UAS Traffic Management Research Articles

Hazardous flight region prediction for a small UAV operated in an urban area using a deep neural network

With an increase of UAVs in logistics and transportation, the safety of UAVs operated in the urban wind environment becomes an important issue. Small UAVs are more sensitive to the wind environment because of their small size, slow cruising speed, and limited endurance. In the unmanned aircraft system traffic management (UTM), a safety risk assessment under bad weather conditions is an important component. In this study, a hazardous flight region prediction system for small UAVs operated in urban areas is developed using a deep neural network (DNN) to support a risk assessment and safe trajectory planning. A large eddy simulation (LES) is applied to reflect the terrain-driven wind environment in the urban area. The result of a weather research and forecasting (WRF) model is used as an initial and boundary condition of the LES to generate a realistic complicated wind environment in an urban area. Furthermore, an iterative nesting algorithm is applied to the LES to obtain a sufficient resolution of the wind environment, which is suitable for the small UAV scale. The deviation distance from the original flight path due to the wind environment is considered as a flight hazard criterion in this study. The proposed system is able to predict deviation distance due to the wind environment over the entire flight space over time by using the DNN model. The training data for the DNN is obtained using the multicopter flight dynamics simulator, which can take into account the influence of a specific wind environment. With the indexes considering this deviation distance and the local topography (distribution of buildings) in the urban area, the hazardous flight region is predicted. The information supplied by the proposed hazardous flight region prediction model can be used for the flight risk assessment and safe flight trajectory planning to increase the flight safety of small UAVs.

Explanation of Machine-Learning Solutions in Air-Traffic Management

Advances in the trusted autonomy of air-traffic management (ATM) systems are currently being pursued to cope with the predicted growth in air-traffic densities in all classes of airspace. Highly automated ATM systems relying on artificial intelligence (AI) algorithms for anomaly detection, pattern identification, accurate inference, and optimal conflict resolution are technically feasible and demonstrably able to take on a wide variety of tasks currently accomplished by humans. However, the opaqueness and inexplicability of most intelligent algorithms restrict the usability of such technology. Consequently, AI-based ATM decision-support systems (DSS) are foreseen to integrate eXplainable AI (XAI) in order to increase interpretability and transparency of the system reasoning and, consequently, build the human operators’ trust in these systems. This research presents a viable solution to implement XAI in ATM DSS, providing explanations that can be appraised and analysed by the human air-traffic control operator (ATCO). The maturity of XAI approaches and their application in ATM operational risk prediction is investigated in this paper, which can support both existing ATM advisory services in uncontrolled airspace (Classes E and F) and also drive the inflation of avoidance volumes in emerging performance-driven autonomy concepts. In particular, aviation occurrences and meteorological databases are exploited to train a machine learning (ML)-based risk-prediction tool capable of real-time situation analysis and operational risk monitoring. The proposed approach is based on the XGBoost library, which is a gradient-boost decision tree algorithm for which post-hoc explanations are produced by SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Results are presented and discussed, and considerations are made on the most promising strategies for evolving the human–machine interactions (HMI) to strengthen the mutual trust between ATCO and systems. The presented approach is not limited only to conventional applications but also suitable for UAS-traffic management (UTM) and other emerging applications.

Integrating runtime verification into an automated UAS traffic management system

Unmanned Aerial Systems (UAS) are quickly integrating into the National Air Space. Doing so safely is a pressing concern, as the US alone has over 1.5 million registered small (under 55 pounds) UAS and the FAA projects further rapid expansion. This drives the need for an intelligent, automated system for UAS Traffic Management (UTM). Even more than for manned aircraft, UTM must integrate runtime checks to ensure system safety, at the very least to make up for the lack of humans on-board to employ the common-sense safety checks ingrained into the culture of human aviation. We overview a candidate automated, intelligent UTM system and propose multiple integration points for runtime verification to ensure that each part of the UTM adheres to safety requirements during operation. We write, validate, and present patterns for formal requirements across multiple subsystems of this UTM framework. We incorporate specifications that use set aggregation as a way of raising their abstraction from single sensors to sets of sensors, which allow us to monitor for system requirement violations with smaller specifications. After encoding our requirements as flight-certifiable runtime observers in the R2U2 RV engine, we execute them in simulation across multiple real-life test flights supplemented with simulated data to cover additional cases that did not occur in flight. Lessons learned accompany an analysis of the efficacy and performance of RV integration into the UTM framework.

