Intelligent Supervision for Robust Plan Execution

  • Roberto Micalizio
  • Enrico Scala
  • Pietro Torasso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6934)

Abstract

The paper addresses the problem of supervising the execution of a plan with durative actions in a just partially known world, where discrepancies between the expected conditions and the ones actually found may arise. The paper advocates a control architecture which exploits additional knowledge to prevent (when possible) action failures by changing the execution modality of actions while these are still in progress. Preliminary experimental results, obtained in a simulated space exploration scenario, are reported.

Keywords

Plan Execution Intelligent Supervision Robotic Agents Control Architecture 

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References

  1. 1.
    Bouguerra, A., Karlsson, L., Saffiotti, A.: Monitoring the execution of robot plans using semantic knowledge. Robotics and Autonomous Systems 56, 942–954 (2008)CrossRefGoogle Scholar
  2. 2.
    Bozzano, M., Cimatti, A., Roveri, M., Tchaltsev, A.: A comprehensive approach to on-board autonomy verification and validation. In: ICAPS 2009 Workshop on Verification and Validation of Planning and Scheduling Systems (2009)Google Scholar
  3. 3.
    Micalizio, R.: A distributed control loop for autonomous recovery in a multi-agent plan. In: Proc. IJCAI 2009, pp. 1760–1765 (2009)Google Scholar
  4. 4.
    Micalizio, R., Torasso, P.: Monitoring the execution of a multi-agent plan: Dealing with partial observability. In: Proc. ECAI 2008, pp. 408–412 (2008)Google Scholar
  5. 5.
    Musso, I., Micalizio, R., Scala, E., et al.: Communication scheduling and plans revision for planetary rovers. In: Proc. of i-SAIRAS 2010 (2010)Google Scholar
  6. 6.
    Fox, M., Long, D.: Pddl2.1: An extension to pddl for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124 (2003)MATHGoogle Scholar
  7. 7.
    Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F.: An architecture for autonomy. International Journal of Robotics Research 17(4), 315–337 (1998)CrossRefGoogle Scholar
  8. 8.
    Calisi, D., Iocchi, L., Nardi, D., Scalzo, C., Ziparo, V.A.: Context-based design of robotic systems. Robotics and Autonomous Systems (RAS) - Special Issue on Semantic Knowledge in Robotics 56(11), 992–1003 (2008)CrossRefGoogle Scholar
  9. 9.
    Dousson, C., Le Maigat, P.: Chronicle recognition improvement using temporal focusing and hierarchization. In: IJCAI 2007, pp. 324–329 (2007)Google Scholar
  10. 10.
    Nesnas, I.A.: Claraty: A collaborative software for advancing robotic technologies. In: Proc. of NASA Science and Technology Conference (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Roberto Micalizio
    • 1
  • Enrico Scala
    • 1
  • Pietro Torasso
    • 1
  1. 1.Università di Torino corsoTorinoItaly

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