UAS Traffic Management Research Articles

DIM-Based Random Number Generation Using Quantum Noise Resources

Currently, unmanned aircraft systems (UASs) or drones are in service in various industrial fields, and each UAS operator establishes and operates their own independent drone system. These individual drone systems interact only with their own components without any integrated management. As the number of UASs is increasing due to the expansion of the drone industry, standardized operation is required. Therefore, to integrate and manage existing drone systems, the Federal Aviation Administration and National Aeronautics and Space Administration devised UAS Traffic Management (UTM). The drone identity module (DIM), which is being developed as a drone identification device, securely stores the remote identification (RID) of each drone and performs a cryptographic operation to secure information between the drone and UTM infrastructure. The DIM performs cryptographic authentication protocols to achieve cryptographic identification and authentication with the UTM infrastructure, which requires random numbers. Modern cryptographic systems rely on difficult computations, and an environment capable of generating secure cryptographic random numbers must be configured to provide high computational costs to attackers. In this paper, we explain the need for random numbers in the DIM, analyze random number generators used in related drone-based studies, and analyze the characteristics of noise resource generation devices that can be used in existing drone systems. Subsequently, based on the analysis results, existing methods are used to generate random numbers in the DIM, and limitations are derived. To overcome these limitations, we propose a method of generating random numbers in the DIM using quantum noise resources. For our proposal, we conduct an analysis of the physical specifications of noise resource generation devices, DIM prototypes, and quantum noise resource generators in existing drone systems, and we present the results of NIST 800-90B entropy measurement using data collected from quantum random number generators.

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.

Lane-Based Large-Scale UAS Traffic Management

The FAA and NASA are developing an Advanced Air Mobility (AAM) capability defining an Unmanned Aircraft Systems (UAS) Traffic Management (UTM) architecture. The combined scale and density of the expected air traffic, as well as the algorithmic complexity of maintaining safe separation, are driving a consensus that a structured airspace will eventually be required. Against this background, a lane-based airspace structure is proposed here whose motivation is to reduce the computational complexity of strategic deconfliction by providing UAS agents with a set of pre-defined airway corridors called lanes. To achieve complexity reduction, an airspace is defined that is composed of a directed graph where every node has either input or output degree equal to one, and flight plans consist of a scheduled sequence of lane traversals. The major results are: (1) the creation and layout of lane structures, (2) an efficient lane-based strategic deconfliction scheduling algorithm, (3) lane-network performance analysis tools, and (4) a tactical deconfliction protocol to handle dynamic contingencies (e.g., failure to follow the nominal flight plan). In conclusion, this approach provides efficient scheduling of safe flight paths, straightforward analysis of stream properties of the transportation system, an effective contingency handling protocol, and scalability to thousands of flights over urban areas.

A route network planning method for urban air delivery

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.

Multi-Agent Path Finding in Unmanned Aircraft System Traffic Management With Scheduling and Speed Variation

The development of an unmanned aircraft system (UAS) traffic management (UTM) system for the safe integration of unmanned aerial vehicles (UAVs) requires pre-flight conflict detection and resolution (CDR) methods to provide collision-free flight paths for all UAVs before takeoff. A popular solution consists in adapting multi-agent path finding (MAPF) techniques. However, standard MAPF solvers consider only a fixed takeoff time and fixed uniform speed for each UAV flight path, which can lead to inefficiencies in the resolution of instances. Therefore, in this article, we propose incorporating scheduling elements into MAPF solvers, which allows us to adjust the takeoff times and speeds of each UAV to solve conflicts. We introduce two time-related resolution techniques: 1) takeoff scheduling, whereby the start time of a UAV agent is delayed, and 2) speed adjustment, wherein the speed of a UAV agent is decreased over a segment on its flight path. Importantly, we present a distinction of conflict types, which enables us to combine replanning resolution to the aforementioned temporal resolution techniques. We evaluate our proposed approaches on a realistic, high-density UAV delivery scenario in Tokyo, Japan. We show that the combination of takeoff scheduling, replanning, and speed-adjustment resolution techniques improves the efficiency of route planning by reducing the average delay per flight path and the number of rejected flight paths.

