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Neural Computing and Applications

, Volume 19, Issue 6, pp 807–823 | Cite as

Collective decision-making based on social odometry

  • Álvaro Gutiérrez
  • Alexandre Campo
  • Félix Monasterio-Huelin
  • Luis Magdalena
  • Marco Dorigo
Swarm Robotics

Abstract

In this paper, we propose a swarm intelligence localization strategy in which robots have to locate different resource areas in a bounded arena and forage between them. The robots have no knowledge of the arena dimensions and of the number of resource areas. The strategy is based on peer-to-peer local communication without the need for any central unit. Social Odometry leads to a self-organized path selection. We show how collective decisions lead the robots to choose the closest resource site from a central place. Results are presented with simulated and real robots.

Keywords

Swarm robotics Self-organization Collective decision Local communication 

Notes

Acknowledgments

A. Campo and M. Dorigo acknowledge support from the Belgian F.R.S.-FNRS, of which they are a Research Fellow and a Research Director, respectively. This work was partially supported by the Gestión de la Demanda Eléctrica Doméstica con Energía Solar Fotovoltaica project, funded by the Plan Nacional de I+D+i 2007-2010 (ENE2007-66135) of the Spanish Ministerio de Educación y Ciencia and the N4C—Networking for Challenged Communications Citizens: Innovative Alliances and Test beds project, funded by the Seventh Framework Program (FP7-ICT-223994-N4C) of the European Commission. The information provided is the sole responsibility of the authors and does not reflect the European Commission’s opinion. The Spanish Ministry and the European Commission are not responsible for any use that might be made of data appearing in this publication.

