Coverage Control for Robotic Networks in Dynamic Environments

The coverage control problem involves spatially disseminating a network of mobile agents using distributed control laws, or coverage controllers, over a desired region to locally minimise an associated cost function. Certain nonlinear controllers derived using Lyapunov theory feature desirable properties, such as distributed communication and computation. Previous approaches to the coverage problem have addressed both static and dynamic environments through the choice of density function and agent sensor models. However, stability guarantees under additional dynamics induced by these choices are restricted by significant technical assumptions that simplify the underlying proofs at the expense of limited applicability. In this work, a generalised coverage controller is presented that guarantees practical stability under relaxed technical assumptions. The algorithm, and its convergence, is illustrated via simulation examples. Asymptotic stability of classical Voronoi-based coverage due to Cortes et al.’s coverage controller and its variations have been studied, however rates of convergence are absent. Although convergence guarantees are provided for many of the variations on the coverage control problem, they rarely demonstrate exponential convergence or rely on conjecture to do so. Local exponential convergence opens new avenues of research within coverage control, such as robust controller design or combination with online estimation. This work provides local exponential convergence properties for a multi-agent network using a coverage controller, as well as the existence of a ball around local equilibria for which this holds under the listed assumptions. Mobile robots using coverage controllers will be subject to various disturbances, such as uncertainty, errors and delays. This work shows that a variation of Cortes et al.’s coverage controller also features robust stability properties, specifically input-to-output stability, under the same set of assumptions. Cons