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Swarm robotics: a review from the swarm engineering perspective

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Abstract

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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References

  • Abbott, R. (2006). Emergence explained. Complexity, 12(1), 13–26.

    MathSciNet  Google Scholar 

  • Agassounon, W., & Martinoli, A. (2002). Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems. In Proceedings of the first international joint conference on autonomous agents and multi-agent systems (pp. 1090–1097). Richland: IFAAMAS.

    Google Scholar 

  • Amé, J., Halloy, J., Rivault, C., Detrain, C., & Deneubourg, J. L. (2006). Collegial decision making based on social amplification leads to optimal group formation. Proceedings of the National Academy of Sciences, 103(15), 5835–5840.

    Google Scholar 

  • Ampatzis, C. (2008). On the evolution of autonomous time-based decision-making and communication in collective robotics. PhD thesis, IRIDIA, Université Libre de Bruxelles, Belgium.

  • Ampatzis, C., Tuci, E., Trianni, V., & Dorigo, M. (2008). Evolution of signaling in a multi-robot system: categorization and communication. Adaptive Behavior, 16(1), 5–26.

    Google Scholar 

  • Ampatzis, C., Tuci, E., Trianni, V., Christensen, A. L., & Dorigo, M. (2009). Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots. Artificial Life, 15, 465–484.

    Google Scholar 

  • Anderson, C., Theraulaz, G., & Deneubourg, J.-L. (2002). Self-assemblages in insect societies. Insectes Sociaux, 49(2), 99–110.

    Google Scholar 

  • Bachrach, J., Beal, J., & McLurkin, J. (2010). Composable continuous-space programs for robotic swarms. Neural Computing & Applications, 19(6), 825–847.

    Google Scholar 

  • Bahçeci, E., & Şahin, E. (2005). Evolving aggregation behaviors for swarm robotic systems: a systematic case study. In Proceedings of the 2005 swarm intelligence symposium, SIS 2005 (pp. 333–340). Piscataway: IEEE Press.

    Google Scholar 

  • Bahçeci, E., Soysal, O., & Şahin, E. (2003). A review: pattern formation and adaptation in multi-robot systems (Technical Report CMU-RI-TR-03-43). Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.

  • Balch, T., & Hybinette, M. (2000). Social potentials for scalable multi-robot formations. In Proceedings of the 2000 IEEE international conference on robotics and automation, ICRA 2000 (pp. 73–80). Piscataway: IEEE Press.

    Google Scholar 

  • Baldassarre, G. (2006). Evolution of collective behaviour: coordination object retrieval in groups of physically-linked simulated robots. URL http://laral.istc.cnr.it/baldassarre/demos/2003swarmobject/swarmobject.htm. Last checked on November 2012.

  • Baldassarre, G., Nolfi, S., & Parisi, D. (2003). Evolving mobile robots able to display collective behaviors. Artificial Life, 9(3), 255–267.

    Google Scholar 

  • Baldassarre, G., Parisi, D., & Nolfi, S. (2006). Distributed coordination of simulated robots based on self-organization. Artificial Life, 12(3), 289–311.

    Google Scholar 

  • Baldassarre, G., Trianni, V., Bonani, M., Mondada, F., Dorigo, M., & Nolfi, S. (2007). Self-organized coordinated motion in groups of physically connected robots. IEEE Transactions on Systems, Man, and Cybernetics. Part B, 37(1), 224–239.

    Google Scholar 

  • Bayindir, L., & Şahin, E. (2007). A review of studies in swarm robotics. Turkish Journal of Electrical Engineering, 15(2), 115–147.

    Google Scholar 

  • Beal, J. (2004). Programming an amorphous computational medium. In Lecture notes in computer science: Vol. 3566. Proceedings of the international workshop on unconventional programming paradigms (UPP) (p. 97). Berlin: Springer.

    Google Scholar 

  • Beckers, R., Holland, O., & Deneubourg, J.-L. (1994). From local actions to global tasks: stigmergy and collective robotics. In Artificial life IV (pp. 181–189). Cambridge: MIT Press.

    Google Scholar 

  • Beer, R. D., & Gallagher, J. C. (1992). Evolving dynamic neural networks for adaptive behavior. Adaptive Behavior, 1(1), 91–122.

    Google Scholar 

  • Beni, G. (2005). From swarm intelligence to swarm robotics. In Lecture notes in computer science: Vol. 3342. Swarm robotics (pp. 1–9). Berlin: Springer.

    Google Scholar 

  • Berman, S., Halász, Á. M., Hsieh, M. A., & Kumar, V. (2009). Optimized stochastic policies for task allocation in swarms of robots. IEEE Transactions on Robotics, 25(4), 927–937.

    Google Scholar 

  • Berman, S., Lindsey, Q., Sakar, M., Kumar, V., & Pratt, S. (2011a). Experimental study and modeling of group retrieval in ants as an approach to collective transport in swarm robotic systems. Proceedings of the IEEE, 99(9), 1470–1481.

    Google Scholar 

  • Berman, S., Nagpal, R., & Halasz, A. (2011b). Optimization of stochastic strategies for spatially inhomogeneous robot swarms: a case study in commercial pollination. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3923–3930).

    Google Scholar 

  • Bonabeau, E., Sobkowski, A., Theraulaz, G., & Deneubourg, J.-L. (1997). Adaptive task allocation inspired by a model of division of labor in social insects. In Biocomputing and emergent computation: proceedings of BCEC97, London, UK (pp. 36–45). Singapore: World Scientific.

    Google Scholar 

  • Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. New York: Oxford University Press.

    MATH  Google Scholar 

  • Brambilla, M., Pinciroli, C., Birattari, M., & Dorigo, M. (2009). A reliable distributed algorithm for group size estimation with minimal communication requirements. In Fourteenth international conference on advanced robotics—ICAR 2009 (p. 6). Proceedings on CD-ROM, paper ID 137.

