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Multi-robot Map Updating in Dynamic Environments

  • Fabrizio Abrate
  • Basilio Bona
  • Marina Indri
  • Stefano Rosa
  • Federico Tibaldi
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 83)

Abstract

Multi-robot systems play an important role in many robotic applications. A prerequisite for a team of robots is the capability of building and maintaining updated maps of the environment. The simultaneous estimation of the trajectory and the map of the environment (known as SLAM) requires many computational resources. Moreover, SLAM is generally performed in environments that do not vary over time (called static environments), whereas real applications commonly require navigation services in dynamic environments. This paper focuses on long term mapping operativity in presence of variations in the map, as in the case of robotic applications in logistic spaces, where rovers have to track the presence of goods in given areas. In this context classical SLAM approaches are generally not directly applicable, since they usually apply in static environments or in dynamic environments where it is possible to model the environment dynamics. This paper proposes a methodology that allows the robots to detect variations in the environment, generate maps containing only the persistent variations, propagate thiem to the team and finally merge the received information in a consistent way. The team of robots is also exploited to assure the coverage of areas not visited for long time, thus improving the knowledge on the present status of the map. The map updating process is demonstrated to be computationally light, in order to be performed in parallel with other tasks (e.g., team coordination and planning, surveillance).

Keywords

Localization Error Mapping Process Goal Point Robotic Application Persistent Variation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abrate, F., Bona, B., Indri, M., Rosa, S., Tibaldi, F.: Switching multirobot collaborative localization in symmetrical environments. In: IEEE International Conference on Intelligent Robots Systems (IROS 2008), 2nd Workshop on Planning, Perception and Navigation for Intelligent Vehicles, PPNIV (2008)Google Scholar
  2. 2.
    Abrate, F., Bona, B., Indri, M., Rosa, S., Tibaldi, F.: Three state multirobot collaborative localization in symmetrical environments. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, pp. 1–6 (2009)Google Scholar
  3. 3.
    Abrate, F., Bona, B., Indri, M., Rosa, S., Tibaldi, F.: Map updating in dynamic environments. In: Proceedings of the 41st International Symposium on Robotics, pp. 296–303 (2010)Google Scholar
  4. 4.
    Birk, A., Carpin, S.: Merging occupancy grid maps from multiple robots. Proceedings of the IEEE 94(7), 1384–1397 (2006), doi:10.1109/JPROC.2006.876965CrossRefGoogle Scholar
  5. 5.
    Carlone, L., Ng, M.K., Du, J., Bona, B., Indri, M.: Rao-blackwellized particle filters multi robot slam with unknown initial correspondences and limited communication. In: Proceedings of IEEE International Conference on Robotics and Automation (2010)Google Scholar
  6. 6.
    Carpin, S.: Fast and accurate map merging for multi-robot systems. Auton. Robots 25(3), 305–316 (2008), doi:http://dx.doi.org/10.1007/s10514-008-9097-4 CrossRefGoogle Scholar
  7. 7.
    Fabrizi, E., Saffiotti, A.: Extracting topology-based maps from gridmaps. In: IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 2972–2978 (2000)Google Scholar
  8. 8.
    Fenwick, J., Newman, P., Leonard, J.: Cooperative concurrent mapping and localization, vol. 2, pp. 1810–1817 (2002), doi:10.1109/ROBOT.2002.1014804Google Scholar
  9. 9.
    Howard, A.: Multi-robot simultaneous localization and mapping using particle filters. In: Robotics: Science and Systems, pp. 201–208 (2005)Google Scholar
  10. 10.
    Howard, A., Sukhatme, G.S., Matarić, M.J.: Multi-robot mapping using manifold representations. Proceedings of the IEEE - Special Issue on Multi-Robot Systems 94(9), 1360–1369 (2006)Google Scholar
  11. 11.
    LaValle, S.: Planning Algorithms. Cambridge University Press (2004)Google Scholar
  12. 12.
    Maragos, P., Saffiotti, A.: Morphological skeleton representation and coding of binary images. IEEE Trans. on Acoustics, Speech, and Signal Processing (1986)Google Scholar
  13. 13.
    Siciliano, B., Khatib, O. (eds.): Springer Handbook of Robotics. Springer, Heidelberg (2008), http://dx.doi.org/10.1007/978-3-540-30301-5 zbMATHGoogle Scholar
  14. 14.
    Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29(12), 1104–1113 (1981)CrossRefGoogle Scholar
  15. 15.
    Thrun, S., Bücken, A.: Integrating grid-based and topological maps for mobile robot navigation. In: Proc. of the National Conference on Artificial Intelligence (1996)Google Scholar
  16. 16.
    Williams, S., Dissanayake, G., Durrant-Whyte, H.: Towards multi-vehicle simultaneous localisation and mapping, pp. 2743–2748 (2002), doi:10.1109/ROBOT.2002.1013647Google Scholar
  17. 17.
    Zhou, X.S., Roumeliotis, S.I.: Multi-robot slam with unknown initial correspondence: The robot rendezvous case. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 1785–1792Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fabrizio Abrate
    • 1
  • Basilio Bona
    • 2
  • Marina Indri
    • 2
  • Stefano Rosa
    • 2
  • Federico Tibaldi
    • 2
  1. 1.Istituto Superiore Mario BoellaTorinoItaly
  2. 2.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTorinoItaly

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