Swarm Intelligence

, Volume 12, Issue 3, pp 227–244 | Cite as

Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor

  • Frank BonnetEmail author
  • Alexey Gribovskiy
  • José Halloy
  • Francesco Mondada


Collective behavior based on self-organization has been observed in populations of animals from insects to vertebrates. These findings have motivated engineers to investigate approaches to control autonomous multi-robot systems able to reproduce collective animal behaviors, and even to collectively interact with groups of animals. In this article, we show collective decision making by a group of autonomous robots and a group of zebrafish, leading to a shared decision about swimming direction. The robots can also modulate the collective decision-making process in biased and non-biased experimental setups. These results demonstrate the possibility of creating mixed societies of vertebrates and robots in order to study or control animal behavior.


Animal–robot interaction Multi-agent systems Collective behavior Zebrafish Mixed societies 



This work was supported by the EU-ICT Project ASSISIbf, No. 601074. The information provided is the sole responsibility of the authors and does not reflect the European Commission’s opinion. The European Commission is not responsible for any use that might be made of data appearing in this publication. We thank Leo Cazenille and Philippe Rétornaz for their assistance during the software and firmware implementation. We would also like to gratefully acknowledge Daniel Burnier and Norbert Crot for their technical support during the design and production of the robotic devices.

Supplementary material

Supplementary material 1 (mp4 20651 KB)


