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Introduction to Swarm Robotics

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Swarm Robotics: A Formal Approach

Abstract

We introduce fundamental concepts of swarm robotics and get a little overview.Swarm robotics is a complex approach that requires an understanding of how to define swarm behavior, whether there is a minimum size of swarms, what are the requirements and properties of swarm systems. We define self-organization and develop an understanding of feedback systems. Swarms do not necessarily need to be homogeneous but can consist of different types of robots making them heterogeneous. We also discus the interaction of robot swarms with human beings as a factor.

But it was one thing to release a population of virtual agents inside a computer’s memory to solve a problem. It was another thing to set real agents free in the real world.

—Michael Crichton, Prey

The flying swarm is immediately sent into the ‘cloud-brain’ formation and its collective memory reawakens.

—Stanisław Lem, The Invincible

In fact, the colony is the real organism, not the individual.

—Daniel Suarez, Kill Decision

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Notes

  1. 1.

    The concept of “agents” is well-defined and complex, see Russell and Norvig [337] and Sect. 2.4.2.

  2. 2.

    “Collective behavior” is a technical term common in sociology which is used in swarm robotics research rather ingenuously. It seems sufficient to read it with the simple meaning “behavior of the whole swarm,” that is, the resulting overall behavior of all swarm members.

  3. 3.

    Open-access book “The Economy,” http://www.core-econ.org/the-economy/book/text/11.html#118-modelling-bubbles-and-crashes.

  4. 4.

    http://www.swarm-bots.org/, funded by the European commission, grant FET IST-2000-31010.

  5. 5.

    Funded by the European commission, grant IST FET-open 507006.

  6. 6.

    http://www.eecs.harvard.edu/ssr/projects/cons/termes.html, funded by the Wyss Institute for Biologically Inspired Engineering, Harvard.

  7. 7.

    http://www.argos-sim.info/.

  8. 8.

    https://www.cyberbotics.com/.

  9. 9.

    http://box2d.org/.

  10. 10.

    http://www.arup.com/homepage_future_of_rail.

  11. 11.

    https://arxiv.org/abs/1606.02583.

  12. 12.

    http://ccl.northwestern.edu/netlogo/models/Fireflies.

References

  1. Abbott, R. (2004). Emergence, entities, entropy, and binding forces. In The Agent 2004 Conference on: Social Dynamics: Interaction, Reflexivity, and Emergence, Argonne National Labs and University of Chicago, October 2004.

    Google Scholar 

  2. Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight, T., et al. (2000). Amorphous computing. Communications of the ACM, 43(5), 74–82.

    Article  Google Scholar 

  3. Adamatzky, A. (2010). Physarum machines: Computers from slime mould. Singapore: World Scientific.

    Book  Google Scholar 

  4. Anderson, C., Boomsma, J. J., & Bartholdi, J. J. (2002). Task partitioning in insect societies: Bucket brigades. Insectes Sociaux, 49, 171–180.

    Article  Google Scholar 

  5. Anderson, P. W. (1972). More is different. Science, 177(4047), 393–396.

    Article  Google Scholar 

  6. Bachrach, J., & Beal, J. (2006). Programming a sensor network as an amorphous medium. In Distributed computing in sensor systems (DCOSS’06, extended abstract).

    Google Scholar 

  7. Bak, P., Tang, C., & Wiesenfeld, K. (1988). Self-organized criticality. Physical Review A, 38(1), 364–374. https://doi.org/10.1103/PhysRevA.38.364

    Article  MathSciNet  MATH  Google Scholar 

  8. Balus̆ka, F., & Levin, M. (2016). On having no head: Cognition throughout biological systems. Frontiers in Psychology, 7, 902. ISSN 1664-1078. https://www.frontiersin.org/article/10.3389/fpsyg.2016.00902

  9. Baran, P. (1960). Reliable digital communications systems using unreliable network repeater nodes. Technical report, The RAND Corporation, Santa Monica, CA.