    Google Scholar 

  • Brambilla, M., Pinciroli, C., Birattari, M., & Dorigo, M. (2012). Property-driven design for swarm robotics. In Proceedings of 11th international conference on autonomous agents and multiagent systems (AAMAS 2012) (pp. 139–146). Richland: IFAAMAS.

    Google Scholar 

  • Breder, C. M. Jr. (1954). Equations descriptive of fish schools and other animal aggregations. Ecology, 35(3), 361–370.

    Google Scholar 

  • Brooks, R. (1990). Elephants don’t play chess. Robotics and Autonomous Systems, 6(1–2), 3–15.

    Google Scholar 

  • Brooks, R. A. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1), 14–23.

    Google Scholar 

  • Brutschy, A., Pini, G., & Decugnière, A. (2012). Grippable objects for the foot-bot (Technical Report TR/IRIDIA/2012-001). IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.

  • Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organization in biological systems. Princeton studies in complexity. Princeton: Princeton University Press.

    Google Scholar 

  • Campo, A., & Dorigo, M. (2007). Efficient multi-foraging in swarm robotics. In Lecture notes in artificial intelligence: Vol. 4648. Advances in artificial life, proceedings of ECAL 2007 (pp. 696–705). Berlin: Springer.

    Google Scholar 

  • Campo, A., Nouyan, S., Birattari, M., Groß, R., & Dorigo, M. (2006). Enhancing cooperative transport using negotiation of goal direction. In Lecture notes in computer science: Vol. 4150. Proceedings of the fifth international workshop on ant colony optimization and swarm intelligence (ANTS 2006) (pp. 365–366). Berlin: Springer.

    Google Scholar 

  • Campo, A., Garnier, S., Dédriche, O., Zekkri, M., & Dorigo, M. (2011). Self-organized discrimination of resources. PLoS ONE, 6(5), 05.

    Google Scholar 

  • Cao, Y. U., Fukunaga, A. S., Kahng, A. B., & Meng, F. (1997). Cooperative mobile robotics: antecedents and directions. Autonomous Robots, 4(1), 7–27.

    Google Scholar 

  • Çelikkanat, H., & Şahin, E. (2010). Steering self-organized robot flocks through externally guided individuals. Neural Computing & Applications, 19(6), 849–865.

    Google Scholar 

  • Christensen, A. L., O’Grady, R., & Dorigo, M. (2008). SWARMORPH-script: a language for arbitrary morphology generation in self-assembling robots. Swarm Intelligence, 2(2–4), 143–165.

    Google Scholar 

  • Christensen, A. L., O’Grady, R., & Dorigo, M. (2009). From fireflies to fault-tolerant swarms of robots. IEEE Transactions on Evolutionary Computation, 13(4), 754–766.

    Google Scholar 

  • Correll, N. (2008). Parameter estimation and optimal control of swarm-robotic systems: a case study in distributed task allocation. In IEEE international conference on robotics and automation (ICRA) (pp. 3302–3307).

    Google Scholar 

  • Correll, N., & Martinoli, A. (2007). Modeling self-organized aggregation in a swarm of miniature robots. In IEEE international conference on robotics and automation.

    Google Scholar 

  • Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move. Nature, 433(7025), 513–516.

    Google Scholar 

  • Crespi, V., Galstyan, A., & Lerman, K. (2008). Top-down vs bottom-up methodologies in multi-agent system design. Autonomous Robots, 24(3), 303–313.

    Google Scholar 

  • Dantu, K., Berman, S., Kate, B., & Nagpal, R. (2012). A comparison of deterministic and stochastic approaches for allocating spatially dependent tasks in micro-aerial vehicle collectives. In IEEE/RSJ international conference on intelligent robots and systems.

    Google Scholar 

  • 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(2), 159–168.

    Google Scholar 

  • Di Caro, G. A., Ducatelle, F., & Gambardella, L. M. (2009). Wireless communications for distributed navigation in robot swarms. In Lecture notes in computer science: Vol. 5484. Applications of evolutionary computing (pp. 21–30). Berlin: Springer.

    Google Scholar 

  • Dixon, C., Winfield, A., & Fisher, M. (2011). Towards temporal verification of emergent behaviours in swarm robotic systems. In Lecture notes in computer science: Vol. 6856. Towards autonomous robotic systems (pp. 336–347). Berlin: Springer.

    Google Scholar 

  • Donald, B. R., Jennings, J., & Rus, D. (1997). Information invariants for distributed manipulation. The International Journal of Robotics Research, 16(5), 673–702.

    Google Scholar 

  • Dorigo, M., & Birattari, M. (2007). Swarm intelligence. Scholarpedia, 2(9), 1462.

    Google Scholar 

  • Dorigo, M., & Şahin, E. (2004). Guest editorial. Autonomous Robots, 17, 111–113.

    Google Scholar 

  • Dorigo, M., Tuci, E., Trianni, V., Groß, R., Nouyan, S., Ampatzis, C., Labella, T. H., O’Grady, R., Bonani, M., & Mondada, F. (2006). SWARM-BOT: design and implementation of colonies of self-assembling robots. In Computational intelligence: principles and practice (pp. 103–135). New York: IEEE Computational Intelligence Society. Chap. 6.

    Google Scholar 

  • Dorigo, M., Floreano, D., Gambardella, L., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., Bonani, M., Brambilla, M., Brutschy, A., Burnier, D., Campo, A., Christensen, A., Decugnière, A., Di Caro, G., Ducatelle, F., Ferrante, E., Forster, A., Martinez Gonzales, J., Guzzi, J., Longchamp, V., Magnenat, S., Mathews, N., Montes de Oca, M., O’Grady, R., Pinciroli, C., Pini, G., Retornaz, P., Roberts, J., Sperati, V., Stirling, T., Stranieri, A., Stutzle, T., Trianni, V., Tuci, E., Turgut, A., & Vaussard, F. (2012). Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine, in press.

