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Abstract:
Recent research on multi-agent systems has produced
a plethora of decentralized controllers that implicitly
assume various degrees of agent localization. However, many
practical arrangements commonly taken to allow and achieve
localization imply some form of centralization, from the use of
physical tagging to allow the identification of the single agent to
the adoption of global positioning systems based on cameras or
GPS. These devices clearly decrease the system autonomy and
range of applicability, and should be avoided if possible. Following
this guideline, this work addresses the mutual localization
problem with anonymous relative position measures, presenting
a robust solution based on a probabilistic framework. The
proposed localization system exhibits higher accuracy and lower
complexity (O(n2)) than our previous method [1]. Moreover,
with respect to more conventional solutions that could be
conceived on the basis of the current literature, our method
is theoretically suitable for tasks requiring frequent, manyto-
many encounters among agents (e.g., formation control,
cooperative exploration, multiple-view environment sensing).
The proposed localization system has been validated by means
of an extensive experimental study.
