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Self-organised path formation in a swarm of robots

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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.

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References

  • Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping: part II. IEEE Robotics & Automation Magazine, 13(3), 108–117.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Burgard, W., Moors, M., Stachniss, C., & Schneider, F.E. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–386.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Detrain, C., & Denebourg, J.-L. (2009). Collective decision and foraging patterns in ants and honeybees. Advances in Insect Physiology, 35, 123–173.

    Article  Google Scholar 

  • Dorigo, M., & Şahin, E. (2004). Swarm robotics—special issue editorial. Autonomous Robots, 17(2–3), 111–113.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Floreano, D., Mitri, S., Magnenat, S., & Keller, L. (2007). Evolutionary conditions for the emergence of communication in robots. Current Biology, 17, 514–519.

    Article  Google Scholar 

  • Floreano, D., Husband, P., & Nolfi, S. (2008). Evolutionary robotics. In B. Siciliano & O. Khatib (Eds.), Handbook of robotics (pp. 1423–1451). Berlin: Springer.

    Chapter  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Goss, S., Aron, S., Deneubourg, J.-L., & Pasteels, J. M. (1989). Self-organized shortcuts in the Argentine ant. Naturwissenchaften, 76, 579–581.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hafner, V. V. (2005). Cognitive maps in rats and robots. Adaptive Behavior, 13, 87–96.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mamei, M., & Zambonelli, F. (2007). Pervasive pheromone-based interaction with RFID tags. ACM Transactions on Autonomous and Adaptive Systems, 2(2), 1–28.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

  • Maynard-Smith, J., & Harper, D. G. (2003). Animal signals. London: Oxford University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. London: Oxford University Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • Payton, D., Daily, M., Estkowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11(3), 319–324.

    Article  MATH  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Rekleitis, I., Dudek, G., & Milios, E. (2001). Multi-robot collaboration for robust exploration. Annals of Mathematics and Artificial Intelligence, 31, 7–40.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

  • Schmickl, T., & Crailsheim, K. (2008). Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonomous Robots, 25, 171–188.

    Article  Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423, 623–656.

    MATH  MathSciNet  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Trianni, V. (2008). Studies in computational intelligence: Vol. 108. Evolutionary swarm robotics. Evolving self-organising behaviours in groups of autonomous robots. Berlin: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Vickerstaff, R. J., & Di Paolo, E. A. (2005). Evolving neural models of path integration. Journal of Experimental Biology, 208, 3349–3366.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, via San Martino della Battaglia 44, 00185, Rome, Italy

    Valerio Sperati, Vito Trianni & Stefano Nolfi

  2. IRIDIA-CoDE, ULB, Avenue F. Roosevelt, n. 50, CP 194/6, 1050, Brussels, Belgium

    Vito Trianni

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  1. Valerio Sperati
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  2. Vito Trianni
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  3. Stefano Nolfi
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Correspondence to Vito Trianni.

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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

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