Swarm Intelligence

, 5:73 | Cite as

Self-organized cooperation between robotic swarms

  • Frederick Ducatelle
  • Gianni A. Di Caro
  • Carlo Pinciroli
  • Luca M. Gambardella
Article

Abstract

We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading.

Keywords

Swarm robotics Heterogeneous robot swarms Swarm intelligence Self-organization Stigmergy Robot navigation Multi-robot systems Ant foraging 

References

  1. Batalin, M., & Sukhatme, G. (2004). Coverage, exploration and deployment by a mobile robot and communication network. Telecommunication Systems Journal, Special Issue on Wireless Sensor Networks, 26(2), 181–196. Google Scholar
  2. Batalin, M., Sukhatme, G., & Hattig, M. (2004). Mobile robot navigation using a sensor network. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 636–641). Washington: IEEE Computer Society. Google Scholar
  3. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. New York: Oxford University Press. MATHGoogle Scholar
  4. Bonani, M., Longchamp, V., Magnenat, S., Rétornaz, P., Burnier, D., Roulet, G., Vaussard, F., Bleuler, H., & Mondada, F. (2010). The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4187–4193). Washington: IEEE Computer Society. Google Scholar
  5. Corke, P., Peterson, R., & Rus, D. (2005). Localization and navigation assisted by cooperating networked sensors and robots. International Journal of Robotics Research, 24(9), 771–786. CrossRefGoogle Scholar
  6. Detrain, C., & Deneubourg, J.-L. (2006). Self-organized structures in a superorganism: do ants “behave” like molecules? Physics of Life Reviews, 3, 162–187. CrossRefGoogle Scholar
  7. Dorigo, M., & Birattari, M. (2007). Swarm intelligence. Scholarpedia, 2(9), 1462. CrossRefGoogle Scholar
  8. Dorigo, M., & Gambardella, L. M. (1997). Ant Colony System: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66. CrossRefGoogle Scholar
  9. Dorigo, M., & Sahin, E. (2004). Guest editorial: swarm robotics. Autonomous Robotics, 17(2–3), 111–113. CrossRefGoogle Scholar
  10. Dorigo, M., Bonabeau, E., & Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems, 16(8), 851–871. CrossRefGoogle Scholar
  11. Ducatelle, F., Di Caro, G. A., & Gambardella, L. (2010a). Cooperative self-organization in a heterogeneous swarm robotic system. In Proceedings of the genetic and evolutionary computation conference (GECCO) (pp. 87–94). New York: ACM Press. CrossRefGoogle Scholar
  12. Ducatelle, F., Di Caro, G. A., & Gambardella, L. (2010b). Cooperative stigmergic navigation in a heterogeneous robotic swarm. In From animals to animats 11, Proceedings of the 11th international conference on simulation of adaptive behavior (SAB) (pp. 607–617). Berlin: Springer. Google Scholar
  13. Ducatelle, F., Di Caro, G. A., & Gambardella, L. (2010c). Mobile stigmergic markers for navigation in a heterogeneous robotic swarm. In Lecture notes in computer science: Vol. 6234. Proceedings of the 7th international conference on swarm intelligence (ANTS) (pp. 456–463). Berlin: Springer. Google Scholar
  14. Dussutour, A., Fourcassié, V., Helbing, D., & Denebourg, J.-L. (2004). Optimal traffic organization in ants under crowded conditions. Nature, 428, 70–73. CrossRefGoogle Scholar
  15. Fujisawa, R., Dobata, S., Kubota, D., Imamura, H., & Matsuno, F. (2008). Dependency by concentration of pheromone trail for multiple robots. In Lecture notes in computer science: Vol. 5127. Proceedings of the 6th international conference on ant colony optimization and swarm intelligence (ANTS) (pp. 283–290). Berlin: Springer. CrossRefGoogle Scholar
  16. Garnier, S., Tache, F., Combe, M., Grimal, A., & Theraulaz, G. (2007). Alice in pheromone land: an experimental setup for the study of ant-like robots. In Proceedings of the IEEE swarm intelligence symposium (SIS) (pp. 37–44). Washington: IEEE Computer Society. CrossRefGoogle Scholar
  17. Goss, S., Aron, S., Deneubourg, J.-L., & Pasteels, J.-M. (1989). Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76, 579–581. CrossRefGoogle Scholar
  18. Grassé, P. P. (1959). La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. La théorie de la stigmergie: essai d’interprétation du comportement des termites constructeurs. Insectes Sociaux, 6, 41–81. CrossRefGoogle Scholar
  19. Kalra, N., & Martinoli, A. (2006). A comparative study of market-based and threshold-based task allocation. In Proceedings of the 8th international symposium on distributed autonomous robotic systems (DARS) (pp. 91–101). Berlin: Springer. CrossRefGoogle Scholar
  20. Labella, T.H., Dorigo, M., & Deneubourg, J.-L. (2004). Self-organized task allocation in a group of robots. In Proceedings of the 7th international symposium on distributed autonomous robotic systems (DARS) (pp. 389–398). Tokyo: Springer. Google Scholar
  21. Momen, S., & Sharkey, A. (2009). An ant-like task allocation model for a swarm of heterogeneous robots. In Proceedings of the 2nd swarm intelligence algorithms and applications symposium (SIAAS) (pp. 31–38). Brighton: SSAISB. Google Scholar
  22. Momen, S., Amavasai, B., & Siddique, N. (2007). Mixed species flocking for heterogeneous robotic swarms. In Proceedings of IEEE region 8 Eurocon: The international conference on computer as a tool. Washington: IEEE Computer Society. Google Scholar
