3,949 research outputs found
Collective behaviour without collective order in wild swarms of midges
Collective behaviour is a widespread phenomenon in biology, cutting through a
huge span of scales, from cell colonies up to bird flocks and fish schools. The
most prominent trait of collective behaviour is the emergence of global order:
individuals synchronize their states, giving the stunning impression that the
group behaves as one. In many biological systems, though, it is unclear whether
global order is present. A paradigmatic case is that of insect swarms, whose
erratic movements seem to suggest that group formation is a mere epiphenomenon
of the independent interaction of each individual with an external landmark. In
these cases, whether or not the group behaves truly collectively is debated.
Here, we experimentally study swarms of midges in the field and measure how
much the change of direction of one midge affects that of other individuals. We
discover that, despite the lack of collective order, swarms display very strong
correlations, totally incompatible with models of noninteracting particles. We
find that correlation increases sharply with the swarm's density, indicating
that the interaction between midges is based on a metric perception mechanism.
By means of numerical simulations we demonstrate that such growing correlation
is typical of a system close to an ordering transition. Our findings suggest
that correlation, rather than order, is the true hallmark of collective
behaviour in biological systems.Comment: The original version has been split into two parts. This first part
focuses on order vs. correlation. The second part, about finite-size scaling,
will be included in a separate paper. 15 pages, 6 figures, 1 table, 5 video
Wall following to escape local minima for swarms of agents using internal states and emergent behaviour
Natural examples of emergent behaviour, in groups due to interactions among the group's individuals, are numerous. Our aim, in this paper, is to use complex emergent behaviour among agents that interact via pair-wise attractive and repulsive potentials, to solve the local minima problem in the artificial potential based navigation method. We present a modified potential field based path planning algorithm, which uses agent internal states and swarm emergent behaviour to enhance group performance. The algorithm is used successfully to solve a reactive path-planning problem that cannot be solved using conventional static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents to perform problem solving using the dynamic internal states of the agents along with emergent behaviour of the entire group
An emergent wall following behaviour to escape local minima for swarms of agents
Natural examples of emergent behaviour, in groups due to interactions among the group's individuals, are numerous. Our aim, in this paper, is to use complex emergent behaviour among agents that interact via pair-wise attractive and repulsive potentials, to solve the local minima problem in the artificial potential based navigation method. We present a modified potential field based path planning algorithm, which uses agent internal states and swarm emergent behaviour to enhance group performance. The algorithm is used successfully to solve a reactive path-planning problem that cannot be solved using conventional static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents to perform problem solving using the dynamic internal states of the agents along with emergent behaviour of the entire group
Long-range Acoustic Interactions in Insect Swarms: An Adaptive Gravity Model
The collective motion of groups of animals emerges from the net effect of the
interactions between individual members of the group. In many cases, such as
birds, fish, or ungulates, these interactions are mediated by sensory stimuli
that predominantly arise from nearby neighbors. But not all stimuli in animal
groups are short range. Here, we consider mating swarms of midges, which
interact primarily via long-range acoustic stimuli. We exploit the similarity
in form between the decay of acoustic and gravitational sources to build a
model for swarm behavior. By accounting for the adaptive nature of the midges'
acoustic sensing, we show that our "adaptive gravity" model makes mean-field
predictions that agree well with experimental observations of laboratory
swarms. Our results highlight the role of sensory mechanisms and interaction
range in collective animal behavior. The adaptive interactions that we present
here open a new class of equations of motion, which may appear in other
biological contexts.Comment: 25 pages, 15 figure
Solving the potential field local minimum problem using internal agent states
We propose a new, extended artificial potential field method, which uses dynamic internal agent states. The internal states are modelled as a dynamical system of coupled first order differential equations that manipulate the potential field in which the agent is situated. The internal state dynamics are forced by the interaction of the agent with the external environment. Local equilibria in the potential field are then manipulated by the internal states and transformed from stable equilibria to unstable equilibria, allowiong escape from local minima in the potential field. This new methodology successfully solves reactive path planning problems, such as a complex maze with multiple local minima, which cannot be solved using conventional static potential fields
Finite-size scaling as a way to probe near-criticality in natural swarms
Collective behaviour in biological systems is often accompanied by strong
correlations. The question has therefore arisen of whether correlation is
amplified by the vicinity to some critical point in the parameters space.
Biological systems, though, are typically quite far from the thermodynamic
limit, so that the value of the control parameter at which correlation and
susceptibility peak depend on size. Hence, a system would need to readjust its
control parameter according to its size in order to be maximally correlated.
This readjustment, though, has never been observed experimentally. By gathering
three-dimensional data on swarms of midges in the field we find that swarms
tune their control parameter and size so as to maintain a scaling behaviour of
the correlation function. As a consequence, correlation length and
susceptibility scale with the system's size and swarms exhibit a near-maximal
degree of correlation at all sizes.Comment: Selected for Viewpoint in Physics; PRL Editor's Suggestio
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