Traffic management and resource allocation for UAV-based parcel delivery in low-altitude urban space

This research proposes a framework of Unmanned Aircraft Vehicles (UAV) system traffic management in the context of parcel delivery in low-altitude urban airspace, including clustering-based UAV path planning, Unmanned Aircraft System Traffic Management (UTM) with conflict detection and resolution (CD&R), and mechanism design for airspace resource allocation. For UAV path planning, we develop a procedure by first clustering a large variety of obstacles that arise from building heights and terrain topology and can impede UAV flying. Based on the clustered obstacles, Saturated Fast-Marching Square (Saturated FM2) algorithm is then employed to generate optimal and alternative paths for each UAV mission. While identifying the optimal and alternative paths does not consider UAV traffic interactions, several traffic management models are proposed to efficiently allocate spatial and temporal airspace resources to UAV missions. The UTM models determine the departure time and the path to take for each UAV flight while resolving path conflicts from different perspectives. Specifically, four UTM models are proposed: Sequential Delay (SD) Model, Sequential Delay/Reroute (SDR) Model, Full Optimization (FO) Model, and Batch Optimization (BO) Model. Among the four models, the BO model is of particular interest as it strikes a balance between seeking a system optimum solution and maintaining computational tractability. Given that traffic management requires private information from UAV operators, the Vickrey-Clarke-Groves (VCG) mechanism is further adapted to the UTM context, in which airspace resource allocation is performed in conjunction with a payment scheme to incentivize truthful private information reporting by UAV operators. Extensive numerical analysis is conducted with San Francisco as the case study area. The results show the effectiveness of the proposed framework, particularly the scalability of the BO model. We also find that payment by a UAV flight under the adapted VCG mechanism depends critically on traffic density and the extent of interaction the UAV flight has with other flights.

Mobile Networks’ Support for Large-Scale UAV Services

The services based on Unmanned Aerial Vehicles (UAVs) have started to be used in many countries but not on a large scale yet. The paper describes the present status of UAV services and a concept of a solution for large-scale deployment of safe and reliable UAV services that use the 5G network for communication with UAVs. Based on the Standards Developing Organizations and related industry fora activities, unmanned traffic and airspace regulations, especially the commonly supported concept of Unmanned Aircraft Systems Traffic Management (UTM), UAV-related communication requirements and 5G System (5GS) features, we have developed an alternative approach to the integration of UAV and 5GS. The proposed concept includes a set of enablers that can be provided by Mobile Network Operators in order to not only support but also leverage UAV services. As work on many items is still in progress, we identify a list of open issues and challenges and present them at the end of the paper with the main focus on the MNO–UTM provider relationship and safe flights.

Kentsel Hava Sahasında İnsansız Hava Aracı Sistemleri Trafik Yönetimi için Verilmesi Gereken Hizmetler ve Kullanılabilecek Bazı Teknolojiler

İnsansız hava araçları, günümüzde eğlence ve hobi amacıyla tercih edilmesinin yanı sıra, tarımsal faaliyetlerde, güvenlik amaçlı gözetlemede, arama-kurtarma faaliyetlerinde ve daha birçok alanda etkin bir şekilde kullanılmaya başlanmıştır. Yaşanan gelişmeler başta ulaşım ve havacılık sektörü olmak üzere diğer birçok sektörün de ilgisini çekmiştir. Dronlar, uzaktan pilotlu hava aracı sistemleri (UPHAS), otonom hava araçları, henüz konsept teknolojiler olarak karşımıza çıkan sürücülü ve sürücüsüz kişisel hava araçları (KHA) gelecekte günlük yaşantımızın bir parçası olarak insanların yaşadığı yerleşim yerleri ve civarında ciddi bir kentsel hava hareketliliği (KHH) oluşturacaktır. Kentsel hava sahasında gerçekleşecek olan bu hareketliliğin, mevcut hava trafik yönetimi (HTY) ile uyumlu bir şekilde yönetilmesi büyük önem arz etmektedir. İnsansız hava aracı sistemleri trafik yönetiminin (İTY) ihtiyaçları mevcut hava trafik yönetiminden farklıdır. Ancak mevcut teknolojilerle beraber yeni teknolojiler kullanılarak insansız hava araçları trafik yönetimi için çözümler sunulabilir. Bu çalışmada, kentsel hava sahasını kullanacak insansız hava araçlarının emniyetli ve düzenli trafik yönetimi için verilmesi gereken hizmetler ve kullanılabilecek bazı teknolojiler önerilerek kentsel hava hareketliliği sistem altyapısı için temel bir model oluşturulmaya çalışılmıştır.