  1. Abaid, N., Bartolini, T., Macri, S., & Porfiri, M. (2012). Zebrafish responds differentially to a robotic fish of varying aspect ratio, tail beat frequency, noise, and color. Behavioural Brain Research, 233(2), 545–553.CrossRefGoogle Scholar
  2. Abaid, N., Marras, S., Fitzgibbons, C., & Porfiri, M. (2013). Modulation of risk-taking behaviour in golden shiners (Notemigonus crysoleucas) using robotic fish. Behavioural Processes, 100, 9–12.CrossRefGoogle Scholar
  3. Abaid, N., & Porfiri, M. (2010). Fish in a ring: Spatio-temporal pattern formation in one-dimensional animal groups. Journal of The Royal Society Interface, page rsif20100175.Google Scholar
  4. Bartolini, T., Mwaffo, V., Showler, A., Macrì, S., Butail, S., & Porfiri, M. (2016). Zebrafish response to 3D printed shoals of conspecifics: The effect of body size. Bioinspiration & Biomimetics, 11(2), 026003.CrossRefGoogle Scholar
  5. Bonnet, F., Binder, S., de Oliveria, M. E., Halloy, J., & Mondada, F. (2014). A miniature mobile robot developed to be socially integrated with species of small fish. In IEEE international conference on robotics and biomimetics (ROBIO) 2014 (pp. 747–752).Google Scholar
  6. Bonnet, F., Retornaz, P., Halloy, J., Gribovskiy, A., & Mondada, F. (2012). Development of a mobile robot to study the collective behavior of zebrafish. In 2012 4th IEEE RAS EMBS international conference on biomedical robotics and biomechatronics (BioRob) (pp. 437–442).Google Scholar
  7. Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal: Software Tools for the Professional Programmer, 25(11), 120–123.Google Scholar
  8. Braitenberg, V. (1986). Vehicles: Experiments in synthetic psychology. Cambridge: MIT Press.Google Scholar
  9. Butail, S., Bartolini, T., & Porfiri, M. (2013). Collective response of zebrafish shoals to a free-swimming robotic fish. PLoS One, 8(10), e76123.CrossRefGoogle Scholar
  10. Butail, S., Ladu, F., Spinello, D., & Porfiri, M. (2014a). Information flow in animal–robot interactions. Entropy, 16(3), 1315–1330.CrossRefGoogle Scholar
  11. Butail, S., Polverino, G., Phamduy, P., Del Sette, F., & Porfiri, M. (2014b). Fish–robot interactions in a free-swimming environment: Effects of speed and configuration of robots on live fish. In SPIE smart structures and materials \(+\) nondestructive evaluation and health monitoring, 2014 (Vol. 8).Google Scholar
  12. Cazenille, L., Chemtob, Y., Bonnet, F., Gribovskiy, A., Mondada, F., Bredeche, N., & Halloy, J. (2017). Automated calibration of a biomimetic space-dependent model for zebrafish and robot collective behaviour in a structured environment. In Conference on biomimetic and biohybrid systems (pp. 107–118). Berlin: Springer.Google Scholar
  13. Cianca, V., Bartolini, T., Porfiri, M., & Macrì, S. (2013). A robotics-based behavioral paradigm to measure anxiety-related responses in zebrafish. PLoS ONE, 8(7), e69661.CrossRefGoogle Scholar
  14. Correll, N., Schwager, M., & Rus, D. (2008). Social control of herd animals by integration of artificially controlled congeners. In From animals to animats 10: 10th International conference on simulation of adaptive behavior (pp. 437–446).Google Scholar
  15. Donati, E., Worm, M., Mintchev, S., van der Wiel, M., Benelli, G., von der Emde, G., et al. (2016). Investigation of collective behaviour and electrocommunication in the weakly electric fish, mormyrus rume, through a biomimetic robotic dummy fish. Bioinspiration and Biomimetics, 11(6), 066009.CrossRefGoogle Scholar
  16. Faria, J. J., Dyer, J. R., Clément, R. O., Couzin, I. D., Holt, N., Ward, A. J., et al. (2010). A novel method for investigating the collective behaviour of fish: Introducing robofish. Behavioral Ecology and Sociobiology, 64(8), 1211–1218.CrossRefGoogle Scholar
  17. Fleisch, V. C., & Neuhauss, S. C. (2006). Visual behavior in zebrafish. Zebrafish, 3(2), 191–201.CrossRefGoogle Scholar
  18. Garnier, S. (2011). From ants to robots and back: How robotics can contribute to the study of collective animal behavior. In Y. Meng & Y. Jin (Eds.), Bio-inspired self-organizing robotic systems (pp. 105–120). Berlin, Heidelberg: Springer.Google Scholar
