Overcoming Limited Onboard Sensing in Swarm Robotics Through Local Communication

  • Tiago RodriguesEmail author
  • Miguel Duarte
  • Margarida Figueiró
  • Vasco Costa
  • Sancho Moura Oliveira
  • Anders Lyhne Christensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9420)


In swarm robotics systems, the constituent robots are typically equipped with simple onboard sensors of limited quality and range. In this paper, we propose to use local communication to enable sharing of sensory information between neighboring robots to overcome the limitations of onboard sensors. Shared information is used to compute readings for virtual, collective sensors that, to a control program, are indistinguishable from a robot’s onboard sensors. We evaluate two implementations of collective sensors: one that relies on sharing of immediate sensory information within a local frame of reference, and another that relies on sharing of accumulated sensory information within a global frame of reference. We compare performance of swarms using collective sensors with: (i) swarms in which robots only use their onboard sensors, and (ii) swarms in which the robots have idealized sensors. Our experimental results show that collective sensors significantly improve the swarm’s performance by effectively extending the capabilities of the individual robots.


Multirobot systems Evolutionary robotics Situated communication Local collective sensing Predator-prey task Foraging 



This work was supported by Fundação para a Ciência e a Tecnologia (FCT) under the grants, SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013 and UID/EEA/50008/2013.