    Google Scholar 

  10. Barrow-Green, J. (1997). Poincaré and the three body problem. American Mathematical Society, London: London Mathematical Society.

    MATH  Google Scholar 

  11. Bayindir, L. (2015). A review of swarm robotics tasks. Neurocomputing, 172(C), 292–321. http://dx.doi.org/10.1016/j.neucom.2015.05.116

    Google Scholar 

  12. Bayindir, L., & Şahin, E. (2007). A review of studies in swarm robotics. Turkish Journal of Electrical Engineering and Computer Sciences, 15, 115–147. http://journals.tubitak.gov.tr/elektrik/issues/elk-07-15-2/elk-15-2-2-0705-13.pdf

    Google Scholar 

  13. Beckers, R., Holland, O. E., & Deneubourg, J.-L. (1994). From local actions to global tasks: Stigmergy and collective robotics. In Artificial life IV (pp. 189–197). Cambridge, MA: MIT Press.

    Google Scholar 

  14. Bekey, G., Ambrose, R., Kumar, V., Lavery, D., Sanderson, A., Wilcox, B., et al. (2008). Robotics: State of the art and future challenges. Singapore: World Scientific.

    Book  Google Scholar 

  15. Beni, G. (2005). From swarm intelligence to swarm robotics. In E. Şahin & W. M. Spears (Eds.), Swarm Robotics - SAB 2004 International Workshop, Santa Monica, CA, July 2005. Lecture notes in computer science (Vol. 3342, pp. 1–9). Berlin: Springer. http://dx.doi.org/10.1007/978-3-540-30552-1_1

  16. Berea, A., Cohen, I., D’Orsogna, M. R., Ghosh, K., Goldenfeld, N., Goodnight, C. J., et al. (2014). IDR team summary 6. In Collective behavior: From cells to societies. Washington, DC: The National Academies Press.

    Google Scholar 

  17. Bjerknes, J. D., & Winfield, A. (2013). On fault-tolerance and scalability of swarm robotic systems. In A. Martinoli, F. Mondada, N. Correll, G. Mermoud, M. Egerstedt, M. Ani Hsieh, L. E. Parker, & K. Støy (Eds.), Distributed autonomous robotic systems (DARS 2010). Springer tracts in advanced robotics (Vol. 83, pp. 431–444). Berlin: Springer. ISBN 978-3-642-32722-3. http://dx.doi.org/10.1007/978-3-642-32723-0_31

  18. Bjerknes, J. D., Winfield, A., & Melhuish, C. (2007). An analysis of emergent taxis in a wireless connected swarm of mobile robots. In Y. Shi & M. Dorigo (Eds.), IEEE Swarm Intelligence Symposium, Los Alamitos, CA (pp. 45–52). New York: IEEE Press.

    Google Scholar 

  19. Bodi, M., Thenius, R., Szopek, M., Schmickl, T., & Crailsheim, K. (2011). Interaction of robot swarms using the honeybee-inspired control algorithm BEECLUST. Mathematical and Computer Modelling of Dynamical Systems, 18, 87–101. http://www.tandfonline.com/doi/abs/10.1080/13873954.2011.601420

    Article  MATH  Google Scholar 

  20. Bonabeau, E. (2002). Predicting the unpredictable. Harvard Business Review, 80(3), 109–116.

    Google Scholar 

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

    MATH  Google Scholar 

  22. Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41. ISSN 1935-3812. http://dx.doi.org/10.1007/s11721-012-0075-2

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organizing biological systems. Princeton, NJ: Princeton University Press.

    MATH  Google Scholar 

  25. Cavalcanti, A., & Freitas, R. A. Jr. (2005). Nanorobotics control design: A collective behavior approach for medicine. IEEE Transactions on NanoBioscience, 4(2), 133–140.