  • Ducatelle, F., Di Caro, G. A., Pinciroli, C., Mondada, F., & Gambardella, L. M. (2011a). Communication assisted navigation in robotic swarms: self-organization and cooperation. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2011) (pp. 4981–4988). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Ducatelle, F., Di Caro, C. P. G. A., & Gambardella, L. M. (2011b). Self-organized cooperation between robotic swarms. Swarm Intelligence, 5(2), 73–96.

    Google Scholar 

  • Dudek, G., Jenkin, M., Milios, E., & Wilkes, D. (1993). A taxonomy for swarm robots. In Proceedings of the 1993 IEEE/RSJ international conference on intelligent robots and systems, IROS 93 (pp. 441–447). Piscataway: IEEE Press.

    Google Scholar 

  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.

    Google Scholar 

  • Ferrante, E., Turgut, A. E., Mathews, N., Birattari, M., & Dorigo, M. (2010). Flocking in stationary and non-stationary environments: a novel communication strategy for heading alignment. In Lecture notes in computer science: Vol. 6239. Parallel problem solving from nature—PPSN XI: 11th international conference (pp. 331–340). Berlin: Springer.

    Google Scholar 

  • Ferrante, E., Turgut, A. E., Huepe, C., Stranieri, A., Pinciroli, C., & Dorigo, M. (2012). Self-organized flocking with a mobile robot swarm: a novel motion control method. Adaptive Behavior.

  • Ferrante, E., Brambilla, M., Birattari, M., & Dorigo, M. (2013). Socially-mediated negotiation for obstacle avoidance in collective transport. In Springer tracts in advanced robotics: Vol. 83. Proceedings of the international symposium on distributed autonomous robotics systems (DARS 2010) (pp. 571–583). Berlin: Springer.

    Google Scholar 

  • Fine, T. L. (1999). Feedforward neural network methodology. Berlin: Springer.

    MATH  Google Scholar 

  • Flocchini, P., Prencipe, G., Santoro, N., & Widmayer, P. (2008). Arbitrary pattern formation by asynchronous, anonymous, oblivious robots. Theoretical Computer Science, 407(1–3), 412–447.

    MathSciNet  MATH  Google Scholar 

  • Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., & Birattari, M. (2012). Analysing an evolved robotic behaviour using a biological model of collegial decision making. In Lecture notes in computer science: Vol. 7426. Proceedings of the 12th international conference on adaptive behavior (SAB2012) (pp. 381–390). Berlin: Springer.

    Google Scholar 

  • Franks, N., & Sendova-Franks, A. (1992). Brood sorting by ants: distributing the workload over the work-surface. Behavioral Ecology and Sociobiology, 30, 109–123.

    Google Scholar 

  • Friedmann, M. (2010). Simulation of autonomous robot teams with adaptable level of abstraction. Ph.D. thesis, University of Darmstadt, Germany.

  • Frigg, R., & Hartmann, S. (2012). Models in science. In The Stanford encyclopedia of philosophy. Stanford: Stanford University. Spring 2012 edition.

    Google Scholar 

  • Galstyan, A., Hogg, T., & Lerman, K. (2005). Modeling and mathematical analysis of swarms of microscopic robots. In Proceedings of the 2005 swarm intelligence symposium—(SIS 2005) (pp. 201–208). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Garnier, S., Jost, C., Jeanson, R., Gautrais, J., Asadpour, M., Caprari, G., & Theraulaz, G. (2005). Aggregation behaviour as a source of collective decision in a group of cockroach-like robots. In Lecture notes in artificial intelligence: Vol. 3630. Advances in artificial life (pp. 169–178). Berlin: Springer.

    Google Scholar 

  • Garnier, S., Gautrais, J., Asadpour, M., Jost, C., & Theraulaz, G. (2009). Self-organized aggregation triggers collective decision making in a group of cockroach-like robots. Adaptive Behavior, 17(2), 109–133.

    Google Scholar 

  • Gazi, V., & Fidan, B. (2007). Coordination and control of multi-agent dynamic systems: models and approaches. In Lecture notes in computer science: Vol. 4433. Swarm robotics (pp. 71–102). Berlin: Springer.

    Google Scholar 

  • Gazi, V., & Passino, K. M. (2002). Stability analysis of social foraging swarms: combined effects of attractant/repellent profiles. In Proceedings of the 41st IEEE conference on decision and control (Vol. 3, pp. 2848–2853). Piscataway: IEEE Press.

    Google Scholar 

  • Gazi, V., & Passino, K. M. (2003). Stability analysis of swarms. IEEE Transactions on Automatic Control, 48(4), 692–696.

    MathSciNet  Google Scholar 

  • Gazi, V., & Passino, K. M. (2004a). A class of attractions/repulsion functions for stable swarm aggregations. International Journal of Control, 77(18), 1567–1579.

    MathSciNet  MATH  Google Scholar 

  • Gazi, V., & Passino, K. M. (2004b). Stability analysis of social foraging swarms. IEEE Transactions on Systems, Man, and Cybernetics. Part B, 34(1), 539–557.

    Google Scholar 

  • Gazi, V., & Passino, K. M. (2005). Stability of a one-dimensional discrete-time asynchronous swarm. IEEE Transactions on Systems, Man, and Cybernetics. Part B, 35(4), 834–841.

    Google Scholar 

  • Getling, A. V. (1998). Rayleigh–Bénard convection: structures and dynamics (Vol. 11). London: World Scientific.

