Persistent Covering With Latency and Energy Constraints

Most consumer-level low-cost unmanned aerial vehicles (UAVs) have limited battery power and long charging time. Thus, they cannot accomplish some practical tasks such as providing service to cover an area for an extended time, also known as persistent covering. Algorithmic approaches are limited mostly due to the computational complexity and intractability of the problem. Approximation algorithms that assume unlimited energy have been considered to segment a large area into smaller cells that require periodic visits under the latency constraints. In this letter, 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 experimentally demonstrate that the proposed algorithm has better performance than the baseline.