    Article  Google Scholar 

  26. Couzin, I. D., Krause, J., James, R., Ruxton, G. D., & Franks, N. R. (2002). Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology, 218, 1–11. https://doi.org/10.1006/jtbi.2002.3065

    Article  MathSciNet  Google Scholar 

  27. Crutchfield, J. (1994). The calculi of emergence: Computation, dynamics, and induction. Physica D, 75(1–3), 11–54.

    Article  MATH  Google Scholar 

  28. Crutchfield, J. (1994). Is anything ever new? In G. Cowan, D. Pines, & D. Melzner (Eds.), Complexity: metaphors, models, and reality. SFI series in the sciences of complexity proceedings (Vol. 19, pp. 479–497). Reading, MA: Addison-Wesley.

    Google Scholar 

  29. Darley, V. (1994). Emergent phenomena and complexity. In R. Brooks & P. Maes (Eds.), Artificial life IV (pp. 411–416). Cambridge, MA: MIT Press.

    Google Scholar 

  30. De Wolf, T., & Holvoet, T. (2005). Emergence versus self-organisation: Different concepts but promising when combined. In S. Brueckner, G. D. M. Serugendo, A. Karageorgos, & R. Nagpal (Eds.), Proceedings of the Workshop on Engineerings Self Organising Applications. Lecture notes in computer science (Vol. 3464, pp. 1–15). Berlin: Springer.

    Google Scholar 

  31. Deguet, J., Demazeau, Y., & Magnin, L. (2006). Elements about the emergence issue: A survey of emergence definitions. Complexus, 3(1–3), 24–31.

    Article  Google Scholar 

  32. Deng, B. (2015). Machine ethics: The robot’s dilemma. Nature, 523, 24–66. http://dx.doi.org/10.1038/523024a

    Article  Google Scholar 

  33. Dorigo, M., Birattari, M., & Brambilla, M. (2014). Swarm robotics. Scholarpedia, 9(1), 1463. http://dx.doi.org/10.4249/scholarpedia.1463

    Article  Google Scholar 

  34. Dorigo, M., Bonabeau, E., & Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems, 16(9), 851–871.

    Article  Google Scholar 

  35. Dorigo, M., Floreano, D., Gambardella, L. M., Mondada, F., Nolfi, S., Baaboura, T., et al. (2013). Swarmanoid: A novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine, 20(4), 60–71.

    Article  Google Scholar 

  36. Dorigo, M., & Şahin, E. (2004). Guest editorial: Swarm robotics. Autonomous Robots, 17(2–3), 111–113.

    Article  Google Scholar 

  37. Dorigo, M., Trianni, V., Sahin, E., Groß, R., Labella, T. H., Baldassarre, G., et al. (2004). Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots, 17, 223–245. https://doi.org/10.1023/B:AURO.0000033972.50769.1c.

    Article  Google Scholar 

  38. Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S. M., et al. (2016). Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS One, 11(3), 1–25. https://doi.org/10.1371/journal.pone.0151834.

    Article  Google Scholar 

  39. Ducatelle, F., Di Caro, G. A., & Gambardella, L. M. (2010). Cooperative self-organization in a heterogeneous swarm robotic system. In Proceedings of the 12th Conference on Genetic and Evolutionary Computation (GECCO) (pp. 87–94). New York: ACM.

    Chapter  Google Scholar 

  40. Dussutour, A., Fourcassié, V., Helbing, D., & Deneubourg, J.-L. (2004). Optimal traffic organization in ants under crowded conditions. Nature, 428, 70–73.

    Article  Google Scholar 

  41. Eigen, M., & Schuster, P. (1977). A principle of natural self-organization. Naturwissenschaften, 64(11), 541–565. ISSN 0028-1042. http://dx.doi.org/10.1007/BF00450633

    Article  Google Scholar 

  42. Eigen, M., & Schuster, P. (1979). The hypercycle: A principle of natural self organization. Berlin: Springer.

    Book  Google Scholar 

  43. Ferber, J. (1999). Multi-agent systems: An introduction to distributed artificial intelligence. New York: Addison-Wesley.