Operational Testing of Unmanned Aircraft System Traffic Management in Disaster Response

In October 2018, a flight test, which occurred as part of a large-scale disaster drill in Ehime Prefecture, Japan, successfully demonstrated that the Integrated Aircraft Operation System for Disaster Relief (D-NET) and Unmanned Aircraft System Traffic Management (UTM) can contribute to the safe and efficient use of airspace by both manned and unmanned aircraft. This paper presents the technical challenges of operating unmanned aircraft in disaster relief as well as traffic management integration and the technical solutions developed to address these challenges. The scenarios used to test the integration of both systems in a real-world context, together with flight tests results and analysis, are also shown. The flight tests successfully demonstrated the application of unmanned aircraft to disaster response and showed they can safely cooperate with manned aircraft to improve response efficiency.

Development of a Flexible and Expandable UTM Simulator Based on Open Sources and Platforms

In order to study air traffic control of UAS’s (Unmanned Aerial Systems) in very low altitudes, the UTM (UAS Traffic Management) simulator has to be as flexible and expandable as other research simulators because relevant technologies and regulations are not matured enough at this stage. Available approaches using open sources and platforms are investigated to be used in the UTM simulator. The fundamental rationale for selection is availability of necessary resources to build a UTM simulator. Integration efforts to build a UTM simulator are elaborated, using Ardupilot, MavProxi, Cesium, and VWorld, which are selected from the thorough field study. Design requirements of a UTM simulator are determined by analyzing UTM services defined by NASA (National Aeronautics and Space Administration) and Eurocontrol. The UTM simulator, named eUTM, is composed of three components: UOS (UTM Operating System), UTM, and multiple GCSs (Ground Control Stations). GCSs are responsible for generation of flight paths of various UASs. UTM component copies functions of a real UTM such as monitoring and controlling air spaces. UOS provides simulation of environment such as weather, and controls the whole UTM simulator system. UOS also generates operation scenarios of UTM, and resides on the same UTM computer as an independent process. Two GCS simulators are connected to the UTM simulator in the present configuration, but the UTM simulator can be expanded to include up to 10 GCS simulators in the present design. In order to demonstrate the flexibility and expandability of eUTM simulator, several operation scenarios are realized and typical deconfliction scenarios among them are tested with a deconfliction algorithm. During the study, some limits are identified with applied open sources and platforms, which have to be resolved in order to obtain a flexible and expandable UTM simulator supporting relevant studies. Most of them are related to interfacing individual sources and platforms which use different program languages and communication drivers.

Cellular and Virtualization Technologies for UAVs: An Experimental Perspective.

The Unmanned Aircraft System (UAS) ecosystem is exponentially growing in both recreational and professional fields to provide novel services and applications to consumers from multiple engineering fields. However, this technology has only scraped the surface of its potential, especially in those cases that require fast reaction times. Accordingly, the UAS Traffic Management (UTM) project aims at efficiently managing the air traffic for Unmanned Aerial Vehicle (UAV) operations, including those cases where UAVs might be remotely managed from a completely different geographical location. With these considerations in mind, this article presents a cellular-assisted UAVs testbed used to complete a mission managed beyond the radio line-of-sight (BRLoS), as well as introducing a virtualization platform for deploying services using containerization technology. In addition, the article conducts a communication performance evaluation in order to determine if the testbed equipment meets the requirements to carry out this BRLoS management. Finally, indoor flight operations are carried out to demonstrate the feasibility and proper operation of the testbed.

Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services

This paper presents a software architecture for Unmanned aerial system Traffic Management (UTM). The work is framed within the U-space ecosystem, which is the European initiative for UTM in the civil airspace. We propose a system that focuses on providing the required services for automated decision-making during real-time threat management and conflict resolution, which is the main gap in current UTM solutions. Nonetheless, our software architecture follows an open-source design that is modular and flexible enough to accommodate additional U-space services in future developments. In its current implementation, our UTM solution is capable of tracking the aerial operations and monitoring the airspace in real time, in order to perform in-flight emergency management and tactical deconfliction. We show experimental results in order to demonstrate the UTM system working in a realistic simulation setup. For that, we performed our tests with the UTM system and the operators of the aerial aircraft located at remote locations with the consequent communication issues, and we showcased that the system was capable of managing in real time the conflicting events in two different use cases.

Key wireless communication technologies to support traffic management systems of unmanned aerial vehicles for civil application (review of foreign literature)

Not less than one hundred thousand Unmanned Aerial Vehicles (UAVs) are expected to perform flights simultaneously in Russia by 2035. The UAV fleet capacity triggers the development of the systems for informational support, operating control and management of UAV flights (Unmanned Aircraft System Traffic Management (UTM) systems) similar to that one already operating in manned aviation. The challenges arising in the sphere of civil aviation cannot be solved without wireless communication. The goals of this article are as follows: 1) familiarization of communication experts with the latest scientific developments of unmanned aerial technologies 2) description of the telecommunication-related problems of extensive systems of UAV control encountered by development engineers. In this article a schematic architecture and main functions of UTM systems are described as well as the examples of their implementation. Special emphasis is put on enhancing flight safety by means of a rational choice of communication technologies to manage conflicts (Conflict Management) known as "collision avoidance". The article analyzes the application of a wide range of wireless technologies ranging from Wi-Fi and Automatic Dependent Surveillance Broadcast (ADS-B) to 5G cellular networks as well as cell-free networks contributing to the development of 6G communication networks. As a result of the analysis, a list of promising research trends at the intersection of the fields of wireless communication and UAVs for civil application is made.

Unmanned Aircraft System Traffic Management (UTM) Simulation of Drone Delivery Models in 2030 Japan

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.

Communications Standards for Unmanned Aircraft Systems: The 3GPP Perspective and Research Drivers

An unmanned aircraft system (UAS) consists of an unmanned aerial vehicle (UAV) and its controller, which use radios to communicate. While the remote controller (RC) is traditionally operated by a person who is maintaining visual line of sight with the UAV it controls, the trend is moving toward long-range control and autonomous operation. To enable this, reliable and widely available wireless connectivity is needed because it is the only way to remotely control a UAV or take control of an autonomous UAV flight. This article surveys the ongoing Third Generation Partnership Project standardization activities for enabling networked UASs. In particular, we present the requirements, envisaged architecture, and services to be offered to/by UAVs and RCs, which will communicate with one another, with the UAS traffic management (UTM), and with other users through cellular networks. Critical research directions relate to security and spectrum coexistence, among others. We identify major R&D platforms that will drive the standardization of cellular communications networks and applications.

Decentralized Control Synthesis for Air Traffic Management in Urban Air Mobility

Urban air mobility (UAM) refers to air transportation services within an urban area, often in an on-demand fashion. We study air traffic management (ATM) for vehicles in a UAM fleet, while guaranteeing system safety requirements such as traffic separation. Existing ATM methods for unmanned aerial systems, such as UAS traffic management, utilize alternative approaches which do not provide strict safety guarantees. No established infrastructure exists for providing ATM at scale for UAM. We provide a decentralized, hierarchical approach for UAM ATM that allows for scalability to high traffic densities as well as providing theoretical guarantees of correctness with respect to user-provided safety specifications. Our main contributions are two-fold. First, we propose a novel UAM ATM architecture that divides the control authority between vertihubs that are each in charge of all UAM vehicles in their local airspace. Each vertihub also contains a number of vertiports that are in charge of UAM vehicle takeoffs and landings. The resulting architecture is decentralized and hierarchical, which not only enables scalability, but also robustness in the event of any individual vertihub or vertiport no longer being operational. Second, we provide a contract-based correct-by-construction reactive synthesis approach that provably guarantees safety properties with respect to user-provided specifications in linear temporal logic. We demonstrate the approach on large-volume UAM air traffic data.

