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Swarm Intelligence

, Volume 8, Issue 3, pp 227–246 | Cite as

Designing pheromone communication in swarm robotics: Group foraging behavior mediated by chemical substance

  • Ryusuke FujisawaEmail author
  • Shigeto Dobata
  • Ken Sugawara
  • Fumitoshi Matsuno
Article

Abstract

In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm.

Keywords

Swarm robotics Social insects Pheromone communication 

Notes

Acknowledgments

We wish to thank Hikaru Imamura, who assisted with the experiments of attraction and cooperative transport, and Yuki Fujisawa, who despite being pregnant, tolerated the late return of her husband on numerous occasions.

References

  1. Balch, T. (1999). The impact of diversity on performance in multi-robot foraging. In Proceedings of the Third Annual Conference on Autonomous Agents, ACM (pp. 92–99).Google Scholar
  2. Balch, T., & Arkin, R. (1994). Communication in reactive multiagent robotic systems. Autonomous Robots, 1(1), 27–52.CrossRefGoogle Scholar
  3. Beckers, R., Holland, O., & Deneubourg, J. L. (1994). From local actions to global tasks: Stigmergy and collective robotics. In R. Brooks & P. Maes (Eds.), Artificial life IV. Cambridge, MA: MIT Press.Google Scholar
  4. Donald, B., Jennings, J., & Rus, D. (1994) Analyzing teams of cooperating mobile robots. In Proceedings of the 1994 IEEE International Conference on Robotics and Automation, IEEE (pp. 1896–1903).Google Scholar
  5. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1), 29–41.CrossRefGoogle Scholar
  6. Fujisawa, R., Imamura, H., Hashimoto, T., & Matsuno, F. (2007) Swarm intelligence of multi-robot using pheromone trail. In Proceedings of the 2nd Internal Symposium on Mobiligence (pp. 203–206).Google Scholar
  7. Fujisawa, R., Dobata, S., Kubota, D., Imamura, H., & Matsuno, F. (2008a). Dependency by concentration of pheromone trail for multiple robots. In Proceedings of Sixth International Conference on Ant Colony Optimization and Swarm Intelligence, LNCS 5217, Springer (pp. 283–290).Google Scholar
  8. Fujisawa, R., Imamura, H., Hashimoto, T., & Matsuno, F. (2008b). Communication using pheromone field for multiple robots. In Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems, IEEE (pp. 1391–1396).Google Scholar
  9. Fujisawa, R., Imamura, H., Hashimoto, T., & Matsuno, F. (2009). Development of multi-robots communicating by pheromone trail. IPSJ Transactions on Mathematical Modeling and its Applications, 2(2), 81–90. In Japanese.Google Scholar
  10. Fujisawa, R., Imamura, H., & Matsuno, F. (2010) Cooperative transportation by swarm robots using pheromone communication. In Proceedings of the 10th International Symposium on Distributed Autonomous Robotics Systems (DARS2010), Springer.Google Scholar
  11. Fujisawa, R., Dobata, S., Sasaki, Y., Takisawa, R., & Matsuno, F. (2012). Collision-induced “priority rule” governs efficiency of pheromone-communicating swarm robots. In Proceedings of Eighth International Conference on Ant Colony Optimization and Swarm Intelligence, LNCS 7461, Springer (pp. 228–235).Google Scholar
  12. Garnier, S., Tâche, F., Combe, M., Grimal, A., & Theraulaz, G. (2007). Alice in pheromone land: An experimental setup for the study of ant-like robots. In: Swarm Intelligence Symposium, SIS 2007, IEEE (pp. 37–44).Google Scholar
  13. Grassé, P. P. (1959). La reconstruction du nid et les coordinations inter-individuelles chez Bellicositermes Natalensis et Cubitermes sp. La théorie de la stigmergie: Essaid’interpretation du comportement de termites constructeurs. Insectes Sociaux, 6(1), 41–80.CrossRefMathSciNetGoogle Scholar
  14. Hamann, H., & Wörn, H. (2007). An analytical and spatial model of foraging in a swarm of robots. In E. Şahin, W. M. Spears & A.F.T. Winfield (Eds.), Swarm Robotics, LNCS 4433 (pp. 43–55). Berlin: Springer.Google Scholar
