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Get in touch: cooperative decision making based on robot-to-robot collisions

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Abstract

We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which results from executing a very simple bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust. This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.

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References

  1. Ame J.-M., Rivault C., Deneubourg J.-L. (2004) Cockroach aggregation based on strain odour recognition. Animal Behaviour 68: 793–801. doi:10.1016/j.anbehav.2004.01.009

    Article  Google Scholar 

  2. Baldassarre G., Nolfi S., Parisi D. (2003) Evolving mobile robots able to display collective behaviour. Artificial Life 9: 255–267. doi:10.1162/106454603322392460

    Article  Google Scholar 

  3. Beckers R., Deneubourg J.-L., Goss S. (1992) Trails and U-turns in the selection of a path by the ant Lasius niger. Journal of Theoretical Biology 159: 397–415. doi:10.1016/S0022-5193(05)80686-1

    Article  Google Scholar 

  4. Beckers R., Holland O. E., Deneubourg J.-L. (1994) From local actions to global tasks: Stigmergy and collective robotics. Artificial Life IV: 181–189

    Google Scholar 

  5. Beni, G. (2005). From swarm intelligence to swarm robotics. In E. Şahin & W. Spears (Eds.), Proceedings of the SAB 2004 Workshop on Swarm Robotics, Santa Monica, CA, USA, July, 2004. Lecture Notes in Computer Science 3342, pp. 1–9.

  6. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. In Santa Fe Institute Studies in the Sciences of Complexity (306 pp.). New York, NY: Oxford University Press.

  7. Camazine S., Deneubourg J.-L., Franks N.R., Sneyd J., Theraulaz G., Bonabeau E. (2003) Self-organization in biological systems (560 pp.). Princeton University Press, Princeton, NJ

    MATH  Google Scholar 

  8. Corell, N., & Martinoli, A. (2007). Modeling self-organized aggregation in a swarm of miniature robots. In IEEE International Conference on Robotics and Automation, Workshop on collective behaviors inspired by biological and biochemical systems.

  9. Crailsheim K., Eggenreich U., Ressi R., Szolderits M.J. (1999) Temperature preference of honeybee drones (Hymenoptera: Apidae). Entomologia Generalis 24: 37–47

    Google Scholar 

  10. De Schutter G., Theraulaz G., Deneubourgh J.-L. (2001) Animal-robots collective intelligence. Annals of Mathematics and Artificial Intelligence 31: 223–238. doi:10.1023/A:1016638723526

    Article  Google Scholar 

  11. Deneubourg J.-L., Lioni A., Detrain C. (2002) Dynamics of aggregation and emergence of cooperation. The Biological Bulletin 202: 262–267. doi:10.2307/1543477

    Article  Google Scholar 

  12. Depickère S., Fresneau D., Detrain C., Deneubourg J.-L. (2004) Marking as a decision factor in the choice of a new resting site in Lasius niger. Insectes Sociaux 51: 243–246

    Google Scholar 

  13. Dorigo M., Trianni V., Sahin E., Gro R., Labella T.H., Baldassarre G. et al (2004) Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots 17: 223–245. doi:10.1023/B:AURO.0000033973.24945.f3

    Article  Google Scholar 

  14. Garnier, S., Jost, C., Jeanson, R., Gautrais, J., Asadpour, M., Caprari, G., et al. (2005). Collective decision-making by a group of cockroach-like robots. In IEEE Swarm Intelligence Symposium (SIS’05), pp. 233–240.

  15. 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 IEEE Swarm Intelligence Symposium.

  16. Gervasi V., Prencipe G. (2004) Coordination without communication: The case of the flocking problem. Discrete Applied Mathematics 144: 324–344. doi:10.1016/j.dam.2003.11.010

    Article  MATH  MathSciNet  Google Scholar 

  17. Grassé P.P. (1959) La reconstruction du nid et les coordinations inter-individuelles chez Bellicositermes natalensis et Cubitermes sp. La théorie de la stigmergie: Essai d’interprétation des termites constructeurs. Insectes Sociaux 6: 41–83. doi:10.1007/BF02223791

    Google Scholar 

  18. Grodzicki P., Caputa M. (2005) Social versus individual behaviour: A comparative approach to thermal behaviour of the honeybee (Apis mellifera L.) and the American cockroach (Periplaneta americana L.). Journal of Insect Physiology 51: 315–322. doi:10.1016/j.jinsphys.2005.01.001

    Article  Google Scholar 

  19. Halloy J., Sempo G., Caprari G., Rivault C., Asadpour M., Tâche F. et al (2007) Social integration of robots into groups of cockroaches to control self-organized choices. Science 318: 1155–1158. doi:10.1126/science.1144259

    Article  Google Scholar 

  20. Hamann, H., Wörn, H., Crailsheim, K., & Schmickl, T. (2008). Spatial macroscopic models of a bio-inspired robotic swarm algorithm. In Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems.

  21. Holland O., Melhuish C. (1999) Stigmergy, self-organization, and sorting in collective robotics. Artificial Life 5: 173–202. doi:10.1162/106454699568737

    Article  Google Scholar 

  22. Jeanson R., Rivault C., Deneubourg J.-L., Blanco S., Fournier R., Jost C. et al (2005) Self-organized aggregation in cockroaches. Animal Behaviour 69: 169–180. doi:10.1016/j.anbehav.2004.02.009

    Article  Google Scholar 

  23. Kennedy J., Eberhart R.C. (2001) Swarm intelligence (512 pp.). Morgan Kaufmann Publishers, Inc., San Francisco, CA

    Google Scholar 

  24. Kornienko, S., Kornienko, O., & Levi, P. (2005). IR-based communication and perception in microrobotic swarms. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’05), Edmonton, Canada.

