Assessing the limits of centralized unmanned aerial vehicle conflict management in U-Space

There is an important growth of unmanned aerial vehicles (UAVs) performing planned missions in urban environments, which poses significant challenges to the research community. The possibility of collisions represents a critical challenge. UAVs can suffer collisions due to different causes external or internal to their flight plans. In this context, dynamic geo-fencing is a useful approach, whereby each UAV is able to provide a prediction of its future positions within a limited time. These predictions could be used to detect conflicts, allowing to dynamically modify the flight plans so as to avoid imminent collisions. In this work, a conflict detection algorithm/method is proposed, implemented and tested on a central server performing real-time conflict analysis for a large number of UAVs flying in the aerial space of a city (U-Space). The architecture assumes that UAVs send their future locations to a traffic controller. This controller compares the predicted positions of nearby vehicles to detect possible conflicts. The results of this work demonstrate the feasibility of the proposed conflict detection algorithm and its interest to improve the security and efficiency in U-Space environments. The server is able to track thousands of UAVs in real time with a conflict anticipation around 11 s.This work is derived from the following R&D projects: PID2021-122580NB-I00 and RTI2018-098156-B-C52, funded by MCIN/AEI/10.13039/501100011033, “ERDF A way of making Europe”, and SBPLY/19/180501/000159, funded by the Junta de Comunidades de Castilla-La Mancha (JCCM) and the EU through the European Regional Development Fund (ERDF-FEDER)

Contributions to deconfliction advanced U-space services for multiple unmanned aerial systems including field tests validation

Unmanned Aerial Systems (UAS) will become commonplace, the number of UAS flying in European airspace is expected to increase from a few thousand to hundreds of thousands by 2050. To prepare for this approaching, national and international organizations involved in aerial traffic management are now developing new laws and restructuring the airspace to incorporate UAS into civil airspace. The Single European Sky ATM Research considers the development of the U-space, a crucial step to enable the safe, secure, and efficient access of a large set of UAS into airspace. The design, integration, and validation of a set of modules that contribute to our UTM architecture for advanced U-space services are described in this Thesis. With an emphasis on conflict detection and resolution features, the architecture is flexible, modular, and scalable. The UTM is designed to work without the need for human involvement, to achieve U-space required scalability due to the large number of expected operations. However, it recommends actions to the UAS operator since, under current regulations, the operator is accountable for carrying out the recommendations of the UTM. Moreover, our development is based on the Robot Operating System (ROS) and is open source. The main developments of the proposed Thesis are monitoring and tactical deconfliction services, which are in charge of identifying and resolving possible conflicts that arise in the shared airspace of several UAS. By limiting the conflict search to a local search surrounding each waypoint, the proposed conflict detection method aims to improve conflict detection. By splitting the issue down into smaller subproblems with only two waypoints, the conflict resolution method tries to decrease the deviation distance from the initial flight plan. The proposed method for resolving potential threats is based on the premise that UAS can follow trajectories in time and space properly. Therefore, another contribution of the presented Thesis is an UAS 4D trajectory follower that can correct space and temporal deviations while following a given trajectory. Currently, commercial autopilots do not offer this functionality that allows to improve the airspace occupancy using time as an additional dimension. Moreover, the integration of onboard detect and avoid capabilities, as well as the consequences for U-space services are examined in this Thesis. A module capable of detecting large static unexpected obstacles and generating an alternative route to avoid the obstacle online is presented. Finally, the presented UTM architecture has been tested in both software-in-theloop and hardware-in-the-loop development enviroments, but also in real scenarios using unmanned aircraft. These scenarios were designed by selecting the most relevant UAS operation applications, such as the inspection of wind turbines, power lines and precision agriculture, as well as event and forest monitoring. ATLAS and El Arenosillo were the locations of the tests carried out thanks to the European projects SAFEDRONE and GAUSS.Los sistemas aéreos no tripulados (UAS en inglés) se convertirán en algo habitual. Se prevé que el número de UAS que vuelen en el espacio aéreo europeo pase de unos pocos miles a cientos de miles en 2050. Para prepararse para esta aproximación, las organizaciones nacionales e internacionales dedicadas a la gestión del tráfico aéreo están elaborando nuevas leyes y reestructurando el espacio aéreo para incorporar los UAS al espacio aéreo civil. SESAR (del inglés Single European Sky ATM Research) considera el desarrollo de U-space, un paso crucial para permitir el acceso seguro y eficiente de un gran conjunto de UAS al espacio aéreo. En esta Tesis se describe el diseño, la integración y la validación de un conjunto de módulos que contribuyen a nuestra arquitectura UTM (del inglés Unmanned aerial system Traffic Management) para los servicios avanzados del U-space. Con un énfasis en las características de detección y resolución de conflictos, la arquitectura es flexible, modular y escalable. La UTM está diseñada para funcionar sin necesidad de intervención humana, para lograr la escalabilidad requerida por U-space debido al gran número de operaciones previstas. Sin embargo, la UTM únicamente recomienda acciones al operador del UAS ya que, según la normativa vigente, el operador es responsable de las operaciones realizadas. Además, nuestro desarrollo está basado en el Sistema Operativo de Robots (ROS en inglés) y es de código abierto. Los principales desarrollos de la presente Tesis son los servicios de monitorización y evitación de conflictos, que se encargan de identificar y resolver los posibles conflictos que surjan en el espacio aéreo compartido de varios UAS. Limitando la búsqueda de conflictos a una búsqueda local alrededor de cada punto de ruta, el método de detección de conflictos pretende mejorar la detección de conflictos. Al dividir el problema en subproblemas más pequeños con sólo dos puntos de ruta, el método de resolución de conflictos intenta disminuir la distancia de desviación del plan de vuelo inicial. El método de resolución de conflictos propuesto se basa en la premisa de que los UAS pueden seguir las trayectorias en el tiempo y espacio de forma adecuada. Por tanto, otra de las aportaciones de la Tesis presentada es un seguidor de trayectorias 4D de UAS que puede corregir las desviaciones espaciales y temporales mientras sigue una trayectoria determinada. Actualmente, los autopilotos comerciales no ofrecen esta funcionalidad que permite mejorar la ocupación del espacio aéreo utilizando el tiempo como una dimensión adicional. Además, en esta Tesis se examina la capacidad de integración de módulos a bordo de detección y evitación de obstáculos, así como las consecuencias para los servicios de U-space. Se presenta un módulo capaz de detectar grandes obstáculos estáticos inesperados y capaz de generar una ruta alternativa para evitar dicho obstáculo. Por último, la arquitectura UTM presentada ha sido probada en entornos de desarrollo de simulación, pero también en escenarios reales con aeronaves no tripuladas. Estos escenarios se diseñaron seleccionando las aplicaciones de operación de UAS más relevantes, como la inspección de aerogeneradores, líneas eléctricas y agricultura de precisión, así como la monitorización de eventos y bosques. ATLAS y El Arenosillo fueron las sedes de las pruebas realizadas gracias a los proyectos europeos SAFEDRONE y GAUSS

