Sectorial coverage control with load balancing in non-convex hollow environments

It has long been a challenging task for multi-agent systems (MASs) to inexpensively service probable events in non-convex environments. Coverage control provides an efficient framework to address MAS deployment problem for optimizing the cost of tackling unknown events. By means of the divide-and-conquer methodology, this paper proposes a sectorial coverage formulation to configure MASs in non-convex hollow environments while ensuring load balancing among subregions. Thereby, a distributed controller is designed to drive each agent towards a desirable configuration that minimizes the coverage cost by simultaneously adopting sectorial partition mechanism. Theoretical analysis is conducted to ensure the asymptotic stability of closed-loop MASs with exponential convergence of equitable partition. In addition, a circular search algorithm is proposed to identify desirable solutions to such a sectorial coverage problem, which guarantees approximating the optimal deployment of MASs with arbitrarily small tolerance. Finally, both numerical simulations and multi-robot experiments are conducted to substantiate the efficiency of the present sectorial coverage approach.