Abstract
In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to cope with the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. The collective strategy is synthesised through evolutionary robotics techniques, and is based on the emergence of a dynamic structure formed by the robots moving back and forth between the two target areas. Due to this structure, each robot is able to maintain the right heading and to efficiently navigate between the two areas. The evolved behaviour proved to be effective in finding the shortest path, adaptable to new environmental conditions, scalable to larger groups and larger environment size, and robust to individual failures.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping: part II. IEEE Robotics & Automation Magazine, 13(3), 108–117.
Baldassarre, G., & Nolfi, S. (2009). Strengths and synergies of evolved and designed controllers: a study within collective robotics. Journal of Artificial Intelligence, 173, 857–875.
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 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS 2010) (pp. 4187–4193). New York: IEEE Press.
Burgard, W., Moors, M., Stachniss, C., & Schneider, F.E. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–386.
De Greef, J., & Nolfi, S. (2010). Evolution of implicit and explicit communication in a group of mobile robots. In S. Nolfi & M. Mirolli (Eds.), Evolution of communication and language in embodied agents (pp. 179–214). Berlin: Springer.
Detrain, C., & Denebourg, J.-L. (2009). Collective decision and foraging patterns in ants and honeybees. Advances in Insect Physiology, 35, 123–173.
Dorigo, M., & Şahin, E. (2004). Swarm robotics—special issue editorial. Autonomous Robots, 17(2–3), 111–113.
Drogoul, A., & Ferber, J. (1993). From Tom Thumb to the Dockers: Some experiments with foraging robots. In J.-A. Meyer, H. Roitblat, & S. W. Wilson (Eds.), From animals to animats 2. Proceedings of the second international conference on simulation of adaptive behavior (SAB 92) (pp. 451–459). Cambridge: MIT Press.
Ducatelle, F., Di Caro, G. A., Pinciroli, C., & Gambardella, L. M. (2011a). Self-organized cooperation between robotic swarms. Swarm Intelligence, 5(2) (this issue).
Ducatelle, F., Di Caro, G. A., Pinciroli, C., Mondada, F., & Gambardella, L. M. (2011b). Communication assisted navigation in robotic swarms: self-organization and cooperation. Technical report IDISA-04-11, IDISA, Lugano, Switzerland. Submitted to IROS 2011.
Filliat, D., & Meyer, J.-A. (2003). Map-based navigation in mobile robots—I. A review of localization strategies. Journal of Cognitive Systems Research, 4, 243–282.
Floreano, D., Mitri, S., Magnenat, S., & Keller, L. (2007). Evolutionary conditions for the emergence of communication in robots. Current Biology, 17, 514–519.
Floreano, D., Husband, P., & Nolfi, S. (2008). Evolutionary robotics. In B. Siciliano & O. Khatib (Eds.), Handbook of robotics (pp. 1423–1451). Berlin: Springer.
Floreano, D., Mitri, S., & Hubert, J. (2010). A robotic platform for studying the evolution of communication. In S. Nolfi & M. Mirolli (Eds.), Evolution of communication and language in embodied agents (pp. 303–306). Berlin: Springer.
Fujisawa, R., Imamura, H., Hashimoto, T., & Matsuno, F. (2008). Communication using pheromone field for multiple robots. In Proceedings of the 2008 IEEE/RSJ international conference on intelligent robots and systems (IROS 2008) (pp. 1391–1396). New York: IEEE Press.
Garnier, S., Tâche, F., Combe, M., Grimal, A., & Theraulaz, G. (2007). Alice in pheromone land: an experimental setup for the study of ant-like robots. In Proceedings of the 2007 IEEE swarm intelligence symposium (SIS 2007) (pp. 37–44). New York: IEEE Press.
Gigliotta, O., & Nolfi, S. (2008). On the coupling between agent internal and agent/environmental dynamics: Development of spatial representations in evolving autonomous robots. Adaptive Behavior, 16, 148–165.
Goss, S., Aron, S., Deneubourg, J.-L., & Pasteels, J. M. (1989). Self-organized shortcuts in the Argentine ant. Naturwissenchaften, 76, 579–581.
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.
