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deployment, consensus, rendezvous, cohesiveness, etc., are encoded by using aggre-
gate objective functions, then dierent techniques to design a suitable control law
are provided, by exploiting the design of gradient ows, the analysis of emergent be-
haviors, the identication of meaningful local objective functions, the composition
of basic behaviors (heuristics).
Finally, dierent strategies have been introduced to assess the correctness and
performance of coordination algorithms. This can result in a complicated task,
because of the time-variant nature of the connections, the presence of uncertain-
ties, the heterogeneity in the agent population, or even the nondeterministic na-
ture of some control algorithms. Several strategies have been introduced, such as
stochastic linear techniques [Jadbabaie et al. (2003)], circulant matrices [Marshall
et al. (2004)], algebraic graph theory [Olfati-Saber and Murray (2004)], dieren-
tial equations [Justh and Krishnaprasad (2004)], invariance principles [Cortes and
Bullo (2005)], graph grammars [Klavins (2007)], Monte Carlo methods [Pallottino
et al. (2007)], partial dierence equations [Bliman and Ferrari-Trecate (2008)], and
time-scale separation [Nabet et al. (2007)].
The references mentioned in this work depict a variegate range of techniques to
analyze and solve coordination motion problems. Nevertheless, it seems that the
underlying (either implicitly or explicitly) network structure constitutes a common
feature shared by most, if not all, of them. It is our opinion that network science can
be a unifying and cross-fertilizing element between several approaches, towards the
development of both eective analyses of biological collectives and powerful motion
coordination algorithms.
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