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      Spatial flocking: Control by speed, distance, noise and delay

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      1 , 2 , * , 3
      PLoS ONE
      Public Library of Science

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          Abstract

          Fish, birds, insects and robots frequently swim or fly in groups. During their three dimensional collective motion, these agents do not stop, they avoid collisions by strong short-range repulsion, and achieve group cohesion by weak long-range attraction. In a minimal model that is isotropic, and continuous in both space and time, we demonstrate that (i) adjusting speed to a preferred value, combined with (ii) radial repulsion and an (iii) effective long-range attraction are sufficient for the stable ordering of autonomously moving agents in space. Our results imply that beyond these three rules ordering in space requires no further rules, for example, explicit velocity alignment, anisotropy of the interactions or the frequent reversal of the direction of motion, friction, elastic interactions, sticky surfaces, a viscous medium, or vertical separation that prefers interactions within horizontal layers. Noise and delays are inherent to the communication and decisions of all moving agents. Thus, next we investigate their effects on ordering in the model. First, we find that the amount of noise necessary for preventing the ordering of agents is not sufficient for destroying order. In other words, for realistic noise amplitudes the transition between order and disorder is rapid. Second, we demonstrate that ordering is more sensitive to displacements caused by delayed interactions than to uncorrelated noise (random errors). Third, we find that with changing interaction delays the ordered state disappears at roughly the same rate, whereas it emerges with different rates. In summary, we find that the model discussed here is simple enough to allow a fair understanding of the modeled phenomena, yet sufficiently detailed for the description and management of large flocks with noisy and delayed interactions. Our code is available at http://github.com/fij/floc.

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          Coordination of groups of mobile autonomous agents using nearest neighbor rules

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            Novel Type of Phase Transition in a System of Self-Driven Particles

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              Inferring the structure and dynamics of interactions in schooling fish.

              Determining individual-level interactions that govern highly coordinated motion in animal groups or cellular aggregates has been a long-standing challenge, central to understanding the mechanisms and evolution of collective behavior. Numerous models have been proposed, many of which display realistic-looking dynamics, but nonetheless rely on untested assumptions about how individuals integrate information to guide movement. Here we infer behavioral rules directly from experimental data. We begin by analyzing trajectories of golden shiners (Notemigonus crysoleucas) swimming in two-fish and three-fish shoals to map the mean effective forces as a function of fish positions and velocities. Speeding and turning responses are dynamically modulated and clearly delineated. Speed regulation is a dominant component of how fish interact, and changes in speed are transmitted to those both behind and ahead. Alignment emerges from attraction and repulsion, and fish tend to copy directional changes made by those ahead. We find no evidence for explicit matching of body orientation. By comparing data from two-fish and three-fish shoals, we challenge the standard assumption, ubiquitous in physics-inspired models of collective behavior, that individual motion results from averaging responses to each neighbor considered separately; three-body interactions make a substantial contribution to fish dynamics. However, pairwise interactions qualitatively capture the correct spatial interaction structure in small groups, and this structure persists in larger groups of 10 and 30 fish. The interactions revealed here may help account for the rapid changes in speed and direction that enable real animal groups to stay cohesive and amplify important social information.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2018
                4 May 2018
                : 13
                : 5
                : e0191745
                Affiliations
                [1 ] Department of Automation Control, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, China, 430074
                [2 ] MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Pázmány Péter sétány 1A, Budapest, Hungary, 1117
                [3 ] School of Computer Science, Fudan University, 825 Zhangheng Rd, Shanghai, China, 201203
                Rijksuniversiteit Groningen, NETHERLANDS
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-5341-5582
                Article
                PONE-D-17-28718
                10.1371/journal.pone.0191745
                5935395
                29727441
                9586cf7c-da10-4164-b391-c3ab61281c00
                © 2018 Farkas, Wang

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 August 2017
                : 10 January 2018
                Page count
                Figures: 7, Tables: 0, Pages: 12
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003825, Magyar Tudományos Akadémia;
                Award ID: NN 103114
                Award Recipient :
                The authors gratefully acknowledge support from the Hungarian Scientific Research Fund (OTKA NN 103114), and advice received from T. Vicsek, M. Nagy, and H.-T. Zhang. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Velocity
                Research and Analysis Methods
                Simulation and Modeling
                Agent-Based Modeling
                Computer and Information Sciences
                Systems Science
                Agent-Based Modeling
                Physical Sciences
                Mathematics
                Systems Science
                Agent-Based Modeling
                Physical Sciences
                Mathematics
                Numerical Analysis
                Numerical Integration
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Birds
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Physical Sciences
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                Mathematical Physics
                Equations of Motion
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Physical Sciences
                Physics
                Condensed Matter Physics
                Anisotropy
                Physical Sciences
                Materials Science
                Material Properties
                Anisotropy
                Custom metadata
                All relevant data are within the paper. Raw data are available for free upon request. Source code is at http://github.com/fij/floc.

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