Hafner, V. V. (2005). Cognitive maps in rats and robots. Adaptive Behavior, 13, 87–96.
Hauert, S., Zufferey, J.-C., & Floreano, D. (2009a). Evolved swarming without positioning information: an application in aerial communication relay. Autonomous Robots, 26(1), 21–32.
Hauert, S., Zufferey, J.-C., & Floreano, D. (2009b). Reverse-engineering of artificially evolved controllers for swarms of robots. In Proceedings of the 2009 IEEE congress on evolutionary computation (CEC’09) (pp. 55–61). New York: IEEE Press.
Lambrinos, D., Kobayashi, H., Pfeifer, R., Maris, M., Labhart, T., & Wehner, R. (1997). An autonomous agent navigating with a polarized light compass. Adaptive Behavior, 6(1), 131–161.
Mamei, M., & Zambonelli, F. (2007). Pervasive pheromone-based interaction with RFID tags. ACM Transactions on Autonomous and Adaptive Systems, 2(2), 1–28.
Martinelli, A., Pont, F., & Siegwart, R. (2005). Multi-robot localization using relative observations. In Proceedings of the 2005 IEEE international conference on robotics and automation (ICRA 2005) (pp. 2797–2802). New York: IEEE Press.
Mayet, R., Roberz, J., Schmickl, T., & Crailsheim, K. (2010). Antbots: a feasible visual emulation of pheromone trails for swarm robots. In M. Dorigo, M. Birattari, G. A. Di Caro, R. Doursat, A. P. Engelbrecht, D. Floreano, L. M. Gambardella, R. Groß, E. Şahin, T. Stützle, & H. Sayama (Eds.), Lecture notes in computer science: Vol. 6234. Proceedings of the 7th international conference on swarm intelligence (ANTS 2010) (pp. 84–94). Berlin: Springer.
Maynard-Smith, J., & Harper, D. G. (2003). Animal signals. London: Oxford University Press.
Menzel, R., Greggers, U., Smith, A., Berger, S., Brandt, R., Brunke, S., Bundrock, G., Hülse, S., Plümpe, T., Schaupp, F., Schüttler, E., Stach, S., Stindt, J., Stollhoff, N., & Watzl, S. (2005). Honey bees navigate according to a map-like spatial memory. Proceedings of the National Academy of Sciences of the United States of America, 102(8), 3040–3045.
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 P. J. S. Gonçalves, P. J. D. Torres, & C. M. O. Alves (Eds.), Proceedings of the 9th conference on autonomous robot systems and competitions (Vol. 1, pp. 59–65). IPCB: Instituto Politécnico de Castelo Branco, Portugal.
Nolfi, S., & Floreano, D. (2000). Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. Cambridge: MIT Press/Bradford Books.
Nouyan, S., Campo, A., & Dorigo, M. (2008). Path formation in a robot swarm. Self-organised strategies to find your way home. Swarm Intelligence, 2(1), 1–23.
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.
O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. London: Oxford University Press.
Østergaard, E. H., Sukhatme, G. S., & Matarić, M. J. (2001). Emergent bucket brigading: a simple mechanism for improving performance in multi-robot constrained-space foraging tasks. In Proceedings of the fifth international conference on autonomous agents (pp. 2219–2223). New York: ACM Press.
Payton, D., Daily, M., Estkowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11(3), 319–324.
Pfingsthorn, M., Slamet, B., & Visser, A. (2008). A scalable hybrid multi-robot SLAM method for highly detailed maps. In U. Visser, F. Ribeiro, T. Ohashi, & F. Dellaert (Eds.), Lecture notes in computer science: Vol. 5001. RoboCup 2007: robot soccer world cup XI (pp. 457–464). Berlin: Springer.
Rekleitis, I., Dudek, G., & Milios, E. (2001). Multi-robot collaboration for robust exploration. Annals of Mathematics and Artificial Intelligence, 31, 7–40.
Roberts, J. F., Zufferey, J.-C., & Floreano, D. (2008). Energy management for indoor hovering robots. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2008) (pp. 1242–1247). New York: IEEE Press.
