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Scenarios of Swarm Robotics

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Swarm Robotics: A Formal Approach

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Abstract

We do an extensive check of what typical scenarios of swarm robotics have been investigated and what methods have been published.This is an extensive guide through the literature on swarm robotics. It is structured by the investigated scenarios and starts from tasks of low complexity, such as aggregation and dispersion. A discussion of pattern formation, object clustering, sorting, and self-assembly follows. Collective construction is already a rather complex scenario that combines several subtasks, such as collective decision-making and collective transport. We take the example of collective manipulation to discuss the interesting phenomenon of super-linear performance increase. Not only the swarm performance increases with increasing swarm size but even the individual robot’s efficiency. Flocking, collective motion, foraging, and shepherding are discussed as typical examples of swarm behaviors. Bio-hybrid systems as combinations of robots and living organisms are quickly introduced. We conclude with a discussion of what could arguably be called “swarm robotics 2.0”—a few recent very promising approaches, such as error detection, security, swarms as interfaces, and swarm robotics as field robotics.

All this time we’ve been gazing upwards in anticipation of an alien species arriving from space, when intelligent life-forms have been with us all along, inhabiting part of the planet that we’ve never seriously attempted to explore.

—Frank Schätzing, The Swarm

From what you say it follows that robots should be constructed quite differently from the way we’ve been doing it in order to be really universal: you’d have to start with tiny elementary building blocks, primary units, pseudo-cells that can replace each other, if necessary.

—Stanisław Lem, The Invincible

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Notes

  1. 1.

    https://spectrum.ieee.org/automaton/robotics/industrial-robots/swagbot-to-herd-cattle-on-australian-ranches.

  2. 2.

    http://www.subcultron.eu/.

References

  1. Allwright, M., Bhalla, N., & Dorigo, M. (2017). Structure and markings as stimuli for autonomous construction. In 2017 18th International Conference on Advanced Robotics (ICAR) (pp. 296–302). New York: IEEE.

    Chapter  Google Scholar 

  2. Allwright, M., Bhalla, N., El-faham, H., Antoun, A., Pinciroli, C., & Dorigo, M. (2014). SRoCS: Leveraging stigmergy on a multi-robot construction platform for unknown environments. In International Conference on Swarm Intelligence (pp. 158–169). Cham: Springer.

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Arbuckle, D. J. (2007). Self-assembly and Self-repair by Robot Swarms, University of Southern California.

    Google Scholar 

  6. Arbuckle, D. J., & Requicha, A. A. G. (2010). Self-assembly and self-repair of arbitrary shapes by a swarm of reactive robots: Algorithms and simulations. Autonomous Robots, 28(2), 197–211. ISSN 1573-7527. https://doi.org/10.1007/s10514-009-9162-7

    Article  Google Scholar 

  7. Arkin, R. C., & Egerstedt, M. (2015). Temporal heterogeneity and the value of slowness in robotic systems. In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 1000–1005). New York: IEEE.

    Chapter  Google Scholar 

  8. Arvin, F., Turgut, A. E., Bazyari, F., Arikan, K. B., Bellotto, N., & Yue, S. (2014). Cue-based aggregation with a mobile robot swarm: A novel fuzzy-based method. Adaptive Behavior, 22(3), 189–206.

    Article  Google Scholar 

  9. Arvin, F., Turgut, A. E., & Yue, S. (2012). Fuzzy-based aggregation with a mobile robot swarm. In Swarm intelligence (ANTS’12). Lecture notes in computer science (Vol. 7461, pp. 346–347). Berlin: Springer. ISBN 978-3-642-32649-3. https://doi.org/10.1007/978-3-642-32650-9\_39

    Google Scholar 

  10. Augugliaro, F., Lupashin, S., Hamer, M., Male, C., Hehn, M., Mueller, M. W., et al. (2014). The flight assembled architecture installation: Cooperative construction with flying machines. IEEE Control Systems, 34(4), 46–64.

    Article  MathSciNet  Google Scholar 

  11. Balch, T. (2000). Hierarchic social entropy: An information theoretic measure of robot group diversity. Autonomous Robots, 8(3), 209–238. ISSN 0929-5593.

    Article  Google Scholar 

  12. Ball, P. (2015). Forging patterns and making waves from biology to geology: A commentary on Turing (1952) ‘the chemical basis of morphogenesis’. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 370(1666), 20140218. ISSN 0962-8436. https://doi.org/10.1098/rstb.2014.0218

    Article  Google Scholar 

  13. Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., et al. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences of the United States of America, 105(4), 1232–1237.

