Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

Drones have started to be used for surveillance within the cities, visually scanning the predefined zones, quickly detecting abnormal states such as fires, accidents, and pollution, or assessing the disaster zones. Coverage Path Planning (CPP) is a problem that aims to determine the most suitable path or motion plan for a vehicle to cover the entire desired area in the task. So, this paper proposes a novel two-dimensional coverage path planning (CPP) mathematical model with the fact that a single drone may need to be recharged within its route based on its energy consumption, and the obstacles must be avoided while constructing the route. Our study aims to create realistic routes for drones by considering multiple charging stations and obstacles for surveillance. We tested the model for a grid example based on the scenarios obtained by changing the layout, the number of obstacles and recharging stations, and area size using the Python Gurobi Optimization library. As a contribution, we analyzed the impact of the number of existing obstacles and recharging stations, the size and layout of the area to be covered on total energy consumption, and the total solution time of CPP in our study for the first time in the literature, through a detailed Scenario Analysis. Results show that the map size and the number of covered cells affect the total energy consumption, but different layouts with shuffled cells are not effective. The area size to be covered affects the total computation time, significantly. As the number of obstacles and recharging stations increases, the computation time decreases up to a certain limit, then stabilizes.