Swarm Intelligence

, Volume 8, Issue 1, pp 1–33 | Cite as

Cooperative navigation in robotic swarms

  • Frederick Ducatelle
  • Gianni A. Di CaroEmail author
  • Alexander Förster
  • Michael Bonani
  • Marco Dorigo
  • Stéphane Magnenat
  • Francesco Mondada
  • Rehan O’Grady
  • Carlo Pinciroli
  • Philippe Rétornaz
  • Vito Trianni
  • Luca M. Gambardella


We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.


Swarm robotics Cooperative navigation Self-organization 



The research presented in this paper was partially supported by the European Commission via the Future and Emerging Technologies projects SWARMANOID (grant IST-022888), and ASCENS (grant IST-257414), and by the European Research Council via the ERC Advance Grant “E-SWARM: Engineering Swarm Intelligence Systems” (grant 246939).

The work was also supported by the Swiss National Science Foundation through the National Centre of Competence in Research (NCCR) Robotics.

Marco Dorigo and Rehan O’Grady acknowledge support from the Belgian F.R.S.-FNRS, of which they are a research director and a postdoctoral researcher, respectively.

Supplementary material

(MP4 70.3 MB)


  1. Balch, T. (2000). Hierarchic social entropy: an information theoretic measure of robot group diversity. Autonomous Robots, 8(3), 209–238. CrossRefGoogle Scholar
  2. Baldassarre, G. (2008). Self-organization as phase transition in decentralized groups of robots: a study based on Boltzmann entropy. In L. Jain, X. Wu, & M. Prokopenko (Eds.), Advanced information and knowledge processing. Advances in applied self-organizing systems (pp. 127–146). London: Springer. CrossRefGoogle Scholar
  3. Baldassarre, G., Parisi, D., & Nolfi, S. (2004). Measuring coordination as entropy decrease in groups of linked simulated robots. In Proceedings of the fifth international conference on complex systems (ICCS) (pp. 1–14). Boston: The New England Complex Systems Institute. Google Scholar
  4. Batalin, M., & Sukhatme, G. (2004). Coverage, exploration and deployment by a mobile robot and communication network. Telecommunication Systems Journal, 26(2), 181–196. Special Issue on Wireless Sensor Networks. CrossRefGoogle Scholar
  5. Batalin, M., & Sukhatme, G. (2007). The design and analysis of an efficient local algorithm for coverage and exploration based on sensor network deployment. IEEE Transactions on Robotics, 23(4), 661–675. CrossRefGoogle Scholar
  6. Bettstetter, C. (2001). Mobility modeling in wireless networks: categorization, smooth movement, and border effects. ACM SIGMOBILE Mobile Computations and Communications Review, 5(3), 55–66. CrossRefGoogle Scholar
  7. Bettstetter, C., Hartenstein, H., & Pérez-Costa, X. (2004). Stochastic properties of the random waypoint mobility model. Wireless Networks, 10(5), 555–567. CrossRefGoogle Scholar
  8. Blažević, L., Giordano, S., & Le Boudec, J.-Y. (2002). Self organized terminode routing. Cluster Computing, 5(2), 205–218. CrossRefGoogle Scholar
  9. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. New York: Oxford University Press. zbMATHGoogle Scholar
  10. 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
  11. De Wolf, T., & Holvoet, T. (2005). Emergence vs. self-organisation: different concepts but promising when combined. In LNCS: Vol. 3464. Engineering self-organising systems (pp. 77–91). Berlin: Springer. CrossRefGoogle Scholar
  12. Deneubourg, J.-L., Aron, S., Goss, S., & Pasteels, J.-M. (1990). The self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behavior, 3, 159–168. CrossRefGoogle Scholar
  13. Di Caro, G. A., Ducatelle, F., & Gambardella, L. M. (2005). AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications, 16(5), 443–455. CrossRefGoogle Scholar
  14. Dorigo, M., & Sahin, E. (2004). Guest editorial: swarm robotics. Autonomous Robotics, 17(2–3), 111–113. CrossRefGoogle Scholar