Cooperative area surveillance strategies using multiple unmanned systems

Recently, the U.S. Department of Defense placed the technological development of intelligence, surveillance, and reconnaissance (ISR) tools at the top of its priority list. Area surveillance that takes place in an urban setting is an ISR tool of special interest. Unmanned aerial vehicles (UAVs) are ideal candidates to perform area surveillance because they are inexpensive and they do not require a human pilot to be aboard. Multiple unmanned systems increase the rate of information flow from the target region and maintain up to date information. The purpose of the research described in this dissertation is to develop and test a system that coordinates multiple UAVs on a wide area coverage surveillance mission. The research presented in this document implements a waypoint generator for multiple aerial vehicles that is especially suited for large area surveillance. The system chooses initial locations for the vehicles and generates a set of balanced sub-trees which cover the region of interest (ROI) for the vehicles. The sub-trees are then optimally combined to form a single minimal tree that spans the entire region. The system transforms the tree path into a series of waypoints suitable for the aerial vehicles. The output of the system is a set of waypoints for each vehicle assigned to the coverage task. Results from computer simulation and flight testing are presented.Ph.D.Committee Chair: Dr. George Vachtsevanos; Committee Member: Ayanna Howard; Committee Member: Dr. Thomas Michaels; Committee Member: Eric Johnson; Committee Member: Linda Will

Perpetual flight in flow fields

Tese de Doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Distributed Deconfliction Algorithm for Unmanned Aerial Vehicles with Limited Range and Field of View Sensors

This paper proposes a novel approach for collision avoidance between Unmanned Aerial Vehicles (UAVs) with limited range and field of view sensors. The algorithm is designed for unicycle vehicles that need to fly above a specific minimal speed to maintain flight. It uses a navigation function approach when the UAV is clear from conflict and smoothly switches to a collision avoidance maneuver when other UAVs are encountered. We show that the proposed avoidance algorithm can ensure a collision-free path. We also carry out a throughout quantitative analysis of the algorithm singularities and propose heuristic recipes for avoiding deadlock situations. Simulations are performed to show the effectiveness of the algorithm

On the underlying dynamics of traffic conflicts related to stochastic behaviour

The aim of this paper is to analyse why and how air traffic conflicts occur as a result of the stochastic behaviour of both the ownship and the intruder and to show how system-level characteristics can be derived from such an analysis. Ensemble dynamics in a given traffic scenario have already been analysed using multi-agent simulations by many; however, such an analysis is hardly ever backed up and interpreted in terms of an analytical study. By making use of directional conflict probability maps, characteristics of integral, system-level quantities can be explained, providing further insight into the relationship of speed distribution parameters and system performance quantities, namely, safety and throughput