  25. Kornienko, S., Kornienko, O., & Levi, P. (2005). Collective AI: Context awareness via communication. In International Joint Conference on Artificial Intelligence (IJCAI’05), Edinburgh, UK.

  26. Kornienko, S., Thenius, R., Kornienko, O., & Schmickl, T. (accepted). Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic swarm. Accepted for publication in Adaptive Behaviour.

  27. Krieger M.J.B., Billeter J.-B. (2000) The call of duty: Self-organised task allocation in a population of up to twelve mobile robots. Robotics and Autonomous Systems 30: 65–84. doi:10.1016/S0921-8890(99)00065-2

    Article  Google Scholar 

  28. Labella T.H., Dorigo M., Deneubourg J.-L. (2006) Division of labor in a group of robots inspired by ants’ foraging behavior. ACM Transactions on Autonomous and Adaptive Systems 1: 4–25. doi:10.1145/1152934.1152936

    Article  Google Scholar 

  29. Millonas M.M. (1994) Swarms, phase transitions, and collective intelligence. Artificial Life 3: 417–445

    Google Scholar 

  30. Payton D., Daily M., Estowski R., Howard M., Lee C. (2001) Pheromone robotics. Autonomous Robots 11: 319–324. doi:10.1023/A:1012411712038

    Article  MATH  Google Scholar 

  31. Reynolds C.W. (1987) Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Computer Graphics and Interactive Techniques 21: 25–34. doi:10.1145/37402.37406

    Article  Google Scholar 

  32. Şahin E. (2005). Swarm robotics: From sources of inspiration to domains of application. In E. Şahin & W. Spears (Eds.), Proceedings of the SAB 2004 Workshop on Swarm Robotics, Santa Monica, CA, USA, July, 2004. Lecture Notes in Computer Science 3342, pp. 10–20.

  33. Schmickl T., Crailsheim K. (2004) Costs of environmental fluctuations and benefits of dynamic decentralized foraging decisions in honeybees. Adaptive Behavior 12: 263–277. doi:10.1177/105971230401200311

    Article  Google Scholar 

  34. Schmickl, T., & Crailsheim, K. (2007). A navigation algorithm for swarm robotics inspired by slime mold aggregation. In E. Şahin W. Spears, & A. F. T. Winfield (Eds.), Proceedings of the SAB 2006 Workshop on Swarm Robotics, Rome, Italy, September, 2006. Lecture Notes in Computer Science 4433, pp. 1–13.

  35. Schmickl, T., Möslinger, C., & Crailsheim, K. (2007). Collective perception in a robot swarm. In E. Şahin, W. Spears, & A. F. T. Winfield (Eds.), Proceedings of the SAB 2006 Workshop on Swarm Robotics, Rome, Italy, September, 2006. Lecture Notes in Computer Science 4433, pp. 144–157.

  36. Seeley T.D., Camazine S., Sneyd J. (1991) Collective decision-making in honey bees: How colonies choose among nectar sources. Behavioral Ecology and Sociobiology 28: 277–290. doi:10.1007/BF00175101

    Article  Google Scholar 

  37. Seyfried, J., Szymanski, M., Bender, N., Estana, R., Thiel, M., & Wörn, H. (2005). The I-SWARM project: Intelligent small world autonomous robots for micromanipulation. In E. Şahin, & W. Spears (Eds.), Proceedings of the SAB 2004 Workshop on Swarm Robotics, Santa Monica, CA, USA, July, 2004. Lecture Notes in Computer Science 3342, pp. 70–83.

  38. Soysal, O., & Şahin, E. (2007). A macroscopic model for self-organized aggregation in swarm robotic systems. In E. Şahin, W. Spears, & A. F. T. Winfield (Eds.), Proceedings of the SAB 2006 Workshop on Swarm Robotics, Rome, Italy, September, 2006. Lecture Notes in Computer Science 4433, pp. 27–42.

  39. Sugawara, K., Kazama, T., & Watanabe, T. (2004). Foraging behavior of interacting robots with virtual pheromone. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, pp. 3074–3079.

  40. Tâche, F., Asadpour, M., Caprari, G., Karlen, W., & Siegwart, R. (2005). Perception and behavior of InsBot: Robot–animal interaction issues. In IEEE International Conference on Robotics and Biomimetics (ROPIO’05), Shatin, pp. 517–522.

  41. www.swarmrobot.orgAccessed 1 August 2008.

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Authors and Affiliations

  1. Department for Zoology, Karl-Franzens-University Graz, Graz, Austria

    Thomas Schmickl, Ronald Thenius, Gerald Radspieler & Karl Crailsheim

  2. University of Applied Sciences St. Poelten, St. Poelten, Austria

    Christoph Moeslinger

  3. Institute of Parallel and Distributed Systems, University of Stuttgart, Stuttgart, Germany

    Serge Kernbach

  4. Institute for Process Control and Robotics, University Karlsruhe (TH), Karlsruhe, Germany

    Marc Szymanski

Authors
  1. Thomas Schmickl
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  2. Ronald Thenius
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  3. Christoph Moeslinger
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  4. Gerald Radspieler
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  5. Serge Kernbach
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  6. Marc Szymanski
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  7. Karl Crailsheim
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Correspondence to Thomas Schmickl.

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Schmickl, T., Thenius, R., Moeslinger, C. et al. Get in touch: cooperative decision making based on robot-to-robot collisions. Auton Agent Multi-Agent Syst 18, 133–155 (2009). https://doi.org/10.1007/s10458-008-9058-5

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