    MATH  Google Scholar 

  • Giusti, A., Nagi, J., Gambardella, L., & Caro, G. D. (2012). Distributed consensus for interaction between humans and mobile robot swarms. In Proceedings of 11th international conference on autonomous agents and multiagent systems (AAMAS 2012), Richland, SC.

    Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley.

    MATH  Google Scholar 

  • Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.

    Google Scholar 

  • Grassé, P.-P. (1959). La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insectes Sociaux, 6, 41–80.

    Google Scholar 

  • Groß, R., & Dorigo, M. (2008a). Evolution of solitary and group transport behaviors for autonomous robots capable of self-assembling. Adaptive Behavior, 16(5), 285–305.

    Google Scholar 

  • Groß, R., & Dorigo, M. (2008b). Self-assembly at the macroscopic scale. Proceedings of the IEEE, 96(9), 1490–1508.

    Google Scholar 

  • Groß, R., & Dorigo, M. (2009). Towards group transport by swarms of robots. International Journal of Bio-Inspired Computation, 1(1–2), 1–13.

    Google Scholar 

  • Grünbaum, D., & Okubo, A. (1994). Modeling social animal aggregations. Frontiers in Theoretical Biology, 100, 296–325.

    Google Scholar 

  • Gutiérrez, Á., Campo, A., Monasterio-Huelin, F., Magdalena, L., & Dorigo, M. (2010). Collective decision-making based on social odometry. Neural Computing & Applications, 19(6), 807–823.

    Google Scholar 

  • Halász, A., Liang, Y., Hsieh, M., & Lai, H.-J. (2012). Emergence of specialization in a swarm of robots. In Springer tracts in advanced robotics: Vol. 83. Distributed autonomous robotic systems (pp. 403–416). Berlin: Springer.

    Google Scholar 

  • Hamann, H. (2012). Towards swarm calculus: universal properties of swarm performance and collective decisions. In Lecture notes in computer science: Vol. 7461. Swarm intelligence: 8th international conference, ANTS 2012 (pp. 168–179). Berlin: Springer.

    Google Scholar 

  • Hamann, H., & Wörn, H. (2008). A framework of space-time continuous models for algorithm design in swarm robotics. Swarm Intelligence, 2(2–4), 209–239.

    Google Scholar 

  • Hettiarachchi, S. D. (2007). Distributed evolution for swarm robotics. PhD thesis, University of Wyoming, Laramie, WY.

  • Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Cambridge: MIT Press.

    Google Scholar 

  • Howard, A., Matarić, M. J., & Sukhatme, G. S. (2002). Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In Proceedings of the 2002 international symposium on distributed autonomous robotic systems (DARS 2002) (pp. 299–308). Berlin: Springer.

    Google Scholar 

  • Hsieh, M. A., Halász, Á., Berman, S., & Kumar, V. (2008). Biologically inspired redistribution of a swarm of robots among multiple sites. Swarm Intelligence, 2(2–4), 121–141.

    Google Scholar 

  • Iocchi, L., Nardi, D., & Salerno, M. (2001). Reactivity and deliberation: a survey on multi-robot systems. In Lecture notes in computer science: Vol. 2103. Balancing reactivity and social deliberation in multi-agent systems (pp. 9–32). Berlin: Springer.

    Google Scholar 

  • Jeanson, R., Rivault, C., Deneubourg, J.-L., Blanco, S., Fournier, R., Jost, C., & Theraulaz, G. (2005). Self-organized aggregation in cockroaches. Animal Behaviour, 69(1), 169–180.

    Google Scholar 

  • Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4, 237–285.

    Google Scholar 

  • Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1–2), 99–134.

    MathSciNet  MATH  Google Scholar 

  • Kalyanakrishnan, S., & Stone, P. (2007). Batch reinforcement learning in a complex domain. In AAMAS ’07: proceedings of the 6th international joint conference on autonomous agents and multiagent systems. Richland: IFAAMAS.

    Google Scholar 

  • Kaminka, G. A., Schechter-Glick, R., & Sadov, V. (2008). Using sensor morphology for multirobot formations. IEEE Transactions on Robotics, 24(2), 271–282.

    Google Scholar 

  • Kazadi, S. (2000). Swarm engineering. Ph.D. thesis, California Institute of Technology, Pasadena, CA, USA.

  • Kazadi, S. (2009). Model independence in swarm robotics. International Journal of Intelligent Computing and Cybernetics, 2(4), 672–694.

    MathSciNet  MATH  Google Scholar 

  • Kendall, D. G. (1966). Branching processes since 1873. Journal of the London Mathematical Society, 41(1), 386–406.

    Google Scholar 

  • Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1), 90–98.

    MathSciNet  Google Scholar 

  • Kolling, A., Nunnally, S., & Lewis, M. (2012). Towards human control of robot swarms. In Proceedings of the seventh annual ACM/IEEE international conference on human-robot interaction (pp. 89–96). New York: ACM.

    Google Scholar 

  • Konur, S., Dixon, C., & Fisher, M. (2012). Analysing robot swarm behaviour via probabilistic model checking. Robotics and Autonomous Systems, 60(2), 199–213.

    Google Scholar 

  • Kramer, J., & Scheutz, M. (2007). Development environments for autonomous mobile robots: a survey. Autonomous Robots, 22(2), 101–132.

    Google Scholar 

  • Krieger, M. J. B., & Billeter, J.-B. (2000). The call of duty: self-organised task allocation in a population of up to twelve mobile robots. Robotics and Autonomous Systems, 30(1–2), 65–84.

    Google Scholar 

  • Kube, C. R., & Bonabeau, E. (2000). Cooperative transport by ants and robots. Robotics and Autonomous Systems, 30(1–2), 85–101.

    Google Scholar 

  • Labella, T. H., Dorigo, M., & Deneubourg, J.-L. (2006). Division of labour in a group of robots inspired by ants’ foraging behaviour. ACM Transactions on Autonomous and Adaptive Systems, 1(1), 4–25.