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?

Evaluation of a Multi-Mode-Transceiver for Enhanced UAV Visibility and Connectivity in Mixed ATM/UTM Contexts

Visibility and communication are the essential pillars for safe flight operations in dense airspaces. Small Unmanned Aerial Vehicles (UAVs) of the order of up to 25 kg are increasingly being used at airports as a cost-effective alternative for maintenance and calibration work. However, the joint operation of manned and unmanned aircraft in busy airspaces poses a major challenge. Due to the small diameter of such UAVs, the established principle of “see and avoid” is difficult or even impossible to implement, especially during take-off and landing. For this reason, a certified Mode A/C/S transponder supporting ADS-B was extended with an embedded system and a cellular interface to realize a Multi-Mode-Transceiver (MMT). Integrated into a UAV, the MMT can provide aircraft visibility in the context of traditional manned Air Traffic Management (ATM) and future UAS Traffic Management (UTM) at the same time. This multimodal communication approach was investigated in flight test campaigns with two commercially available UAS that were connected to an experimental UTM with a simulated controlled airspace. The results confirm the safety gain of the multimodal cooperative approach. Furthermore, the collaborative interface with ATC enables the digital transmission of transponder codes, entry clearances and emergency procedures without the need for a voice radio communication. However, the parallel operation of both radio technologies in a confined space requires modifications to the transmission power and alignment of the radio antennas to avoid mutual interference. Furthermore, different reference planes of barometric altitude measurement in manned and unmanned aviation pose additional challenges that need to be addressed.

Economic policy choices and trade-offs for Unmanned aircraft systems Traffic Management (UTM): Insights from Europe and the United States

Despite expectations of substantial growth in Unmanned Aircraft Systems (UAS, also called drones) there is currently no large-scale system in place to manage UAS operating in shared airspace. Many countries are developing UAS Traffic Management (UTM) concepts which, unlike conventional air traffic management (ATM), seek to foster a competitive market involving many private actors and service providers and which rely on the cooperative and distributed management of UAS. This paper focusses on the economic policy dimensions of UTM. It describes the main participants, activities and commercial interactions of the emerging UTM concepts in the European Union and the United States. Building on these descriptions, we then consider some of the fundamental choices and trade-offs confronting policymakers when developing an economic policy framework for UTM such as: which activities should be competitive; how can access to scarce airspace be allocated in a safe, fair and efficient way; on what commercial terms will data be shared among participants; what infrastructure can cater for large scale UAS operations; and to what extent should the economic regulatory frameworks for UTM and ATM be aligned.

UAS Traffic Management Communications: The Legacy of ADS-B, New Establishment of Remote ID, or Leverage of ADS-B-Like Systems?

Unmanned aerial system (UAS) traffic management (UTM) requires each UAS to communicate with each other and to other stakeholders involved in the operation. In practice, there are two types of wireless communication systems established in the UAS community: automatic dependent surveillance broadcast (ADS-B) and remote identification (Remote ID). In between these two systems, there is ADS-B-like communication which leverages using other types of communications available in the market for the purpose of UTM. This review aims to provide an insight into those three systems, based on the published standard documents and latest research development. It also suggests how to construct a feasible communication architecture. The integrative approach is used in this literature review. The review categorization includes definition, data format, technology used, and research applications, and any remaining issues are discussed. The similarities and differences of each system are elaborated, covering practical findings. In addition, the SWOT analysis is conducted based on the findings. Lastly, multi-channel communication for UTM is proposed as a feasible solution in the UTM operation.

