Literature DB >> 28484319

Geometric decompositions of collective motion.

Matteo Mischiati1,2,3, P S Krishnaprasad1,2.   

Abstract

Collective motion in nature is a captivating phenomenon. Revealing the underlying mechanisms, which are of biological and theoretical interest, will require empirical data, modelling and analysis techniques. Here, we contribute a geometric viewpoint, yielding a novel method of analysing movement. Snapshots of collective motion are portrayed as tangent vectors on configuration space, with length determined by the total kinetic energy. Using the geometry of fibre bundles and connections, this portrait is split into orthogonal components each tangential to a lower dimensional manifold derived from configuration space. The resulting decomposition, when interleaved with classical shape space construction, is categorized into a family of kinematic modes-including rigid translations, rigid rotations, inertia tensor transformations, expansions and compressions. Snapshots of empirical data from natural collectives can be allocated to these modes and weighted by fractions of total kinetic energy. Such quantitative measures can provide insight into the variation of the driving goals of a collective, as illustrated by applying these methods to a publicly available dataset of pigeon flocking. The geometric framework may also be profitably employed in the control of artificial systems of interacting agents such as robots.

Entities:  

Keywords:  collective motion; connections; energy decomposition; ensemble properties; fibre bundles; n-body problem

Year:  2017        PMID: 28484319      PMCID: PMC5415679          DOI: 10.1098/rspa.2016.0571

Source DB:  PubMed          Journal:  Proc Math Phys Eng Sci        ISSN: 1364-5021            Impact factor:   2.704


  7 in total

1.  Novel type of phase transition in a system of self-driven particles.

Authors: 
Journal:  Phys Rev Lett       Date:  1995-08-07       Impact factor: 9.161

2.  Order and flexibility in the motion of fish schools.

Authors:  Yoshinobu Inada; Keiji Kawachi
Journal:  J Theor Biol       Date:  2002-02-07       Impact factor: 2.691

3.  Statistical mechanics for natural flocks of birds.

Authors:  William Bialek; Andrea Cavagna; Irene Giardina; Thierry Mora; Edmondo Silvestri; Massimiliano Viale; Aleksandra M Walczak
Journal:  Proc Natl Acad Sci U S A       Date:  2012-03-16       Impact factor: 11.205

4.  Hierarchical group dynamics in pigeon flocks.

Authors:  Máté Nagy; Zsuzsa Akos; Dora Biro; Tamás Vicsek
Journal:  Nature       Date:  2010-04-08       Impact factor: 49.962

5.  Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study.

Authors:  M Ballerini; N Cabibbo; R Candelier; A Cavagna; E Cisbani; I Giardina; V Lecomte; A Orlandi; G Parisi; A Procaccini; M Viale; V Zdravkovic
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-28       Impact factor: 11.205

6.  Finite-size scaling as a way to probe near-criticality in natural swarms.

Authors:  Alessandro Attanasi; Andrea Cavagna; Lorenzo Del Castello; Irene Giardina; Stefania Melillo; Leonardo Parisi; Oliver Pohl; Bruno Rossaro; Edward Shen; Edmondo Silvestri; Massimiliano Viale
Journal:  Phys Rev Lett       Date:  2014-12-01       Impact factor: 9.161

7.  Symmetry and reduction in collectives: low-dimensional cyclic pursuit.

Authors:  Kevin S Galloway; Eric W Justh; P S Krishnaprasad
Journal:  Proc Math Phys Eng Sci       Date:  2016-10       Impact factor: 2.704

  7 in total

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