Planner9, a HTN Planner Distributed on Groups of Miniature Mobile Robots

  • Stéphane Magnenat
  • Martin Voelkle
  • Francesco Mondada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5928)

Abstract

Autonomous mobile robots are promising tools for operations in environments that are difficult to access for humans. When these environments are dynamic and non-deterministic, like in collapsed buildings, the robots must coordinate their actions and the use of resources using planning. This paper presents Planner9, a hierarchical task network (htn) planner that runs on groups of miniature mobile robots. These robots have limited computational power and memory, but are well connected through Wi-Fi. Planner9 takes advantage of this connectivity to distribute the planning over different robots. We have adapted the htn algorithm to perform parallel search using A* and to limit the number of search nodes through lifting. We show that Planner9 scales well with the number of robots, even on non-linear tasks that involve recursions in their decompositions. We show that contrary to JSHOP2, Planner9 finds optimal plans.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kumar, V., Rus, D., Singh, S.: Robot and sensor networks for first responders. IEEE Pervasive Computing 3(4), 24–33 (2004)CrossRefGoogle Scholar
  2. 2.
    Stormont, D.: Autonomous rescue robot swarms for first responders. In: Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, pp. 151–157. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  3. 3.
    Ghallab, M., Nau, D., Traverso, P.: Automated Planning: theory and practice. Morgan Kaufmann, San Francisco (2004)MATHGoogle Scholar
  4. 4.
    Mondada, F., Pettinaro, G.C., Guignard, A., Kwee, I., Floreano, D., Deneubourg, J.L., Nolfi, S., Gambardella, L., Dorigo, M.: SWARM-BOT: a New Distributed Robotic Concept. Autonomous Robots, special Issue on Swarm Robotics 17(2-3), 193–221 (2004)Google Scholar
  5. 5.
    Erol, K., Hendler, J.A., Nau, D.S.: HTN Planning: Complexity and Expressivity. In: AAAI, pp. 1123–1128. AAAI Press, Menlo Park (1994)Google Scholar
  6. 6.
    Obst, O., Boedecker, J.: Flexible Coordination of Multiagent Team Behavior Using HTN Planning. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 521–528. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Dix, J., Munoz-Avila, H., Nau, D., Zhang, L.: IMPACTing SHOP: Putting an AI planner into a multi-agent environment. Annals of Mathematics and Artificial Intelligence 37(4), 381–407 (2003)MATHCrossRefGoogle Scholar
  8. 8.
    Durfee, E.: Distributed problem solving and planning. Multiagent systems: a modern approach to distributed artificial intelligence, 121–164 (1999)Google Scholar
  9. 9.
    DesJardins, M., Durfee, E., Ortiz, C., Wolverton, M.: A survey of research in distributed, continual planning. AI Magazine (1999)Google Scholar
  10. 10.
    Dias, M., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE 94(7), 1257–1270 (2006)CrossRefGoogle Scholar
  11. 11.
    Zlot, R., Stentz, A.: Market-based multirobot coordination for complex tasks. The International Journal of Robotics Research 25(1), 73–102 (2006)CrossRefGoogle Scholar
  12. 12.
    Pellier, D., Fiorino, H.: A Unified Framework Based on HTN and POP Approaches for Multi-Agent Planning. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 285–288. IEEE Press, Los Alamitos (2007)CrossRefGoogle Scholar
  13. 13.
    Hayashi, H., Tokura, S., Ozaki, F.: Towards Real-World HTN Planning Agents. Knowledge Processing and Decision Making in Agent-based Systems 170, 13–41 (2009)CrossRefGoogle Scholar
  14. 14.
    Nau, D., Au, T., Ilghami, O., Kuter, U., Murdock, W., Wu, D., Yaman, F.: SHOP2: An HTN planning system. Journal of Artificial Intelligence Research 20(1), 379–404 (2003)MATHGoogle Scholar
  15. 15.
    Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. Commun. ACM 5(7), 394–397 (1962)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4, 100–107Google Scholar
  17. 17.
    Rao, V.N., Kumar, V.: Superlinear speedup in parallel state-space search. In: Proceedings of the Eighth Conference on Foundations of Software Technology and Theoretical Computer Science, London, UK, pp. 161–174. Springer, Heidelberg (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stéphane Magnenat
    • 1
  • Martin Voelkle
    • 1
  • Francesco Mondada
    • 1
  1. 1.LSRO laboratory, EPFLLausanneSwitzerland

Personalised recommendations