  1. 1.
    Balch, T., Arkin, R.C.: Communication in reactive multiagent robotic systems. Auton. Robots 1(1), 27–52 (1994)CrossRefGoogle Scholar
  2. 2.
    Bassler, B.L.: How bacteria talk to each other: regulation of gene expression by quorum sensing. Curr. Opin. Microbiol. 2(6), 582–587 (1999)CrossRefGoogle Scholar
  3. 3.
    Beckers, R., Holland, O.E., Deneubourg, J.L.: From local actions to global tasks: stigmergy and collective robotics. In: Proceedings of the International Workshop on the Synthesis and Simulation of Living Systems (ALIFE), pp. 181–189. MIT Press, Cambridge (1994)Google Scholar
  4. 4.
    Beer, R.D., Gallagher, J.C.: Evolving dynamical neural networks for adaptive behavior. Adapt. Behav. 1(1), 91–122 (1992)CrossRefGoogle Scholar
  5. 5.
    Chaimowicz, L., Campos, M.F.M., Kumar, R.V.: Dynamic role assignment for cooperative robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), vol. 1, pp. 293–298. IEEE Press, Piscataway (2002)Google Scholar
  6. 6.
    Chandrasekaran, S., Hougen, D.F.: Swarm intelligence for cooperation of bio-nano robots using quorum sensing. In: Proceedings of the Bio Micro and Nanosystems Conference (BMN), p. 104. IEEE Press, Piscataway (2006)Google Scholar
  7. 7.
    Christensen, A.L., O’Grady, R., Dorigo, M.: SWARMORPH-script: a language for arbitrary morphology generation in self-assembling robots. Swarm Intell. 2(2–4), 143–165 (2008)CrossRefGoogle Scholar
  8. 8.
    Christensen, A.L., O’Grady, R., Dorigo, M.: From fireflies to fault tolerant swarms of robots. IEEE Trans. Evol. Comput. 13(4), 754–766 (2009)CrossRefGoogle Scholar
  9. 9.
    Christensen, A.L., Oliveira, S., Postolache, O., de Oliveira, M.J., Sargento, S., Santana, P., Nunes, L., Velez, F., Sebastiao, P., Costa, V., et al.: Design of communication and control for swarms of aquatic surface drones. In: 7th International Conference on Agents and Artificial Intelligence (ICAART). SciTePress, Lisbon (2015)Google Scholar
  10. 10.
    Cianci, C.M., Raemy, X., Pugh, J., Martinoli, A.: Communication in a swarm of miniature robots: the e-puck as an educational tool for swarm robotics. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SAB 2006 Ws 2007. LNCS, vol. 4433, pp. 103–115. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  11. 11.
    Correll, N., Martinoli, A.: Collective inspection of regular structures using a swarm of miniature robots. In: Ang Jr., M.H., Khatib, O. (eds.) Experimental Robotics IX. STAR, vol. 21, pp. 375–386. Springer, Berlin (2006)CrossRefGoogle Scholar
  12. 12.
    Dias, M.B., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: a survey and analysis. Proc. IEEE 94(7), 1257–1270 (2006)CrossRefGoogle Scholar
  13. 13.
    Dias, M.B., Ghanem, B., Stentz, A.: Improving cost estimation in market-based coordination of a distributed sensing task. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3972–3977. IEEE Press, Piscataway (2005)Google Scholar
  14. 14.
    Dietl, M., Gutmann, J.S., Nebel, B.: Cooperative sensing in dynamic environments. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1706–1713. IEEE Press, Piscataway (2001)Google Scholar
  15. 15.
    Dorigo, M., Floreano, D., Gambardella, L.M., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., Bonani, M., Brambilla, M., Brutschy, A., Burnier, D., Campo, A., Christensen, A.L., Decugnière, A., Di Caro, G., Ducatelle, F., Ferrante, E., Förster, A., Guzzi, J., Longchamp, V., Magnenat, S., Martinez Gonzales, J., Mathews, N., O’Grady, R., Pinciroli, C., Pini, G., Rétornaz, P., Roberts, J., Sperati, V., Stirling, T., Stranieri, A., Stützle, T., Trianni, V., Tuci, E., Turgut, A.E., Vaussard, F., Montes de Oca, M.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20(4), 60–71 (2013)CrossRefGoogle Scholar
  16. 16.
    Dorigo, M., Trianni, V., Şahin, E., Groß, R., Labella, T., Baldassarre, G., Nolfi, S., Deneubourg, J., Mondada, F., Floreano, D., Gambardella, L.M.: Evolving self-organizing behaviors for a swarm-bot. Auton. Robots 17(2), 223–245 (2004)CrossRefGoogle Scholar
  17. 17.
    Duarte, M., Christensen, A.L., Oliveira, S.: Towards artificial evolution of complex behaviors observed in insect colonies. In: Antunes, L., Pinto, H.S. (eds.) EPIA 2011. LNCS, vol. 7026, pp. 153–167. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  18. 18.
    Duarte, M., Silva, F., Rodrigues, T., Oliveira, S.M., Christensen, A.L.: JBotEvolver: a versatile simulation platform for evolutionary robotics. In: Proceedings of the International Conference on the Synthesis & Simulation of Living Systems (ALIFE), pp. 210–211. MIT Press, Cambridge (2014)Google Scholar
  19. 19.
    Einolghozati, A., Sardari, M., Fekri, F.: Collective sensing-capacity of bacteria populations. In: Proceedings of the IEEE International Symposium on Information Theory (ISIT), pp. 2959–2963. IEEE Press, Piscataway (2012)Google Scholar
  20. 20.
    Floreano, D., Keller, L.: Evolution of adaptive behaviour in robots by means of Darwinian selection. PLoS Biol. 8(1), e1000292 (2010)CrossRefGoogle Scholar
  21. 21.
    Floreano, D., Mitri, S., Magnenat, S., Keller, L.: Evolutionary conditions for the emergence of communication in robots. Curr. Biol. 17(6), 514–519 (2007)CrossRefGoogle Scholar
  22. 22.
    Fredslund, J., Matarić, M.J.: A general algorithm for robot formations using local sensing and minimal communication. IEEE Trans. Robot. Autom. 18(5), 837–846 (2002)CrossRefGoogle Scholar
  23. 23.
    Ayorkor Korsah, G., Dias, M.B., Stentz, A.: A comprehensive taxonomy for multi-robot task allocation. Int. J. Robot. Res. 32(12), 1495–1512 (2013)CrossRefGoogle Scholar
  24. 24.