An overview of flight demonstration of NASA unmanned aircraft system traffic management system

An overview of flight demonstration of NASA unmanned aircraft system traffic management system

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.

Prototyping operational autonomy for Space Traffic Management

Current state of the art in Space Traffic Management (STM) relies on a handful of providers for surveillance and collision prediction, and manual coordination between operators. Neither is scalable to support the expected 10x increase in active spacecraft population in less than 10 years, nor does it support automated maneuver planning. We present a software prototype of an STM architecture based on open Application Programming Interfaces (APIs), and drawing on insights from NASA's architecture for low-altitude Unmanned Aerial System Traffic Management. The STM architecture is designed to provide structure to the interactions between spacecraft operators, various regulatory bodies, and STM service suppliers, while maintaining flexibility of these interactions and the ability for new market participants to enter easily. Autonomy will be an indispensable part of the proposed architecture in enabling efficient data sharing, coordination between STM participants and safe flight operations (e.g. select spacecraft maneuvers to prevent impending conjunctions between multiple spacecraft).The STM prototype is based on modern micro-service architecture adhering to OpenAPI standards and deployed in industry-standard virtualized containers, facilitating easy communication between different participants or services. The system architecture is designed to facilitate adding and replacing services with minimal disruption. We have implemented some example participant services (e.g. a space situational awareness/SSA provider, a conjunction assessment supplier/CAS, an automated maneuver advisor/AMA) within the prototype. Different services, with creative algorithms folded into them, can fulfill similar functional roles within the STM architecture by flexibly connecting to it using pre-defined APIs and data models, thereby lowering the barrier to entry of new players in the STM marketplace.We demonstrate the STM prototype on a multiple conjunction scenario with multiple maneuverable spacecraft, where an example CAS and AMA can recommend optimal maneuvers to the spacecraft operators, based on a pre-defined reward function. Such tools can intelligently search the space of potential collision avoidance maneuvers with varying parameters like lead time and propellant usage, to optimize a customized reward function, and be implemented as a scheduling service within the STM architecture. The case study shows an example of autonomous maneuver planning using the API-based framework. As satellite populations and predicted conjunctions increase, an STM architecture can facilitate seamless information exchange related to collision prediction and mitigation among various service applications on different platforms and servers. The availability of such an STM network also opens up new research topics on satellite maneuver planning, scheduling and negotiation across disjoint entities.

SE프로세스를 적용한 UTM 환경의 항법 오차 산출 필요성 검토

This study carries out a basic study of ways to calculate navigation errors for aircraft operating in the unmanned aerial system traffic management(UTM). Recently, research by UTM has been carried out both at home and abroad, along with the initial study of system definitions at the basic stage, operational techniques of the aircraft, and the practicality of the concept of necessary operations at the actual operational stage. This study presented after a review the factors that should be considered for the calculation of navigation errors among the factors that examine whether the actual low altitude aircraft can operate properly within UTM during its actual operation and the need to apply them in practice.

A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management

Recent evolutions of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept are driving the introduction of new airspace structures and classifications, which must be suitable for low-altitude airspace and provide the required level of safety and flexibility, particularly in dense urban and suburban areas. Therefore, airspace classifications and structures need to evolve based on appropriate performance metrics, while new models and tools are needed to address UTM operational requirements, with an increasing focus on the coexistence of manned and unmanned Urban Air Mobility (UAM) vehicles and associated Communication, Navigation and Surveillance (CNS) infrastructure. This paper presents a novel airspace model for UTM adopting Performance-Based Operation (PBO) criteria, and specifically addressing urban airspace requirements. In particular, a novel airspace discretisation methodology is introduced, which allows dynamic management of airspace resources based on navigation and surveillance performance. Additionally, an airspace sectorisation methodology is developed balancing the trade-off between communication overhead and computational complexity of trajectory planning and re-planning. Two simulation case studies are conducted: over the skyline and below the skyline in Melbourne central business district, utilising Global Navigation Satellite Systems (GNSS) and Automatic Dependent Surveillance-Broadcast (ADS-B). The results confirm that the proposed airspace sectorisation methodology promotes operational safety and efficiency and enhances the UTM operators’ situational awareness under dense traffic conditions introducing a new effective 3D airspace visualisation scheme, which is suitable both for mission planning and pre-tactical UTM operations. Additionally, the proposed performance-based methodology can accommodate the diversity of infrastructure and vehicle performance requirements currently envisaged in the UTM context. This facilitates the adoption of this methodology for low-level airspace integration of UAS (which may differ significantly in terms of their avionics CNS capabilities) and set foundations for future work on tactical online UTM operations.