    Google Scholar 

  44. Floreano, D., & Mattiussi, C. (2008). Bio-inspired artificial intelligence: Theories, methods, and technologies. Cambridge, MA: MIT Press.

    Google Scholar 

  45. Frei, R., & Serugendo, G. D. M. (2012). The future of complexity engineering. Centreal European Journal of Engineering, 2(2), 164–188. http://dx.doi.org/10.2478/s13531-011-0071-0

    Google Scholar 

  46. Garnier, S., Murphy, T., Lutz, M., Hurme, E., Leblanc, S., & Couzin, I. D. (2013). Stability and responsiveness in a self-organized living architecture. PLoS Computational Biology, 9(3), e1002984. https://doi.org/10.1371/journal.pcbi.1002984.

    Article  Google Scholar 

  47. Gates, B. (2007). A robot in every home. Scientific American, 296(1), 58–65.

    Article  Google Scholar 

  48. Gerkey, B., Vaughan, R. T., & Howard, A. (2003). The player/stage project: Tools for multi-robot and distributed sensor systems. In Proceedings of the 11th International Conference on Advanced Robotics (ICAR 2003) (pp. 317–323).

    Google Scholar 

  49. Gierer, A., & Meinhardt, H. (1972). A theory of biological pattern formation. Biological Cybernetics, 12(1), 30–39. http://dx.doi.org/10.1007/BF00289234

    MATH  Google Scholar 

  50. 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, 41–83.

    Article  Google Scholar 

  51. Grimmett, G. (1999). Percolation. Grundlehren der mathematischen Wissenschaften (Vol. 321). Berlin: Springer.

    Google Scholar 

  52. Groß, R., Magnenat, S., & Mondada, F. (2009). Segregation in swarms of mobile robots based on the Brazil nut effect. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009) (pp. 4349–4356). New York: IEEE.

    Chapter  Google Scholar 

  53. Guillermo, M. (2005). Morphogens and synaptogenesis in drosophila. Journal of Neurobiology, 64(4), 417–434. http://dx.doi.org/10.1002/neu.20165

    Article  Google Scholar 

  54. Gunther, N. J. (1993). A simple capacity model of massively parallel transaction systems. In CMG National Conference (pp. 1035–1044).

    Google Scholar 

  55. Gunther, N. J., Puglia, P., & Tomasette, K. (2015). Hadoop super-linear scalability: The perpetual motion of parallel performance. ACM Queue, 13(5), 46–55.

    Google Scholar 

  56. Haken, H. (1977). Synergetics - An introduction. Berlin: Springer.

    Book  MATH  Google Scholar 

  57. Haken, H. (2004). Synergetics: Introduction and advanced topics. Berlin: Springer.

    Book  Google Scholar 

  58. Hamann, H. (2006). Modeling and Investigation of Robot Swarms. Master’s thesis, University of Stuttgart, Germany.

    Google Scholar 

  59. Hamann, H. (2013). Towards swarm calculus: Urn models of collective decisions and universal properties of swarm performance. Swarm Intelligence, 7(2–3), 145–172. http://dx.doi.org/10.1007/s11721-013-0080-0

    Article  Google Scholar 

  60. Hamann, H., Schmickl, T., Wörn, H., & Crailsheim, K. (2012). Analysis of emergent symmetry breaking in collective decision making. Neural Computing & Applications, 21(2), 207–218. http://dx.doi.org/10.1007/s00521-010-0368-6

    Article  Google Scholar 

  61. Hayes, A. T. (2002). How many robots? group size and efficiency in collective search tasks. In H. Asama, T. Arai, T. Fukuda, & T. Hasegawa (Eds.), Distributed autonomous robotic systems 5 (pp. 289–298). Tokyo: Springer. ISBN 978-4-431-65941-9. http://dx.doi.org/10.1007/978-4-431-65941-9_29

