Large-Scale UAS Traffic Management (UTM) Structure

An Unmanned Aircraft System (UAS) Traffic Management System (UTM) relies significantly on automation, introducing the need for efficient and accurate trajectory computation to enable coordination and safety. The main objective of this paper is to present and to organize prior work and relevant concepts with the goal of developing a framework for UAS trajectory prediction in the presence of anomalous events. Literature documenting UAS safety and risk assessment has provided multiple pointers for identification and characterization of system failures that cause trajectory deviations or changes to its associated qualities. A UAS trajectory modeling framework considering endogenous and exogenous factors affecting the trajectory is introduced and used in this exposition. In addition, a general formulation of the trajectory computation challenge is presented along with key requirements for potential solution approaches.

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.

The Unmanned Aircraft System (UAS) Traffic Management (UTM) effort at NASA aims to enable access to low-altitude airspace for small UAS. This goal is being pursued partly through partnerships that NASA has developed with the UAS stakeholder community, the FAA, other government agencies, and the designated FAA UAS Test Sites. By partnering with the FAA UAS Test Sites, NASA's UTM project has performed a geographically diverse, simultaneous set of UAS operations at locations in six states. The demonstrations used an architecture that was developed by NASA in partnership with the FAA to safely coordinate such operations. These demonstrations—the second or Technical Capability Level (TCL 2) National Campaign of UTM testing—was performed from May 15 through June 9, 2017. Multiple UAS operations occurred during the testing at sites located in Alaska, Nevada, Texas, North Dakota, Virginia, and New York with multiple organizations serving as UAS Service Suppliers and/or UAS Operators per the specifications provided by NASA. By engaging various members of the UAS community in development and operational roles, this campaign provided initial validation of different aspects of the UTM concept including: UAS Service Supplier technologies and procedures; geofencing technologies/conformance monitoring; ground-based surveillance/sense and avoid; airborne sense and avoid; communication, navigation, surveillance; and human factors related to UTM data creation and display. Additionally, measures of performance were defined and calculated from the flight data to establish quantitative bases for comparing flight test activities and to provide potential metrics that might be routinely monitored in future operational UTM systems.

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.

Natural disasters, such as flooding, wildfire, hurricane, tornadoes, earthquakes and tsunamis, pose challenges in preserving human life and minimizing the damages to a region. During catastrophic events, timely response of disaster relief personnel, an efficient deployment of resources in the recovery effort, and coordinated information sharing amongst different relief agencies can make a substantial difference in responding to those impacted by the disaster. Many relief activities currently utilize both ground personnel and manned airborne assets during different phases of the disaster response. Typically, multiple organizations support relief activities and this often creates logistics coordination challenges between agencies which can result in wasted time or resources. The Japan Aerospace Exploration Agency (JAXA) has been developing an Integrated aircraft operation system for disaster relief (D-NET), which assists collection and sharing of disaster information through the integrated operation of aircraft such as helicopters, aircraft, and satellites, for efficient and safe rescue operations by disaster relief aircraft. Due to the advancement in unmanned aircraft systems (UAS) technologies, public safety organizations have started incorporating small UAS (sUAS) as an asset in their disasters response activities. To address the airspace integration challenges of the influx of sUAS in the United States the National Aeronautics and Space Administration (NASA), under the UAS Traffic Management (UTM) project, has been engaged in research to enable large-scale commercial applications of sUAS operating in low altitude airspace. This paper presents the integration of D-NET, which incorporate sUAS in the planning, information sharing, and operation support of disasters response activities, and UTM, which provides airspace management to enable large scale high density operations. The integration of the DNET and UTM systems enables coordination, data sharing, and airspace management to improve the timeliness of the disaster response, enable relief organization to reduce cost and overhead by using UAS assets and still maintain airspace safety during the relief activities.