Uncertainty-Driven Ontology for Decision Support System in Air Transport

Recent electronics advances for air transport have increased aircraft density, volume, and frequency in the airspace. These advances come with control requirements for precise navigation, coordinated Air Traffic Management (ATM) or Unmanned aircraft system Traffic Management (UTM), and proactive security. The tight tolerances of aircraft control necessitate management of spatial uncertainty, timeliness precision, and confidence assessment, which have, respectively, variance, reliability, and veracity situation awareness and assessment metrics. Meeting such airspace requirements involves the ability to evaluate how those metrics impact ATM/UTM operations, making the complex interrelationships between them a key aspect for coping with the fast worldwide growth of air transport. To support such growth, ontologies have been proposed as a promising technology for making such interrelationships explicit, while facilitating communication between avionics devices. This paper investigates the use of ontologies in support of electronic ATM/UTM operations, highlighting the use of Uncertainty Representation and the Reasoning Evaluation Framework (URREF) in realizing the ability for Air Traffic Controllers (ATCs) to semantically communicate with aircraft operators concerning physical airspace coordination. Using Avionics Analytics Ontology (AAO) endowed with the URREF, application examples based on two airspace situations are presented. Example results for northeast coast of Brazil atmospheric volcanic ash as well as for the Eyjafjallajokull volcano eruption show a 65–80% success in providing warnings to ATCs for airspace control. The paper demonstrates that an ontology-based UTM enhances the capability and accuracy of an ATM to suggest rerouting in the presence of remarkably deteriorated weather conditions.

Investigation of Using Sky Openness Ratio as Predictor for Navigation Performance in Urban-like Environment to Support PBN in UTM.

One of the causes of positioning inaccuracies in the Unmanned Aircraft System (UAS) is navigation error. In urban environment operations, multipaths could be the dominant contributor to navigation errors. This paper presents a study on how the operation environment affects the lateral (horizontal) navigation performance when a self-built UAS is going near different types of urban obstructions in real flight tests. Selected test sites are representative of urban environments, including open carparks, flight paths obstructed by buildings along one or both sides, changing sky access when flying towards corners formed by two buildings or dead ends, and buildings with reflective glass-clad surfaces. The data was analysed to obtain the horizontal position error between Global Positioning System (GPS) position and ground truth derived from Real Time Kinematics (RTK), with considerations for (1) horizontal position uncertainty estimate (EPH) reported by the GPS receiver, (2) no. of visible satellites, and (3) percentage of sky visible (or sky openness ratio, SOR) at various altitudes along the flight paths inside the aforementioned urban environments. The investigation showed that there is no direct correlation between the measured horizontal position error and the reported EPH; thus, the EPH could not be used for the purpose of monitoring navigation performance. The investigation further concluded that there is no universal correlation between the sky openness ratio (SOR) seen by the UAS and the resulting horizontal position error, and a more complex model would need to be considered to translate 3D urban models to expected horizontal navigation uncertainty for the UAS Traffic Management (UTM) airspace.

Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems

Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM.

Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning

Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM). There are a variety of techniques for real-time robust drone guidance, but numerous of them solve in discretized airspace and control, which would require an additional path smoothing step to provide flexible commands for UAS. To deliver safe and computationally efficient guidance for UAS operations, we explore the use of a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to lead autonomous UAS to their destinations while bypassing obstacles through continuous control. The proposed scenario state representation and reward function can map the continuous state space to continuous control for both heading angle and speed. To verify the effectiveness of the proposed learning framework, we conducted numerical experiments with static and moving obstacles. Uncertainties associated with the environments and safety operation bounds are investigated in detail. Results show that the proposed model is able to provide accurate and robust guidance and resolve conflict with a success rate of over 99%.

