Generalized Coverage Control for Time-Varying Density Functions

Abstract

The coverage control problem for robotic networks focuses on distributively coordinating the positioning of multiple dynamic agents to provide sensor coverage across a bounded region in two dimensional space. The associated optimal coverage problem seeks to position these agents so as to minimize an associated coverage cost. This coverage cost is typically defined with respect to a density function that is used to bias the network towards desired configurations. Previous approaches to this optimal coverage problem have addressed both static and dynamic environments through the choice of density function; however, stability guarantees for time-varying densities are restricted by significant technical assumptions that simplify the underlying proofs at the expense of limited applicability. In this paper, a generalized algorithm is presented that guarantees practical stability under relaxed technical assumptions. The algorithm, and its convergence, is illustrated via simulation examples.