    Google Scholar 

  • Langer, J. S. (1980). Instabilities and pattern formation in crystal growth. Reviews of Modern Physics, 52(1), 1–28.

    Google Scholar 

  • Lee, J., & Arkin, R. C. (2003). Adaptive multi-robot behavior via learning momentum. In IEEE international conference on intelligent robots and systems (IROS 2003) (Vol. 2). Piscataway: IEEE Press.

    Google Scholar 

  • Lerman, K., & Galstyan, A. (2002). Mathematical model of foraging in a group of robots: effect of interference. Autonomous Robots, 13(2), 127–141.

    MATH  Google Scholar 

  • Lerman, K., Galstyan, A., Martinoli, A., & Ijspeert, A. J. (2001). A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life, 7(4), 375–393.

    Google Scholar 

  • Levi, P., & Kernbach, S. (2010). Symbiotic multi-robot organisms. Berlin: Springer.

    MATH  Google Scholar 

  • Li, L., Martinoli, A., & Abu-Mostafa, Y. S. (2004). Learning and measuring specialization in collaborative swarm systems. Adaptive Behavior, 12(3–4), 199–212.

    Google Scholar 

  • Lindsey, Q., Mellinger, D., & Kumar, V. (2012). Construction with quadrotor teams. Autonomous Robots, 33, 323–336.

    Google Scholar 

  • Liu, W. (2007). Modelling of adaptive foraging in swarm robotic systems. URL http://www.brl.ac.uk/researchthemes/swarmrobotics/swarmroboticsystems.aspx. Last checked on November 2012.

  • Liu, W., & Winfield, A. (2010). Modeling and optimization of adaptive foraging in swarm robotic systems. International Journal of Robotics Research, 29(14), 1743–1760.

    Google Scholar 

  • Liu, W., Winfield, A. F. T., Sa, J., Chen, J., & Dou, L. (2007). Towards energy optimization: emergent task allocation in a swarm of foraging robots. Adaptive Behavior, 15(3), 289–305.

    Google Scholar 

  • Liu, Y., & Passino, K. M. (2004). Stable social foraging swarms in a noisy environment. IEEE Transactions on Automatic Control, 49(1), 30–44.

    MathSciNet  Google Scholar 

  • Liu, Y., Passino, K. M., & Polycarpou, M. M. (2003). Stability analysis of m-dimensional asynchronous swarms with a fixed communication topology. IEEE Transactions on Automatic Control, 48(1), 76–95.

    MathSciNet  Google Scholar 

  • Martinoli, A., Ijspeert, A. J., & Mondada, F. (1999). Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1), 51–63.

    Google Scholar 

  • Martinoli, A., Easton, K., & Agassounon, W. (2004). Modeling swarm robotic systems: a case study in collaborative distributed manipulation. The International Journal of Robotics Research, 23(4–5), 415–436.

    Google Scholar 

  • Massink, M., Brambilla, M., Latella, D., Dorigo, M., & Birattari, M. (2012). Analysing robot swarm decision-making with bio-pepa. In Lecture notes in computer science: Vol. 7461. Swarm intelligence (pp. 25–36). Berlin: Springer.

    Google Scholar 

  • Matarić, M. J. (1997). Reinforcement learning in the multi-robot domain. Autonomous Robots, 4(1), 73–83.

    Google Scholar 

  • Matarić, M. J. (1998). Using communication to reduce locality in distributed multi-agent learning. Journal of Experimental and Theoretical Artificial Intelligence, 10(3), 357–369.

    MATH  Google Scholar 

  • Matarić, M. J., & Cliff, D. (1996). Challenges in evolving controllers for physical robots. Robotics and Autonomous Systems, 19(1), 67–83.

    Google Scholar 

  • Mathews, N., Christensen, A. L., Ferrante, E., O’Grady, R., & Dorigo, M. (2010). Establishing spatially targeted communication in a heterogeneous robot swarm. In Proceedings of 9th international conference on autonomous agents and multiagent systems (AAMAS 2010) (pp. 939–946). Richland: IFAAMAS.

    Google Scholar 

  • Mathews, N., Christensen, A. L., O’Grady, R., & Dorigo, M. (2012). Spatially targeted communication and self-assembly. In Proceedings of the 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS 2012) (pp. 2678–2679). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Maxim, P. M., Spears, W. M., & Spears, D. F. (2009). Robotic chain formations. In Proceedings of the IFAC workshop on networked robotics (pp. 19–24). Oxford: Elsevier.

    Google Scholar 

  • McLurkin, J., Smith, J., Frankel, J., Sotkowitz, D., Blau, D., & Schmidt, B. (2006). Speaking swarmish: human–robot interface design for large swarms of autonomous mobile robots. In 2006 AAAI spring symposium (pp. 72–75). Menlo Park: AAAI.

    Google Scholar 

  • Meinhardt, H. (1982). Models of biological pattern formation (Vol. 6). London: Academic Press.

    Google Scholar 

  • Melhuish, C. (1999). Intelligent Autonomous Systems Laboratory. URL http://www.ias.uwe.ac.uk/. Last checked on November 2012.

  • Melhuish, C., Holland, O., & Hoddell, S. (1999a). Convoying: using chorusing for the formation of travelling groups of minimal agents. Robotics and Autonomous Systems, 28(2–3), 207–216.

    Google Scholar 

  • Melhuish, C., Welsby, J., & Edwards, C. (1999b). Using templates for defensive wall building with autonomous mobile ant-like robots. In Proceedings of towards intelligent and autonomous mobile robots (Vol. 99).

    Google Scholar 

  • Minsky, M. (1967). Computation: finite and infinite machines. Upper Saddle River: Prentice-Hall.

