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Coordinating heterogeneous teams of robots using temporal symbolic planning

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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.

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References

  • 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.

  • Bonet, B., & Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1), 5–33.

    Article  MATH  MathSciNet  Google Scholar 

  • Burgard, W., Moors, M., Stachniss, C., & Schneider, F. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–378.

    Article  Google Scholar 

  • Bylander, T. (1994). The computational complexity of propositional STRIPS planning. Artificial Intelligence, 69(1–2), 165–204.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Cao, Y. U., Fukunaga, A. S., & Khang, A. B. (1997). Cooperative mobile robotics: Antecedents and directions. Autonomous Robots, 4(1), 7–27.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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 

  • Dudek, G., Jenkin, M., Milios, E., & Wilkes, D. (1996). A taxonomy for multi-agent robotics. Autonomous Robots, 3(4), 375–397.

    Article  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

  • 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).

  • 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 

  • Fikes, R., & Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, 189–208.

    Article  MATH  Google Scholar 

  • 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.

    MATH  Google Scholar 

  • Gerevini, A., & Serina, I. (1999). Fast planning through greedy action graphs. In Proceedings of the National Conference on Artificial Intelligence (AAAI), (pp. 503–510).

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

  • Helmert, M., & Geffner, H. (2008). Unifying the causal graph and additive heuristics. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS).

  • 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).

  • Helmert, M. (2006). The fast downward planning system. Journal of Artificial Intelligence Research, 26, 191–246.

    Article  MATH  Google Scholar 

  • Hoffmann, J., & Nebel, B. (2001). The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14, 253–302.

    MATH  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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).

  • 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).

  • Kautz, H., & Selman, B. (1992). Planning as satisfiability. In Procedings of the European Conference on Artificial Intelligence (ECAI).

  • 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).

  • 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).

  • 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).

  • Papadimitriou, C. H., & Yannakakis, M. (1991). Shortest paths without a map. Theoretical Computer Science, 84(1), 127–150.

    Google Scholar 

  • Richter, S., & Westphal, M. (2010). The LAMA planner: Guiding cost-based anytime planning with landmarks. Journal of Artificial Intelligence Research (JAIR), 39, 127–177.

    MATH  Google Scholar 

  • Rintanen, J. (2007). Complexity of concurrent temporal planning. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS).

  • Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach. Englewood Cliffs: Prentice hall.

    Google Scholar 

  • 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).

  • 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.

  • 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 

  • Stachniss, C. (2009). Robotic Mapping and Exploration. Heidelberg: Springer.

    Book  Google Scholar 

  • 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).

  • 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).

  • 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).

  • 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).

  • Yamauchi, B. (1998). Frontier-based exploration using multiple robots. In Proceedings of the International Conference on Autonomous Agents, (pp. 47–53).

  • 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).

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 079, 79110 , Freiburg, Germany

    Kai M. Wurm, Wolfram Burgard & Cyrill Stachniss

  2. Departmant of Computer Science, University of Freiburg, Georges-Köhler-Allee 052, 79110 , Freiburg, Germany

    Christian Dornhege & Bernhard Nebel

Authors
  1. Kai M. Wurm
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  2. Christian Dornhege
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  3. Bernhard Nebel
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  4. Wolfram Burgard
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  5. Cyrill Stachniss
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Corresponding author

Correspondence to Kai M. Wurm.

Additional information

This work was supported by the Deutsche Forschungsgemeinschaft (DFG) in the Transregional Collaborative Research Center SFB/TR8 Spatial Cognition projects A3-[MultiBot] and R7-[PlanSpace] and by Microsoft Research, Redmond.

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Wurm, K.M., Dornhege, C., Nebel, B. et al. Coordinating heterogeneous teams of robots using temporal symbolic planning. Auton Robot 34, 277–294 (2013). https://doi.org/10.1007/s10514-012-9320-1

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