    Chapter  Google Scholar 

  62. Heinrich, M. K., Wahby, M., Soorati, M. D., Hofstadler, D. N., Zahadat, P., Ayres, P., et al. (2016). Self-organized construction with continuous building material: Higher flexibility based on braided structures. In Proceedings of the 1st International Workshop on Self-Organising Construction (SOCO) (pp. 154–159). New York: IEEE. https://doi.org/10.1109/FAS-W.2016.43

    Google Scholar 

  63. Hogg, T. (2006). Coordinating microscopic robots in viscous fluids. Autonomous Agents and Multi-Agent Systems, 14(3), 271–305.

    Article  Google Scholar 

  64. Holland, J. H. (1998). Emergence - From chaos to order. New York: Oxford University Press.

    MATH  Google Scholar 

  65. Ingham, A. G., Levinger, G., Graves, J., & Peckham, V. (1974). The Ringelmann effect: Studies of group size and group performance. Journal of Experimental Social Psychology, 10(4), 371–384. ISSN 0022-1031. https://doi.org/10.1016/0022-1031(74)90033-X.

    Article  Google Scholar 

  66. Jaeger, J., Surkova, S., Blagov, M., Janssens, H., Kosman, D., Kozlov, K. N., et al. (2004). Dynamic control of positional information in the early Drosophila embryo. Nature, 430, 368–371. http://dx.doi.org/10.1038/nature02678

    Article  Google Scholar 

  67. Jansson, F., Hartley, M., Hinsch, M., Slavkov, I., Carranza, N., Olsson, T. S. G., et al. (2015). Kilombo: A Kilobot simulator to enable effective research in swarm robotics. Preprint arXiv:1511.04285.

    Google Scholar 

  68. Jeanne, R. L., & Nordheim, E. V. (1996). Productivity in a social wasp: Per capita output increases with swarm size. Behavioral Ecology, 7(1), 43–48.

    Article  Google Scholar 

  69. Johnson, S. (2001). Emergence: The connected lives of ants, brains, cities, and software. New York: Scribner.

    Google Scholar 

  70. Kalthoff, K. (1978). Pattern formation in early insect embryogenesis - data calling for modification of a recent model. Journal of Cell Science, 29(1), 1–15.

    Google Scholar 

  71. Kapellmann-Zafra, G., Salomons, N., Kolling, A., & Groß, R. (2016). Human-robot swarm interaction with limited situational awareness. In M. Dorigo (Ed.), International Conference on Swarm Intelligence (ANTS 2016). Lecture notes in computer science (pp. 125–136). Berlin: Springer.

    Google Scholar 

  72. Kengyel, D., Hamann, H., Zahadat, P., Radspieler, G., Wotawa, F., & Schmickl, T. (2015). Potential of heterogeneity in collective behaviors: A case study on heterogeneous swarms. In Q. Chen, P. Torroni, S. Villata, J. Hsu, & A. Omicini (Eds.), PRIMA 2015: Principles and practice of multi-agent systems. Lecture notes in computer science (Vol. 9387, pp. 201–217). Berlin: Springer.

    Google Scholar 

  73. Kennedy, J., & Eberhart, R. C. (2001). Swarm intelligence. Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  74. Khaluf, Y., Birattari, M., & Rammig, F. (2013). Probabilistic analysis of long-term swarm performance under spatial interferences. In A.-H. Dediu, C. Martín-Vide, B. Truthe, & M. A. Vega-Rodríguez (Eds.), Proceedings of Theory and Practice of Natural Computing (pp. 121–132). Berlin/Heidelberg: Springer. ISBN 978-3-642-45008-2. http://dx.doi.org/10.1007/978-3-642-45008-2_10

    Chapter  Google Scholar 

  75. Kim, L. H., & Follmer, S. (2017). UbiSwarm: Ubiquitous robotic interfaces and investigation of abstract motion as a display. The Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 66:1–66:20. ISSN 2474-9567. http://doi.acm.org/10.1145/3130931

  76. Klein, J. (2003). Continuous 3D agent-based simulations in the breve simulation environment. In Proceedings of NAACSOS Conference (North American Association for Computational, Social, and Organizational Sciences).