    MATH  Google Scholar 

  • Mondada, F. (2005). Swarm-bots. URL http://www.swarm-bot.org/. Last checked on November 2012.

  • Mondada, F., Bonani, M., Guignard, A., Magnenat, S., Studer, C., & Floreano, D. (2005). Superlinear physical performances in a SWARM-BOT. In Lecture notes in computer science: Vol. 3630. Proceedings of the VIIIth European conference on artificial life (pp. 282–291). Berlin: Springer.

    Google Scholar 

  • Montes de Oca, M. A., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., & Dorigo, M. (2011). Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intelligence, 5(3–4), 305–327.

    Google Scholar 

  • Naghsh, A., Gancet, J., Tanoto, A., & Roast, C. (2008). Analysis and design of human-robot swarm interaction in firefighting. In Proceedings of the 17th IEEE international symposium on the robot and human interactive communication (Ro-man 2008) (pp. 255–260).

    Google Scholar 

  • Nolfi, S., & Floreano, D. (2000). Evolutionary robotics. intelligent robots and autonomous agents. Cambridge: MIT Press.

    Google Scholar 

  • 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.

    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.

    Google Scholar 

  • O’Grady, R., Christensen, A., & Dorigo, M. (2009a). SWARMORPH: multi-robot morphogenesis using directional self-assembly. IEEE Transactions on Robotics, 25(3), 738–743.

    Google Scholar 

  • O’Grady, R., Pinciroli, C., Christensen, A. L., & Dorigo, M. (2009b). Supervised group size regulation in a heterogeneous robotic swarm. In 9th conference on autonomous robot systems and competitions, robótica 2009 (pp. 113–119). Castelo Branco: IPCB-Instituto Politécnico de Castelo Branco.

    Google Scholar 

  • O’Grady, R., Groß, R., Christensen, A. L., & Dorigo, M. (2010). Self-assembly strategies in a group of autonomous mobile robots. Autonomous Robots, 28(4), 439–455.

    Google Scholar 

  • O’Hara, K. J., & Balch, T. (2007). Pervasive sensor-less networks for cooperative multi-robot tasks. In Distributed autonomous robotic systems 6 (pp. 305–314). Tokyo: Springer.

    Google Scholar 

  • Okubo, A. (1986). Dynamical aspects of animal grouping: swarms, schools, flocks, and herds. Advances in Biophysics, 22(0), 1–94.

    Google Scholar 

  • Panait, L., & Luke, S. (2005). Cooperative multi-agent learning: the state of the art. Autonomous Agents and Multi-Agent Systems, 11(3), 387–434.

    Google Scholar 

  • Parker, C. A. C., & Zhang, H. (2011). Biologically inspired collective comparisons by robotic swarms. International Journal of Robotics Research, 30(5), 524–535.

    Google Scholar 

  • Parker, L. E. (1996). L-ALLIANCE: task-oriented multi-robot learning in behavior-based systems. Advanced Robotics, 11(4), 305–322.

    Google Scholar 

  • Parrish, J. K., Viscido, S. V., & Grünbaum, D. (2002). Self-organized fish schools: an examination of emergent properties. Biological Bulletin, 202(3), 296–305.

    Google Scholar 

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

    MATH  Google Scholar 

  • Pinciroli, C., O’Grady, R., Christensen, A. L., & Dorigo, M. (2009). Self-organised recruitment in a heterogeneous swarm. In Fourteenth international conference on advanced robotics—ICAR 2009 (p. 6). Proceedings on CD-ROM, paper ID 176.

    Google Scholar 

  • Pinciroli, C., O’Grady, R., Christensen, A. L., & Dorigo, M. (2010). Heterogeneous swarms through minimal communication between homogeneous sub-swarms. In Lecture notes in computer science: Vol. 6234. Proceedings of the seventh international conference on ant colony optimization and swarm intelligence (ANTS-2010) (pp. 558–559). Berlin: Springer.

    Google Scholar 

  • 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).

  • Pini, G. (2011). Task partitioning in swarms of robots an adaptive method for strategy selection. URL http://iridia.ulb.ac.be/supp/IridiaSupp2011-003/index.html. Last checked on November 2012.

  • Pini, G., & Tuci, E. (2008). On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach. Connection Science, 20(2–3), 211–230.

    Google Scholar 

  • Pini, G., Brutschy, A., Birattari, M., & Dorigo, M. (2009). Interference reduction through task partitioning in a robotic swarm. In IEEE international conference on neural networks: IEEE world congress on computational intelligence. Setubal: INSTICC Press.

    Google Scholar 

  • Pini, G., Brutschy, A., Frison, M., Roli, A., Dorigo, M., & Birattari, M. (2011). Task partitioning in swarms of robots: an adaptive method for strategy selection. Swarm Intelligence, 5(3–4), 283–304.

    Google Scholar 

  • Podevijn, G., O’Grady, R., & Dorigo, M. (2012). Self-organised feedback in human swarm interaction. In Proceedings of the workshop on robot feedback in human-robot interaction: how to make a robot readable for a human interaction partner (Ro-Man 2012).

    Google Scholar 

  • Prorok, A., Correll, N., & Martinoli, A. (2011). Multi-level spatial modeling for stochastic distributed robotic systems. The International Journal of Robotics Research, 30(5), 574–589.

    Google Scholar 

  • Pugh, J., & Martinoli, A. (2007). Parallel learning in heterogeneous multi-robot swarms. In Proceedings of the IEEE congress on evolutionary computation (pp. 3839–3846). Piscataway: IEEE Press.

    Google Scholar 

  • Reif, J. H., & Wang, J. (1999). Social potential fields: a distributed behavioral control for autonomous robots. Robotics and Autonomous Systems, 27(3), 171–194.

    Google Scholar 

  • Reynolds, C. (1987a). Boids (Flocks, herds, and schools: a distributed behavioral model). URL http://www.red3d.com/cwr/boids/. Last checked on November 2012.