    Article  Google Scholar 

  14. 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 

  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. Berman, S., Lindsey, Q., Sakar, M. S., Kumar, V., & Pratt, S. C. (2011). 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. ISSN 0018-9219. https://doi.org/10.1109/JPROC.2011.2111450

    Article  Google Scholar 

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

    MATH  Google Scholar 

  18. Bonnet, F., Cazenille, L., Gribovskiy, A., Halloy, J., & Mondada, F. (2017). Multi-robot control and tracking framework for bio-hybrid systems with closed-loop interaction. In 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017 (pp. 4449–4456). https://doi.org/10.1109/ICRA.2017.7989515

  19. 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 

  20. Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., & Dorigo, M. (2014). Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Autonomous Agents and Multi-Agent Systems, 28(1), 101–125. ISSN 1387-2532. http://dx.doi.org/10.1007/s10458-012-9212-y

    Article  Google Scholar 

  21. Buhl, J., Sumpter, D. J. T., Couzin, I. D., Hale, J. J., Despland, E., Miller, E. R., & Simpson, S. J. (2006). From disorder to order in marching locusts. Science, 312(5778), 1402–1406. https://doi.org/10.1126/science.1125142

    Article  Google Scholar 

  22. Caprari, G., Colot, A., Siegwart, R., Halloy, J., & Deneubourg, J.-L. (2005). Animal and robot mixed societies: Building cooperation between microrobots and cockroaches. IEEE Robotics & Automation Magazine, 12(2), 58–65. https://doi.org/10.1109/MRA.2005.1458325

    Article  Google Scholar 

  23. Carlsson, H., & Van Damme, E. (1993). Global games and equilibrium selection. Econometrica: Journal of the Econometric Society, 6(5), 989–1018.

    Article  MathSciNet  MATH  Google Scholar 

  24. Carneiro, J., Leon, K., Caramalho, Í., Van Den Dool, C., Gardner, R., Oliveira, V., et al. (2007). When three is not a crowd: A crossregulation model of the dynamics and repertoire selection of regulatory cd4+ t cells. Immunological Reviews, 216(1), 48–68.

    Article  Google Scholar 

  25. Chazelle, B. (2015). An algorithmic approach to collective behavior. Journal of Statistical Physics, 158(3), 514–548.

    Article  MathSciNet  MATH  Google Scholar 

  26. Chen, J., Gauci, M., Price, M. J., & Groß, R. (2012). Segregation in swarms of e-puck robots based on the Brazil nut effect. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), Richland, SC (pp. 163–170). IFAAMAS.

    Google Scholar 

  27. 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. ISSN 1089-778X. https://doi.org/10.1109/TEVC.2009.2017516

    Article  Google Scholar 

  28. Correll, N., Schwager, M., & Rus, D. (2008). Social control of herd animals by integration of artificially controlled congeners. In Proceedings of the 10th International Conference on Simulation of Adaptive Behavior: From Animals to Animats. Lecture notes in computer science (Vol. 5040, pp. 437–446). Berlin: Springer.

    Google Scholar 

  29. 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 

  30. da Silva Guerra, R., Aonuma, H., Hosoda, K., & Asada, M. (2010). Behavior change of crickets in a robot-mixed society. Journal of Robotics and Mechatronics, 22, 526–531.

    Article  Google Scholar 

  31. De Nardi, R., & Holland, O. E. (2007). Ultraswarm: A further step towards a flock of miniature helicopters. 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, pp. 116–128). Berlin: Springer.

    Google Scholar 

  32. Ding, H., & Hamann, H. (2014). Sorting in swarm robots using communication-based cluster size estimation. In M. Dorigo, M. Birattari, S. Garnier, H. Hamann, M. M. de Oca, C. Solnon, & T. Stützle (Eds.), Ninth International Conference on Swarm Intelligence (ANTS 2014). Lecture notes in computer science (Vol. 8667, pp. 262–269). Berlin: Springer.

    Google Scholar 

  33. Divband Soorati, M., & Hamann, H. (2016). Robot self-assembly as adaptive growth process: Collective selection of seed position and self-organizing tree-structures. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016) (pp. 5745–5750). New York: IEEE. http://dx.doi.org/10.1109/IROS.2016.7759845

    Chapter  Google Scholar 

  34. 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 

  35. Dorigo, M., Tuci, E., Trianni, V., Groß, R., Nouyan, S., Ampatzis, C., et al. (2006). SWARM-BOT: Design and implementation of colonies of self-assembling robots. In G. Y. Yen & D. B. Fogel (Eds.), Computational intelligence: Principles and practice (pp. 103–135). Los Alamitos, CA: IEEE Press.

    Google Scholar 

  36. 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 

  37. Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S. M., et al. (2016). Unleashing the potential of evolutionary swarm robotics in the real world. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, GECCO ’16 Companion, New York, NY, USA (pp. 159–160). New York: ACM. ISBN 978-1-4503-4323-7. http://doi.acm.org/10.1145/2908961.2930951

    Chapter  Google Scholar 

  38. 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 

  39. Farrow, N., Klingner, J., Reishus, D., & Correll, N. (2014). Miniature six-channel range and bearing system: Algorithm, analysis and experimental validation. In 2014 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6180–6185). New York: IEEE.

    Chapter  Google Scholar 

  40. Ferrante, E., Turgut, A. E., Duéñez-Guzmàn, E., Dorigo, M., & Wenseleers, T. (2015). Evolution of self-organized task specialization in robot swarms. PLoS Computational Biology, 11(8), e1004273. https://doi.org/10.1371/journal.pcbi.1004273.

