Coverage control in an isotropic Gaussian mixture environment

In this work we address the problem of coverage control of a group of mobile sensors in a convex and bounded environment which is subject to an isotropic Gaussian mixture (GM) model. We consider the case that the GM model is introduced by a known number of information sources with time-varying locations. We show by examples that in such a dynamic environment, it may not be possible and feasible for sensors to gain a nondecreasing and maximal reward. Instead, in the case of allowing sensors act autonomously in the environment, we present the upper and lower bound of rewards of sensors. Finally, we give an algorithm using the sweep-pursuit-capture strategy for a group of sensors. Our result shows that after a finite time, we can possess upper and lower bounds of rewards for sensors at each time step before their next deployment.