![](bg6.png)
because if we keep increasing it, accuracy decreases
again. In this case, for example, a breaking distance
value of 3.0 involves average errors of 0.04, 0.5, 0.9,
0.1.
6 RELATED WORK
Multi-agent robotic systems have been intensively
studied by the scientific community over the past
decade ((Brooks et al., 1990) (Johnson and Bay,
1995)). The main reason for this is that, despite the
limitations of single robots for accomplishing general
tasks such as foraging, transportation, construction or
surveillance, these tasks can be successfully achieved
by coordinated groups of robots. Furthermore, some
of these tasks can be outperformed when the group of
robots form specific spatial distributions (Fredslung
and Mataric, 2002a), what it is usually known as ro-
bot formations.
This paper presents a parameterization of basic be-
haviors whose combination yields to the emergence
of a more complex global behavior that consists on
formation maintenance while following a trajectory.
In particular, robots have proven to be able to main-
tain three different formations just by using local in-
formation and without having the concept of forma-
tion explicitly. Local information refers to reference
robots in the neighborhood, similarly to friend ro-
bots in (Fredslung and Mataric, 2002b). Our “Pri-
ority respect” behavior is also analogous to its robot
ID ordering. Nevertheless, following its ‘friendship’
nomenclature, the “Waiting for the follower” behav-
ior results in a more tight double-linked chain (i.e.,
reciprocal-friendship) than the single-linked chain of
friendships of Fredslund and Mataric.
On the other hand, this “Waiting for the follower”
behavior is related to the unsupervised formation
maintenance work by Yamaguchi et al. (Yamaguchi
et al., 2001), where attractions between robots are
symmetrical. As in our case, the validity of their re-
sults was supported by computer simulations, but they
study mathematically the stabilization of the forma-
tion by means of formation vectors that do apply in
the formation creation rather than in the formation
maintenance in movement. These formation vectors
are also related to the attractive and repulsive gradi-
ent forces implemented by Feddema et al. (Feddema
et al., 2004). Their work has a system control per-
spective that focuses on stability rather than, as in our
case, in following accurately a trajectory while main-
taining the formation.
7 CONCLUSIONS AND FUTURE
WORK
Our work is based on the parameterization of basic
behaviors to optimize the performance of robot for-
mations empirically. Despite the potential loss of gen-
erality, this tuning strategy applies for different queue,
inverted V and rectangle formations, and tries to pose
a step forward in the solution of the formation main-
tenance problem when using local information. Fu-
ture work will focus on the way adaptation can be
achieved automatically: since we work on simula-
tions, we envision genetic algorithms as an alterna-
tive, were the set of parameters codify the population
and the error measure can be used as objective func-
tion to be optimized.
ACKNOWLEDGEMENTS
Bernat Grau’s implementation has been key for this
work.
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