Improved conflict detection and resolution for service UAVs in shared airspace

In future UAV-based services, UAV (Unmanned Aerial Vehicle) fleets will be managed by several independent flight operation service providers in shared low-altitude airspace. Therefore, Conflict Detection and Resolution (CDR) methods that can solve conflicts---possible collisions between UAVs of different service providers---are a key element of the Unmanned Aircraft System Traffic Management (UTM) system. As our CDR method, we introduce an adaptation of ORCA, which is a state-of-the-art collision avoidance algorithm hitherto mainly used in a limited theoretical scope, to realistic UAV operations. Our approach, called Adapted ORCA, addresses practical considerations that are inherent to the deployment of UAVs in shared airspace, such as navigation inaccuracies, communication overhead, and flight phases. We validate our approach through simulations. First, by empirically tuning the ORCA parameters look-ahead time window and deconfliction distance, we are able to minimize the ORCA generated deviations from the nominal flight path. Second, by simulating realistic UAV traffic for delivery, we can determine a value for separation distance between UAVs that uses airspace efficiently

Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish realism and relevancy in  unmanned aerial vehicle flight planning. Furthermore, this study incorporates one of the most comprehensive set of criteria identified during our literature search. The simulation results clearly show that the 4D path planning approach is able to provide solutions in complex dynamic environments in which the 3D approach could not find a solution

Using collision cones to assess biological deconfliction methods

Biological systems consistently outperform autonomous systems governed by engineered algorithms in their ability to reactively avoid collisions. To better understand this discrepancy, a collision avoidance algorithm was applied to frames of digitized video trajectory data from bats, swallows and fish (Myotis velifer, Petrochelidon pyrrhonota and Danio aequipinnatus). Information available from visual cues, specifically relative position and velocity, was provided to the algorithm which used this information to define collision cones that allowed the algorithm to find a safe velocity requiring minimal deviation from the original velocity. The subset of obstacles provided to the algorithm was determined by the animal's sensing range in terms of metric and topological distance. The algorithmic calculated velocities showed good agreement with observed biological velocities, indicating that the algorithm was an informative basis for comparison with the three species and could potentially be improved for engineered applications with further study

Learning-based perception and control with adaptive stress testing for safe autonomous air mobility

The use of electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient, high-speed, on-demand air transportation within a metropolitan area is a topic of increasing interest, which is expected to bring fundamental changes to the city infrastructures and daily commutes. NASA, Uber, and Airbus have been exploring this exciting concept of Urban Air Mobility (UAM), which has the potential to provide meaningful door-to-door trip time savings compared with automobiles. However, successfully bringing such vehicles and airspace operations to fruition will require introducing orders-of-magnitude more aircraft to a given airspace volume, and the ability to manage many of these eVTOL aircraft safely in a congested urban area presents a challenge unprecedented in air traffic management. Although there are existing solutions for communication technology, onboard computing capability, and sensor technology, the computation guidance algorithm to enable safe, efficient, and scalable flight operations for dense self-organizing air traffic still remains an open question. In order to enable safe and efficient autonomous on-demand free flight operations in this UAM concept, a suite of tools in learning-based perception and control systems with stress testing for safe autonomous air mobility is proposed in this dissertation. First, a key component for the safe autonomous operation of unmanned aircraft is an effective onboard perception system, which will support sense-and-avoid functions. For example, in a package delivery mission, or an emergency landing event, pedestrian detection could help unmanned aircraft with safe landing zone identification. In this dissertation, we developed a deep-learning-based onboard computer vision algorithm on unmanned aircraft for pedestrian detection and tracking. In contrast with existing research with ground-level pedestrian detection, the developed algorithm achieves highly accurate multiple pedestrian detection from a bird-eye view, when both the pedestrians and the aircraft platform are moving. Second, for the aircraft guidance, a message-based decentralized computational guidance algorithm with separation assurance capability for single aircraft case and multiple cooperative aircraft case is designed and analyzed in this dissertation. The algorithm proposed in this work is to formulate this problem as a Markov Decision Process (MDP) and solve it using an online algorithm Monte Carlo Tree Search (MCTS). For the multiple cooperative aircraft case, a novel coordination strategy is introduced by using the logit level-$k$ model in behavioral game theory. To achieve higher scalability, we introduce the airspace sector concept into the UAM environment by dividing the airspace into sectors, so that each aircraft only needs to coordinate with aircraft in the same sector. At each decision step, all of the aircraft will run the proposed computational guidance algorithm onboard, which can guide all the aircraft to their respective destinations while avoiding potential conflicts among them. In addition, to make the proposed algorithm more practical, we also consider the communication constraints and communication loss among the aircraft by modifying our computational guidance algorithms given certain communication constraints (time, bandwidth, and communication loss) and designing air-to-air and air-to-ground communication frameworks to facilitate the computational guidance algorithm. To demonstrate the performance of the proposed computational guidance algorithm, a free-flight airspace simulator that incorporates environment uncertainty is built in an OpenAI Gym environment. Numerical experiment results over several case studies including the roundabout test problem show that the proposed computational guidance algorithm has promising performance even with the high-density air traffic case. Third, to ensure the developed autonomous systems meet the high safety standards of aviation, we propose a novel, simulation driven approach for validation that can automatically discover the failure modes of a decision-making system, and optimize the parameters that configure the system to improve its safety performance. Using simulation, we demonstrate that the proposed validation algorithm is able to discover failure modes in the system that would be challenging for humans to find and fix, and we show how the algorithm can learn from these failure modes to improve the performance of the decision-making system under test