Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

On-board Obstacle Avoidance in the Teleoperation of Unmanned Aerial Vehicles

Teleoperation von Drohnen in Umgebungen ohne GPS-Verbindung und wenig Bewegungsspielraum stellt den Operator vor besondere Herausforderungen. Hindernisse in einer unbekannten Umgebung erfordern eine zuverlässige Zustandsschätzung und Algorithmen zur Vermeidung von Kollisionen. In dieser Dissertation präsentieren wir ein System zur kollisionsfreien Navigation einer ferngesteuerten Drohne mit vier Propellern (Quadcopter) in abgeschlossenen Räumen. Die Plattform ist mit einem Miniaturcomputer und dem Minimum an Sensoren ausgestattet. Diese Ausstattung genügt den Anforderungen an die Rechenleistung. Dieses Setup ermöglicht des Weiteren eine hochgenaue Zustandsschätzung mit Hilfe einer Kaskaden-Architektur, sehr gutes Folgeverhalten bezüglich der kommandierten Geschwindigkeit, sowie eine kollisionsfreie Navigation. Ein Komplementärfilter berechnet die Höhe der Drohne, während ein Kalman-Filter Beschleunigung durch eine IMU und Messungen eines Optical-Flow Sensors fusioniert und in die Softwarearchitektur integriert. Eine RGB-D Kamera stellt dem Operator ein visuelles Feedback, sowie Distanzmessungen zur Verfügung, um ein Roboter-zentriertes Modell umliegender Hindernisse mit Hilfe eines Bin-Occupancy-Filters zu erstellen. Der Algorithmus speichert die Position dieser Hindernisse, auch wenn sie das Sehfeld des Sensors verlassen, mit Hilfe des geschätzten Zustandes des Roboters. Das Prinzip des Ausweich-Algorithmus basiert auf dem Ansatz einer modell-prädiktiven Regelung. Durch Vorhersage der wahrscheinlichen Position eines Hindernisses werden die durch den Operator kommandierten Sollwerte gefiltert, um eine mögliche Kollision mit einem Hindernis zu vermeiden. Die Plattform wurde experimentell sowohl in einer räumlich abgeschlossenen Umgebung mit zahlreichen Hindernissen als auch bei Testflügen in offener Umgebung mit natürlichen Hindernissen wie z.B. Bäume getestet. Fliegende Roboter bergen das Risiko, im Fall eines Fehlers, sei es ein Bedienungs- oder Berechnungsfehler, durch einen Aufprall am Boden oder an Hindernissen Schaden zu nehmen. Aus diesem Grund nimmt die Entwicklung von Algorithmen dieser Roboter ein hohes Maß an Zeit und Ressourcen in Anspruch. In dieser Arbeit präsentieren wir zwei Methoden (Software-in-the-loop- und Hardware-in-the-loop-Simulation) um den Entwicklungsprozess zu vereinfachen. Via Software-in-the-loop-Simulation konnte der Zustandsschätzer mit Hilfe simulierter Sensoren und zuvor aufgenommener Datensätze verbessert werden. Eine Hardware-in-the-loop Simulation ermöglichte uns, den Roboter in Gazebo (ein bekannter frei verfügbarer ROS-Simulator) mit zusätzlicher auf dem Roboter installierter Hardware in Simulation zu bewegen. Ebenso können wir damit die Echtzeitfähigkeit der Algorithmen direkt auf der Hardware validieren und verifizieren. Zu guter Letzt analysierten wir den Einfluss der Roboterbewegung auf das visuelle Feedback des Operators. Obwohl einige Drohnen die Möglichkeit einer mechanischen Stabilisierung der Kamera besitzen, können unsere Drohnen aufgrund von Gewichtsbeschränkungen nicht auf diese Unterstützung zurückgreifen. Eine Fixierung der Kamera verursacht, während der Roboter sich bewegt, oft unstetige Bewegungen des Bildes und beeinträchtigt damit negativ die Manövrierbarkeit des Roboters. Viele wissenschaftliche Arbeiten beschäftigen sich mit der Lösung dieses Problems durch Feature-Tracking. Damit kann die Bewegung der Kamera rekonstruiert und das Videosignal stabilisiert werden. Wir zeigen, dass diese Methode stark vereinfacht werden kann, durch die Verwendung der Roboter-internen IMU. Unsere Ergebnisse belegen, dass unser Algorithmus das Kamerabild erfolgreich stabilisieren und der rechnerische Aufwand deutlich reduziert werden kann. Ebenso präsentieren wir ein neues Design eines Quadcopters, um dessen Ausrichtung von der lateralen Bewegung zu entkoppeln. Unser Konzept erlaubt die Neigung der Propellerblätter unabhängig von der Ausrichtung des Roboters mit Hilfe zweier zusätzlicher Aktuatoren. Nachdem wir das dynamische Modell dieses Systems hergeleitet haben, synthetisierten wir