This study examines whether unmanned aircraft systems (UAS) operated in the context of UAS traffic management (UTM) can be properly operated in its flight environment. In detail, this study examines the influencing navigation factors affecting UASs during flight and examines factors affecting the navigation of UASs under UTM. After deriving various factors affecting navigation, their importance are determined by applying the analytic hierarchy process technique, and the important influencing factors are examined. For low-altitude UAS navigation, errors are classified into navigation-system and flight-technical errors, and a hierarchy is constructed for their sub-factors affecting the influencers. Through this, influencing factors for precise navigation of low-altitude UAS are analyzed, and high importance items are identified

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.

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management.

This paper develops a novel physics-inspired traffic coordination approach and applies it to Unmanned Aircraft System (UAS) traffic management. We extend available physics-inspired approaches previously applied to 1-D traffic flow on highways and urban streets to support models of traffic coordination in higher dimension airspace for cases where no predefined paths exist. The paper considers airspace as a finite control volume while UAS coordination, treated as continuum deformation, is controlled at the airspace boundaries. By partitioning airspace into planned and unplanned spaces, the paper models nominal coordination in the planned airspace as the solution of a partial differential equation with spatiotemporal parameters. This paper also improves resilience to vehicle failures with a resilient boundary control algorithm to update the geometry of the planned space when UAS problems threaten safe coordination in existing navigable airspace channels. To support UAS coordination at the microscopic level, we propose clustering vehicles based on vehicle performance limits. UAS clusters, with each UAS treated as a particle of a virtual rigid body, use leader-follower containment to acquire the macroscopic desired trajectory.

An unmanned aircraft system traffic management (UTM) system to support flights beyond visual line-of-sight is considered necessary for the promotion of commercial drone use. In the research and development of UTM systems, cost and time constraints make it difficult to actually fly a large number of drones in the same airspace, so research is mainly conducted using a simulator. This paper presents details of a UTM simulator named the “scalable simulator for knowledge of low-altitude environment” (SKALE) developed by the Japan Aerospace Exploration Agency (JAXA), with respect to the construction of a model case of drone delivery model set in 2030 in Japan. Moreover, UTM concepts for airspace safety and efficient airspace utilization (parcel transport) are proposed and evaluated using JAXA’s UTM simulator and drone delivery model cases. Simulation results are discussed, and the knowledge gained for the improvement of airspace safety and airspace utilization (parcel transport) efficiency is documented.

Urban air mobility is gaining interest as the need for On Demand Mobility in today's congested traffic is becoming high in metropolitan areas. Urban Air Mobility (UAM) is envisioned as a concept to transport passengers and cargo safely and efficiently using innovative aircraft in the urban areas. It is expected to improve mobility for the general public, decongest road traffic, reduce transport time and reduce the strain on existing public transport networks. There exist several challenges to Urban Air Mobility (UAM) such as integration of procedures with airspace and the airport, noise levels that are acceptable to the general public, public safety, public acceptance, vehicle certification, and more. Most of the research in the United States and European skies (DLR - German Aerospace Center) related to urban areas has focused on small UAS (Unmanned Aircraft Systems) flights (NASA's UTM (UAS Traffic Management) research) and their integration with the airspace and building safe operations in densely populated areas. Previous studies on UAM have focused on fast time simulations of the routes that are separated via a separation service and network of routes. Similarly, research in Europe has focused on the approach profile for these innovative aircraft, vertiports and battery life among others. UAM as a part of the On-Demand Mobility effort has provided some guidelines for operations as shown below: Does not require additional ATC (Air Traffic Control) infrastructure; Does not impose additional workload on ATC; Does not restrict operations of traditional airspace users; Will meet appropriate safety thresholds and requirements; Will prioritize operational scalability; Will allow flexibility where possible and structure where necessary. This paper explores potential routes and procedures in a Human-In-The-Loop (HITL) experiment that could be applied in the near-term to allow integration of UAM flights into the airspace as well as a large airport. The airspace that was explored was Dallas Fort Worth (DFW) airspace managed by the DFW East Tower in South Flow only. In addition, Dallas Love Field (DAL) and Addison (ADS) airspace were also part of the testbed. The initial set of routes investigated in this study were published helicopter routes in the DFW area. Figure 1 shows class B airspace in DFW area and the origin/destination city pairs where UAM flights flew along with helicopter routes shown in blue. The research focused on exploring procedures for integrating UAM flights into Class Bravo and Class Delta airspace. Three different communication procedures, evaluated with three different levels of UAM traffic, are shown in Table 1. The current day routes were evaluated with current day communication procedures were explored as the first condition. The current day routes were also evaluated in the second condition with reduced communications, which was assumed due to the presence of a Letter Of Agreement (LOA). The purpose of the LOA was to reduce the verbiage associated with pilots getting clearance to Class B airspace from the controllers, pre-assigning beacons codes to the UAM flights, separate routes by assigning altitudes and speeds to flights going in any one direction. Flights were expected to automatically change frequency when exiting Class B airspace, thus transition points for entry and exit points were also specified in the LOA.

