Indirect/Direct Learning Coverage Control for Wireless Sensor and Mobile Robot Networks

Abstract

This article proposes indirect/direct learning control schemes for wireless sensor and mobile robot networks to cover an environment according to the density function, which is the distribution of an important quantity within the environment. When stationary sensors cooperate with mobile robots, the density estimation can be enhanced by using nonstationary basis functions to relax the assumption of matching conditions in the previous approach. To improve the density function estimation, this study employs an expectation–maximization algorithm and log-likelihood, which maximizes the similarity between the proposed normalized density and normalized coverage function. Subsequently, the adaptive weighting algorithm is combined with the proposed indirect coverage control for tunable basis centers and the weighting of the basis functions. For direct coverage control, mobile robots are driven to cover the regions of higher importance while simultaneously estimating the density function utilizing a sensory model function. We prove that the Lloyd algorithm is a special case of the direct method when the density function and Voronoi partitions are available. The efficiency of the proposed methods is confirmed in numerical examples and semiexperiments.