techniques to plan and inform about the paths the users can follow. The AI system is called PIPSS and integrates planning techniques and scheduling methods.


AI Planning & Scheduling Montecarlo method RFID Location & Orientation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bonet, B., Geffner, H.: Planning as Heuristic Search: New results. In: Biundo, S., Fox, M. (eds.) ECP 1999. LNCS, vol. 1809. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Castano, B., R-Moreno, M.D.: An Artificial Intelligence and RFID System for People Detection and Orientation in Big Surfaces. In: Procs. of the 6th International Conference on Computing, Communications and Control Technologies (CCT 2008), Florida, USA (2008)Google Scholar
  3. 3.
    Cesta, A., Oddi, A., Smith, S.F.: An Iterative Sampling Procedure for Resource Constrained Project Scheduling with Time Windows. In: Proceedings of the 16th Int. Joint Conference on Artificial Intelligence (IJCAI 1999) (1999)Google Scholar
  4. 4.
    Engels, D.V.: RFID: The technical Reality. In: Proceedings of Workshop on Radio Frequency Identification: Applications and Implications for Consumers, Washington, DC (2004)Google Scholar
  5. 5.
    Gerevini, A., Long, D.: Plan Constraints and Preferences in PDDL3. The Language of the Fifth International Planning Competition. Tech. Rep. Technical Report, Department of Electronics for Automation, University of Brescia, Italy (2005)Google Scholar
  6. 6.
    Gerevini, A., Saetti, A., Serina, I.: An Approach to Temporal Planning and Scheduling in Domains with Predicatable Exogenous Events. Jair 25, 187–213 (2006)zbMATHGoogle Scholar
  7. 7.
    Halsey, K., Long, D., Fox, M.: CRIKEY - A Planner Looking at the Integration of Scheduling and Planning. In: Procs. of the Workshop on Integration Scheduling Into Planning at 13th International Conference on Automated Planning and Scheduling (ICAPS 2003), pp. 46–52 (2004)Google Scholar
  8. 8.
    Hoffmann, J., Nebel, B.: The ff Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research 14, 253–302 (2001)zbMATHGoogle Scholar
  9. 9.
    Plaza, J., R-Moreno, M.D., Castano, B., Carbajo, M., Moreno, A.: PIPSS: Parallel Integrated Planning and Scheduling System. In: The 27th Annual Workshop of the UK Planning and Scheduling Special Interest Group (PLANSIG 2005), London, UK (2008)Google Scholar
  10. 10.
    R-Moreno, M.D., Camacho, D., Moreno, A.: HPP: A Heuristic Progressive Planner. In: The 24th Annual Workshop of the UK Planning and Scheduling Special Interest Group (PLANSIG 2005), London, UK (2005)Google Scholar
  11. 11.
    Sabelfeld, K.K.: Monte Carlo Methods in Boundary Value Problems. Springer, Heidelberg (1991)Google Scholar
  12. 12.
    Vidal, V., Geffner, H.: Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming. Artificial Intelligence 3 170, 298–335 (2006)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Departamento de AutomáticaUniversidad de AlcaláMadridSpain
  2. 2.Departamento de MatemáticasUniversidad de AlcaláMadridSpain

Personalised recommendations