References

  1. Balch T, Arkin RC (1994) Communication in reactive multiagent robotic systems. Auton Robots 1(1):1–25Google Scholar
  2. Beckers R, Deneubourg J-L, Goss S (1992) Trails and U-turns in the selection of a path by the ant Lasius niger. J Theor Biol 159(4):397–415CrossRefGoogle Scholar
  3. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New YorkzbMATHGoogle Scholar
  4. Brooks RA (1986) A robust layered control system for a mobile robot. IEEE J Robotics Autom 2(1): 14–23Google Scholar
  5. Burgard W, Fox D, Hennig D, Schmidt T (1996) Estimating the absolute position of a mobile robot using position probability grids. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence. AAAI Press/MIT Press, Cambridge, pp 896–901Google Scholar
  6. Burgard W, Derr A, Fox D, Cremers AB (1998) Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IEEE Press, Piscataway, pp 1–6Google Scholar
  7. Camazine S (1993) The regulation of pollen foraging by honey bees: how foragers assess the colony’s need for pollen. Behav Ecol Sociobiol 32(4):265–273CrossRefGoogle Scholar
  8. Camazine S, Deneubourg J-L, Franks NR, Sneyd J, Theraulaz G, Bonabeau E (2003) Self-organization in biological systems. Princeton studies in complexity. Princeton University Press, PrincetonGoogle Scholar
  9. Campo A, Nouyan S, Birattari M, Groß R, Dorigo M (2006) Negotiation of goal direction for cooperative transport. In: Ant colony optimization and swarm intelligence, 5th International Workshop, ANTS 2006, Brussels, Belgium, September 4–7, 2006, Proceedings. Lecture Notes in Computer Science. Springer, Berlin, pp 191–202Google Scholar
  10. Cassandra AR, Kaelbling LP, Kurien JA (1996) Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems. IEEE Press, Piscataway, pp 963–972Google Scholar
  11. Chong K, Kleeman L (1997) Accurate odometry and error modelling for a mobile robot. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE Press, Piscataway, pp 2783–2788Google Scholar
  12. Christensen AL (2005) Efficient neuro-evolution of hole-avoidance and phototaxis for a swarm-bot. Tech. Rep. 2005-014, IRIDIA, Université Libre de Bruxelles, Brussels, BelgiumGoogle Scholar
  13. Corradi P, Schmickl T, Scholz P, Menciassi A, Dario P (2009) Optical networking in a swarm of microrobots. In: Cheng MX (ed) NanoNet. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer, pp 107–119Google Scholar
  14. Crailsheim K (1992) The flow of jelly within a honeybee colony. J Comp Physiol B 162(8):681–689CrossRefGoogle Scholar
  15. Deneubourg J-L, Aron S, Goss S, Pasteels JM (1990) The self-organizing exploratory pattern of the Argentine ant. J Insect Behav 3(1):159–168CrossRefGoogle Scholar
  16. Dudek G, Mackenzie P (1993) Model-based map construction for robot localization. In: Proceedings of Vision Interface. CIPPR Society Press, Toronto, pp 97–102Google Scholar
  17. Feng L, Borenstein J, Everett H (1994) Where am I? Sensors and methods for autonomous mobile robot positioning. University of Michigan Press, Ann ArborGoogle Scholar
  18. Grabowski R, Navarro-Serment L, Paredis C, Khosla P (2000) Heterogeneous teams of modular robots for mapping and exploration. Auton Robots 8(2):293–308CrossRefGoogle Scholar
  19. Gutiérrez A, Campo A, Dorigo M, Amor D, Magdalena L, Monasterio-Huelin F (2008a) An open localization and local communication embodied sensor. Sensors 8(11):7545–7563CrossRefGoogle Scholar
  20. Gutiérrez A, Campo A, Santos FC, Pinciroli C, Dorigo M (2008b) Social odometry in populations of autonomous robots. In: Ant Colony Optimization and Swarm Intelligence, 6th International Conference, ANTS 2008, Brussels, Belgium, September 2008, Proceedings. Lecture Notes in Computer Science. Springer, Berlin, Germany, pp 371–378Google Scholar
  21. Gutiérrez A, Campo A, Dorigo M, Donate J, Monasterio-Huelin F, Magdalena L (2009a) Open e-puck range and bearing miniaturized board for local communication in swarm robotics. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE Press, Piscataway, pp 3111–3116Google Scholar
  22. Gutiérrez A, Campo A, Fernández D, Monasterio-Huelin F, Magdalena L, Dorigo M (2009b) Social odometry: a self-organized distributed location algorithm. Tech Rep 2009-014, IRIDIA, Université Libre de Bruxelles, Brusseles, BelgiumGoogle Scholar
  23. Gutiérrez A, Campo A, Santos FC, Monasterio-Huelin F, Dorigo M (2009c) Imitation based odometry in collective robotics. Int J Adv Rob Sys 6(2):129–136Google Scholar
  24. Gutmann J, Weigel T, Nebel B (2001) A fast, accurate, and robust method for self-localization in polygonal environments using laser-rangefinders. Adv Robotics J 14(1):1–17Google Scholar
  25. Gutmann JS, Weigel T, Nebel B (1999) Fast, accurate and robust self-localization in polygonal environments. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems. IEEE Press, Piscataway, pp 1412–1419Google Scholar
  26. Hammerstein P (2003) Genetic and cultural evolution of cooperation. MIT Press, CambridgeGoogle Scholar
  27. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng Ser D 82(1):35–45Google Scholar
  28. Korst P, Velthuis H (1982) The nature of trophallaxis in honeybees. Insectes Sociaux 29(2):209–221CrossRefGoogle Scholar
  29. Kurazume R, Hirose S, Nagata S, Sashida N (1996) Study on cooperative positioning system (basic principle and measurement experiment). In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems. IEEE Press, Piscataway, pp 1421–1426Google Scholar
  30. Larsen T, Bak M, Andersen N, Ravn O (1998) Location estimation for autonomously guided vehicle using an augmented Kalman filter to autocalibrate the odometry. In: FUSION 98 Spie Conference. CSREA Press, Las Vegas, NV, pp 33–39Google Scholar
  31. Liebig J, Heinze J, Hölldobler B (1997) Trophallaxis and aggression in the ponerine ant, Ponera coarctata: implications for the evolution of liquid food exchange in the hymenoptera. Ethology 103(9):707–722CrossRefGoogle Scholar
