  Most consumer-level low-cost unmanned aerial vehicles (UAVs) have limited
battery power and long charging time. Due to these energy constraints, they
cannot accomplish many practical tasks, such as monitoring a sport or political
event for hours. The problem of providing the service to cover an area for an
extended time is known as persistent covering in the literature. In the past,
researchers have proposed various hardware platforms, such as battery-swapping
mechanisms, to provide persistent covering. However, algorithmic approaches are
limited mostly due to the computational complexity and intractability of the
problem. Approximation algorithms have been considered to segment a large area
into smaller cells that require periodic visits under the latency constraints.
However, these methods assume unlimited energy. In this paper, we explore
geometric and topological properties that allow us to significantly reduce the
size of the optimization problem. Consequently, the proposed method can
efficiently determine the minimum number of UAVs needed and schedule their
routes to cover an area persistently. We demonstrated experimentally that the
proposed algorithm has better performance than the baseline methods.