    Google Scholar 

  77. Kubík, A. (2001). On emergence in evolutionary multiagent systems. In Proceedings of the 6th European Conference on Artificial Life (pp. 326–337).

    Google Scholar 

  78. Kubík, A. (2003). Toward a formalization of emergence. Artificial Life, 9, 41–65.

    Article  Google Scholar 

  79. Lazer, D., & Friedman, A. (2007). The network structure of exploration and exploitation. Administrative Science Quarterly, 52, 667–694.

    Article  Google Scholar 

  80. Le Goc, M., Kim, L. H., Parsaei, A., Fekete, J.-D., Dragicevic, P., & Follmer, S. (2016). Zooids: Building blocks for swarm user interfaces. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (pp. 97–109). New York: ACM.

    Chapter  Google Scholar 

  81. Lenaghan, S. C., Wang, Y., Xi, N., Fukuda, T., Tarn, T., Hamel, W. R., et al. (2013). Grand challenges in bioengineered nanorobotics for cancer therapy. IEEE Transactions on Biomedical Engineering, 60(3), 667–673.

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  83. Luke, S., Cioffi-Revilla, C., Panait, L., & Sullivan, K. (2004). Mason: A new multi-agent simulation toolkit. In Proceedings of the 2004 Swarmfest Workshop (Vol. 8, p. 44).

    Google Scholar 

  84. Mack, C. A. (2011). Fifty years of Moore’s law. IEEE Transactions on Semiconductor Manufacturing, 24(2), 202–207.

    Article  Google Scholar 

  85. Matarić, M. J. (1992). Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3), 304–312.

    Article  Google Scholar 

  86. Matarić, M. J. (1992). Minimizing complexity in controlling a mobile robot population. In IEEE International Conference on Robotics and Automation (pp. 830–835). New York: IEEE.

    Google Scholar 

  87. Matarić, M. J. (1993). Designing emergent behaviors: From local interactions to collective intelligence. Proceedings of the Second International Conference on From Animals to Animats 2: Simulation of Adaptive Behavior (pp. 432–441).

    Google Scholar 

  88. 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 AAAI Spring Symposium: To Boldly Go Where No Human-Robot Team has Gone Before (pp. 72–75).

    Google Scholar 

  89. Meinhardt, H. (1982). Models of biological pattern formation. New York: Academic.

    Google Scholar 

  90. Meinhardt, H., & Gierer, A. (1974). Applications of a theory of biological pattern formation based on lateral inhibition. Journal of Cell Science, 15(2), 321–346. ISSN 0021-9533. http://view.ncbi.nlm.nih.gov/pubmed/4859215

    MATH  Google Scholar 

  91. Meinhardt, H., & Gierer, A. (2000). Pattern formation by local self-activation and lateral inhibition. Bioessays, 22, 753–760.

    Article  Google Scholar 

  92. Merkle, D., Middendorf, M., & Scheidler, A. (2007). Swarm controlled emergence-designing an anti-clustering ant system. In IEEE Swarm Intelligence Symposium (pp. 242–249). New York: IEEE.

    Google Scholar 

  93. Merkle, D., Middendorf, M., & Scheidler, A. (2008). Organic computing and swarm intelligence. In C. Blum & D. Merkle (Eds.), Swarm intelligence: Introduction and applications. Berlin: Springer.

    Google Scholar 

  94. Meyer, B., Renner, C., & Maehle, E. (2016). Versatile sensor and communication expansion set for the autonomous underwater vehicle MONSUN. In Advances in Cooperative Robotics: Proceedings of the 19th International Conference on CLAWAR 2016 (pp. 250–257). Singapore: World Scientific.

