ICAROUS: Integrated configurable algorithms for reliable operations of unmanned systems

The hierarchical unmanned aerial systems (UAS) traffic management (UTM) is proposed for UAS operation in Taiwan. The proposed UTM is constructed using the similar concept of ATM from the transport category aviation system. Based on the airspace being divided by 400 feet of altitude, the RUTM (regional UTM) is managed by the local government and the NUTM (national UTM) by the Civil Aeronautical Administration (CAA). Under construction of the UTM system infrastructure, this trial tests examine the effectiveness of UAV surveillance under 400 feet using automatic dependent surveillance-broadcast (ADS-B)-like on-board units (OBU). The ground transceiver station (GTS) is designed with the adoptable systems. In these implementation tests, five long-range wide area network (LoRa) gateways and one automatic packet reporting system (APRS) I-Gate are deployed to cover the Tainan Metropolitan area. The data rates are set in different systems from 8 to 12 s to prevent from data conflict or congestion. The signal coverage, time delay, data distribution, and data variance in communication are recorded and analyzed for RUTM operation. Data streaming and Internet manipulation are verified with cloud system stability and availability. Simple operational procedures are defined with priority for detect and avoid (DAA) for unmanned aerial vehicles (UAVs). Mobile communication and Zello broadcasts are introduced and applied to establish controller-to-pilot communication (CPC) for DAA. The UAV flight tests are generally beyond visual line-of-sight (BVLOS) near suburban areas with flight distances to 8 km. On the GTS deployment, six test locations examine communication coverage and effectiveness using ADS-B like OBUs. In system verification, the proposed ADS-B like OBU works well in the UTM infrastructure. The system feasibility is proven with support of receiving data analysis and transceiver efficiency. The trial test supports RUTM in Taiwan for UAV operations.

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.

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 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 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.

Japan Unmanned System Traffic & Radio Management Consortium conducted a large flight demonstration and made about 120 flights of 20 small Unmanned Aerial Systems (UAS), which includes one simulated aircraft, in October 2017. All the flights were under a traffic management, and all the operators submitted a flight plan, reserved an airspace and followed the navigation of the controller. This paper describes the overview of a concept of UAS traffic management (UTM) and the demonstration, and discusses the feasibility and challenges of the UTM concept.

The rapid growth of urban mobility and logistics demands innovative solutions to address congestion, inefficiencies, and sustainability challenges. Unmanned Aerial Systems (UAS) offer a promising alternative for urban transportation, particularly in last-mile deliveries and emergency response. To accommodate the anticipated scale of UAS operations, potentially involving millions of daily flights, the UAS Traffic Management (UTM) ecosystem must support safe, efficient, and fair airspace access while minimizing disruption. UAS operations in dense urban areas must be supported by efficient and scalable path planning and deconflicting mechanisms which compute collision-free trajectories. A dynamic rerouting mechanism allows UAS trajectories to dynamically adapt to real-time deviations caused by environmental factors, communication errors, or dynamic airspace constraints. In this paper, we introduce a UAS Path Planning with Dynamic Rerouting framework that leverages a space-time graph model to ensure safe and efficient variable speed UAS navigation in urban environments. Our approach provides collision-free trajectories by construction and enables real-time path adjustments by dynamically rerouting UAS in case of slowdown events. By incorporating real-time request management and collision-free trajectories, our model enhances the feasibility of integrating UAS into multimodal urban transportation networks. We analyze the computational efficiency of our approach and demonstrate its scalability through simulation experiments using a real-world urban map in very dense scenarios with up to 14000 UAS vehicles. Our results indicate that dynamic rerouting with collision-free trajectory computation improves UAS route availability while maintaining airspace safety and operational efficiency.

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.

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.

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.

Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fly in urban airspaces within a decade. This is in addition to goals for increased Urban Air Mobility (UAM): larger transportation UAS and piloted air vehicles, including air taxi service and innovative aviation designs to revolutionize transportation. A significant problem is the lack of current management systems for this extremely large volume of air traffic, composed of heterogeneous drone shapes, sizes, and onboard equipment capabilities, flying adjacent to larger UAM vehicles in spaces previously unoccupied by General Aviation (GA). Robust and resilient detection, identification, localization, and tracking of sUAS sharing UAM airspace is complicated by urban factors such as non-transmitting, non-cooperative drones. Exacerbating these challenges, current technological limitations and vulnerabilities in Global Positioning System (GPS), Automatic Dependent Surveillance-Broadcast (ADS-B), and Inertial Navigation Systems (INS) can pose safety and security threats. To streamline a vision of safe and secure sUAS-UAM shared airspace, we propose Drone Net, a UAS Traffic Management (UTM) network of multi-modal ground and flight instruments. Drone Net is an architecture fusing a network of passive visual and acoustic sensor nodes with active Radio Detection and Ranging (radar) and Light Detection and Ranging (lidar) sensing methods for UTM, designed for integration with existing Air Traffic Control (ATC) and future UAM systems. The purpose of the Drone Net system is to evaluate use of Electro-Optical/Infrared (EO/IR) and acoustic arrays networked within an urban UAS operating region such as uncontrolled Class-G and waiver-granted Class-D airspace. The Drone Net approach combines detection, tracking, and localization estimation from radar, ADS-B, and EO/IR such that the design is robust to sensor errors, sample loss, and spoofing or other types of attacks. The system experimental design allows for emulation of corrupted ADS-B, GPS, and INS data on our flight platform including capability to communicate with ground radar and EO/IR to recover from flight instrument major and minor malfunctions such that a Drone Net cooperative UAS can be engineered to be fail-safe and fail-secure. We hypothesize that the Drone Net network of rooftop sensors, shown below, provides this conceptualized fail-safe, fail-secure property based upon the combination of self-localization with backup and confirming data from the ground sensor network. Further, with ground-system sensor fusion as well as UTM flight plan and registration information fusion, the overall system can manage small UAS that are both compliant and non-compliant alongside GA and UAM more safely than a single mode like ADS-B alone. To test our hypothesis, we envision two experiments this year. First, a test to confirm that we can emulate ADS-B, GPS, and INS sensor data corruption, leading to recovery using backup flight (lidar, EO/IR) or ground (radar, EO/IR) data to safely land an ALTA6 or other test UAS. Second, a series of Sense-and-Avoid (SAA) experiments between our test UAS and a tethered aerial obstacle. In the future, we will use this experience with fail-safe, fail-secure methods and software to further test more advanced scenarios between multiple UAS. The network of Drone Net instruments in a local area as well as regional and more global cloud-based networks will allow for heterogeneous information fusion and algorithm development for multi-sensor drone detection, classification, and identification with more accuracy than a single database or sensor system. The latent power of the Drone Net project is in its open-design ground and flight sensor network with data sharing capability to improve data mining and machine learning over time for analysis and security applications. More concrete applications for Drone Net include UTM integration with ATC and UAM, but also airspace safety and security for campus and public venues in general. Herein, we will present our experimental design and current test data from Embry-Riddle Aeronautical University in collaboration with University of Colorado Boulder toward completing the open specification for ground and flight instruments. We would additionally like to invite others to participate in growing the network and the data available for UTM, UAM, and GA shared airspace research.

Recently, the number of drones for personal leisure as well as Unmanned Aerial Vehicles (UAVs) for such as military, commercial, agricultural, disaster response is increasing rapidly. When a large number of UAVs fly simultaneously in airspace, collision problems between UAVs can occur, which is likely to lead directly to human accidents or economic loss. Accordingly, there are growing needs for UAS Traffic Management (UTM) systems. In this phenomenon, various communication protocols for UTM are emerging. In this paper, especially, the application of UTM system based on Wireless Access in Vehicular Environment (WAVE) protocol is introduced as one of most efficient and stable protocols in environments with high-speed mobility and dynamic changing topology. Furthermore, we design a network architecture that supports direct communication among UAVs as well as UAV and ground station within the UTM framework. By applying WAVE protocol in the UTM system, it is possible to transmit messages promptly and accurately to prevent collision in the event of an emergency, and to configure the UTM system to manage all UAVs by applying a WAVE-based ad-hoc network.

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

The advent of large-scale Unmanned Aircraft Systems (UAS) exploitation for urban tasks, such as delivery, has led to a great deal of research and development in the UAS Traffic Management (UTM) domain. The general approach at this time is to define a grid network for the area of operation, and then have UAS Service Suppliers (USS) pairwise deconflict any overlapping grid elements for their flights. Moreover, this analysis is performed on arbitrary flight paths through the airspace, and thus may impose a substantial computational burden in order to ensure strategic deconfliction (that is, no two flights are ever closer than the minimum required separation). However, the biggest drawback to this approach is the impact of contingencies on UTM operations. For example, if one UAS slows down, or goes off course, then strategic deconfliction is no longer guaranteed, and this can have a disastrous snowballing effect on a large number of flights. We propose a lane-based approach which not only allows a one-dimensional strategic deconfliction method, but provides structural support for alternative contingency handling methods with minimal impact on the overall UTM system. Methods for lane creation, path assignment through lanes, flight strategic deconfliction, and contingency handling are provided here.

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.