    Garnier, S., Jost, C., Gautrais, J., Asadpour, M., Caprari, G., Jeanson, R., Grimal, A., Theraulaz, G.: The embodiment of cockroach aggregation behavior in a group of micro-robots. Artif. Life 14(4), 387–408 (2008)CrossRefGoogle Scholar
  25. 25.
    Gerkey, B.P., Matarić, M.J.: Pusher-watcher: an approach to fault-tolerant tightly-coupled robot coordination. In: Proceedings of IEEE International Conference on Robotics and Automation, (ICRA), pp. 464–469. IEEE Press, Piscataway (2002)Google Scholar
  26. 26.
    Gerkey, B.P., Matarić, M.J.: Sold!: Auction methods for multirobot coordination. IEEE Trans. Robot. Autom. 18(5), 758–768 (2002)CrossRefGoogle Scholar
  27. 27.
    Gutiérrez, A., Campo, A., Dorigo, M., Amor, D., Magdalena, L., Monasterio-Huelin, F.: An open localization and local communication embodied sensor. Sensors 8(11), 7545–7563 (2008)CrossRefGoogle Scholar
  28. 28.
    Hoff, N.R., Sagoff, A., Wood, R.J., Nagpal, R.: Two foraging algorithms for robot swarms using only local communication. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 123–130. IEEE Press, Piscataway (2010)Google Scholar
  29. 29.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Fluids Eng. 82(1), 35–45 (1960)Google Scholar
  30. 30.
    Karol, A., Williams, M.-A.: Distributed sensor fusion for object tracking. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 504–511. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  31. 31.
    Mamei, M., Zambonelli, F.: Physical deployment of digital pheromones through RFID technology. In: Proceedings of the IEEE Swarm Intelligence Symposium (SIS), pp. 281–288. IEEE Press, Piscataway (2005)Google Scholar
  32. 32.
    Mathews, N., Valentini, G., Christensen, A.L., O’Grady, R., Brutschy, A., Dorigo, M.: Spatially targeted communication in decentralized multirobot systems. Auton. Robots 38(4), 439–457 (2015)CrossRefGoogle Scholar
  33. 33.
    Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.C., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: Proceedings of the IEEE International Conference on Autonomous Robot Systems and Competitions (ROBOTICA), pp. 59–65. IPCB: Instituto Politécnico de Castelo Branco (2009)Google Scholar
  34. 34.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, And Technology of Self-organizing Machines. MIT Press, Cambridge (2000) Google Scholar
  35. 35.
    Pagello, E., D’Angelo, A., Menegatti, E.: Cooperation issues and distributed sensing for multirobot systems. Proc. IEEE 94(7), 1370–1383 (2006)CrossRefGoogle Scholar
  36. 36.
    Parker, L.E.: Alliance: an architecture for fault tolerant multirobot cooperation. IEEE Trans. Robot. Autom. 14(2), 220–240 (1998)CrossRefGoogle Scholar
  37. 37.
    Payton, D.W., Daily, M.J., Hoff, B., Howard, M.D., Lee, C.L.: Pheromone robotics. Auton. Robots 11(3), 319–324 (2001)zbMATHCrossRefGoogle Scholar
  38. 38.
    Roth, M., Vail, D., Veloso, M.: A real-time world model for multi-robot teams with high-latency communication. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2494–2499. IEEE Press, Piscataway (2003)Google Scholar
  39. 39.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  40. 40.
    Schmickl, T., Crailsheim, K.: Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Auton. Robots 25(1–2), 171–188 (2008)CrossRefGoogle Scholar
  41. 41.
    Stamatis, P.N., Zaharakis, I.D., Kameas, A.D.: A study of bio-inspired communication scheme in swarm robotics. In: Demazeau, Y., Pavón, J., Corchado, J.M., Bajo, J. (eds.) 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAMS). AISC, vol. 55, pp. 383–391. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  42. 42.
    Stone, P., Veloso, M.: Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork. Artif. Intell. 110, 241–273 (1999)zbMATHCrossRefGoogle Scholar
  43. 43.
    Støy, K.: Using situated communication in distributed autonomous mobile robotics. In: Proceedings of the Scandinavian Conference on Artificial Intelligence (SCAI), pp. 44–52. IOS Press, Amsterdam (2001)Google Scholar
  44. 44.
    Stroupe, A.W., Martin, M.C., Balch, T.: Distributed sensor fusion for object position estimation by multi-robot systems. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, pp. 1092–1098. IEEE Press, Piscataway (2001)Google Scholar
  45. 45.
    Svennebring, J., Koenig, S.: Building terrain-covering ant robots: a feasibility study. Auton. Robots 16(3), 313–332 (2004)CrossRefGoogle Scholar
  46. 46.
    Trianni, V., Groß, R., Labella, T.H., Şahin, E., Dorigo, M.: Evolving aggregation behaviors in a swarm of robots. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 865–874. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  47. 47.
    Turgut, A.E., Çelikkanat, H., Gökçe, F., Şahin, E.: Self-organized flocking with a mobile robot swarm. In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 39–46. IFAAMAS, Richland (2008)Google Scholar
  48. 48.
    Zlot, R., Stentz, A., Dias, M.B., Thayer, S.: Multi-robot exploration controlled by a market economy. In: Proceedings 2002 IEEE International Conference on Robotics and Automation, (ICRA), vol. 3, pp. 3016–3023. IEEE Press, Piscataway (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Tiago Rodrigues
    • 1
    • 2
    • 3
    Email author
  • Miguel Duarte
    • 1
    • 2
    • 3
  • Margarida Figueiró
    • 1
    • 2
    • 3
  • Vasco Costa
    • 1
    • 2
    • 3
  • Sancho Moura Oliveira
    • 1
    • 2
    • 3
  • Anders Lyhne Christensen
    • 1
    • 2
    • 3
  1. 1.Bio-inspired Computation and Intelligent Machines LabLisbonPortugal
  2. 2.Instituto de TelecomunicaçõesLisbonPortugal
  3. 3.Instituto Universitário de Lisboa (ISCTE-IUL)LisbonPortugal

Personalised recommendations