Generalized Path Planning for UTM Systems With a Space-Time Graph

Motivated by the increased use of UAS in commercial applications, in this paper we tackle the problem of path planning when requests are submitted by UAS managed by different operators. We propose the new problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalized path planning</i> for UAS Traffic Management, where the UAS path is described by operators with a sequence of waypoint groups and a solution trajectory must pass through a waypoint in each group. This problem is typical for applications where multiple charging stations and pickup/drop-off locations are distributed in a flight area. Our solution builds upon prior work on discretized space-time graph path planning and proposes a novel multi-source/multi-destination graph search algorithm that generates collision-free trajectories for pre-flight CDR. Our efficient algorithm has runtime proportional to the number of groups and avoids combinatorial explosion. We apply our mechanism to the energy-constrained UAS package delivery problem with multiple warehouses and battery charging stations. Simulation results show that our algorithm is efficient and scalable with the number of requests and graph size. The addition of charging stations and the option for multiple warehouses increases the request admission ratio and reduces the overall trajectory duration, effectively improving both the planner’s quality of service and the efficiency of airspace usage.

Automated Playbook for UAV Traffic Management Based on Spatiotemporal Scenario Data

This paper develops a decision framework to automate the playbook for UAS traffic management (UTM) under uncertain environmental conditions based on spatiotemporal scenario data. Motivated by the traditional air traffic management (ATM) which uses the playbook to guide traffic using pre-validated routes under convective weather, the proposed UTM playbook leverages a database to store optimal UAS routes tagged with spatiotemporal wind scenarios to automate the UAS trajectory management. Our perspective is that the UASs, and many other modern systems, operate in spatiotemporally evolving environments, and similar spatiotemporal scenarios are tied with similar management decisions. Motivated by this feature, our automated playbook solution integrates the offline operations, online operations and a database to enable real-time UAS trajectory management decisions. The solution features the use of similarity between spatiotemporal scenarios to retrieve offline decisions as the initial solution for online fine tuning, which significantly shortens the online decision time. A fast query algorithm that exploits the correlation of spatiotemporal scenarios is utilized in the decision framework to quickly retrieve the best offline decisions. The online fine tuning adapts to trajectory deviations and subject to collision avoidance among UASs. The solution is demonstrated using simulation studies, and can be utilized in other applications, where quick decisions are desired and spatiotemporal environments play a crucial role in the decision process.

Efficient and fair traffic flow management for on-demand air mobility

The increased use of drones and air-taxis is expected to make airspace resources more congested, necessitating the use of unmanned aircraft systems traffic management (UTM) initiatives to ensure safe and efficient operations. Typically, strategic UTM involves solving an optimization problem that ensures that proposed flight schedules do not exceed airspace and vertiport capacities. However, the dynamic nature and low lead-time of applications such as on-demand delivery and urban air mobility traffic may reduce the efficiency and fairness of strategic UTM. We first discuss the adaptation of three fairness metrics into a traffic flow management problem (TFMP). Then, with computational simulations of a drone package delivery scenario in Toulouse, we evaluate trade-offs in the TFMP between efficiency and fairness, as well as between different fairness metrics. We show that system fairness can be improved with little loss in efficiency. We also consider two approaches to the integrated scheduling of both high lead-time flights (i.e., flights with a schedule known in advance) and low lead-time flights in a rolling horizon optimization framework. We compare the performance of both approaches for different horizon lengths and under varying proportions of high and low lead-time flights.

Guest Editorial Introduction to the Special Issue on Unmanned Aircraft System Traffic Management

Advances of unmanned aircraft system (UAS) technology have spurred a rapid investment of commercial UAS use in broad public domains, such as cargo transport, agriculture support, emergency response, on-demand communication, and infrastructure health monitoring. Urban unmanned aerial transportation that can transport passengers over short distances is also on the way. With the forthcoming dense operations of UAS particularly over urban regions, ensuring airspace safety becomes an urgent issue.