Abstract
This paper presents a distributed coverage algorithm for a network of mobile agents. Unlike previous work that uses a simple gradient descent algorithm, here we employ an existing deterministic annealing (DA) technique to achieve more optimal convergence values since typical coverage objective functions contain many local minima. We replicate the results of the classical DA algorithm while imposing a limited-range constraint to sensors. As the temperature is decreased, phase changes lead to a regrouping of agents, which is decided through a distributed task allocation algorithm. While simple gradient descent algorithms are heavily dependent on initial conditions for such non-convex coverage objective functions, annealing techniques are generally less prone to this phenomena. The results of our simulations confirm this fact, as we show in the manuscript.
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