  • Reynolds, C. W. (1987b). Flocks, herds and schools: a distributed behavioral model. Computer Graphics, 21(4), 25–34.

    Google Scholar 

  • Riedmiller, M., Gabel, T., Hafner, R., & Lange, S. (2009). Reinforcement learning for robot soccer. Autonomous Robots, 27(1), 55–73.

    Google Scholar 

  • Rosenfeld, A., Kaminka, G. A., Kraus, S., & Shehory, O. (2008). A study of mechanisms for improving robotic group performance. Artificial Intelligence, 172(6–7), 633–655.

    MATH  Google Scholar 

  • Şahin, E. (2005). Swarm robotics: from sources of inspiration to domains of application. In Lecture notes in computer science: Vol. 3342. Swarm robotics (pp. 10–20). Berlin: Springer.

    Google Scholar 

  • Scheidler, A. (2011). Dynamics of majority rule with differential latencies. Physical Review E, 83(3), 031116.

    Google Scholar 

  • Schmickl, T., Hamann, H., Wörn, H., & Crailsheim, K. (2009). Two different approaches to a macroscopic model of a bio-inspired robotic swarm. Robotics and Autonomous Systems, 57(9), 913–921.

    Google Scholar 

  • Schwager, M., Michael, N., Kumar, V., & Rus, D. (2011). Time scales and stability in networked multi-robot systems. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 3855–3862).

    Google Scholar 

  • Shucker, B., & Bennett, J. K. (2007). Scalable control of distributed robotic macrosensors. In Distributed autonomous robotic systems 6 (pp. 379–388). Tokyo: Springer.

    Google Scholar 

  • Shucker, B., Murphey, T., & Bennett, J. (2008). Convergence-preserving switching for topology-dependent decentralized systems. IEEE Transactions on Robotics, 24(6), 1405–1415.

    Google Scholar 

  • Soysal, O., & Şahin, E. (2005). Probabilistic aggregation strategies in swarm robotic systems. In Proceedings of the IEEE swarm intelligence symposium (pp. 325–332). Piscataway: IEEE Press.

    Google Scholar 

  • Soysal, O., & Şahin, E. (2007). A macroscopic model for self-organized aggregation in swarm robotic systems. In Lecture notes in computer science: Vol. 4433. Swarm robotics (pp. 27–42). Berlin: Springer.

    Google Scholar 

  • Soysal, O., Bahçeci, E., & Şahin, E. (2007). Aggregation in swarm robotic systems: evolution and probabilistic control. Turkish Journal of Electrical Engineering and Computer Sciences, 15(2), 199–225.

    Google Scholar 

  • Spears, W. M., & Spears, D. F. (2012). Physics-based swarm intelligence. Berlin: Springer.

    Google Scholar 

  • Spears, W. M., Spears, D. F., Hamann, J. C., & Heil, R. (2004). Distributed, physics-based control of swarms of vehicles. Autonomous Robots, 17(2–3), 137–162.

    Google Scholar 

  • Sperati, V., Trianni, V., & Nolfi, S. (2008). Evolving coordinated group behaviours through maximization of mean mutual information. Swarm Intelligence, 2(2–4), 73–95.

    Google Scholar 

  • Sperati, V., Trianni, V., & Nolfi, S. (2011). Self-organised path formation in a swarm of robots. Swarm Intelligence, 5, 97–119.

    Google Scholar 

  • Stewart, R. L., & Russell, R. A. (2006). A distributed feedback mechanism to regulate wall construction by a robotic swarm. Adaptive Behavior, 14, 21–51.

    Google Scholar 

  • Stirling, T., & Floreano, D. (2010). Energy efficient swarm deployment for search in unknown environments. In Lecture notes in computer science. Proceedings of the 7th international conference on swarm intelligence (ANTS 2010) (pp. 562–563). Berlin: Springer.

    Google Scholar 

  • Stone, P., & Veloso, M. M. (2000). Multiagent systems: a survey from a machine learning perspective. Autonomous Robots, 8(3), 345–383.

    Google Scholar 

  • Stranieri, A., Ferrante, E., Turgut, A. E., Trianni, V., Pinciroli, C., Birattari, M., & Dorigo, M. (2011). Self-organized flocking with a heterogeneous mobile robot swarm. In Advances in artificial life, ECAL 2011 (pp. 789–796). Cambridge: MIT Press.

    Google Scholar 

  • Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: an introduction. Cambridge: MIT Press.

    Google Scholar 

  • Theraulaz, G., Goss, S., Gervet, J., & Deneubourg, J.-L. (1990). Task differentiation in polistes wasp colonies: a model for self-organizing groups of robots. In Proceedings of the first international conference on simulation of adaptive behavior on from animals to animats (pp. 346–355). Cambridge: MIT Press.

    Google Scholar 

  • Theraulaz, G., Bonabeau, E., & Deneubourg, J.-L. (1998). Response threshold reinforcements and division of labour in insect societies. Proceedings of the Royal Society B. Biological Sciences, 265(1393), 327–332.

    Google Scholar 

  • Trianni, V., & Dorigo, M. (2006). Self-organisation and communication in groups of simulated and physical robots. Biological Cybernetics, 95, 213–231.

    MATH  Google Scholar 

  • Trianni, V., Labella, T. H., Groß, R., Şahin, E., Dorigo, M., & Deneubourg, J.-L. (2002). Modeling pattern formation in a swarm of self-assembling robots (Technical Report TR/IRIDIA/2002-12). IRIDIA, Université Libre de Bruxelles, Belgium.

  • Trianni, V., Groß, R., Labella, T. H., Şahin, E., & Dorigo, M. (2003). Evolving aggregation behaviors in a swarm of robots. In Lecture notes in artificial intelligence: Vol. 2801. Advances in artificial life: 7th European conference—ECAL 2003 (pp. 865–874). Berlin: Springer.

