  It is always a challenging task to service sudden events in non-convex and
uncertain environments, and multi-agent coverage control provides a powerful
theoretical framework to investigate the deployment problem of mobile robotic
networks for minimizing the cost of handling random events. Inspired by the
divide-and-conquer methodology, this paper proposes a novel coverage
formulation to control multi-agent systems in the non-convex region while
equalizing the workload among subregions. Thereby, a distributed coverage
controller is designed to drive each agent towards the desired configurations
that minimize the service cost by integrating with the rotational partition
strategy. In addition, a circular search algorithm is proposed to identify
optimal solutions to the problem of lowering service cost. Moreover, it is
proved that this search algorithm enables to approximate the optimal
configuration of multi-agent systems with the arbitrary small tolerance.
Finally, numerical simulations are implemented to substantiate the efficacy of
proposed coverage control approach.
