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Artificial Life and Robotics

, Volume 24, Issue 4, pp 460–470 | Cite as

Analysis of push-forward model for swarm-like collective motions

  • Yuichiro SueokaEmail author
  • Yuto Sato
  • Makihiko Ishitani
  • Koichi Osuka
Original Article
  • 49 Downloads

Abstract

A system that operates as a whole through interactions between its constituent individuals, each of which operates autonomously, is called an autonomous decentralized system. This type of system is increasingly attracting attention in the fields of biology and engineering as a system that flexibly adapts to changes in external environments. In this paper, a push-forward model is proposed as a simple model to generate various collective motions in groups. The push-forward model can be used to generate four types of collective motions, including one where “if an individual flies out of the group, the direction in which the entire group is heading changes accordingly.” This motion could not be generated using conventional group models. In this study, the group collective motions obtained from the push-forward model are classified using evaluation indices, and the mechanisms of their development are discussed. Moreover, an evaluation is conducted using scale-free correlation, an index that expresses “group likeness,” which is derived from actual observations of bird flocks.

Keywords

Distributed group model Swarm-like behavior Scale-free correlation 

Notes

References

  1. 1.
    Tinbergen N (1951) The study of instinct. Oxford university Press, OxfordzbMATHGoogle Scholar
  2. 2.
    Couzin ID et al (2002) Collective memory and spatial sorting in animal groups. J Theor Biol 218:1–11MathSciNetCrossRefGoogle Scholar
  3. 3.
    Strömbom D et al (2017) Solving the shepherding problem: heuristics for herding autonomous, interacting agents. J R Soc Interface 11(100):20140719CrossRefGoogle Scholar
  4. 4.
    Aoki I (1982) A simulation study on the schooling mechanism in fish. Bull Jpn Soc Sci Fish 48(8):1081–1088CrossRefGoogle Scholar
  5. 5.
    Oboshi T, Kato S, Mutoh A, Itoh H (2003) A simulation study on the form of fish schooling for escape from predator. Forma 18(2):119–131Google Scholar
  6. 6.
    Nguyen LTH, Ta VT, Yagi A (2016) Obstacle avoiding patterns and cohesiveness of fish school. J Theor Biol 406:116–123CrossRefGoogle Scholar
  7. 7.
    Aoki I (1980) An analysis of the schooling behavior of fish. Bull Ocean Res Inst 12:1–65MathSciNetGoogle Scholar
  8. 8.
    Partridge BL (1982) The structure and function of fish schools. Sci Am 246(6):114–123CrossRefGoogle Scholar
  9. 9.
    Romey WL (1996) Individual differences make a difference in the trajectories of simulated schools of fish. Ecol Model 92:65–77CrossRefGoogle Scholar
  10. 10.
    Couzin ID, Krause J (2003) Self-organization and collective behavior in vertebrates. Adv Stud Behav 32:1–75CrossRefGoogle Scholar
  11. 11.
    Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioral model. Comput Gr 22:25–33CrossRefGoogle Scholar
  12. 12.
    Vicsek T, Czirok A, Jacob EB, Shochet O (1995) Novel type of phase transition in a system of self-driven particles. Phys Rev Lett 75:1226–1229MathSciNetCrossRefGoogle Scholar
  13. 13.
    Niizato T, Gunji YP (2011) Metric-topological interaction model of collective behavior. Ecol Model 222:3041–3049CrossRefGoogle Scholar
  14. 14.
    Ballerini M, Cabibbo V, Candelier R, Cisbani E, Dina IG, Lecomte V, Orlamdi A, Parisi G, Procaccini A, Viale M, Zdravkovic V (2008) Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proc Natl Acad Sci 105:1232–1237CrossRefGoogle Scholar
  15. 15.
    Tunstrom K, Katz Y, Loannou C, Huepe C, Lutz J, Couzin D (2013) Collective states, multistability and transitional behavior in schooling fish. PLOS Comput Biol 9:e1002915MathSciNetCrossRefGoogle Scholar
  16. 16.
    Couzin D, Loannou C, Demirel G, Gross T, Torney C, Hartnett A, Conradt L, Levin S, Leonard N (2011) Uninformed individuals promote democratic consensus in animal groups. Science 334:1578–1580CrossRefGoogle Scholar
  17. 17.
    Murakami H, Tomaru T, Niizato T, Nishiyama Y, Sonoda K, Moriyama T, Gunji Y (2014) Collective behavior of soldier crab swarm in both ring- and round-shaped arenas. In: Proc. of the 20th international Symposium on Artificial Life and RoboticsGoogle Scholar
  18. 18.
    Cavagna A (2010) Scale-free correlations in starling flocks. Proc Natl Acad Sci 107:11865–11870CrossRefGoogle Scholar

Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2019

Authors and Affiliations

  • Yuichiro Sueoka
    • 1
    Email author
  • Yuto Sato
    • 1
  • Makihiko Ishitani
    • 1
  • Koichi Osuka
    • 1
  1. 1.SuitaJapan

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