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ABSTRACT
Dynamic task allocation is an essential requirement for
multi-robot systems operating in unknown dynamic environments. It
allows robots to change their behavior in response to
environmental changes or actions of other robots in order to
improve overall system performance. Emergent coordination
algorithms for task allocation that use only local sensing and no
direct communication between robots are attractive because they
are robust and scalable. However, a lack of formal analysis tools
makes emergent coordination algorithms difficult to design. In
this paper we present a mathematical model of a general dynamic
task allocation mechanism. Robots using this mechanism have to
choose between two types of task, and the goal is to achieve a
desired task division in the absence of explicit communication and
global knowledge. Robots estimate the state of the environment
from repeated local observations and decide which task to choose
based on these observations. We model the robots and observations
as stochastic processes and study the dynamics of the collective
behavior. Specifically, we analyze the effect that the number of
observations and the choice of the decision function have on the
performance of the system. The mathematical models are validated
in a multi-robot multi-foraging scenario. The model's predictions
agree very closely with experimental results from sensor-based
simulations.
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