einen auf Feedback-Linearisierung basierten Regler. Simulationen bestätigen unsere Überlegungen und heben die Verbesserung der Manövrierfähigkeit dieses neuartigen Designs hervor.The teleoperation of unmanned aerial vehicles (UAVs), especially in cramped, GPS-restricted, environments, poses many challenges. The presence of obstacles in an unfamiliar environment requires reliable state estimation and active algorithms to prevent collisions. In this dissertation, we present a collision-free indoor navigation system for a teleoperated quadrotor UAV. The platform is equipped with an on-board miniature computer and a minimal set of sensors for this task and is self-sufficient with respect to external tracking systems and computation. The platform is capable of highly accurate state-estimation, tracking of the velocity commanded by the user and collision-free navigation. The robot estimates its state in a cascade architecture. The attitude of the platform is calculated with a complementary filter and its linear velocity through a Kalman filter integration of inertial and optical flow measurements. An RGB-D camera serves the purpose of providing visual feedback to the operator and depth measurements to build a probabilistic, robot-centric obstacle state with a bin-occupancy filter. The algorithm tracks the obstacles when they leave the field of view of the sensor by updating their positions with the estimate of the robot's motion. The avoidance part of our navigation system is based on the Model Predictive Control approach. By predicting the possible future obstacles states, the UAV filters the operator commands by altering them to prevent collisions. Experiments in obstacle-rich indoor and outdoor environments validate the efficiency of the proposed setup. Flying robots are highly prone to damage in cases of control errors, as these most likely will cause them to fall to the ground. Therefore, the development of algorithm for UAVs entails considerable amount of time and resources. In this dissertation we present two simulation methods, i.e. software- and hardware-in-the-loop simulations, to facilitate this process. The software-in-the-loop testing was used for the development and tuning of the state estimator for our robot using both the simulated sensors and pre-recorded datasets of sensor measurements, e.g., from real robotic experiments. With hardware-in-the-loop simulations, we are able to command the robot simulated in Gazebo, a popular open source ROS-enabled physical simulator, using computational units that are embedded on our quadrotor UAVs. Hence, we can test in simulation not only the correct execution of algorithms, but also the computational feasibility directly on the robot's hardware. Lastly, we analyze the influence of the robot's motion on the visual feedback provided to the operator. While some UAVs have the capacity to carry mechanically stabilized camera equipment, weight limits or other problems may make mechanical stabilization impractical. With a fixed camera, the video stream is often unsteady due to the multirotor's movement and can impair the operator's situation awareness. There has been significant research on how to stabilize videos using feature tracking to determine camera movement, which in turn is used to manipulate frames and stabilize the camera stream. However, we believe that this process could be greatly simplified by using data from a UAV’s on-board inertial measurement unit to stabilize the camera feed. Our results show that our algorithm successfully stabilizes the camera stream with the added benefit of requiring less computational power. We also propose a novel quadrotor design concept to decouple its orientation from the lateral motion of the quadrotor. In our design the tilt angles of the propellers with respect to the quadrotor body are being simultaneously controlled with two additional actuators by employing the parallelogram principle. After deriving the dynamic model of this design, we propose a controller for this platform based on feedback linearization. Simulation results confirm our theoretical findings, highlighting the improved motion capabilities of this novel design with respect to standard quadrotors