  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. A., Ducatelle, F., Ferrante, E., Förster, A., Gonzales, J. M., Guzzi, J., Longchamp, V., Magnenat, S., Mathews, N., de Oca, M. M., 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. (2013). Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine, 20(4). Google Scholar
  16. Dousse, O., Thiran, P., & Hasler, M. (2002). Connectivity in ad-hoc and hybrid networks. In Proceedings of the 21th IEEE conference on computer communications (INFOCOM) (pp. 1079–1088). New York: IEEE Press. Google Scholar
  17. Dubois-Ferriere, H., Grossglauser, M., & Vetterli, M. (2003). Age matters: efficient route discovery in mobile ad hoc networks using encounter ages. In Proceedings of the 4th ACM international symposium on mobile ad hoc networking & computing (MobiHoc) (pp. 257–266). New York: ACM. Google Scholar
  18. Ducatelle, F., Foerster, A., Di Caro, G. A., & Gambardella, L. M. (2009). Supporting navigation in multi-robot systems through delay tolerant network communication. In Proc. of the IFAC workshop on networked robotics (NetRob) (pp. 25–30). Philadelphia: Elsevier. Google Scholar
  19. Ducatelle, F., Di Caro, G. A., Pinciroli, C., & Gambardella, L. M. (2011a). Self-organized cooperation between robotic swarms. Swarm Intelligence, 5(2), 73–96. CrossRefGoogle Scholar
  20. Ducatelle, F., Di Caro, G. A., Pinciroli, C., Mondada, F., & Gambardella, L. M. (2011b). Communication assisted navigation in robotic swarms: self-organization and cooperation. In Proceedings of the 24th IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4981–4988). Washington: IEEE Computer Society. Google Scholar
  21. Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localisation and mapping (SLAM): part I. IEEE Robotics & Automation Magazine, 13(2), 99–110. CrossRefGoogle Scholar
  22. El-Shehawey, M. (2009). On the gambler’s ruin problem for a finite Markov chain. Statistics & Probability Letters, 79(14), 1590–1595. CrossRefzbMATHMathSciNetGoogle Scholar
  23. Fall, K. (2003). A delay-tolerant network architecture for challenged internets. In Proceedings of the ACM conference on applications, technologies, architectures, and protocols for computer communications, SIGCOMM (pp. 27–34). New York: ACM. Google Scholar
  24. Fujisawa, R., Dobata, S., Kubota, D., Imamura, H., & Matsuno, F. (2008). Dependency by concentration of pheromone trail for multiple robots. In LNCS: Vol. 4217. Proceedings of ANTS 2008, 6th international workshop on ant algorithms and swarm intelligence (pp. 283–290). Berlin: Springer. Google Scholar
  25. 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 Proc. of the IEEE swarm intelligence symp. (SIS) (pp. 37–44). Washington: IEEE Computer Society. Google Scholar
  26. Groenevelt, R., Nain, P., & Koole, G. (2005). The message delay in mobile ad hoc networks. Performance Evaluation, 62(1–4), 210–228. CrossRefGoogle Scholar
  27. Grossglauser, M., & Vetterli, M. (2006). Locating mobile nodes with ease: learning efficient routes from encounter histories alone. IEEE/ACM Transactions on Networking, 14(3), 457–469. CrossRefGoogle Scholar
  28. Gutiérrez, A., Campo, A., Dorigo, M., Amor, D., Magdalena, L., & Monasterio-Huelin, F. (2008). An open localization and local communication embodied sensor. Sensors, 8(11), 7545–7563. CrossRefGoogle Scholar
  29. Gutiérrez, A., Campo, A., Monasterio-Huelin, F., Magdalena, L., & Dorigo, M. (2010). Collective decision-making based on social odometry. Neural Computing & Applications, 19(6), 807–823. CrossRefGoogle Scholar
  30. Jacquet, P., Mans, B., & Rodolakis, G. (2010). Information propagation speed in mobile and delay tolerant networks. IEEE Transactions on Information Theory, 56(10), 5001–5015. CrossRefMathSciNetGoogle Scholar