    Article  Google Scholar 

  41. Ferrer, E. C. (2016). The blockchain: A new framework for robotic swarm systems. Preprint arXiv:1608.00695. https://arxiv.org/pdf/1608.00695

    Google Scholar 

  42. Flora robotica. (2017). Project website. http://www.florarobotica.eu

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

    Google Scholar 

  44. Ford, L. R. Jr, & Fulkerson, D. R. (2015). Flows in networks. Princeton, NJ: Princeton University Press.

    MATH  Google Scholar 

  45. Franks, N. R., & Sendova-Franks, A. B. (1992). Brood sorting by ants: Distributing the workload over the work-surface. Behavioral Ecology and Sociobiology, 30(2), 109–123.

    Article  Google Scholar 

  46. Franks, N. R., Wilby, A., Silverman, B. W., & Tofts, C. (1992). Self-organizing nest construction in ants: Sophisticated building by blind bulldozing. Animal Behaviour, 44, 357–375.

    Article  Google Scholar 

  47. Gauci, M., Nagpal, R., & Rubenstein, M. (2016). Programmable self-disassembly for shape formation in large-scale robot collectives. In 13th International Symposium on Distributed Autonomous Robotic Systems (DARS 16).

    Google Scholar 

  48. Gauci, M., Ortiz, M. E., Rubenstein, M., & Nagpal, R. (2017). Error cascades in collective behavior: A case study of the gradient algorithm on 1000 physical agents. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (pp. 1404–1412). International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar 

  49. Gerling, V., & von Mammen, S. (2016). Robotics for self-organised construction. In 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W), September 2016 (pp. 162–167). https://doi.org/10.1109/FAS-W.2016.45

  50. 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 

  51. Goldstein, I., & Pauzner, A. (2005). Demand–deposit contracts and the probability of bank runs. The Journal of Finance, 60(3), 1293–1327.

    Article  Google Scholar 

  52. Gomes, J., Mariano, P., & Christensen, A. L. (2015). Cooperative coevolution of partially heterogeneous multiagent systems. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (pp. 297–305). International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar 

  53. Gordon, D. M. (1996). The organization of work in social insect colonies. Nature, 380, 121–124. https://doi.org/10.1038/380121a0

    Article  Google Scholar 

  54. Gribovskiy, A., Halloy, J., Deneubourg, J.-L., Bleuler, H., & Mondada, F. (2010). Towards mixed societies of chickens and robots. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4722–4728). New York: IEEE.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  56. 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 

  57. Habibi, G., Xie, W., Jellins, M., & McLurkin, J. (2016). Distributed path planning for collective transport using homogeneous multi-robot systems. In N.-Y. Chong & Y.-J. Cho (Eds.), Distributed Autonomous Robotic Systems: The 12th International Symposium (pp. 151–164). Tokyo: Springer. ISBN 978-4-431-55879-8. https://doi.org/10.1007/978-4-431-55879-8_11

    Chapter  Google Scholar 

  58. Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tâche, F., et al. (2007). Social integration of robots into groups of cockroaches to control self-organized choices. Science, 318(5853), 1155–1158. http://dx.doi.org/10.1126/science.1144259

    Article  Google Scholar 

  59. Hamann, H., Divband Soorati, M., Heinrich, M. K., Hofstadler, D. N., Kuksin, I., Veenstra, F., et al. (2017). Flora robotica - An architectural system combining living natural plants and distributed robots. Preprint arXiv:1709.04291.

    Google Scholar 

  60. Hamann, H., Meyer, B., Schmickl, T., & Crailsheim, K. (2010). A model of symmetry breaking in collective decision-making. In S. Doncieux, B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, & J.-B. Mouret (Eds.), From animals to animats 11. Lecture notes in artificial intelligence (Vol. 6226, pp. 639–648), Berlin: Springer. http://dx.doi.org/10.1007/978-3-642-15193-4_60

  61. Hamann, H., Schmickl, T., & Crailsheim, K. (2012). A hormone-based controller for evaluation-minimal evolution in decentrally controlled systems. Artificial Life, 18(2), 165–198. http://dx.doi.org/10.1162/artl_a_00058

    Article  Google Scholar 

  62. Hamann, H., Schmickl, T., & Crailsheim, K. (2012). Self-organized pattern formation in a swarm system as a transient phenomenon of non-linear dynamics. Mathematical and Computer Modelling of Dynamical Systems, 18(1), 39–50. http://www.tandfonline.com/doi/abs/10.1080/13873954.2011.601418

    Article  MathSciNet  MATH  Google Scholar 

  63. 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 

  64. Hamann, H., Stradner, J., Schmickl, T., & Crailsheim, K. (2010). A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’10) (pp. 244–251).

