Distributed Coverage Control of Mobile Sensors: Generalized Approach using Distance Functions

Abstract

This paper proposes a general framework for distributed coverage control of mobile robotic sensors. We pose the multiagent coverage problem as an optimization problem over the space of density functions. We show that the popular locational optimization framework for coverage can be viewed as a special case of optimizing the Kullback-Leibler divergence in the space of density functions. We also see that more general approaches to distributed coverage control can be formulated based on minimizing different notions of distances. In particular we consider the $\mathcal{L}^{2}$ -distance as a possible metric to design distributed coverage control laws.