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N n
r
(a)
(b)
Fig. 17.11.
Order parameters v a . The parameters have been chosen as: N = 100, v = 0:03,
= 4.
groups based on r-disk interactions very often break into more than one component,
while topological groups exhibit a greater cohesion.
The smashing observation of topological interactions between the elements of an
animal group opens the way to new investigations. On the other hand, in the per-
spective of developing distributed control algorithms inspired to animal grouping,
exploiting the advantages of this topological construction of the proximity graph
requires the adoption of sensory and communication equipments able to locate and
communicate with arbitrarily distant units.
17.4. From Biological Networks to Engineering Problems
As discussed above, the development of theory and applications on motion coordi-
nation encompasses a twofold interest. On one hand, the interest is focused on the
development of suitable models able to emulate and, to some extent, predict, the
behavior of animal collectives, such as ocks, herds, swarms, schools, etc. On the
other hand, engineers are interested in taking inspiration from the animal world to
develop strategies for coordinating the motion of embedded systems, like teams of
mobile robots, mobile sensor networks, unmanned vehicles, and so on. Generally
speaking, the interest is focused on those applications which require the adoption
of a team of autonomous agents with computing, communication, and mobility ca-
pabilities. The eld of application is wide, covering distributed sensing, search and
rescue, environmental modeling, surveillance, exploration [Mart nez et al. (2007)].
In this framework, the trend is to design and implement decentralized coordination
and control strategies. That is, the control algorithm implemented on a given agent
must rely on information about the agent itself and a few agents laying on a given
neighborood. In this way, at least four advantageous features can be achieved:
 
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