    Google Scholar 

  65. Hamann, H., Szymanski, M., & Wörn, H. (2007). Orientation in a trail network by exploiting its geometry for swarm robotics. In Y. Shi & M. Dorigo (Eds.), IEEE Swarm Intelligence Symposium, Honolulu, USA, April 1–5, Los Alamitos, CA, April 2007 (pp. 310–315). New York: IEEE Press.

    Chapter  Google Scholar 

  66. Hamann, H., Wahby, M., Schmickl, T., Zahadat, P., Hofstadler, D., Støy, K., et al. (2015). Flora robotica – Mixed societies of symbiotic robot-plant bio-hybrids. In Proceedings of IEEE Symposium on Computational Intelligence (IEEE SSCI 2015) (pp. 1102–1109). New York: IEEE. http://dx.doi.org/10.1109/SSCI.2015.158

    Chapter  Google Scholar 

  67. Hamann, H., & Wörn, H. (2007). An analytical and spatial model of foraging in a swarm of robots. In E. Şahin, W. Spears, & A. F. T. Winfield (Eds.), Swarm Robotics - Second SAB 2006 International Workshop. Lecture notes in computer science (Vol. 4433, pp. 43–55). Berlin/Heidelberg: Springer.

    Google Scholar 

  68. Hansell, M. H. (1984). Animal architecture and building behaviour. London: Longman.

    Google Scholar 

  69. Harada, K., Corradi, P., Popesku, S., & Liedke, J. (2010). Heterogeneous multi-robot systems. In P. Levi & S. Kernbach (Eds.), Symbiotic multi-robot organisms: Reliability, adaptability, evolution. Cognitive systems monographs (Vol. 7, pp. 79–163). Berlin: Springer.

    Google Scholar 

  70. Harriott, C. E., Seiffert, A. E., Hayes, S. T., & Adams, J. A. (2014). Biologically-inspired human-swarm interaction metrics. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 1471–1475. https://doi.org/10.1177/1541931214581307

    Article  Google Scholar 

  71. 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 

  72. Helbing, D., Keltsch, J., & Molnár, P. (1997). Modelling the evolution of human trail systems. Nature, 388, 47–50.

    Article  Google Scholar 

  73. Helbing, D., Schweitzer, F., Keltsch, J., & Molnár, P. (1997). Active walker model for the formation of human and animal trail systems. Physical Review E, 56(3), 2527–2539.

    Article  Google Scholar 

  74. Hereford, J. M. (2011). Analysis of BEECLUST swarm algorithm. In Proceedings of the IEEE Symposium on Swarm Intelligence (SIS 2011) (pp. 192–198). New York: IEEE.

    Google Scholar 

  75. Hoddell, S., Melhuish, C., & Holland, O. (1998). Collective sorting and segregation in robots with minimal sensing. In 5th International Conference on the Simulation of Adaptive Behaviour (SAB). Cambridge, MA: MIT Press.

    Google Scholar 

  76. Ijspeert, A. J., Martinoli, A., Billard, A., & Gambardella, L. M. (2001). Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots, 11, 149–171. ISSN 0929-5593. https://doi.org/10.1023/A:1011227210047.

    Article  MATH  Google Scholar 

  77. Jackson, D. E., Holcombe, M., & Ratnieks, F. L. W. (2004). Trail geometry gives polarity to ant foraging networks. Nature, 432, 907–909.

    Article  Google Scholar 

  78. Jones, J. (2010). The emergence and dynamical evolution of complex transport networks from simple low-level behaviours. International Journal of Unconventional Computing, 6(2), 125–144.

    Google Scholar 

  79. Jones, J. (2010). Characteristics of pattern formation and evolution in approximations of physarum transport networks. Artificial Life, 16(2), 127–153.

    Article  Google Scholar 

  80. 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 

  81. Kanakia, A. P. (2015). Response Threshold Based Task Allocation in Multi-Agent Systems Performing Concurrent Benefit Tasks with Limited Information. PhD thesis, University of Colorado Boulder.

    Google Scholar 

  82. Karsai, I., & Schmickl, T. (2011). Regulation of task partitioning by a “common stomach”: A model of nest construction in social wasps. Behavioral Ecology, 22, 819–830. https://doi.org/10.1093/beheco/arr060

    Article  Google Scholar 

  83. 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 

  84. Kernbach, S., Thenius, R., Kernbach, O., & Schmickl, T. (2009). Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic swarm. Adaptive Behavior, 17, 237–259.

    Article  Google Scholar 

  85. Kessler, M. A., & Werner, B. T. (2003). Self-organization of sorted patterned ground. Science, 299, 380–383.

    Article  Google Scholar 

  86. Khaluf, Y., Birattari, M., & Hamann, H. (2014). A swarm robotics approach to task allocation under soft deadlines and negligible switching costs. In A. P. del Pobil, E. Chinellato, E. Martinez-Martin, J. Hallam, E. Cervera, & A. Morales (Eds.), Simulation of adaptive behavior (SAB 2014). Lecture notes in computer science (Vol. 8575, pp. 270–279). Berlin: Springer.

