Skip to main content
SpringerLink
Log in

A social approach for target localization: simulation and implementation in the marXbot robot

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

Foraging is a common benchmark problem in collective robotics in which a robot (the forager) explores a given environment while collecting items for further deposition at specific locations. A typical real-world application of foraging is garbage collection where robots collect garbage for further disposal in pre-defined locations. This work proposes a method to cooperatively perform the task of finding such locations: instead of using local or global localization strategies relying on pre-installed infrastructure, the proposed approach takes advantage of the knowledge gathered by a population about the localization of the targets. In our approach, robots communicate in an intrinsic way the estimation about how near they are from a target; these estimations are used by neighbour robots for estimating their proximity, and for guiding the navigation of the whole population when looking for these specific areas. We performed several tests in a simulator, and we validated our approach on a population of real robots. For the validation tests we used a mobile robot called marXbot. In both cases (i.e., simulation and implementation on real robots), we found that the proposed approach efficiently guides the robots towards the pre-specified targets while allowing the modulation of their speed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Arkin RC (1992) Cooperation without communication: multiagent schema-based robot navigation. J Robot Syst 9(3): 351–364

    Article  Google Scholar 

  2. Arkin, RC, Bekey, GA (eds) (1997) Robot colonies. Kluwer, Norwell

    MATH  Google Scholar 

  3. Balch T, Arkin RC (1994) Communication in reactive multiagent robotic systems. Auton Robots 1(1): 27–52

    Article  Google Scholar 

  4. Bekey GA (2005) Autonomous robots: from biological inspiration to implementation and control (Intelligent Robotics and Autonomous Agents). The MIT Press, Cambridge

    Google Scholar 

  5. Bencina R, Kaltenbrunner M, Jorda S (2005) Improved topological fiducial tracking in the reactivision system. In: IEEE computer society conference on computer vision and pattern recognition-workshops, 2005. CVPR workshops. IEEE, p 99

  6. Bonani M, Baaboura T, Retornaz P, Vaussard F, Magnenat S, Burnier D, Longchamp V, Mondada F (2009) The marxbot—a modular all-terrain experimentation robot. http://mobots.epfl.ch/marxbot.html

  7. Bonani M, Longchamp V, Magnenat S, Rtornaz 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: 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS 2010). http://mobots.epfl.ch/

  8. Braitenberg V (1984) Vehicles: experiments in synthetic psychology. The MIT Press, Cambridge

    Google Scholar 

  9. Camazine S, Crailsheim K, Hrassnigg N, Robinson GE, Leonhard B, Kropiunigg H (1998) Protein trophallaxis and the regulation of pollen foraging by honey bees (Apis mellifera L.). Apidologie 29(1–2): 113–126

    Article  Google Scholar 

  10. Camazine S, Deneubourg JL, Franks NR, Sneyd J, Theraulaz G, Bonabeau E (2001) Self-organization in biological systems. Princeton University Press, Princeton

    Google Scholar 

  11. Campo A, Gutiérrez A, Nouyan S, Pinciroli C, Longchamp V, Garnier S, Dorigo M (2010) Artificial pheromone for path selection by a foraging swarm of robots. Biol Cybernet 103: 339–352. doi:10.1007/s00422-010-0402-x

    Article  Google Scholar 

  12. Deneubourg JL, Goss S (1989) Collective patterns and decision making. Ethol Ecol Evol 1: 295–311

    Article  Google Scholar 

  13. Floreano D, Mattiussi C (2008) Bio-inspired artificial intelligence: theories, methods, and technologies. The MIT Press, Cambridge

    Google Scholar 

  14. Gadagkar R (1997) Survival strategies: cooperation and conflict in animal societies. Harvard University Press, USA

    Google Scholar 

  15. Ijspeert A, Martinoli A, Billard A, Gambardella LM (2001) Collaboration through the exploitation of local interactions in autonomous collective robotics: the stick pulling experiment. Auton Robots 11(2): 149–171

    Article  MATH  Google Scholar 

  16. Kuniyoshi Y, Kita N, Rougeaux S, Sakane S, Ishii M, Kakikua M (1994) Cooperation by observation: the framework and basic task patterns. In: IEEE international conference on robotics and automation, 1994. Proceedings 1994, vol 1, pp 767–774

  17. Kuniyoshi Y, Rickki J, Ishii M, Rougeaux S, Kita N, Sakane S, Kakikura M (1994) Vision-based behaviors for multi-robot cooperation. In: Proceedings of the IEEE/RSJ/GI international conference on intelligent robots and systems ’94, vol 2. ‘Advanced robotic systems and the real world’, IROS ’94, pp 925–932

  18. Lambrinos D, Roggendorf T, Pfeifer R (2001) Insect strategies of visual homing in mobile robots. In: Biorobotics—methods and applications. AAAI Press, pp 37–66

  19. Magnenat S, Waibel M, Beyeler A (2009) Enki—an open source fast 2d robot simulator. http://home.gna.org/enki/

  20. Magnenat S, Rtornaz P, Bonani M, Longchamp V, Mondada F (2010) ASEBA: a modular architecture for event-based control of complex robots. IEEE/ASME transactions on mechatronics PP(99):1–9. doi:10.1109/TMECH.2010.2042722. http://www.ieee-asme-mechatronics.org/

  21. Nouyan S, Gross R, Dorigo M, Bonani M, Mondada F (2005) Group transport along a robot chain in a self-organised robot colony. In: Proceedings of the 9th international conference on intelligent autonomous systems, IOS. IOS Press, pp 433–442

  22. Satizábal HF, Upegui A, Pérez-Uribe A (2010) Social target localization in a population of foragers. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Studies in computational intelligence, vol 284. NICSO. Springer, Berlin, pp 13–24

    Google Scholar 

  23. Schmickl T, Crailsheim K (2006) Trophallaxis among swarm-robots: a biologically inspired strategy for swarm robotics. In: The first IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics, 2006. BioRob 2006, pp 377–382. doi:10.1109/BIOROB.2006.1639116

  24. Smith JM, Szathmary E (2000) The origins of life: from the birth of life to the origin of language. Oxford University Press, USA

    Google Scholar 

  25. Sugawara K, Kazama T, Watanabe T (2004) Foraging behavior of interacting robots with virtual pheromone. In: Proceedings 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004), vol 3, pp 3074–3079. doi:10.1109/IROS.2004.1389878

  26. Werger B, Mataric MJ (1996) Robotic “food” chains: externalization of state and program for minimal-agent foraging. In: Proceedings of 4th internationl conference simulation of adaptive behavior: from animals to animats, vol 4. The MIT Press, pp 625–634

  27. Winfield A (2009) Towards an engineering science of robot foraging. Distrib Auton Robot Syst 8: 185–192

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. REDS, University of Applied Sciences Western Switzerland, Yverdon-les-Bains, Switzerland

    Héctor F. Satizábal, Andres Upegui & Andres Perez-Uribe

  2. LSRO-MOBOTS, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Philippe Rétornaz & Francesco Mondada

Authors
  1. Héctor F. Satizábal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andres Upegui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andres Perez-Uribe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philippe Rétornaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francesco Mondada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Héctor F. Satizábal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Satizábal, H.F., Upegui, A., Perez-Uribe, A. et al. A social approach for target localization: simulation and implementation in the marXbot robot. Memetic Comp. 3, 245–259 (2011). https://doi.org/10.1007/s12293-011-0061-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-011-0061-z

Keywords

Search

Navigation