Context-aware coverage path planning for a swarm of UAVs using mobile ground stations for battery-swapping

Abstract The usage of swarms of drones is expected to continue growing in the next years, particularly in dangerous scenarios, such as monitoring and rescue missions in hostile and disaster areas. Small-sized Unmanned Aerial Vehicles (UAVs) are highly suitable for use in such scenarios due to their agility and maneuverability. On the other hand, their limited battery capacity poses significant challenges, especially during missions requiring full coverage of large areas in a short time and extreme weather conditions. This work proposed an energy efficiency approach, which makes use of mobile ground-based battery-swapping stations (BSSes), to speed up the UAV’s battery replacement and reduce energy waste in the round trip to the charging station. Specifically, a Context-Aware Coverage Path Planning (CACPP) problem has been formulated to determine the complete coverage path of a large area by a swarm of UAVs, minimizing the path overlapping and UAV battery swapping. The model takes into account the need to continue re-planning the mission, depending on the weather conditions (i.e., temperature and wind), the presence of obstacles, and the residual energy levels of the drones, as well as the relative positions of the drones and mobile BSSes. To solve the CACPP problem, an iterative approach leveraging two synchronized optimization models for planning UAV paths and BSS routes has been presented. As the CACPP problem is NP-hard, a heuristic procedure for solving it has also been evaluated. Experimental results show that it can be appropriate for large instances of the problem.