    Google Scholar 

  87. 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

  88. Kolling, A., Walker, P., Chakraborty, N., Sycara, K., & Lewis, M. (2016). Human interaction with robot swarms: A survey. IEEE Transactions on Human-Machine Systems, 46(1), 9–26. ISSN 2168-2291. https://doi.org/10.1109/THMS.2015.2480801

    Article  Google Scholar 

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

    Article  Google Scholar 

  90. Lee, R. E. Jr. (1980). Aggregation of lady beetles on the shores of lakes (coleoptera: Coccinellidae). American Midland Naturalist, 104(2), 295–304.

    Article  MathSciNet  Google Scholar 

  91. Lemaire, T., Alami, R., & Lacroix, S. (2004). A distributed tasks allocation scheme in multi-UAV context. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’04) (Vol. 4, pp. 3622–3627). New York: IEEE Press. https://doi.org/10.1109/ROBOT.2004.1308816

  92. 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 

  93. Levi, P., & Kernbach, S. (Eds.). (2010). Symbiotic multi-robot organisms: Reliability, adaptability, evolution. Berlin: Springer.

    MATH  Google Scholar 

  94. Ludwig, L., & Gini, M. (2006). Robotic swarm dispersion using wireless intensity signals. In Distributed autonomous robotic systems 7 (pp. 135–144). Berlin: Springer.

    Chapter  Google Scholar 

  95. Madhavan, R., Fregene, K., & Parker, L. E. (2004). Terrain aided distributed heterogeneous multirobot localization and mapping. Autonomous Robots, 17, 23–39.

    Article  Google Scholar 

  96. Mariano, P., Salem, Z., Mills, R., Zahadat, P., Correia, L., & Schmickl, T. (2017). Design choices for adapting bio-hybrid systems with evolutionary computation. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’17, New York, NY, USA (pp. 211–212). New York: ACM. ISBN 978-1-4503-4939-0. http://doi.acm.org/10.1145/3067695.3076044

    Chapter  Google Scholar 

  97. 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 the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (pp. 939–946). International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar 

  98. 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, H. Sayama, & T. Stützle (Eds.), Swarm Intelligence: 7th International Conference, ANTS 2010. Lecture notes in computer science (Vol. 6234, pp. 84–94). Berlin/Heidelberg/New York: Springer. ISBN 978-3-642-15460-7. https://doi.org/10.1007/978-3-642-15461-4

  99. McEvoy, M. A., & Correll, N. (2015). Materials that couple sensing, actuation, computation, and communication. Science, 347(6228), 1261689. ISSN 0036-8075. https://doi.org/10.1126/science.1261689.

    Article  Google Scholar 

  100. McLurkin, J., Lynch, A. J., Rixner, S., Barr, T. W., Chou, A., Foster, K., et al. (2013). A low-cost multi-robot system for research, teaching, and outreach. In Distributed autonomous robotic systems (pp. 597–609). Berlin: Springer.

    Chapter  Google Scholar 

  101. McLurkin, J., & Smith, J. (2004). Distributed algorithms for dispersion in indoor environments using a swarm of autonomous mobile robots. In Distributed Autonomous Robotic Systems Conference.

    Google Scholar 

  102. Meinhardt, H. (2003). The algorithmic beauty of sea shells. Berlin: Springer.

    Book  MATH  Google Scholar 

  103. Meinhardt, H., & Klingler, M. (1987). A model for pattern formation on the shells of molluscs. Journal of Theoretical Biology, 126, 63–69.

    Article  MathSciNet  MATH  Google Scholar 

  104. Melhuish, C., Wilson, M., & Sendova-Franks, A. (2001). Patch sorting: Multi-object clustering using minimalist robots. In J. Kelemen & P. Sosík (Eds.), Advances in Artificial Life: 6th European Conference, ECAL 2001 Prague, Czech Republic, September 10–14, 2001 Proceedings (pp. 543–552). Berlin/Heidelberg: Springer. ISBN 978-3-540-44811-2. https://doi.org/10.1007/3-540-44811-X_62

    Chapter  Google Scholar 

  105. Mellinger, D., Shomin, M., Michael, N., & Kumar, V. (2013). Cooperative grasping and transport using multiple quadrotors. In Distributed autonomous robotic systems (pp. 545–558). Berlin: Springer.

    Chapter  Google Scholar 

  106. 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 

  107. Meyer, B., Beekman, M., & Dussutour, A. (2008). Noise-induced adaptive decision-making in ant-foraging. In Simulation of adaptive behavior (SAB). Lecture notes in computer science (Vol. 5040, pp. 415–425). Berlin: Springer.

    Google Scholar 

  108. 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 

  109. Moeslinger, C., Schmickl, T., & Crailsheim, K. (2010). Emergent flocking with low-end swarm robots. In M. Dorigo, M. Birattari, G. Di Caro, R. Doursat, A. Engelbrecht, D. Floreano, L. Gambardella, R. Groß, E. Sahin, H. Sayama, & T. Stützle (Eds.), Swarm intelligence. Lecture notes in computer science (Vol. 6234, pp. 424–431). Berlin/Heidelberg: Springer.

