A Multi-robot Coverage Approach Based on Stigmergic Communication

  • Bijan Ranjbar-Sahraei
  • Gerhard Weiss
  • Ali Nakisaee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7598)

Abstract

Recent years have witnessed a rapidly growing interest in using teams of mobile robots for autonomously covering environments. In this paper a novel approach for multi-robot coverage is described which is based on the principle of pheromone-based communication. According to this approach, called StiCo (for “Stigmergic Coverage”), the robots communicate indirectly via depositing/detecting markers in the environment to be covered. Although the movement policies of each robot are very simple, complex and efficient coverage behavior is achieved at the team level. StiCo shows several desirable features such as robustness, scalability and functional extensibility. Two extensions are described, including A-StiCo for dealing with dynamic environments and ID-StiCo for handling intruder detection. These features make StiCo an interesting alternative to graph-based multi-robot coverage approaches which currently are dominant in the field. Moreover, because of these features StiCo has a broad application potential. Simulation results are shown which clearly demonstrate the strong coverage abilities of StiCo in different environmental settings.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Roman-Ballesteros, I., Pfeiffer, C.F.: A framework for cooperative multi-robot surveillance tasks. In: Electronics, Robotics and Automotive Mechanics Conference, vol. 2, pp. 163 –170 (September 2006)Google Scholar
  2. 2.
    Schwager, M., Rus, D., Slotine, J.J.: Decentralized, adaptive coverage control for networked robots. International Journal of Robotics Research 28(3), 357–375 (2009)CrossRefGoogle Scholar
  3. 3.
    Cortes, J., Martinez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation 20(2), 243–255 (2004)CrossRefGoogle Scholar
  4. 4.
    Ranjbar-Sahraei, B., Weiss, G., Nakisaee, A.: Stigmergic coverage algorithm for multi-robot systems (demonstration). In: Proceedings of the Eleventh International Conference on Autonomous Agents and Multiagent Systems, AAMAS (2012)Google Scholar
  5. 5.
    Dorigo, M.: Optimization, Learning and Natural Algorithms. Thesis report, Politecnico di Milano, Italy (1992)Google Scholar
  6. 6.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)Google Scholar
  7. 7.
    Johansson, R., Saffiotti, A.: Navigating by stigmergy: A realization on an rfid floor for minimalistic robots. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 245–252 (May 2009)Google Scholar
  8. 8.
    Herianto, Sakakibara, T., Kurabayashi, D.: Artificial pheromone system using rifd for navigation of autonomous robots. Journal of Bionic Engineering 4(4), 245–253 (2007)CrossRefGoogle Scholar
  9. 9.
    Wagner, I.A., Lindenbaum, M., Bruckstein, A.M.: Distributed covering by ant-robots using evaporating traces. IEEE Transactions on Robotics and Automation 15(5), 918–933 (1999)CrossRefGoogle Scholar
  10. 10.
    Elor, Y., Bruckstein, A.M.: Autonomous Multi-agent Cycle Based Patrolling. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 119–130. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Elor, Y., Bruckstein, A.M.: Multi-a(ge)nt graph patrolling and partitioning. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02, WI-IAT 2009, pp. 52–57. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  12. 12.
    Glad, A., Simonin, O., Buffet, O., Charpillet, F.: Influence of different execution models on patrolling ant behaviors: from agents to robots. In: Proceedings of the Ninth International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2010 (2010)Google Scholar
  13. 13.
    Glad, A., Simonin, O., Buffet, O., Charpillet, F.: Theoretical study of ant-based algorithms for multi-agent patrolling. In: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence, pp. 626–630. IOS Press, Amsterdam (2008)Google Scholar
  14. 14.
    Yanovski, V., Wagner, I.A., Bruckstein, A.M.: A distributed ant algorithm for efficiently patrolling a network. Algorithmica 37, 165–186 (2003)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Cortes, J., Martinez, S., Bullo, F.: Spatially-distributed coverage optimization and control with limited-range interactions. ESAIM: Control, Optimisation and Calculus of Variations 11, 691–719 (2005)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Schwager, M., Rus, D., Slotine, J.J.: Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment. International Journal of Robotics Research 30(3), 371–383 (2011)CrossRefGoogle Scholar
  17. 17.
    Breitenmoser, A., Schwager, M., Metzger, J.C., Siegwart, R., Rus, D.: Voronoi coverage of non-convex environments with a group of networked robots. In: Proc. of the International Conference on Robotics and Automation (ICRA 2010), pp. 4982–4989 (May 2010)Google Scholar
  18. 18.
    Fujisawa, R., Imamura, H., Hashimoto, T., Matsuno, F.: Communication using pheromone field for multiple robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 1391–1396 (September 2008)Google Scholar
  19. 19.
    Ziparo, V.A., Kleiner, A., Marchetti, L., Farinelli, A., Nardi, D.: Cooperative exploration for USAR robots with indirect communication. In: Proc.of 6th IFAC Symposium on Intelligent Autonomous Vehicles, IAV 2007 (2007)Google Scholar
  20. 20.
    Bullo, F., Cortes, J., Martinez, S.: Distributed Control of Robotic Networks. Applied Mathematics Series (2009), http://www.coordinationbook.info
  21. 21.
    Dubins, L.E.: On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal positions and tangents. American Journal of Mathematics 79, 497–516 (1957)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bijan Ranjbar-Sahraei
    • 1
  • Gerhard Weiss
    • 1
  • Ali Nakisaee
    • 2
  1. 1.Dept. of Knowledge EngineeringMaastricht UniversityThe Netherlands
  2. 2.National Organization for Development of Exceptional TalentsShirazIran

Personalised recommendations