    Chapter  Google Scholar 

  95. Mill, J. S. (1843). A system of logic: Ratiocinative and inductive. London: John W. Parker and Son.

    Google Scholar 

  96. Möbius, M. E., Lauderdale, B. E., Nagel, S. R., & Jaeger, H. M. (2001). Brazil-nut effect: Size separation of granular particles. Nature, 414(6861), 270.

    Article  Google Scholar 

  97. Murray, J. D. (1981). A prepattern formation mechanism for animal coat markings. Journal of Theoretical Biology, 88, 161–199.

    Article  MathSciNet  Google Scholar 

  98. Olfati-Saber, R., Fax, A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.

    Article  MATH  Google Scholar 

  99. Østergaard, E. H., Sukhatme, G. S., & Matarić, M. J. (2001). Emergent bucket brigading: A simple mechanisms for improving performance in multi-robot constrained-space foraging tasks. In E. André, S. Sen, C. Frasson, & J. P. Müller (Eds.), Proceedings of the Fifth International Conference on Autonomous Agents (AGENTS’01), New York, NY, USA (pp. 29–35). New York: ACM. ISBN 1-58113-326-X. http://doi.acm.org/10.1145/375735.375825

    Chapter  Google Scholar 

  100. Parrish, J. K., & Edelstein-Keshet, L. (1999). Complexity, pattern, and evolutionary trade-offs in animal aggregation. Science, 284(5411), 99–101. ISSN 0036-8075. https://doi.org/10.1126/science.284.5411.99

    Article  Google Scholar 

  101. Petersen, K., Nagpal, R., & Werfel, J. (2011). TERMES: An autonomous robotic system for three-dimensional collective construction. Proceedings Robotics: Science & Systems VII (pp. 257–264).

    Google Scholar 

  102. Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., et al. (2012). ARGoS: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intelligence, 6(4), 271–295. ISSN 1935-3812. http://dx.doi.org/10.1007/s11721-012-0072-5

    Article  Google Scholar 

  103. Podevijn, G., O’Grady, R., Mathews, N., Gilles, A., Fantini-Hauwel, C., & Dorigo, M. (2016). Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction. Swarm Intelligence, 10(3), 1–18. ISSN 1935-3820. http://dx.doi.org/10.1007/s11721-016-0124-3

    Article  Google Scholar 

  104. Prigogine, I. (1997). The end of certainty: Time, chaos, and the new laws of nature. New York: Free Press.

    Google Scholar 

  105. Prorok, A., Ani Hsieh, M., & Kumar, V. (2015). Fast redistribution of a swarm of heterogeneous robots. In International Conference on Bio-inspired Information and Communications Technologies (BICT).

    Google Scholar 

  106. Rubenstein, M., Ahler, C., & Nagpal, R. (2012). Kilobot: A low cost scalable robot system for collective behaviors. In IEEE International Conference on Robotics and Automation (ICRA 2012) (pp. 3293–3298). https://doi.org/10.1109/ICRA.2012.6224638

  107. Rubenstein, M., Cornejo, A., & Nagpal, R. (2014). Programmable self-assembly in a thousand-robot swarm. Science, 345(6198), 795–799. http://dx.doi.org/10.1126/science.1254295

    Article  Google Scholar 

  108. Russell, B. (1923). Vagueness. Australasian Journal of Psychology and Philosophy, 1(2), 84–92. http://dx.doi.org/10.1080/00048402308540623

    Article  Google Scholar 

  109. Russell, S. J., & Norvig, P. (1995). Artificial intelligence: A modern approach. Englewood, Cliffs, NJ: Prentice Hall.