  31. Johnson, D., & Maltz, D. (1996). Dynamic source routing in ad hoc wireless networks. In T. Imielinski & H. Korth (Eds.), Mobile computing (pp. 153–181). Dordrecht: Kluwer Academic. CrossRefGoogle Scholar
  32. Karlsson, G., Almeroth, K., Fall, K., May, M., Yates, R., & Lea, C.-T. (Eds.), (2008). Special issue on Delay and disruption tolerant wireless communication. IEEE Journal on Selected Areas in Communications, 26(5), 745–840. Google Scholar
  33. Klein, D., Hespanha, J., & Madhow, U. (2010). A reaction-diffusion model for epidemic routing in sparsely connected manets. In Proceedings of the 29th IEEE international conference on computer communications (INFOCOM) (pp. 884–892). Piscataway: IEEE Press. Google Scholar
  34. Kuiper, F., & Fisher, L. (1975). A Monte Carlo comparison for six clustering procedures. Biometrics, 31, 777–784. CrossRefzbMATHGoogle Scholar
  35. Li, Q., & Rus, D. (2005). Navigation protocols in sensor networks. Transactions on Sensor Networks, 1(1), 3–35. CrossRefGoogle Scholar
  36. Mayet, R., Roberz, J., Schmickl, T., & Crailsheim, K. (2010). Antbots: a feasible visual emulation of pheromone trails for swarm robots. In Proceedings of the 7th international conference on swarm intelligence (ANTS) (pp. 84–94). Google Scholar
  37. Mirats Tur, J., Zinggerling, C., & Corominas-Murtra, A. (2009). Geographical information systems for map based navigation in urban environments. Robotics and Autonomous Systems, 57(9), 922–930. CrossRefGoogle Scholar
  38. Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., 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, Instituto Politécnico de Castelo Branco (IPCB), Portugal (Vol. 1, pp. 59–65). Google Scholar
  39. Nain, P., Towsley, D., Benyuan, L., & Zhen, L. (2005). Properties of random direction models. In Proceedings of the 24th IEEE conference on computer communications (INFOCOM) (Vol. 3, pp. 1897–1907). Google Scholar
  40. 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
  41. 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
  42. O’Hara, K., & Balch, T. (2004). Pervasive sensor-less networks for cooperative multi-robot tasks. In Proc. of the 7th int. symp. on distributed autonomous robot systems (DARS) (pp. 305–314). Tokyo: Springer. Google Scholar
  43. O’Hara, K., Walker, D., & Balch, T. (2008). Physical path planning using a pervasive embedded network. IEEE Transactions on Robotics, 24(3), 741–746. CrossRefGoogle Scholar
  44. Payton, D., Daily, M., Estowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11(3), 319–324. CrossRefzbMATHGoogle Scholar
  45. Perkins, C., & Bhagwat, P. (1994). Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. In ACM SIGCOMM conference on communications architectures, protocols and applications (pp. 234–244). New York: ACM. CrossRefGoogle Scholar
  46. Perkins, C. E., & Royer, E. M. (1999). Ad-hoc on-demand distance vector routing. In Proc. of the 2nd IEEE workshop on mobile computing systems and applications (WMCSA) (pp. 90–100). Washington: IEEE Computer Society. CrossRefGoogle Scholar
  47. Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., Birattari, M., Gambardella, L. M., & Dorigo, M. (2012). ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intelligence, 6(4), 271–295. CrossRefGoogle Scholar
  48. Prigogine, I., & Stengers, I. (1984). Order out of chaos. New York: Bantam. Google Scholar
  49. Pugh, J., & Martinoli, A. (2006). Relative localization and communication module for small-scale multi-robot systems. In Proc. of the IEEE int. conf. on robotics and automation (ICRA) (pp. 188–193). Washington: IEEE Computer Society. Google Scholar
  50. Roberts, J., Stirling, T., Zufferey, J., & Floreano, D. (2009). 2.5d infrared range and bearing system for collective robotics. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3659–3664). Washington: IEEE Computer Society. Google Scholar