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Introducing autonomous aerial robots in industrial manufacturing

Although ground robots have been successfully used for many years in manufacturing, the capability of aerial robots to agilely navigate in the often sparse and static upper part of factories makes them suitable for performing tasks of interest in many industrial sectors. This paper presents the design, development, and validation of a fully autonomous aerial robotic system for manufacturing industries. It includes modules for accurate pose estimation without using a Global Navigation Satellite System (GNSS), autonomous navigation, radio-based localization, and obstacle avoidance, among others, providing a fully onboard solution capable of autonomously performing complex tasks in dynamic indoor environments in which all necessary sensors, electronics, and processing are on the robot. It was developed to fulfill two use cases relevant in many industries: light object logistics and missing tool search. The presented robotic system, functionalities, and use cases have been extensively validated with Technology Readiness Level 7 (TRL-7) in the Centro Bahía de C´ adiz (CBC) Airbus D&S factory in fully working conditions.Comisión Europea 60884Horizonte 2020 (Unión Europea) 871479Plan Nacional de I+D+I DPI2017-8979-

Aerial Robotics for Inspection and Maintenance

Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

Навігація БПЛА в приміщенні на основі TDOA методу

Робота публікується згідно наказу ректора від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник дипломної роботи: професор сумісник кафедри авіоніки, Сібрук Леонід ВікторовичThe popularity of UAV’s during last years is greatly increasing. Drones are getting more broad use in various commercial applications. They are used for mapping, monitoring, logistics, media, search and rescue operations and many more possible use cases. One of the recently emerged UAV’s type are indoor drones. Such drones are mostly used for inspections, security monitoring, warehouse operations and public safety. On this basis, a demand for indoor navigation system arises. The specifics of indoor operations of drones, creates unique technical challenges. Development of reliable and precise navigational systems, will allow to implement autonomous UAV system, which will vastly increase efficiency of indoor drone operations. Studies on this topic are sparse and require further investigations and development. For development of navigation systems, it is possible to rely on existing technologies from different areas, such as indoor positioning for pedestrian navigation, or positioning algorithms, used in aviation. Estimation of theoretical performance and accuracy of indoor navigational algorithms and technologies can allow further improvements and implementation of new technologies for practical use. The developed mathematical model is used for analysis of TDOA-based positioning algorithm, which can be used in such positioning systems.Популярность БПЛА в последние годы значительно возрастает. Дроны получают все более широкое применение в различных коммерческих приложениях. Они используются для картирования, мониторинг, логистика, средства массовой информации, поисково-спасательные операции и многое другое возможное использование случаи. Одним из недавно появившихся типов БПЛА являются внутренние дроны. Такие дроны чаще всего используется для инспекций, мониторинга безопасности, складских операций и общественной безопасности. На этом основе возникает спрос на внутреннюю навигационную систему. Специфика работы внутри помещений дронов, создает уникальные технические проблемы. Разработка надежных и точных навигационных систем, позволит реализовать автономную систему БПЛА, что значительно повысить эффективность работы дронов внутри помещений. Исследования по этой теме немногочисленны и требуют дальнейших исследований и разработок. Для разработки навигационных систем можно полагаться на существующие технологии от различные области, такие как позиционирование в помещении для пешеходной навигации или позиционирование алгоритмы, используемые в авиации. Оценка теоретической производительности и точности алгоритмов внутренней навигации и технологии могут позволить дальнейшие улучшения и внедрение новых технологий для практического использования. Разработанная математическая модель используется для анализа алгоритм позиционирования, который можно использовать в таких системах позиционирования

Using Automated Task Solution Synthesis to Generate Critical Junctures for Management of Planned and Reactive Cooperation between a Human-Controlled Blimp and an Autonomous Ground Robot

This thesis documents the use of an approach for automated task solution synthesis that algorithmically and automatically identifies periods during which a team of less-than-fully capable robots benefit from tightly-coupled, coordinated, cooperative behavior. I test two hypotheses: 1) That a team’s performance can be increased by cooperating during certain specific periods of a mission and 2) That these periods can be identified automatically and algorithmically. I also demonstrate how identification of cooperative periods can be performed both off-line prior to the application and reactively during mission execution. I validate these premises in a real-world experiment using a human-piloted Unmanned Aerial Vehicle (UAV) and an autonomous mobile robot. For this experiment I construct a UAV and use an off-the-shelf robot. To identify the cooperative periods I use the ASyMTRe task solution synthesis system, and I use the Player robot server for control tasks such as navigation and path planning. My results show that teams employing cooperative behaviors during algorithmically identified cooperative periods exhibit better performance than non-cooperative teams in a target localization task. I also present results showing an increased time cost for cooperative behaviors and compare the increased time cost of two cooperative approaches that generate cooperative periods prior to and during mission execution