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Autonomous Robots

, Volume 34, Issue 4, pp 277–294 | Cite as

Coordinating heterogeneous teams of robots using temporal symbolic planning

  • Kai M. Wurm
  • Christian Dornhege
  • Bernhard Nebel
  • Wolfram Burgard
  • Cyrill Stachniss
Article

Abstract

The efficient coordination of a team of heterogeneous robots is an important requirement for exploration, rescue, and disaster recovery missions. In this paper, we present a novel approach to target assignment for heterogeneous teams of robots. It goes beyond existing target assignment algorithms in that it explicitly takes symbolic actions into account. Such actions include the deployment and retrieval of other robots or manipulation tasks. Our method integrates a temporal planning approach with a traditional cost-based planner. The proposed approach was implemented and evaluated in two distinct settings. First, we coordinated teams of marsupial robots. Such robots are able to deploy and pickup smaller robots. Second, we simulated a disaster scenario where the task is to clear blockades and reach certain critical locations in the environment. A similar setting was also investigated using a team of real robots. The results show that our approach outperforms ad-hoc extensions of state-of-the-art cost-based coordination methods and that the approach is able to efficiently coordinate teams of heterogeneous robots and to consider symbolic actions.

Keywords

Multi-robot coordination Temporal planning Exploration Heterogeneous robots Marsupial robots 

Notes

Acknowledgments

We wish to thank Marc Gissler, Christoph Sprunk, and Matthias Westphal for their assistance during the real world experiments.

