UAS Traffic Management System Research Articles

UAV Detect and Avoid from UTM-Dependent Surveillance

A hierarchical unmanned aircraft system (UAS) traffic management (UTM) system has deployed 45 ground transceiver stations (GTS) for UAS services in Taiwan. This UTM system covers most areas for UAV-dependent surveillance using ADS-B Like technology. UTM Controller can monitor all UAV flights under transparent surveillance in low airspace. Controller-initiated UAV “detect and avoid” (DAA) mechanism assists UAV separation to ensure flight safety on UTM for small multirotor UAVs. From similar concept to traffic alert and collision avoidance system (TCAS) for the manned aircraft system, the UTM software executes DAA functions to generate approach alerts to UTM Controller. Conflict is detected by heading arrow extrapolation from multiple approaching UAVs by their time to conflict (TTC) on icons. Traffic advisory (TA) and resolution advisory (RA) are pronounced on UTM console to controllers. The less priority UAV pilot will receive the controller-pilot communication (CPC) to perform avoidance resolution. In UTM, the surveillance data period is broadcasting at 5~8 seconds on LoRa (long-range wide-area network) chip. Referring to TA=48 seconds and RA=24 seconds, the signal delay in ADS-B Like system to UTM server is about 0.5 seconds and CPC response is measured about 3~5 seconds. From real flight tests, the RA is enough for the less priority pilot to maneuver UAV for avoidance. From real flight tests, the proposed DAA mechanism based on UTM-dependent surveillance is feasible to resolve multiple approaches. The developing UTM system using ADS-B Like technology is also examined of high availability with redundant reliability and performance stability for flight safety.

Dynamic Capacity Management for Air Traffic Operations in High Density Constrained Urban Airspace

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used.

Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with ATM

UTM Architecture and Flight Demonstration in Korea

Unmanned Aircraft System Traffic Management (UTM) is a traffic management system enabling drones to safely and efficiently fly in low-altitude airspace below 120~150m (400~500ft). UTM provides services such as communication, flight route management, location monitoring, and collision avoidance so that drones completing various missions can fly beyond visual line of sight (BVLOS) safely and increase the usability of airspace. In other words, UTM is a new air traffic management for drones with high levels of automation, advanced decision making and control. Many countries around the world are developing UTM systems that systematically manage the traffic of drones flying at low altitude. In Korea, UTM research has been ongoing as an R&D project since 2017. The purpose of this paper is to introduce the Korean UTM system and to apply it to actual flight demonstration through the developed operational procedures. The approach of this article is to establish Korean UTM architecture through existing references and examples from other countries, devise an operational procedure suitable for the system, and describe the results of using it for flight demonstration. In other words, this paper covers Korea’s UTM architecture, operational procedures, and flight demonstration through a macro approach to UTM. In addition, this paper presents policy and technical challenges that UTM must go through and that need to be solved in the future, which are classified into four categories.

An assessment of frameworks for heterogeneous aircraft operations in low-altitude airspace

In the past decade, the utilization of drones has been increasing for various civilian and military applications. The drones’ operations occupy Class G airspace, which is uncontrolled, though it is regulated. The growing number of applications for drones indicates future traffic in Class G airspace. Therefore, many researchers, in both academia and industry, have proposed conceptual frameworks for Unmanned Aircraft Systems Traffic Management (UTM). Implementation of a traffic management system in the Class G airspace is deemed crucial to ensure safe and efficient management of the aircraft – both manned and unmanned – flying in the airspace. This research aimed to investigate if the existing frameworks for UTM comply with the operational and system requirements of an air traffic management system and more precisely for unmanned aircraft operations in a mixed-mode operational environment. This research answers the following research questions: (i) What are the existing UTM frameworks? (ii) What are the operational requirements for a UTM system? (iii) What are the system requirements for a UTM system? (iv) What is the compliance level of all existing UTM frameworks?

Geofence Definition and Deconfliction for UAS Traffic Management

Before Unmanned Aircraft Systems (UAS) can be widely deployed over complex terrain and urban regions, a methodology is needed to deconflict and track UAS flights. This paper presents a formal geofence definition and a methodology to use geofences to temporally and spatially organize the airspace in support of a UAS Traffic Management (UTM) system. Algorithms are presented to temporally and spatially deconflict requested geofences from existing approved geofences in the UTM system. A case study shows how deconflicted geofences impact UAS flight.

