Air Traffic Control and Flight Planning for Cargo UAVS in a Single Airspace

Abstract

This article piece analyzes in detail the challenges and approaches to incorporating large cargo Unmanned Aerial Vehicles into existing airspace, focusing on air traffic management and flight operations planning. Modifications to air traffic control models required as a result of the inclusion of Unmanned Aerial Vehicles are discussed, with a focus on managing the various stochastic parameters that affect flight quality. The importance of maintaining safe takeoff and landing intervals is also emphasized. In addition, the article delves into the application of machine learning techniques in air traffic flow management, exploring the complexities of planning scheduled and special flights under conditions of incomplete information and stochastic uncertainty. The article highlights the importance of optimizing the payload-to-range ratio of Unmanned Aerial Vehicles, taking into account factors such as wind conditions and battery efficiency to improve cargo transport efficiency. In addition, the article presents a two-stage stochastic programming model for flight planning aimed at minimizing costs and efficiently allocating flights and resources. The impact of drones on "last mile" delivery logistics is also discussed, emphasizing the potential benefits of drones in terms of increased speed, cost reduction, and access to remote areas.