  19. Griparic, K., Haus, T., Miklic, D., & Bogdan, S. (2015). Combined actuator sensor unit for interaction with honeybees. In Sensors applications symposium (SAS), 2015 (pp. 1–5).Google Scholar
  20. Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tache, F., et al. (2007). Social integration of robots into groups of cockroaches to control self-organized choices. Science, 318(5853), 1155–1158.CrossRefGoogle Scholar
  21. Jiang, L., Giuggioli, L., Perna, A., Escobedo, R., Lecheval, V., Sire, C., et al. (2017). Identifying influential neighbors in animal flocking. PLoS Computational Biology, 13(11), e1005822.CrossRefGoogle Scholar
  22. KaewTraKulPong, P., & Bowden, R. (2002). An improved adaptive background mixture model for real-time tracking with shadow detection. Video-Based Surveillance Systems, 1, 135–144.CrossRefGoogle Scholar
  23. Kawabata, K., Aonuma, H., Hosoda, K., & Xue, J. (2013). A system for automated interaction with the cricket utilizing a micro mobile robot. Journal of Robotics and Mechatronics, 25(2), 333–339.CrossRefGoogle Scholar
  24. Kopman, V., Laut, J., Polverino, G., & Porfiri, M. (2013). Closed-loop control of zebrafish response using a bioinspired robotic-fish in a preference test. Journal of the Royal Society Interface, 10(78), 20120540.CrossRefGoogle Scholar
  25. Krause, J., Hoare, D., Krause, S., Hemelrijk, C., & Rubenstein, D. (2000). Leadership in fish shoals. Fish and Fisheries, 1(1), 82–89.CrossRefGoogle Scholar
  26. Krause, J., Winfield, A. F., & Deneubourg, J.-L. (2011). Interactive robots in experimental biology. Trends in Ecology and Evolution, 26(7), 369–375.CrossRefGoogle Scholar
  27. Ladu, F., Bartolini, T., Panitz, S. G., Chiarotti, F., Butail, S., Macrì, S., et al. (2015a). Live predators, robots, and computer-animated images elicit differential avoidance responses in zebrafish. Zebrafish, 12(3), 205–214.CrossRefGoogle Scholar
  28. Ladu, F., Mwaffo, V., Li, J., Macrì, S., & Porfiri, M. (2015b). Acute caffeine administration affects zebrafish response to a robotic stimulus. Behavioural Brain Research, 289, 48–54.CrossRefGoogle Scholar
  29. Landgraf, T., Bierbach, D., Nguyen, H., Muggelberg, N., Romanczuk, P., & Krause, J. (2016). Robofish: Increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live Trinidadian guppies. Bioinspiration & biomimetics, 11(1), 015001.CrossRefGoogle Scholar
  30. Landgraf, T., Nguyen, H., Forgo, S., Schneider, J., Schröer, J., Krüger, C., et al. (2013). Interactive robotic fish for the analysis of swarm behavior. In International conference in swarm intelligence (pp. 1–10). Berlin: Springer.Google Scholar
  31. Landgraf, T., Oertel, M., Rhiel, D., & Rojas, R. (2010). A biomimetic honeybee robot for the analysis of the honeybee dance communication system. In IEEE/RSJ international conference on intelligent robots and systems (IROS), 2010, (pp. 3097–3102).Google Scholar
  32. Laschi, C., Mazzolai, B., Patanè, F., Mattoli, V., Dario, P., Ishii, H., et al. (2006). Design and development of a legged rat robot for studying animal–robot interaction. In The first IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics (BioRob) 2006 (pp. 631–636).Google Scholar
  33. Le Maho, Y., Whittington, J. D., Hanuise, N., Pereira, L., Boureau, M., Brucker, M., et al. (2014). Rovers minimize human disturbance in research on wild animals. Nature Methods, 11(12), 1242–1244.CrossRefGoogle Scholar
  34. Maaswinkel, H., & Li, L. (2003). Spatio-temporal frequency characteristics of the optomotor response in zebrafish. Vision Research, 43(1), 21–30.CrossRefGoogle Scholar
  35. Mondada, F., Martinoli, A., Correll, N., Gribovskiy, A., Halloy, J. I., Siegwart, R., & Deneubourg, J.-L. (2011). A general methodology for the control of mixed natural–artificial societies. Handbook of collective robotics (pp. 399–428).Google Scholar
  36. Pérez-Escudero, A., Vicente-Page, J., Hinz, R. C., Arganda, S., & de Polavieja, G. G. (2014). idTracker: Tracking individuals in a group by automatic identification of unmarked animals. Nature Methods, 11(7), 743–748.CrossRefGoogle Scholar