    Google Scholar 

  110. Moeslinger, C., Schmickl, T., & Crailsheim, K. (2011). A minimalist flocking algorithm for swarm robots. In Advances in Artificial Life: Darwin Meets von Neumann (ECAL’09). Lecture notes in computer science (Vol. 5778, pp. 357–382). Heidelberg/Berlin: Springer.

    Google Scholar 

  111. Monajjemi, V. M., Wawerla, J., Vaughan, R., & Mori, G. (2013). HRI in the sky: Creating and commanding teams of UAVs with a vision-mediated gestural interface. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2013 (pp. 617–623). https://doi.org/10.1109/IROS.2013.6696415

  112. Mondada, L., Karim, M. E., & Mondada, F. (2016). Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms. Swarm Intelligence, 10(4), 247–265. ISSN 1935-3820. https://doi.org/10.1007/s11721-016-0127-0

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  114. Murray, J. D. (2003). On the mechanochemical theory of biological pattern formation with application to vasculogenesis. Comptes Rendus Biologies, 326(2), 239–252.

    Article  Google Scholar 

  115. Nagi, J., Giusti, A., Gambardella, L. M., & Di Caro, G. A. (2014). Human-swarm interaction using spatial gestures. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 2014 (pp. 3834–3841). https://doi.org/10.1109/IROS.2014.6943101

  116. Nair, R., Ito, T., Tambe, M., & Marsella, S. (2002). Task allocation in the RoboCup rescue simulation domain: A short note. In A. Birk, S. Coradeschi, & S. Tadokoro (Eds.), RoboCup 2001: Robot Soccer World Cup V (Vol. 2377, pp. 1–22). Berlin/Heidelberg: Springer. http://dx.doi.org/10.1007/3-540-45603-1_129

    Google Scholar 

  117. 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.

    Article  Google Scholar 

  118. 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 

  119. O’Grady, R., Groß, R., Mondada, F., Bonani, M., & Dorigo, M. (2005). Self-assembly on demand in a group of physical autonomous mobile robots navigating rough terrain. In Advances in Artificial Life, 8th European Conference (ECAL) (pp. 272–281). Berlin: Springer.

    Chapter  Google Scholar 

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

    Article  MATH  Google Scholar 

  121. Pini, G., Brutschy, A., Francesca, G., Dorigo, M., & Birattari, M. (2012). Multi-armed bandit formulation of the task partitioning problem in swarm robotics. In 8th International Conference on Swarm Intelligence (ANTS) (pp. 109–120). Berlin: Springer.

    Chapter  Google Scholar 

  122. Popkin, G. (2016). The physics of life. Nature, 529, 16–18. https://doi.org/10.1038/529016a

    Article  Google Scholar 

  123. Potter, M. A., Meeden, L. A., & Schultz, A. C. (2001). Heterogeneity in the coevolved behaviors of mobile robots: The emergence of specialists. In International Joint Conference on Artificial Intelligence (IJCAI) (pp. 1337–1343). Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  124. Pourmehr, S., Monajjemi, V. M., Vaughan, R., & Mori, G. (2013). “you two! Take off!”: Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2013 (pp. 137–142). https://doi.org/10.1109/IROS.2013.6696344

  125. Prorok, A., Ani Hsieh, M., & Kumar, V. (2016). Formalizing the impact of diversity on performance in a heterogeneous swarm of robots. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5364–5371). https://doi.org/10.1109/ICRA.2016.7487748

  126. Ratnieks, F. L. W., & Anderson, C. (1999). Task partitioning in insect societies. Insectes Sociaux, 46(2), 95–108. https://doi.org/10.1007/s000400050119

    Article  Google Scholar 

  127. Resnick, M. (1994). Turtles, termites, and traffic jams. Cambridge, MA: MIT Press.

    Google Scholar 

  128. Reynolds, C. W. (1987). Flocks, herds, and schools. Computer Graphics, 21(4), 25–34.

    Article  Google Scholar 

  129. 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 

  130. Rubenstein, M., & Shen, W.-M. (2009). Scalable self-assembly and self-repair in a collective of robots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, USA, October 2009.

    Google Scholar 

  131. Russell, R. A. (1997). Heat trails as short-lived navigational markers for mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation (Vol. 4, pp. 3534–3539).

    Google Scholar 

  132. Savkin, A. V. (2004). Coordinated collective motion of groups of autonomous mobile robots: Analysis of Vicsek’s model. IEEE Transactions on Automatic Control, 49(6), 981–982.

    Article  MathSciNet  MATH  Google Scholar 

  133. Scheidler, A., Merkle, D., & Middendorf, M. (2013). Swarm controlled emergence for ant clustering. International Journal of Intelligent Computing and Cybernetics, 6(1), 62–82.

    Article  MathSciNet  Google Scholar 

  134. Schmickl, T., & Crailsheim, K. (2004). Costs of environmental fluctuations and benefits of dynamic decentralized foraging decisions in honey bees. Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems, 12, 263–277.