  32. Martinelli A, Siegwart R (2003) Estimating the odometry error of a mobile robot during navigation. In: Proceedings of the 1st European Conference on Mobile Robots. Warszawa, Poland: Zturek Research Scientific Inst. Press, pp 218–223Google Scholar
  33. Matarić MJ (1997) Learning social behavior. Robotics Auton Syst 20(2):191–204CrossRefGoogle Scholar
  34. McLurkin J, Smith J (2004) Distributed algorithms for dispersion in indoor environments using a swarm of autonomous mobile robots. In: Proceedings of the Seventh International Symposium on Distributed Autonomous Robotic Systems. Springer, Berlin, pp 131–142Google Scholar
  35. Mondada F, Bonani M, Raemy X, Pugh J, Cianci C, Klaptocz A, Magnenat S, Zufferey JC, Floreano D, Martinoli A (2009) The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions. IPCB-Instituto Politécnico de Castelo Branco, Castelo Branco, Portugal, pp 59–65Google Scholar
  36. Nouyan S, Campo A, Dorigo M (2008) Path formation in a robot swarm: self-organized strategies to find your way home. Swarm Intell 2(1):1–23CrossRefGoogle Scholar
  37. Nouyan S, Groß R, Bonani M, Mondada F, Dorigo M (2009) Teamwork in self-organized robot colonies. IEEE Trans Evol Comput 13(4):695–711Google Scholar
  38. Parker LE (1998) Alliance: an architecture for fault tolerant multirobot cooperation. IEEE Trans Robotics Autom 14(2):220–240CrossRefGoogle Scholar
  39. Payton D, Daily M, Estowski R, Howard M, Lee C (2001) Pheromone robotics. Auton Robots 11(3):319–324zbMATHCrossRefGoogle Scholar
  40. Payton D, Estkowski R, Howard M (2003) Compound behaviors in pheromone robotics. Robotics and Auton Syst 44(3):229–240CrossRefGoogle Scholar
  41. Purnamadjaja AH, Russell RA (2004) Pheromone communicate: implementation of necrophoric bee behaviour in a robot swarm. In: Proceedings of the IEEE Conference on Robotics, Automation and Mechatronics. IEEE Press, Piscataway, pp 638–643Google Scholar
  42. Rekleitis I, Dudek G, Milios E (2001) Multi-robot collaboration for robust exploration. Ann Math Artif Intell 31(1):7–40CrossRefGoogle Scholar
  43. Rekleitis I, Dudek G, Milios E (2003) Probabilistic cooperative localization and mapping in practice. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE Press, Piscataway, pp 1907–1912Google Scholar
  44. Russell R (1995) Laying and sensing odor markings as a strategy for assisting mobile robots navigation tasks. IEEE Robotics Autom Mag 2(3):3–9CrossRefGoogle Scholar
  45. Russell R (1999) Ant trails—an example for robots to follow? In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE Press, Piscataway, pp 2698–2703Google Scholar
  46. Santos FC, Pacheco JM, Lenaerts T (2006) Cooperation prevails when individuals adjust their social ties. PLoS Comput Biol 2(10):e140CrossRefGoogle Scholar
  47. Schmickl T, Crailsheim K (2008) Throphallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Auton Robots 25(1):171–188CrossRefGoogle Scholar
  48. Simmons R, Koenig S (1995) Probabilistic robot navigation in partially observable environments. In: Proceedings of the International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Mateo, pp 1080–1087Google Scholar
  49. Simon D (2006) Optimal state estimation: Kalman, H Infinity, and nonlinear approaches. Wiley, LondonGoogle Scholar
  50. Smith RC, Cheeseman P (1987) On the representation and estimation of spatial uncertainly. Int J Robotics Res 5(4):56–68Google Scholar
  51. Stella E, Musio F, Vasanelli L, Distante A (1995) Goal-oriented mobile robot navigation using an odour sensor. In: Proceedings of the Intelligent Vehicles Symposium. IEEE Press, Piscataway, pp 147–151Google Scholar
  52. Svennebring J, Koenig S (2003) Trail-laying robots for robust terrain coverage. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE Press, Piscataway, pp 75–82Google Scholar
  53. Szymanski M, Breitling T, Seyfried J, Wörn H (2006) Distributed shortest-path finding by a micro-robot swarm. In Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, Brussels, Belgium, September 4–7, 2006, Proceedings, Lecture Notes in Computer Science, Springer, Germany, pp 404–411Google Scholar
  54. Traulsen A, Nowak M, Pacheco J (2006) Stochastic dynamics of invasion and fixation. Phys Rev Ser E 74(1):011,909.1–011,909.5Google Scholar
  55. Traulsen A, Pacheco J, Nowak M (2007) Pairwise comparison and selection temperature in evolutionary game dynamics. J Theo Biol 246(3):522–529CrossRefMathSciNetGoogle Scholar
  56. Valdastri P, Corradi P, Menciassi A, Schmickl T, Crailsheim K, Seyfired J, Dario P (2006) Micromanipulation, communication and swarm intelligence issues in a swarm microrobotic platform. Robotics Auton Syst 54(10):789–804CrossRefGoogle Scholar
  57. Vaughan R, Støy K, Sukhatme G, Matarić M (2002) LOST: localization-space trails for robot teams. IEEE Trans Robotics Autom 18(5):796–812CrossRefGoogle Scholar
  58. Wagner IA, Lindembaum M, Bruckstein AM (1999) Distributed covering by ant-robots using evaporating traces. IEEE Trans Robotics Autom 15(5):918–933CrossRefGoogle Scholar
  59. Wang CM (1988) Location estimation and uncertainty analysis for mobile robots. Auton Robot Veh 1(1):1230–1235Google Scholar
  60. Wheeler W (1918) A study of some ant larvae, with consideration of the origin and meaning of the social habit among insects. Proc Am Phil Soc 57(1):293–343Google Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Álvaro Gutiérrez
    • 1
  • Alexandre Campo
    • 2
  • Félix Monasterio-Huelin
    • 1
  • Luis Magdalena
    • 3
  • Marco Dorigo
    • 2
  1. 1.ETSIT, Universidad Politécnica de MadridMadridSpain
  2. 2.IRIDIA, CoDE, Université Libre de BruxellesBrusselsBelgium
  3. 3.European Centre for Soft ComputingAsturiasSpain

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