Russell, A., Thiel, D., Deveza, R., & Mackay-Sim, A. (1994). Sensing odour trails for mobile robot navigation. In Proceedings of the 1994 IEEE international conference on robotics and automation (ICRA’94) (pp. 2672–2677). New York: IEEE Press.
Sadat, S. A., & Vaughan, R. T. (2010). SO-LOST an ant-trail algorithm for multi-robot navigation with active interference reduction. In H. Fellermann, M. Dorr, M. Hanczyc, L. Ladegaard Laursen, S. Maurer, D. Merkle, P.-A. Monnard, K. Støy, & S. Rasmussen (Eds.), Artificial life XII: proceedings of the twelfth international conference on the simulation and synthesis of living systems (pp. 687–693). Cambridge: MIT Press.
Schmickl, T., & Crailsheim, K. (2008). Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonomous Robots, 25, 171–188.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423, 623–656.
Sperati, V., Trianni, V., & Nolfi, S. (2010). Evolution of self-organised path formation in a swarm of robots. In M. Dorigo, M. Birattari, G. A. Di Caro, R. Doursat, A. P. Engelbrecht, D. Floreano, L. M. Gambardella, R. Gross, E. Şahin, T. Stützle, & H. Sayama (Eds.), Lecture notes in computer science: Vol. 6234. Proceedings of the 7th international conference on swarm intelligence (ANTS 2010) (pp. 155–166). Berlin: Springer.
Stirling, T., Wischmann, S., & Floreano, D. (2010). Energy-efficient indoor search by swarms of simulated flying robots without global information. Swarm Intelligence, 4, 117–143.
Thrun, S. (2003). Robotic mapping: a survey. In G. Gerhard Lakemeyer & B. Nebel (Eds.), Exploring artificial intelligence in the new millennium (pp. 1–35). San Francisco: Morgan Kaufmann.
Thrun, S., & Liu, Y. (2005). Multi-robot SLAM with sparse extended information filters. In P. Dario & R. Chatila (Eds.), Springer tracts in advanced robotics: Vol. 15. Robotics research. The eleventh international symposium (pp. 254–266). Berlin: Springer.
Trianni, V. (2008). Studies in computational intelligence: Vol. 108. Evolutionary swarm robotics. Evolving self-organising behaviours in groups of autonomous robots. Berlin: Springer.
Trianni, V., & Nolfi, S. (2011). Engineering the evolution of self-organising behaviours in swarm robotics: A case study. Artificial Life, 17(3) (to appear).
Vaughan, R. T., Støy, K., Sukhatme, G. S., & Matarić, M. J. (2002). LOST localization-space trails for robot teams. IEEE Transactions on Robotics and Automation, 18(5), 796–812.
Vickerstaff, R. J., & Di Paolo, E. A. (2005). Evolving neural models of path integration. Journal of Experimental Biology, 208, 3349–3366.
Wehner, R. (2003). Desert ant navigation: how miniature brains solve complex tasks. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural and Behavioral Physiology, 189(8), 579–588.
Werger, B., & Matarić, M. J. (1996). Robotic “food” chains: Externalization of state and program for minimal-agent foraging. In P. Maes, M. J. Matarić, J.-A. Meyer, J. Pollack, & S. W. Wilson (Eds.), From animals to animats 4. Proceedings of the fourth international conference on simulation of adaptive behavior (SAB 96) (pp. 625–634). Cambridge: MIT Press.
Zeil, J., Boeddeker, N., & Stürzl, W. (2009). Visual homing in insects and robots. In D. Floreano, J.-C. Zufferey, M. V. Srinivasan, & C. Ellington (Eds.), Flying insects and robots (pp. 87–100). Berlin: Springer.
Author information
Authors and Affiliations
Corresponding author
Electronic Supplementary Material
Below are the links to the electronic supplementary material.
(MPEG 27.1 MB)
(MPEG 27.4 MB)
(MPEG 59.8 MB)
(MPEG 12.3 MB)
Rights and permissions
About this article
Cite this article
Sperati, V., Trianni, V. & Nolfi, S. Self-organised path formation in a swarm of robots. Swarm Intell 5, 97–119 (2011). https://doi.org/10.1007/s11721-011-0055-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11721-011-0055-y