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With the recent advances in robotic technologies, field coverage using mobile sensors is now possible, so that a small set of sensors can be mounted on mobile robots and move to desired areas. Compared to static settings, area coverage is more complicated in a mobile sensor network due to the dynamics arising from the continuous movement of the sensors. This complication is even higher in the more realistic case where little or no prior metric information is available about the sensor field. In this paper, we consider the problem of self-deployment of a set of mobile sensors which have no knowledge of the area, the number of nodes, their location, and even the distances to each other. In this restricted setting, we formulate the problem as a multi-player game in which each sensor tries to maximize its coverage while considering the overlapping sensing areas by its neighbors. We propose a distributed learning algorithm for coordinating the movement of the sensors in the field, and prove its convergence to the equilibria of the formulated game. Simulation results demonstrate that for moderate density deployments, the proposed algorithm competes with the existing location-dependent mobility strategies, while outperforming location-free algorithms.This is one of the several papers published in Autonomous Robots comprising the Special Issue on Multi-Robot and Multi-Agent Systems.

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