    Google Scholar 

  135. Schmickl, T., & Hamann, H. (2011). BEECLUST: A swarm algorithm derived from honeybees. In Y. Xiao (Ed.), Bio-inspired computing and communication networks (pp. 95–137). Boca Raton, FL: CRC Press.

    Google Scholar 

  136. Schmickl, T., Hamann, H., & Crailsheim, K. (2011). Modelling a hormone-inspired controller for individual- and multi-modular robotic systems. Mathematical and Computer Modelling of Dynamical Systems, 17(3), 221–242.

    Article  MATH  Google Scholar 

  137. Schmickl, T., Stradner, J., Hamann, H., Winkler, L., & Crailsheim, K. (2011). Major feedback loops supporting artificial evolution in multi-modular robotics. In S. Doncieux, N. Bredèche, & J.-B. Mouret (Eds.), New horizons in evolutionary robotics. Studies in computational intelligence (Vol. 341, pp. 195–209). Berlin/Heidelberg: Springer. ISBN 978-3-642-18271-6. https://doi.org/10.1007/978-3-642-18272-3

  138. Schmickl, T., Thenius, R., Möslinger, C., Radspieler, G., Kernbach, S., & Crailsheim, K. (2008). Get in touch: Cooperative decision making based on robot-to-robot collisions. Autonomous Agents and Multi-Agent Systems, 18(1), 133–155.

    Article  Google Scholar 

  139. Schultz, A. C., Grefenstette, J. J., & Adams, W. (1996). Robo-Shepherd: Learning complex robotic behaviors. In M. Jamshidi, F. Pin, & P. Dauchez (Eds.), Proceedings of the International Symposium on Robotics and Automation (ICRA’96) (Vol. 6, pp. 763–768). New York, NY: ASME Press.

    Google Scholar 

  140. Sempo, G., Depickère, S., Amé, J.-M., Detrain, C., Halloy, J., & Deneubourg, J.-L. (2006). Integration of an autonomous artificial agent in an insect society: Experimental validation. In S. Nolfi, G. Baldassarre, R. Calabretta, J. C. T. Hallam, D. Marocco, J.-A. Meyer, O. Miglino, & D. Parisi (Eds.), From Animals to Animats 9: 9th International Conference on Simulation of Adaptive Behavior, SAB 2006, Rome, Italy, September 25–29, 2006. Proceedings (pp. 703–712). Berlin/Heidelberg: Springer. ISBN 978-3-540-38615-5. https://doi.org/10.1007/11840541_58

  141. Støy, K., & Nagpal, R. (2004). Self-repair through scale independent self-reconfiguration. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004 (IROS 2004). Proceedings (Vol. 2, pp. 2062–2067). NewYork: IEEE.

    Google Scholar 

  142. Sugawara, K., Kazama, T., & Watanabe, T. (2004). Foraging behavior of interacting robots with virtual pheromone. In Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Los Alamitos, CA (pp. 3074–3079). New York: IEEE Press.

    Google Scholar 

  143. Sugawara, K., & Sno, M. (1997). Cooperative acceleration of task performance: Foraging behavior of interacting multi-robots system. Physica D, 100, 343–354.

    Article  MATH  Google Scholar 

  144. Szopek, M., Schmickl, T., Thenius, R., Radspieler, G., & Crailsheim, K. (2013). Dynamics of collective decision making of honeybees in complex temperature fields. PLoS One, 8(10), e76250. https://doi.org/10.1371/journal.pone.0076250. http://dx.doi.org/10.1371/journal.pone.0076250

  145. Tarapore, D., Christensen, A. L., & Timmis, J. (2017). Generic, scalable and decentralized fault detection for robot swarms. PLoS One, 12(8), 1–29. https://doi.org/10.1371/journal.pone.0182058

    Article  Google Scholar 

  146. Tarapore, D., Lima, P. U., Carneiro, J., & Christensen, A. L. (2015). To err is robotic, to tolerate immunological: Fault detection in multirobot systems. Bioinspiration & Biomimetics, 10(1), 016014.

    Article  Google Scholar 

  147. Theraulaz, G., & Bonabeau, E. (1995). Coordination in distributed building. Science, 269, 686–688.

    Article  Google Scholar 

  148. Theraulaz, G., & Bonabeau, E. (1995). Modelling the collective building of complex architectures in social insects with lattice swarms. Journal of Theoretical Biology, 177, 381–400.

    Article  Google Scholar 

  149. Thompson, D. W. (1917). On growth and form: The complete revised edition. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  150. Toner, J., & Tu, Y. (1998). Flocks, herds, and schools: A quantitative theory of flocking. Physical Review E, 58(4), 4828–4858.

    Article  MathSciNet  Google Scholar 

  151. Trianni, V., Ijsselmuiden, J., & Haken, R. (2016). The SAGA concept: Swarm robotics for agricultural applications. Technical report. http://laral.istc.cnr.it/saga/wp-content/uploads/2016/09/saga-dars2016.pdf

    Google Scholar 

  152. Tuci, E., Groß, R., Trianni, V., Mondada, F., Bonani, M., & Dorigo, M. (2006). Cooperation through self-assembly in multi-robot systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 1(2), 115–150.