  37. Phamduy, P., Polverino, G., Fuller, R., & Porfiri, M. (2014). Fish and robot dancing together: Bluefin killifish females respond differently to the courtship of a robot with varying color morphs. Bioinspiration & Biomimetics, 9(3), 036021.CrossRefGoogle Scholar
  38. Polverino, G., Abaid, N., Kopman, V., Macr, S., & Porfiri, M. (2012). Zebrafish response to robotic fish: Preference experiments on isolated individuals and small shoals. Bioinspiration & Biomimetics, 7(3), 036019.CrossRefGoogle Scholar
  39. Polverino, G., & Porfiri, M. (2013a). Mosquitofish (Gambusia affinis) responds differentially to a robotic fish of varying swimming depth and aspect ratio. Behavioural Brain Research, 250, 133–138.CrossRefGoogle Scholar
  40. Polverino, G., & Porfiri, M. (2013b). Zebrafish (Danio rerio) behavioural response to bioinspired robotic fish and mosquitofish (Gambusia affinis). Bioinspiration and Biomimetics, 8(4), 044001.CrossRefGoogle Scholar
  41. Rashid, M. T., Frasca, M., Ali, A. A., Ali, R. S., Fortuna, L., & Xibilia, M. G. (2012). Artemia swarm dynamics and path tracking. Nonlinear Dynamics, 68(4), 555–563.CrossRefGoogle Scholar
  42. Romano, D., Benelli, G., Donati, E., Remorini, D., Canale, A., & Stefanini, C. (2017). Multiple cues produced by a robotic fish modulate aggressive behaviour in siamese fighting fishes. Scientific Reports, 7, 4667.CrossRefGoogle Scholar
  43. Ruberto, T., Mwaffo, V., Singh, S., Neri, D., & Porfiri, M. (2016). Zebrafish response to a robotic replica in three dimensions. Royal Society Open Science, 3(10), 160505.CrossRefGoogle Scholar
  44. Ruberto, T., Polverino, G., & Porfiri, M. (2017). How different is a 3D-printed replica from a conspecific in the eyes of a zebrafish? Journal of the Experimental Analysis of Behavior, 107(2), 279–293.CrossRefGoogle Scholar
  45. Rundus, A. S., Owings, D. H., Joshi, S. S., Chinn, E., & Giannini, N. (2007). Ground squirrels use an infrared signal to deter rattlesnake predation. Proceedings of the National Academy of Sciences, 104(36), 14372–14376.CrossRefGoogle Scholar
  46. Séguret, A., Collignon, B., & Halloy, J. (2016). Strain differences in the collective behaviour of zebrafish (Danio rerio) in heterogeneous environment. Royal Society Open Science, 3(10), 160451.CrossRefGoogle Scholar
  47. Shi, J., & Tomasi, C. (1994). Good features to track. In IEEE computer society conference on computer vision and pattern recognition (CVPR), 1994 (pp. 593–600).Google Scholar
  48. Shi, Q., Miyagishima, S., Konno, S., Fumino, S., Ishii, H., Takanishii, A., et al. (2010). Development of the hybrid wheel-legged mobile robot WR-3 designed to interact with rats. In 3rd IEEE RAS and EMBS international conference on biomedical robotics and biomechatronics (BioRob) (pp. 887–892).Google Scholar
  49. Spence, R., Gerlach, G., Lawrence, C., & Smith, C. (2008). The behaviour and ecology of the zebrafish, Danio rerio. Biological Reviews of the Cambridge Philosophical Society, 83(1), 13–34.CrossRefGoogle Scholar
  50. Spinello, C., Macrì, S., & Porfiri, M. (2013). Acute ethanol administration affects zebrafish preference for a biologically inspired robot. Alcohol, 47(5), 391–398.CrossRefGoogle Scholar
  51. Swain, D. T., Couzin, I. D., & Leonard, N. E. (2012). Real-time feedback-controlled robotic fish for behavioral experiments with fish schools. Proceedings of the IEEE, 100(1), 150–163.CrossRefGoogle Scholar
  52. Taylor, R. C., Klein, B. A., Stein, J., & Ryan, M. J. (2008). Faux frogs: Multimodal signalling and the value of robotics in animal behaviour. Animal Behaviour, 76(3), 1089–1097.CrossRefGoogle Scholar
  53. Vaughan, R. T., Sumpter, N., Henderson, J., Frost, A., & Cameron, S. (2000). Experiments in automatic flock control. Robotics and Autonomous Systems, 31(1–2), 109–117.CrossRefGoogle Scholar
  54. Ward, A. J., Herbert-Read, J. E., Jordan, L. A., James, R., Krause, J., Ma, Q., et al. (2013). Initiators, leaders, and recruitment mechanisms in the collective movements of damselfish. The American Naturalist, 181(6), 748–760.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ecole Polytechnique Fédérale de Lausanne, EPFL STI IMT LSRO, ME B3 30 (Batiment ME)LausanneSwitzerland
  2. 2.Université Paris Diderot, Sorbonne Paris Cité, LIED, UMR 8236ParisFrance

Personalised recommendations