Risk Assessment for UAS Logistic Delivery under UAS Traffic Management Environment

Resulting from a mature accomplishment of the unmanned aircraft system (UAS), it is feasible to be adopted into logistic delivery services. The supporting technologies should be identified and examined, accompanying with the risk assessment. This paper surveys the risk assessment studies for UAVs. The expected level of safety (ELS) analysis is a key factor to safety concerns. By introducing the UTM infrastructure, the UAS implementation can be monitored. From the NASA technical capability level (TCL), UAV in beyond visual line of sight (BVLOS) flights would need certain verifications. Two UAS logistic delivery case studies are tested to assert the UAS services. To examine the ELS to ground risk and air risk, the case studies result in acceptable data to support the UAS logistic delivery with adequate path planning in the remote and suburban areas in Taiwan.

Integration of Unmanned Aircraft Systems into the National Airspace System-Efforts by the University of Alaska to Support the FAA/NASA UAS Traffic Management Program

Over the past decade Unmanned Aircraft Systems (UAS, aka “drones”) have become pervasive, touching virtually all aspects of our world. While UAS offer great opportunity to better our lives and strengthen economies, at the same time these can significantly disrupt manned flight operations and put our very lives in peril. Balancing the demanding and competing requirements of safely integrating UAS into the United States (US) National Airspace System (NAS) has been a top priority of the Federal Aviation Administration (FAA) for several years. This paper outlines efforts taken by the FAA and the National Aeronautics and Space Administration (NASA) to create the UAS Traffic Management (UTM) system as a means to address this capability gap. It highlights the perspectives and experiences gained by the University of Alaska Fairbanks (UAF) Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) as one of the FAA’s six UAS test sites participating in the NASA-led UTM program. The paper summarizes UAF’s participation in the UTM Technical Capability Level (TCL1-3) campaigns, including flight results, technical capabilities achieved, lessons learned, and continuing challenges regarding the implementation of UTM in the NAS. It also details future efforts needed to enable practical Beyond-Visual-Line-of-Sight (BVLOS) flights for UAS operations in rural Alaska.

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.

ADS-B Like UTM Surveillance Using APRS Infrastructure

Automatic packet reporting system (APRS) is selected as a candidate for automatic dependent surveillance-broadcast (ADS-B) like solution for unmanned aircraft system traffic management (UTM). The APRS on-board unit (OBU) at 0.5 W radio power and a proper ground transceiver station (GTS) deployment together makes up the infrastructure for unmanned aerial vehicle (UAV) surveillance. The airborne APRS OBU, designed and built using an available LightAPRS module, and the GTS to relay received data into the UTM Cloud is developed in this study. By system integration, the APRS OBU reports position data and flight data periodically to UTM Cloud. This paper presents the development of the ADS-B like operation for UTM using APRS. To avoid communication jamming by HAMs, the adopted APRS shifts its carrying frequency from 144.64 MHz to 144.61 MHz. In addition, the data period is tuned to around 10 s to test its functional capability. The APRS OBU downlinks 90 bytes of surveillance data onto the UTM cloud using the Internet, including position and flight data from Pixhawk flight controller (FC). A series of system performance verifications are conducted to examine APRS ADS-B like reliability and continuity with coverage limit. Through 19 flight tests, the GTS collected 1330 packets of data out of 1331 transmitted from the APRS OBU. Each data packet has the complete 90 bytes for surveillance with position and six degree-of-freedom (DoF) flight data on the UTM cloud. The APRS GTS deployment, with a low rate of missing data, covers a 40 km radius at the specific frequency of 144.61 MHz. The test results verify the functional capability of APRS to support an ADS-B like for UTM in Taiwan.