    Google Scholar 

  • Tuci, E., Trianni, V., & Dorigo, M. (2004). ‘Feeling’ the flow of time through sensorymotor coordination. Connection Science, 16(4), 301–324.

    Google Scholar 

  • Turgut, A. E., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008a). Self-organized flocking in mobile robot swarms. Swarm Intelligence, 2(2–4), 97–120.

    Google Scholar 

  • Turgut, A. E., Huepe, C., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008b). Modeling phase transition in self-organized mobile robot flocks. In Lecture notes in computer science: Vol. 5217. Proceedings of the 6th international conference on ant colony optimization and swarm intelligence, ANTS 2008 (pp. 108–119). Berlin: Springer.

    Google Scholar 

  • Turing, A. (1953). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society. Part B, 237, 37–72.

    Google Scholar 

  • Varghese, B., & McKee, G. (2009). A review and implementation of swarm pattern formation and transformation models. International Journal of Intelligent Computing and Cybernetics, 2(4), 786–817.

    MathSciNet  MATH  Google Scholar 

  • Vaughan, R. T. (2008). Massively multi-robot simulation in stage. Swarm Intelligence, 2(2–4), 189–208.

    Google Scholar 

  • Waibel, M., Keller, L., & Floreano, D. (2009). Genetic team composition and level of selection in the evolution of cooperation. IEEE Transactions on Evolutionary Computation, 13(3), 648–660.

    Google Scholar 

  • Wang, B., Lim, H. B., & Ma, D. (2009). A survey of movement strategies for improving network coverage in wireless sensor networks. Computer Communications, 32(13–14), 1427–1436.

    Google Scholar 

  • Wawerla, J., Sukhatme, G. S., & Matarić, M. J. (2002). Collective construction with multiple robots. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2696–2701).

    Google Scholar 

  • Werfel, J. (2006). Extended stigmergy in collective construction. IEEE Intelligent Systems, 21, 20–28.

    Google Scholar 

  • Werfel, J. (2011). Distributed multi-robot algorithms for the TERMES 3D collective construction system. URL http://www.eecs.harvard.edu/ssr/publications/. Last checked on November 2012.

  • Werfel, J., & Nagpal, R. (2008). Three-dimensional construction with mobile robots and modular blocks. International Journal of Robotics Research, 27(3–4), 463–479.

    Google Scholar 

  • Werfel, J., Petersen, K., & Nagpal, R. (2011). Distributed multi-robot algorithms for the TERMES 3D collective construction system. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS).

    Google Scholar 

  • Wessnitzer, J., & Melhuish, C. (2003). Collective decision-making and behaviour transitions in distributed ad hoc wireless networks of mobile robots: target-hunting. In Lecture notes in computer science: Vol. 2801. Advances in artificial life (pp. 893–902). Berlin: Springer.

    Google Scholar 

  • Winfield, A. F. T. (2009). Towards an engineering science of robot foraging. In Distributed autonomous robotic systems 8 (pp. 185–192). Berlin: Springer.

    Google Scholar 

  • Winfield, A. F. T., Harper, C. J., & Nembrini, J. (2004). Towards dependable swarms and a new discipline of swarm engineering. In Lecture notes in computer science: Vol. 3342. Proceedings of the international workshop on simulation of adaptive behavior, SAB 2004 (pp. 126–142). Berlin: Springer.

    Google Scholar 

  • Winfield, A. F. T., Sa, J., Fernandez-Gago, M. C., Dixon, C., & Fisher, M. (2005). On formal specification of emergent behaviours in swarm robotic systems. International Journal of Advanced Robotic Systems, 2(4), 363–370.

    Google Scholar 

  • Winfield, A. F. T., Liu, W., Nembrini, J., & Martinoli, A. (2008). Modelling a wireless connected swarm of mobile robots. Swarm Intelligence, 2(2–4), 241–266.

    Google Scholar 

  • Wolpert, D. H., & Tumer, K. (1999). An introduction to collective intelligence (Technical Report NASA-ARC-IC-99-63). NASA Ames Research Center.

  • Yang, E., & Gu, D. (2005). A survey on multiagent reinforcement learning towards multi-robot systems. In Proceedings of IEEE symposium on computational intelligence and games. Piscataway: IEEE Press.

    Google Scholar 

  • Yun, S., Schwager, M., & Rus, D. (2009). Coordinating construction of truss structures using distributed equal-mass partitioning. In Springer tracts in advanced robotics: Vol. 70. Proc. of the 14th international symposium on robotics research (pp. 607–623). Berlin: Springer.

    Google Scholar 

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Acknowledgements

We thank the editor Lynne E. Parker and the anonymous reviewers for their feedback that helped improving the paper. We also thank the authors of the images reproduced in this paper for granting us publication permissions.

The research leading to the results presented in this paper has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement n 246939.

Manuele Brambilla, Mauro Birattari and Marco Dorigo acknowledge support from the F.R.S.-FNRS of Belgium’s Wallonia-Brussels Federation, of which they are a F.R.I.A. Research Fellow, a Research Associate and a Research Director, respectively.

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  1. IRIDIA, CoDE, Université Libre de Bruxelles, 50 Av. Franklin Roosevelt, CP 194/6, 1050, Brussels, Belgium

    Manuele Brambilla, Eliseo Ferrante, Mauro Birattari & Marco Dorigo

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  1. Manuele Brambilla
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Correspondence to Manuele Brambilla.

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Brambilla, M., Ferrante, E., Birattari, M. et al. Swarm robotics: a review from the swarm engineering perspective. Swarm Intell 7, 1–41 (2013). https://doi.org/10.1007/s11721-012-0075-2

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