Exact and Heuristic Approaches to Surveillance Routing with a Minimum Number of Drones

Abstract

The rising cost and scarcity of human labor pose challenges in security patrolling tasks, such as facility security. Drones offer a promising solution to replace human patrols. This paper proposes two methods for finding the minimum number of drones required for efficient surveillance routing: an ILP-based method and a greedy method. We evaluate these methods through experiments, comparing the minimum number of required drones and algorithm runtime. The findings indicate that the ILP-based method consistently yields the same or a lower number of drones needed for surveillance compared to the greedy method, with a 73.3% success rate in achieving better results. However, the greedy method consistently finishes within one second, whereas the ILP-based method sometimes significantly increases when dealing with 14 more locations. As a case study, we apply the greedy method to identify the minimum drone surveillance route for the Osaka-Ibaraki Campus of Ritsumeikan University.