| Popis: |
Recently, the emergence of Unmanned Aerial Vehicles (UAVs) has garnered significant attention due to their widespread applications, such as surveillance, mapping, reconnaissance, as well as commercial delivery, and photography. Despite the tremendous applications of UAVs, there are potential risks associated with drones that may impact flight safety. For instance, launching and releasing drones near airfields can pose serious threats to flight safety. Another challenge is the flying altitude. Flying at high altitudes might cause a collision with other aircraft, and flying at low altitudes can also pose a significant threat due to obstacles in the environment. Various regulations, such as airspace restrictions, flight altitude limits, and safety requirements, can limit the number of UAVs operating in a particular area. This, in turn, makes it challenging to specify the number of UAVs that can operate safely. To address these challenges, we propose an optimization strategy to maximize the number of UAVs that can operate while adhering to regulatory constraints. The problem is formulated and solved using an improved version of a population-based meta-heuristic, IPSO. In the proposed approach, we consider two distinct objective functions. The first one is the local objective function, which aims to minimize the energy consumption of the generated path by IPSO. This objective function is crucial in ensuring that the generated path is energy-efficient. The second objective function is the global objective function of the proposed approach, and aims to maximize the number of UAVs that can operate in a specific area. The proposed approach studies the impact of regulations such as obstacles and flying altitude on a region capacity. The results show that the proposed approach successfully increases the the region capacity, i.e., number of UAVs, to the maximum possible while ensuring safety and regulatory constraints. |