    MATH  Google Scholar 

  110. Şahin, E. (2005). Swarm robotics: From sources of inspiration to domains of application. In E. Şahin & W. M. Spears (Eds.), Swarm Robotics - SAB 2004 International Workshop. Lecture notes in computer science (Vol. 3342, pp. 10–20). Berlin: Springer.

    Google Scholar 

  111. Schmickl, T., Möslinger, C., & Crailsheim, K. (2007). Collective perception in a robot swarm. In E. Şahin, W. M. Spears, & A. F. T. Winfield (Eds.), Swarm Robotics - Second SAB 2006 International Workshop. Lecture notes in computer science (Vol. 4433). Heidelberg/Berlin: Springer.

    Google Scholar 

  112. Schumacher, R. (2002). Book review: Achim Stephan: Emergenz. Von der Unvorhersagbarkeit zur Selbstorganisation. European Journal of Philosophy, 10(3), 415–419 (Dresden/München: Dresden University Press, 1999).

    Google Scholar 

  113. Seyfried, J., Szymanski, M., Bender, N., Estaña, R., Thiel, M., & Wörn, H. (2005). The I-SWARM project: Intelligent small world autonomous robots for micro-manipulation. In E. Şahin & W. M. Spears (Eds.), Swarm Robotics Workshop: State-of-the-Art Survey (pp. 70–83). Berlin: Springer.

    Chapter  Google Scholar 

  114. Sharkey, A. J. C. (2007). Swarm robotics and minimalism. Connection Science, 19(3), 245–260.

    Article  Google Scholar 

  115. Stephan, A. (1999). Emergenz: Von der Unvorhersagbarkeit zur Selbstorganisation. Dresden, Munich: Dresden University Press.

    Google Scholar 

  116. Tyrrell, A., Auer, G., & Bettstetter, C. (2006). Fireflies as role models for synchronization in ad hoc networks. In Proceedings of the 1st International Conference on Bio-inspired Models of Network, Information and Computing Systems. New York: ACM.

    Google Scholar 

  117. Valentini, G., Brambilla, D., Hamann, H., & Dorigo, M. (2016). Collective perception of environmental features in a robot swarm. In 10th International Conference on Swarm Intelligence, ANTS 2016. Lecture notes in computer science (Vol. 9882, pp. 65–76). Berlin: Springer.

    Google Scholar 

  118. Valentini, G., Ferrante, E., Hamann, H., & Dorigo, M. (2016). Collective decision with 100 Kilobots: Speed vs accuracy in binary discrimination problems. Journal of Autonomous Agents and Multi-Agent Systems, 30(3), 553–580. http://dx.doi.org/10.1007/s10458-015-9323-3

    Article  Google Scholar 

  119. Weinberg, S. (1995). Reductionism redux. The New York Review of Books, 42(15), 5.

    Google Scholar 

  120. Witten, T. A. Jr, & Sander, L. M. (1981). Diffusion-limited aggregation, a kinetic critical phenomenon. Physical Review Letters, 47(19), 1400–1403. https://doi.org/10.1103/PhysRevLett.47.1400

    Article  Google Scholar 

  121. Wolpert, L. (1996). One hundred years of positional information. Trends in Genetics, 12(9), 359–364. ISSN 0168-9525. http://view.ncbi.nlm.nih.gov/pubmed/8855666

    Article  Google Scholar 

  122. Yamins, D. (2005). Towards a theory of “local to global” in distributed multi-agent systems. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS’05) (pp. 183–190).

    Google Scholar 

  123. Yamins, D., & Nagpal, R. (2008). Automated global-to-local programming in 1-D spatial multi-agent systems. In L. Padgham, D. C. Parkes, J. P. Müller, & S. Parsons (Eds.), Proceedings of 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 2008.

    Google Scholar 

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    Heiko Hamann

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Hamann, H. (2018). Introduction to Swarm Robotics. In: Swarm Robotics: A Formal Approach. Springer, Cham. https://doi.org/10.1007/978-3-319-74528-2_1

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