Adaptive Coverage Control for Heterogeneous Mobile Sensor Networks in an Unknown Environment

This article addresses the coverage control problems for heterogeneous mobile sensor networks (MSNs) in an environment with unknown event density functions. In contrast to existing works, unknown heterogeneous sensing abilities of the mobile sensor network (MSN) are considered by leveraging a weighted Voronoi diagram, namely, the Power diagram. To guarantee that the time-varying Power diagram converges to that defined by the true sensing weights, an online weight learning law is designed. Moreover, to handle certain applications such as forest fire investigation or nuclear radiation leakage mapping where the density information for the events of interest is not known to the MSN, an adaptive law is presented so that the event density approximation of each sensor converges to the real one along its trajectory. In addition, a move-to-centroid control law is proposed to drive the MSN to a near-optimal coverage configuration as time goes to infinity. Finally, the effectiveness of the proposed approach is illustrated by an example.