Search Results (3)

Controlled Traffic Regions (CTRs) around major airports pose an important challenge to Unmanned Aerial System (UAS) traffic management. Current regulations highly restrict UAS missions in these areas by confining them to segregated areas. This paper makes a proposal to allow more ambitious UAS missions inside CTRs, such as paths across the CTR or between heliports inside the CTR, based on self-separation. This proposal faces two important problems: on the one hand, the adaptive response to the dynamic airspace reconfiguration of a CTR without necessarily terminating the flight, and on the other, a self-managed conflict resolution that allows maintaining traffic separations without the intervention of air traffic controllers. This paper proposes a solution named Reinforcement Learning Multi-Agent Separation Management (RL-MASM). It employs a multi-agent reinforcement learning system with a fully decentralized decision-making scheme, although it uses a common information source of the environment. The proposed system is evaluated against classical control algorithms for obstacle avoidance to determine the potential benefits of AI-based methods. Results show that AI-based methods can benefit from knowing the intent of a UAS. This leads to increased performance in intrusions into no-fly zones or collisions, and also solves some challenging scenarios for classical control algorithms. From the aeronautical point of view, the proposed solution also introduces important advantages in terms of efficiency, scalability, and decentralization. Full article

The operation of unmanned aircraft systems in shared airspace can serve as an accelerator for the global economy and a sensitive addition to the existing mix of transportation modes. For these reasons, concepts of Unmanned Traffic Management have been recently published, defining advanced rules for all potential participants in the operation of unmanned systems. Airspace primarily dedicated to automated unmanned system operations, referred to as U-space in Europe, needs to be designated with consideration for the surrounding airspace. This is especially important in cases where the airspace is controlled, and when declaring U-space airspace, it is necessary to pay particular attention to the density of surrounding air traffic. The goal of this article is to assess the suitability of establishing U-space airspace for Urban Air Mobility in terms of traffic density in a controlled area above the selected metropolis, which is Prague, Czech Republic. To achieve this goal, data on air traffic in the given area were analyzed to obtain precise information about the traffic distribution. Areas in which the establishment of U-space airspace is possible both without implementing dynamic reconfiguration and with the application of the dynamic reconfiguration concept were also selected. The result is the determination of whether it is possible to establish U-space in airspace, as in the analyzed case of the Ruzyně CTR, U-space can be introduced in 83 % of the territory. Full article

With the growth of air traffic demand in busy airspace, there is an urgent need for airspace sectorization to increase air traffic throughput and ease the pressure on controllers. The purpose of this paper is to develop a method framework that can perform airspace sectorization automatically, reasonably, which can be used as an advisory tool for controllers as an automatic system, especially for eliminating irregular sector shapes generated by simulated annealing algorithm (SAA) based on the region growth method. The two graph cutting method, dynamic Monte Carlo method by changing location of flexible vertices (MC-CLFV) and Monte Carlo method by radius changing (MC-RC) were developed to eliminate irregular sector shapes generated by SAA in post-processing. The experimental results show that the proposed method framework of airspace sectorization (AS) can automatically and reasonably generate sector design schemes that meet the design criteria. Our methodology framework and software can provide assistant design and analysis tools for airspace planners to design airspace, improve the reliability and efficiency of airspace design, and reduce the burden of airspace planners. In addition, this lays the foundation for reconstructing airspace with the more intelligent method. Full article