  15. Hangartner, W. (1967). Spezifität und Inaktivierung des Spurpheromons von Lasium fulginosus Latr. und Orientierung der Arbeiterinnen im Duftfeld. Zeitschrift für Vergleichende Physiologie, 57, 103–136.CrossRefGoogle Scholar
  16. Hayes, A., Martinoli, A., & Goodman, R. (2002). Distributed odor source localization. IEEE Sensors Journal, 2(3), 260–271.CrossRefGoogle Scholar
  17. Hölldobler, B., & Wilson, E. (1990). The ants. Cambridge, MA: The Belknap Press of Harvard University Press.CrossRefGoogle Scholar
  18. Ichikawa, S., & Hara, F. (1996). Experimental characteristics of multiple-robots behaviors in communication network expansion and object-fetching. In Proceedings of the 3rd International Symposium Distributed Autonomous Robotic System, Springer (pp. 183–194).Google Scholar
  19. Krieger, M., Billeter, J., & Keller, L. (2000). Ant-like task allocation and recruitment in cooperative robots. Nature, 406(6799), 992–995.CrossRefGoogle Scholar
  20. Kube, C., & Zhang, H. (1993). Collective robotics: From social insects to robots. Adaptive Behavior, 2(2), 189–218.CrossRefGoogle Scholar
  21. Kube, C., & Bonabeau, E. (2000). Cooperative transport by ants and robots. Robotics and Autonomous Systems, 30(1), 85–101.CrossRefGoogle Scholar
  22. Kurabayashi, D., & Asama, H. (2000). Knowledge sharing and cooperation of autonomous robots by intelligent data carrier system. In Proceedings of the 2000 IEEE International Conference on Robotics and Automation, IEEE (Vol. 1, pp. 464–469).Google Scholar
  23. Kurumatani, K. (2000). Macro-model generation for emergent cooperative behaviors in ant colony’s foraging (1)—a simple model case. Journal of the Japanese Society for Artificial Intelligence, 15(5), 829–837. (In Japanese).Google Scholar
  24. Matarić, M., Nilsson, M., & Simsarin, K. (1995). Cooperative multi-robot box-pushing. In IEEE/RSJ International Conference on Intelligent Robots and Systems 95’. Human Robot Interaction and Cooperative Robots’, Proceedings of the 1995, IEEE (Vol. 3, pp. 556–561).Google Scholar
  25. Nakamichi, Y., & Arita, T. (2004). An evolutionary simulation of the origin of pheromone communication. In ISPJ Symposium Series, Information Processing Society of Japan (Vol. 2004, pp. 51–58).Google Scholar
  26. Nouyan, S., Campo, A., & Dorigo, M. (2008). Path formation in a robot swarm. Swarm Intelligence, 2(1), 1–23.CrossRefGoogle Scholar
  27. Nouyan, S., Groß, R., Bonani, M., Mondada, F., & Dorigo, M. (2009). Teamwork in self-organized robot colonies. IEEE Transactions on Evolutionary Computation, 13(4), 695–711.CrossRefGoogle Scholar
  28. Purnamadjaja, A. H., & Russell, R. A. (2007). Guiding robots’ behaviors using pheromone communication. Autonomous Robots, 23(2), 113–130.CrossRefGoogle Scholar
  29. R Core Team: R. (2013). A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved August 20, 2014 from http://www.R-project.org/.
  30. Russell, R. (1997). Heat trails as short-lived navigational markers for mobile robots. In Proceedings of the 1997 IEEE International Conference on Robotics and Automation, IEEE (Vol. 4, pp. 3534–3539).Google Scholar
  31. Steels, L. (1990). Cooperation between distributed agents through self-organisation. In Proceedings of the 1990 IEEE International Conference on Intelligent Robots and Systems, IEEE (pp. 8–14).Google Scholar
  32. Sugawara, K., Kazama, T., & Watanabe, T. (2004). Foraging behavior of interacting robots with virtual pheromone. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2004), IEEE (Vol. 3, pp. 3074–3079).Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ryusuke Fujisawa
    • 1
    Email author
  • Shigeto Dobata
    • 2
  • Ken Sugawara
    • 3
  • Fumitoshi Matsuno
    • 4
  1. 1.Department of Mechanical EngineeringHachinohe Institute of TechnologyHachinoheJapan
  2. 2.Department of Ecology and EvolutionUniversity of LausanneLausanneSwitzerland
  3. 3.Faculty of Liberal ArtsTohoku Gakuin UniversitySendaiJapan
  4. 4.Graduate School of EngineeringKyoto UniversityKyotoJapan

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