  23. Möslinger, C., Schmickl, T., & Crailsheim, K. (2009). A minimalist flocking algorithm for swarm robots. In Lecture notes in computer science. Proceedings of the 10th European conference on artificial life (ECAL). Berlin: Springer (to be published). Google Scholar
  24. Nouyan, S., Campo, A., & Dorigo, M. (2008). Path formation in a robot swarm. Self-organized strategies to find your way home. Swarm Intelligence, 2(1), 1–23. CrossRefGoogle Scholar
  25. 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
  26. O’Hara, K., & Balch, T. (2004). Pervasive sensor-less networks for cooperative multi-robot tasks. In Proceedings of the seventh international symposium on distributed autonomous robot systems (DARS) (pp. 305–314). Tokyo: Springer. Google Scholar
  27. O’Hara, K., Bigio, V., Whitt, S., Walker, D., & Balch, T. (2006). Evaluation of a large scale pervasive embedded network for robot path planning. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 2072–2077). Washington: IEEE Computer Society. Google Scholar
  28. Panait, L., & Luke, S. (2004). Ant foraging revisited. In Proceedings of the ninth international conference on the simulation and synthesis of living systems (ALIFE) (pp. 569–574). Cambridge: MIT Press. Google Scholar
  29. Payton, D., Daily, M., Estowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11(3), 319–324. CrossRefMATHGoogle Scholar
  30. Pinciroli, C., O’Grady, R., Christensen, A., & Dorigo, M. (2009). Self-organised recruitment in a heterogeneous swarm. In Proceedings of the 14th international conference on advanced robotics (ICAR) (pp. 1–8). Washington: IEEE Computer Society. Google Scholar
  31. Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G. A., Ducatelle, F., Stirling, T., Gutiérrez, A., Gambardella, L., & Dorigo, M. (2010). ARGoS: a Pluggable, Multi-Physics Engine Simulator for Heterogeneous Swarm Robotics (Technical Report TR/IRIDIA/2010-026). IRIDIA, Université Libre de Bruxelles, Brussels, Belgium. Google Scholar
  32. Reina, A., Di Caro, G. A., Ducatelle, F., & Gambardella, L. M. (2010). A distributed approach to holonomic path planning. In Electronic proceedings of the workshop on motion planning: from theory to practice, robotics: science and systems (RSS) conference. Google Scholar
  33. Roberts, J., Stirling, T., Zufferey, J.-C., & Floreano, D. (2011). 3-D range and bearing sensor for collective flying robots. Journal of Field Robotics (submitted). Google Scholar
  34. Roberts, J., Zufferey, J.-C., & Floreano, D. (2008). Energy management for indoor hovering robots. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1242–1247). Washington: IEEE Computer Society. Google Scholar
  35. Royer, E., Melliar-Smith, P. M., & Moser, L. (2001). An analysis of the optimum node density for ad hoc mobile networks. In Proceedings of the IEEE international conference on communications (ICC) (pp. 857–861). Washington: IEEE Computer Society. Google Scholar
  36. Sharpe, T., & Webb, B. (1999). Simulated and situated models of chemical trail following in ants. In From animals to animats 5, Proceedings of the 5th international conference on the simulation of adaptive behavior (SAB) (pp. 195–204). Cambridge: MIT Press. Google Scholar
  37. Sit, T., Liu, Z., Ang Jr., M., & Seah, W. (2007). Multi-robot mobility enhanced hop-count based localization in ad hoc networks. Robotics and Autonomous Systems, 55(3), 244–252. CrossRefGoogle Scholar
  38. Stirling, T., Wischmann, S., & Floreano, D. (2010). Energy-efficient indoor search by swarms of simulated flying robots without global information. Swarm Intelligence, 4(2), 117–143. CrossRefGoogle Scholar
  39. 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 (IROS) (pp. 3074–3079). Washington: IEEE Computer Society. Google Scholar
  40. Vaughan, R., Støy, K., Sukhatme, G., & Matarić, M. (2000). Whistling in the dark: cooperative trail following in uncertain localization space. In Proceedings of the fourth international conference on autonomous agents (pp. 187–194). New York: ACM Press. CrossRefGoogle Scholar
  41. Vigorito, C. (2007). Distributed path planning for mobile robots using a swarm of interacting reinforcement learners. In Proceedings of the sixth international joint conference on autonomous agents and multiagent systems (AAMAS) (pp. 782–789). New York: ACM Press. Google Scholar
  42. Werger, B. B., & Matarić, M. J. (1996). Robotic food chains: externalization of state and program for minimal-agent foraging. In From animals to animats 4, Proceedings of the 4th international conference on simulation of adaptive behavior (SAB) (pp. 625–626). Cambridge: MIT Press. Google Scholar
  43. Witkowski, U., El-Habbal, M., Herbrechtsmeier, S., Tanoto, A., Penders, J., Alboul, L., & Gazi, V. (2008). Ad-hoc network communication infrastructure for multi-robot systems in disaster scenarios. In Proceedings of the IARP/EURON workshop on robotics for risky interventions and surveillance of the environment (RISE) (Published online). Google Scholar
  44. Wodrich, M., & Bilchev, G. (1997). Cooperative distributed search: the ants’ way. Control and Cybernetics, 26, 413–446. MathSciNetMATHGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2011

Authors and Affiliations

  • Frederick Ducatelle
    • 1
  • Gianni A. Di Caro
    • 1
  • Carlo Pinciroli
    • 2
  • Luca M. Gambardella
    • 1
  1. 1.“Dalle Molle” Institute for Artificial Intelligence Studies (IDSIA)MannoSwitzerland
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

Personalised recommendations