  51. Royer, E. M., & Toh, C.-K. (1999). A review of current routing protocols for ad hoc mobile wireless networks. IEEE Personal Communications, 6(2), 46–55. CrossRefGoogle Scholar
  52. Russell, R. (1999). Ant trails—an example for robots to follow? In Proceedings of IEEE international conf. on robotics and automation (ICRA) (pp. 2698–2703). Washington: IEEE Computer Society. Google Scholar
  53. Schmickl, T., & Crailsheim, K. (2008). Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonomous Robots, 25(1–2), 171–188. CrossRefGoogle Scholar
  54. Sgorbissa, A., & Arkin, R. C. (2003). Local navigation strategies for a team of robots. Robotica, 21, 461–473. CrossRefGoogle Scholar
  55. Shalizi, C., Shalizi, K., & Haslinger, R. (2004). Quantifying self-organization with optimal predictors. Physical Review Letters, 93(11), 118701 (4 pages). CrossRefGoogle Scholar
  56. Shannon, C. (1949). The mathematical theory of communication. Urbana-Champaign: University of Illinois Press. zbMATHGoogle Scholar
  57. Sharpe, T., & Webb, B. (1999). Simulated and situated models of chemical trail following in ants. In Proc. of the 5th int. conf. on the simulation of adaptive behavior (SAB) (pp. 195–204). Cambridge: MIT Press. Google Scholar
  58. Sperati, V., Trianni, V., & Nolfi, S. (2011). Self-organized path formation in a swarm of robots. Swarm Intelligence, 5(2), 97–119. CrossRefGoogle Scholar
  59. Spyropoulos, T., Psounis, K., & Raghavendra, C. (2004). Single-copy routing in intermittently connected mobile networks. In Proceedings of the first annual IEEE communications society conference on sensor and ad hoc communications and networks (SECON) (pp. 235–244). Google Scholar
  60. Spyropoulos, T., Psounis, K., & Raghavendra, C. (2006). Performance analysis of mobility-assisted routing. In Proc. of the 7th ACM int. symp. on mobile ad hoc networking and computing (MobiHoc) (pp. 49–60). New York: ACM. Google Scholar
  61. Spyropoulos, T., Psounis, K., & Raghavendra, C. (2008). Efficient routing in intermittently connected mobile networks: the single-copy case. IEEE/ACM Transactions on Networking, 16(1), 63–76. CrossRefGoogle Scholar
  62. Sugawara, K., Kazama, T., & Watanabe, T. (2004). Foraging behavior of interacting robots with virtual pheromone. In Proc. of the IEEE/RSJ int. conf. on intelligent robots and systems (IROS) (pp. 3074–3079). Washington: IEEE Computer Society. Google Scholar
  63. Vaughan, R., Støy, K., Sukhatme, G., & Matarić, M. (2002). Lost: localization-space trails for robot teams. IEEE Transactions on Robotics and Automation, 18(5), 796–812. CrossRefGoogle Scholar
  64. Werger, B. B., & Matarić, M. J. (1996). Robotic food chains: externalization of state and program for minimal-agent foraging. In Proc. of the 4th int. conf. on the simulation of adaptive behavior (SAB) (pp. 625–634). Cambridge: MIT Press. Google Scholar
  65. 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 at Google Scholar
  66. Wodrich, M., & Bilchev, G. (1997). Cooperative distributed search: the ants’ way. Control and Cybernetics, 26, 413–446. zbMATHMathSciNetGoogle Scholar
  67. Zhang, X., Neglia, G., Kurose, J., & Towsley, D. (2007). Performance modeling of epidemic routing. Computer Networks, 51(10), 2867–2891. CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Frederick Ducatelle
    • 1
  • Gianni A. Di Caro
    • 1
    Email author
  • Alexander Förster
    • 1
  • Michael Bonani
    • 2
  • Marco Dorigo
    • 3
  • Stéphane Magnenat
    • 2
  • Francesco Mondada
    • 2
  • Rehan O’Grady
    • 3
  • Carlo Pinciroli
    • 3
  • Philippe Rétornaz
    • 2
  • Vito Trianni
    • 3
  • Luca M. Gambardella
    • 1
  1. 1.IDSIAUSI/SUPSIManno-LuganoSwitzerland
  2. 2.EPFLME A3 484 (Bâtiment ME)LausanneSwitzerland
  3. 3.IRIDIACoDE, ULBBrusselsBelgium

Personalised recommendations