Decentralized Multi-Agent Path Finding for UAV Traffic Management

The development of a real-world Unmanned Aircraft System (UAS) Traffic Management (UTM) system to ensure the safe integration of Unmanned Aerial Vehicles (UAVs) in low altitude airspace, has recently generated novel research challenges. A key problem is the development of Pre-Flight Conflict Detection and Resolution (CDR) methods that provide collision-free flight paths to all UAVs before their takeoff. Such problem can be represented as a Multi-Agent Path Finding (MAPF) problem. Currently, most MAPF methods assume that the UTM system is a centralized entity in charge of CDR. However, recent discussions on UTM suggest that such centralized control might not be practical or desirable. Therefore, we explore Pre-Flight CDR methods where independent UAS Service Providers (UASSPs) with their own interests, communicate with each other to resolve conflicts among their UAV operations—without centralized UTM directives. We propose a novel MAPF model that supports the decentralized resolution of conflicts, whereby different ‘agents’, here UASSPs, manage their UAV operations. We present two approaches: (1) a prioritization approach and (2) a simple yet practical pairwise negotiation approach where UASSPs agents determine an agreement to solve conflicts between their UAV operations. We evaluate the performance of our proposed approaches with simulation scenarios based on a consultancy study of predicted UAV traffic for delivery services in Sendai, Japan, 2030. We demonstrate that our negotiation approach improves the “fairness” between UASSPs, i.e. the distribution of costs between UASSPs in terms of total delays and rejected operations due to replanning is more balanced when compared to the prioritization approach.

Traffic management for drones flying in the city

Air Traffic Management (ATM) is designed based on the notion of a pilot navigating an aircraft from within the aircraft. Increasing demand for Unmanned Aircraft Systems (UAS) usage and its safe integration into segregated/non-segregated airspace, on the other hand, have raised a question on the adoption of the current ATM for the UAS Traffic Management (UTM). The Procedures for Air Navigation Services-Air Traffic Management (PAN-ATM) and Procedures for Air Navigation Services - aircraft operations (PAN-OPS) rules are both developed for manned flight operations. Therefore, this work (i) defines the UTM system and describes its envisioned functionalities; (ii) performs an exploratory research to identify the distinctions between a manned and an unmanned flight operation and consequently the possible challenges to implement UTM with regard to the ATM; (iii) discusses potential Communication, Navigation and Surveillance (CNS) technologies to support the UTM system; and (iv) proposes an architectural framework for UTM based on the findings. It is important to highlight that this work is developed based on the assumption that the UAS will operate in the class G airspace (500 ft and below) in urban areas where the UAS are exposed to various obstacles such as static objects: high-rise buildings, trees, lamp posts, over-ground rail tracks and dynamic objects: cranes.

Pre-Flight Conflict Detection and Resolution for UAV Integration in Shared Airspace: Sendai 2030 Model Case

The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and “batch” processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case.

Unmanned Aircraft System traffic management: Concept of operation and system architecture

Abstract Within the past few years, civilian demand for small unmanned aircraft systems (sUAS), commonly referred to as drones, has skyrocketed. The passage of the Federal Aviation Administration (FAA) Modernization and Reform Act in 2012 acknowledged this fact, and has since prompted expedited research and development for civilian sUAS. As proposed at a recent National Aeronautics and Space Administration (NASA) Convention, central to the safe and efficient operations of sUAS will be an unmanned aircraft system traffic management (UTM) system. Such a UTM system will borrow fundamental ideas from large-scale air-traffic control, albeit with several key differences that provide for sUAS which vary in method of control, maneuverability, function, range, and operational constraints. Ultimately, an expansion of UTM infrastructure, a decentralization of governing authority over sUAS operations, and the establishment of a web-interface for pilots to submit flight plans and access crucial data will allow for sUAS operations to shift from being a science-fiction gimmick to an element of daily life. The major objectives of this paper are to: (1) define what a UTM system is; (2) review current UTM practice from industry partners; (3) describe how sUAS pilots would use a typical UTM system, and who has authority over UTM; and (4) determine what physical architecture is required in a UTM system which handles a large variety of sUAS.