In this work, we propose a route planning scheme for large scale Unmanned Aircraft System (UAS) or multiple-drone operations in complex urban air space. The route planning system consists of a path finding algorithm and a scheduling system for safe and efficient uses of airspace. The work flow of the system is: first, generate shortest paths between origination and destination points via heuristic search and second, schedule the submitted flights to avoid possible conflicts. The scheduling process is performed by Evolutionary Algorithm (EA). Such a system would postpone or delay flight requests that may collide with previous flights and reject the conflicted flights. We also propose a fitness function for the EA to minimize both the total delay of the flight requests and the possibility of UAS collision. To demonstrate the feasibility of the proposed route planning models, preliminary simulations in a local town in Singapore are presented and discussed.

Safe planning for fleets of Unmaned Aircraft Systems (UAS) performing complex missions in urban environments has typically been a challenging problem. In the United States of America, the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) have been studying the regulation of the airspace when multiple such fleets of autonomous UAS share the same airspace, outlined in the Concept of Operations document (ConOps). While the focus is on the infrastructure and management of the airspace, the Unmanned Aircraft System (UAS) Traffic Management (UTM) ConOps also outline a potential airspace reservation based system for operation where operators reserve a volume of the airspace for a given time interval to operate in, but it makes clear that the safety (separation from other aircraft, terrain, and other hazards) is a responsibility of the drone fleet operators. In this work, we present a tool that allows an operator to plan out missions for fleets of multi-rotor UAS, performing complex time-bound missions. The tool builds upon a correct-by-construction planning method by translating missions to Signal Temporal Logic (STL). Along with a simple user interface, it also has fast and scalable mission planning abilities. We demonstrate our tool for one such mission.

This chapter describes low‐altitude small unmanned aircraft system (sUAS) missions and anticipated operational paradigms. Since low‐altitude operations occur close to the ground, flight operation zones are defined based on overflown property type or zone. Urban and suburban operations ranging from package delivery to aerial surveillance will require high‐integrity sUAS to assure safety to overflown people and property, whereas low‐altitude flight in rural and other sparsely populated or segregated zones can safely support flights with low‐integrity sUAS such as the existing multicopter. Vehicle and system‐wide support to enable high‐integrity sUAS are summarized, and zone‐specific public and civil sUAS mission types are overviewed with consideration of compelling use cases for each zone. Requirements for UAS traffic management (UTM) support to coordinate safe sUAS transitions between low‐altitude zones and between low‐ and high‐altitude airspace are summarized.

Unmanned Aircraft Systems (UAS) constitute the platform of many applications such as efficient package delivery, monitoring and surveillance, search and rescue, and more. It is predicted that more than seven million UAS will be flying over the United States by the year 2020. In such an environment, it becomes critical to deploy an efficient and scalable UAS Traffic Management System that coordinates UAS trajectories. In this paper we contribute two innovative trajectory scheduling algorithms, an iterative approach and a MIP algorithm, which are collision-free and deadlock-free. The trajectories are computed such that obstacles and forbidden areas are avoided and at all times vehicles maintain a safe distance between them. We analyze the performance of our algorithms using simulations. Various parameters are measured, such as the running time and the overall finish time.