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Technical Report

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Abstract or Description

Distributed algorithms for (re)configuring sensors to cover a given area are important for autonomous multi-robot operations in application areas such as surveillance and environmental monitoring. Depending on the assumptions about the choice of the environment, the sensor models, the coverage metric, and the motion models of sensor nodes, there are different versions of the problem that have been formulated and studied. In this work, we consider the problem of (re)configuring systems equipped with anisotropic sensors (e.g., mobile robot with limited field of view cameras) that cover a polygonal region with polygonal obstacles for detecting interesting events. We assume that a given probability distribution of the events over this polygonal region is known. Our model has two key distinguishing features that are inherently present in covering problems with anisotropic sensors, but are not addressed adequately in the literature. First, we allow for the fact that the sensing performance may not be a monotonically decreasing function of distance. Second, motivated by scenarios where the sensing performance not only depends on the resolution of sensing, but also on the relative orientation between the sensing axis and the event, we assume that the probability of detection of an event depends on both sensing parameters and the angle of observation. We present a distributed gradient-ascent algorithm for (re)configuring the system of mobile sensors so that the joint probability of detection of events over the whole region is maximized. Simulation results illustrating the performance of our algorithms on different systems, namely, mobile camera networks, mobile acoustic sensor networks, and static pan-tilt-zoom camera networks are presented.



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