    Article  Google Scholar 

  153. Turgut, A., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking in mobile robot swarms. Swarm Intelligence, 2(2), 97–120. http://dx.doi.org/10.1007/s11721-008-0016-2

    Article  Google Scholar 

  154. Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, B237(641), 37–72.

    Article  MathSciNet  Google Scholar 

  155. Twu, P., Mostofi, Y., & Egerstedt, M. (2014). A measure of heterogeneity in multi-agent systems. In American Control Conference (pp. 3972–3977).

    Google Scholar 

  156. Vestartas, P., Heinrich, M. K., Zwierzycki, M., Leon, D. A., Cheheltan, A., La Magna, R., & Ayres, P. (2018). Design tools and workflows for braided structures. In K. De Rycke, C. Gengnagel, O. Baverel, J. Burry, C. Mueller, M. M. Nguyen, P. Rahm, & M. R. Thomsen (Eds.), Humanizing Digital Reality: Design Modelling Symposium Paris 2017 (pp. 671–681). Singapore: Springer. ISBN 978-981-10-6611-5. https://doi.org/10.1007/978-981-10-6611-5_55

    Chapter  Google Scholar 

  157. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 6(75), 1226–1229.

    Article  MathSciNet  Google Scholar 

  158. Vicsek, T., & Zafeiris, A. (2012). Collective motion. Physics Reports, 517(3–4), 71–140.

    Article  Google Scholar 

  159. von Frisch, K. (1974). Animal architecture. San Diego, CA: Harcourt.

    Google Scholar 

  160. Wells, H., Wells, P. H., & Cook, P. (1990). The importance of overwinter aggregation for reproductive success of monarch butterflies (danaus plexippus l.). Journal of Theoretical Biology, 147(1), 115–131. ISSN 0022-5193. http://dx.doi.org/10.1016/S0022-5193(05)80255-3.

    Article  Google Scholar 

  161. Werfel, J., Petersen, K., & Nagpal, R. (2014). Designing collective behavior in a termite-inspired robot construction team. Science, 343(6172), 754–758. http://dx.doi.org/10.1126/science.1245842

    Article  Google Scholar 

  162. Wilson, M., Melhuish, C., Sendova-Franks, A. B., & Scholes, S. (2004). Algorithms for building annular structures with minimalist robots inspired by brood sorting in ant colonies. Autonomous Robots, 17, 115–136.

    Article  Google Scholar 

  163. Wilson, S., Pavlic, T. P., Kumar, G. P., Buffin, A., Pratt, S. C., & Berman, S. (2014). Design of ant-inspired stochastic control policies for collective transport by robotic swarms. Swarm Intelligence, 8(4), 303–327.

    Article  Google Scholar 

  164. Yamaguchi, H., Arai, T., & Beni, G. (2001). A distributed control scheme for multiple robotic vehicles to make group formations. Robotics and Autonomous systems, 36(4), 125–147.

    Article  Google Scholar 

  165. Yates, C. A., Erban, R., Escudero, C., Couzin, I. D., Buhl, J., Kevrekidis, I. G., et al. (2009). Inherent noise can facilitate coherence in collective swarm motion. Proceedings of the National Academy of Sciences of the United States of America, 106(14), 5464–5469. https://doi.org/10.1073/pnas.0811195106. http://www.pnas.org/content/106/14/5464.abstract

  166. Zahadat, P., Christensen, D. J., Katebi, S. D., & Støy, K. (2010). Sensor-coupled fractal gene regulatory networks for locomotion control of a modular snake robot. In Proceedings of the 10th International Symposium on Distributed Autonomous Robotic Systems (DARS) (pp. 517–530).

    Google Scholar 

  167. Zahadat, P., Hahshold, S., Thenius, R., Crailsheim, K., & Schmickl, T. (2015). From honeybees to robots and back: Division of labor based on partitioning social inhibition. Bioinspiration & Biomimetics, 10(6), 066005. https://doi.org/10.1088/1748-3190/10/6/066005

    Article  Google Scholar 

  168. Zahadat, P., Hofstadler, D. N., & Schmickl, T. (2017). Vascular morphogenesis controller: A generative model for developing morphology of artificial structures. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’17, New York, NY, USA (pp. 163–170). New York: ACM. ISBN 978-1-4503-4920-8. http://doi.acm.org/10.1145/3071178.3071247

    Chapter  Google Scholar 

  169. Zahadat, P., & Schmickl, T. (2016). Division of labor in a swarm of autonomous underwater robots by improved partitioning social inhibition. Adaptive Behavior, 24(2), 87–101. https://doi.org/10.1177/1059712316633028

    Article  Google Scholar 

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Hamann, H. (2018). Scenarios of Swarm Robotics. In: Swarm Robotics: A Formal Approach. Springer, Cham. https://doi.org/10.1007/978-3-319-74528-2_4

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