8,085 research outputs found
Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion
Several models of flocking have been promoted based on simulations with
qualitatively naturalistic behavior. In this paper we provide the first direct
application of computational modeling methods to infer flocking behavior from
experimental field data. We show that this approach is able to infer general
rules for interaction, or lack of interaction, among members of a flock or,
more generally, any community. Using experimental field measurements of homing
pigeons in flight we demonstrate the existence of a basic distance dependent
attraction/repulsion relationship and show that this rule is sufficient to
explain collective behavior observed in nature. Positional data of individuals
over time are used as input data to a computational algorithm capable of
building complex nonlinear functions that can represent the system behavior.
Topological nearest neighbor interactions are considered to characterize the
components within this model. The efficacy of this method is demonstrated with
simulated noisy data generated from the classical (two dimensional) Vicsek
model. When applied to experimental data from homing pigeon flights we show
that the more complex three dimensional models are capable of predicting and
simulating trajectories, as well as exhibiting realistic collective dynamics.
The simulations of the reconstructed models are used to extract properties of
the collective behavior in pigeons, and how it is affected by changing the
initial conditions of the system. Our results demonstrate that this approach
may be applied to construct models capable of simulating trajectories and
collective dynamics using experimental field measurements of herd movement.
From these models, the behavior of the individual agents (animals) may be
inferred
Collective Motion with Anticipation: Flocking, Spinning, and Swarming
We investigate the collective dynamics of self-propelled particles able to
probe and anticipate the orientation of their neighbors. We show that a simple
anticipation strategy hinders the emergence of homogeneous flocking patterns.
Yet, anticipation promotes two other forms of self-organization: collective
spinning and swarming. In the spinning phase, all particles follow synchronous
circular orbits, while in the swarming phase, the population condensates into a
single compact swarm that cruises coherently without requiring any cohesive
interactions. We quantitatively characterize and rationalize these phases of
polar active matter and discuss potential applications to the design of
swarming robots.Comment: 6 pages, 4 figure
Declarative vs Rule-based Control for Flocking Dynamics
The popularity of rule-based flocking models, such as Reynolds' classic
flocking model, raises the question of whether more declarative flocking models
are possible. This question is motivated by the observation that declarative
models are generally simpler and easier to design, understand, and analyze than
operational models. We introduce a very simple control law for flocking based
on a cost function capturing cohesion (agents want to stay together) and
separation (agents do not want to get too close). We refer to it as {\textit
declarative flocking} (DF). We use model-predictive control (MPC) to define
controllers for DF in centralized and distributed settings. A thorough
performance comparison of our declarative flocking with Reynolds' model, and
with more recent flocking models that use MPC with a cost function based on
lattice structures, demonstrate that DF-MPC yields the best cohesion and least
fragmentation, and maintains a surprisingly good level of geometric regularity
while still producing natural flock shapes similar to those produced by
Reynolds' model. We also show that DF-MPC has high resilience to sensor noise.Comment: 7 Page
Binary interaction algorithms for the simulation of flocking and swarming dynamics
Microscopic models of flocking and swarming takes in account large numbers of
interacting individ- uals. Numerical resolution of large flocks implies huge
computational costs. Typically for interacting individuals we have a cost
of . We tackle the problem numerically by considering approximated
binary interaction dynamics described by kinetic equations and simulating such
equations by suitable stochastic methods. This approach permits to compute
approximate solutions as functions of a small scaling parameter
at a reduced complexity of O(N) operations. Several numerical results show the
efficiency of the algorithms proposed
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