References

  1. Berhault, M., Huang, H., Keskinocak, P., Koenig, S., Elmaghraby, W., Griffin, P. & Kleywegt, A. (2003). Robot exploration with combinatorial auctions: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1957–1962). Vilamoura, Algarve.Google Scholar
  2. Bonet, B., & Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1), 5–33.zbMATHCrossRefMathSciNetGoogle Scholar
  3. Burgard, W., Moors, M., Stachniss, C., & Schneider, F. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–378.CrossRefGoogle Scholar
  4. Bylander, T. (1994). The computational complexity of propositional STRIPS planning. Artificial Intelligence, 69(1–2), 165–204.zbMATHCrossRefMathSciNetGoogle Scholar
  5. Cambon, S., Alami, R., & Gravot, F. (2009). A hybrid approach to intricate motion, manipulation and task planning. International Journal of Robotics Research, 28(1), 104–126.CrossRefGoogle Scholar
  6. Cao, Y. U., Fukunaga, A. S., & Khang, A. B. (1997). Cooperative mobile robotics: Antecedents and directions. Autonomous Robots, 4(1), 7–27.CrossRefGoogle Scholar
  7. Chen, Y., Wah, B. W., & Hsu, C.-W. (2006). Temporal planning using subgoal partitioning and resolution in SGPlan. Journal of Artificial Intelligence Research, 26, 323–369.Google Scholar
  8. Cordes, F., Ahrns, I., Bartsch, S., Birnschein, T., Dettmann, A., Estable, S., et al. (2011). Lunares: Lunar crater exploration with heterogeneous multi robot systems. Intelligent Service Robotics, 4(1), 1–29.CrossRefGoogle Scholar
  9. Dellaert, F., Balch, T., Kaess, M., Ravichandran, R., Alegre, F., Berhault, M., & Walker, D (2002). The Georgia Tech yellow jackets: A marsupial team for urban search and rescue. In AAAI Mobile Robot Competition Workshop, Alberta.Google Scholar
  10. Dornhege, C., Eyerich, P., Keller, T., Trüg, S., Brenner, M., & Nebel, B. (2009). Semantic attachments for domain-independent planning systems. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), (pp. 114–121). Thessaloniki, Greece.Google Scholar
  11. Drenner, A., & Papanikolopoulos, N. (2007). A framework for large-scale multi-robot teams. In P. A. Ioannou & A. Pitsillides (Eds.), Modeling and control of complex systems (p. 297). Boca Raton: CRC press.Google Scholar
  12. Dudek, G., Jenkin, M., Milios, E., & Wilkes, D. (1996). A taxonomy for multi-agent robotics. Autonomous Robots, 3(4), 375–397.CrossRefGoogle Scholar
  13. Erol, K., Nau, D. S., & Subrahmanian, V. S. (1995). Complexity, decidability and undecidability results for domain-independent planning. Artificial Intelligence, 76(1–2), 75–88.zbMATHCrossRefMathSciNetGoogle Scholar
  14. European Space Agency ESA. (2008). ESA’s lunar robotics challenge website. Retrieved from http://www.esa.int/esaCP/SEMGAASHKHF_index_0.html. Accessed 10 Jan 2013.
  15. Eyerich, P., Mattmüller, R., & Röger, G. (2009). Using the context-enhanced additive heuristic for temporal and numeric planning. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), (pp. 130–137).Google Scholar
  16. Eyerich, P., Keller, T., & Helmert, M. (2010). High-quality policies for the canadian traveler’s problem Menlo Park. In F. Maria & P. David (Eds.), Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI). Menlo Park: AAAI Press.Google Scholar
  17. Fikes, R., & Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, 189–208.zbMATHCrossRefGoogle Scholar
  18. Fox, M., & Long, D. (2003). PDDL2.1: An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research (JAIR), 20(1), 61–124.zbMATHGoogle Scholar
  19. Gerevini, A., & Serina, I. (1999). Fast planning through greedy action graphs. In Proceedings of the National Conference on Artificial Intelligence (AAAI), (pp. 503–510).Google Scholar
  20. Gerevini, A., Saetti, A., & Serina, I. (2008). An approach to efficient planning with numerical fluents and multi-criteria plan quality. Artificial Intelligence, 172(8–9), 899–944.zbMATHCrossRefMathSciNetGoogle Scholar
  21. Grabowski, R., Navarro-Serment, L. E., Paredis, C. J. J., & Khosla, P. K. (2000). Heterogeneous teams of modular robots for mapping and exploration. Autonomous Robots, 8(3), 293–308.CrossRefGoogle Scholar
  22. Gregory, P., Long, D., Fox, M., & Beck, J. C. (2012). Planning modulo theories: Extending the planning paradigm. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS).Google Scholar
  23. Helmert, M., & Geffner, H. (2008). Unifying the causal graph and additive heuristics. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS).Google Scholar
  24. Helmert, M. (2002). Decidability and undecidability results for planning with numerical state variables. In Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling, (pp. 44–53).Google Scholar
  25. Helmert, M. (2006). The fast downward planning system. Journal of Artificial Intelligence Research, 26, 191–246.zbMATHCrossRefGoogle Scholar
  26. Hoffmann, J., & Nebel, B. (2001). The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14, 253–302.zbMATHGoogle Scholar
  27. Howard, A., Parker, L. E., & Sukhatme, G. S. (2006). Experiments with a large heterogeneous mobile robot team: Exploration, mapping, deployment and detection. International Journal of Robotics Research, 25(5–6), 447.Google Scholar
  28. Jones, E. G., Dias, M. B., & Stentz, A. (2011). Time-extended multi-robot coordination for domains with intra-path constraints. Autonomous Robots, 30(1), 41–56.CrossRefGoogle Scholar
  29. Kadioglu, E., & Papanikolopoulos, N. (2003). A method for transporting a team of miniature robots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 2297–2302).Google Scholar
  30. Kaelbling, L. P., & Lozano-Perez, T. (2011). Hierarchical task and motion planning in the now. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA).Google Scholar
  31. Kautz, H., & Selman, B. (1992). Planning as satisfiability. In Procedings of the European Conference on Artificial Intelligence (ECAI).Google Scholar
  32. Ko, J., Stewart, B., Fox, D., Konolige, K., & Limketkai, B. (2003). A practical, decision-theoretic approach to multi-robot mapping and exploration. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 3232–3238).Google Scholar
  33. Koes, M., Nourbakhsh, I., & Sycara, K. (2005). Heterogeneous multirobot coordination with spatial and temporal constraints. In Proceedings of the National Conference on Artificial Intelligence, (p. 1292).Google Scholar
  34. Murphy, R. R., Ausmus, M., Bugajska, M., Ellis, T., Johnson, T., Kelley, N., Kiefer, J., & Pollock, L. (1999). Marsupial-like mobile robot societies. In Proceedings of the Annual Conference on Autonomous Agents, (p. 365).Google Scholar
  35. Papadimitriou, C. H., & Yannakakis, M. (1991). Shortest paths without a map. Theoretical Computer Science, 84(1), 127–150. Google Scholar
  36. Richter, S., & Westphal, M. (2010). The LAMA planner: Guiding cost-based anytime planning with landmarks. Journal of Artificial Intelligence Research (JAIR), 39, 127–177.zbMATHGoogle Scholar
  37. Rintanen, J. (2007). Complexity of concurrent temporal planning. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS).Google Scholar
  38. Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach. Englewood Cliffs: Prentice hall.Google Scholar
  39. Rybski, P. E., Stoeter, S. A., Erickson, M. D., Gini, M., Hougen, D. F., & Papanikolopoulos, N. (2000). A team of robotic agents for surveillance. In Proceedings of the International Conference on Autonomous Agents, (pp. 9–16).Google Scholar
  40. Singh, K., & Fujimura, K. (1993). Map making by cooperating mobile robots. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), (pp. 254–259). Atlanta.Google Scholar
  41. Stachniss, C., Martinez Mozos, O., & Burgard, W. (2009). Efficient exploration of unknown indoor environments using a team of mobile robots. Annals of Mathematics and Artificial Intelligence, 52(2–4), 205.Google Scholar
  42. Stachniss, C. (2009). Robotic Mapping and Exploration. Heidelberg: Springer.CrossRefGoogle Scholar
  43. Wurm, K. M., Stachniss, C., & Burgard, W. (2008). Coordinated multi-robot exploration using a segmentation of the environment. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  44. Wurm, K. M., Kümmerle, R., Stachniss, C., & Burgard, W. (2009). Improving robot navigation in structured outdoor environments by identifying vegetation from laser data. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  45. Wurm, K. M., Dornhege, C., Eyerich, P., Stachniss, C., Nebel, B., & Burgard, W. (2010). Coordinated exploration with marsupial teams of robots using temporal symbolic planning. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  46. Yamauchi, B. (1997). A frontier based approach for autonomous exploration. In Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), (pp. 146–151).Google Scholar
  47. Yamauchi, B. (1998). Frontier-based exploration using multiple robots. In Proceedings of the International Conference on Autonomous Agents, (pp. 47–53).Google Scholar
  48. Zlot, R., Stenz, A. T., Dias, M. B., & Thayer, S. (2002). Multi-robot exploration controlled by a market economy. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA).Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Kai M. Wurm
    • 1
  • Christian Dornhege
    • 2
  • Bernhard Nebel
    • 2
  • Wolfram Burgard
    • 1
  • Cyrill Stachniss
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany
  2. 2.Departmant of Computer ScienceUniversity of FreiburgFreiburgGermany

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