Yes / Unmanned Aerial Vehicles (UAVs) are considered, nowadays, as a futuristic and robust paradigm for 5G wireless networks, in terms of providing Internet connectivity services onto infrastructure cellular networks. In this paper, the interference regime caused by multiple downlink aerial wireless transmission beams has been highlighted. This has been introduced by estimating the UAVs coverage area that is analytically derived in a tractable closed-form expression. The rationale of the analysed coverage approach relies on observing and adapting the joint aerial distance between the aerial base stations. This can minimize the intra-overlapped coverage and ultimately maximize the overall coverage performance for a better quality of service demands. The novelty of our approach brings useful design insights for UAVs system-level performance that technically helps in aerial coverage computations without the need of performing an aerial deployment setup. To the end, the performance effectiveness of our methodology has been tested under an urban propagation environment conditions, in which the original probabilistic channel model approximation has been taken into account. Moreover, this paper identifies the interference issue of such an aerial network as a shrinkage or distortion phenomenon.

Die Energieversorgung unbemannter Flugobjekte (UAV) erfolgt gegenwärtig über Batterie-Module. Diese sind als zusätzliche Bauteile in die Struktur eingebracht. Daher erhöht sich das Gesamtgewicht der Struktur deutlich. Im Sinne der Energiespeicherung existieren verschiedene multifunktionale Konzeptansätze. Hierunter zählen strukturelle, elektrische Energiespeicherungssysteme (SEES). Bei diesen Konzepten erfolgt die Energiespeicherung in den Bauteilen bei gleichzeitiger Erfüllung struktureller Eigenschaften. Somit gelten diese als masselose Energiespeicherungssysteme. Im Rahmen dieser Thesis erfolgt eine Betrachtung verschiedener SEES. Schließlich werden strukturelle Superkondensatoren (SSC) zur Integration in ein UAV ausgewählt. Als Integrationsobjekt dient die Drohne DJI Matrice 600 Pro. Ein SSC mit besten Eigenschaften wird anhand einer systematischen Methode aus der aktuellen Literatur ermittelt. Dieser Favorit wird konzeptionell in die Drohne integriert. Diesbezüglich erfolgen verschiedene, physikalische Berechnungen zu elektrischen Eigenschaften und anliegenden Kräften, sodass Rückschlüsse zur Leistungsfähigkeit getroffen werden können. Im weiteren Verlauf wird eine Mehrkörpersimulation mit der Finite-Elemente-Methode (FEM) am Untersuchungsobjekt durchgeführt. Mit der Kenntnis über die anliegenden Beanspruchungen erfolgt weiterführend eine detailgetreue, strukturmechanische Analyse des SSC unter Verwendung der FEM an repräsentativen Volumenelementen. Fortan wird das multiphysikalische Kopplungsphänomen im strukturellen Elektrolyten simuliert. Hierfür werden mathematische Abhängigkeiten von mechanischen Einwirkungen auf geometrisch, veränderliche Größen ermittelt. Diese werden in eine elektrochemische Simulation überführt, sodass das multiphysikalische Kopplungsphänomen berechnet wird. Als Ergebnis zeigt sich, dass die Kompression des Elektrolyten negative Auswirkungen auf die elektrochemischen Eigenschaften hat...:Symbol- und Abkürzungsverzeichnis Abbildungsverzeichnis Tabellenverzeichnis 1 Einleitung 2 Grundlagen 2.1 Strukturelle elektrische Energiespeicherungssysteme 2.2 Superkondensatoren – Aufbau und Funktionsweise 2.3 Berechnungsgrößen am strukturellen Superkondensator 3 Stand der Forschung 3.1 Literaturrecherche – Strukturelle Superkondensatoren 3.2 Festlegung von Parametern und Auswahl des SSC 4 Anwendungsfall: DJI Matrice 600 Pro 4.1 Produktanalyse DJI Matrice 600 Pro 4.2 Integration des strukturellen Superkondensators in die Struktur 4.3 Berechnung elektrischer Eigenschaften 4.4 Analyse und Berechnung der wirkenden Kräfte 4.5 FEM-Mehrkörpersimulation am UAV-Anwendungsfall 5 Strukturmechanische Simulation am SSC 5.1 SSC-Bereichsanalyse und Simulationsaufgabe 5.2 Repräsentative Volumenelemente und Einheitszelle 5.3 Simulation Bereich 1: Poröse Faser in der Matrix 5.4 Simulation Bereich 2: Fasern in der Matrix 5.5 Simulation Bereich 3: Poröser Elektrolyt 6 Multiphysikalische SSC-Simulation 6.1 Multiphysikalischer Kopplungseffekt 6.2 Analyse der geometrischen Größen Porosität und Tortuosität 6.3 Multiphysikalische Simulation mit COMSOL Multiphysics Zusammenfassung und Ausblick Literatur / Unmanned aerial vehicles (UAV) are currently powered by batteries, which are integrated as additional components within their structure. However, the substantial weight of these batteries leads to increased energy consumption and reduced flight time. In addition to battery-based energy systems, there are alternative concepts that serve multifunctional roles. Structural electrical energy storage systems (SEES) for example carry loads and offer electrical energy storage functions at the same time. In this work, structural Supercapacitors (SSC) are selected as SEES candidates. A systematic approach is employed to integrate an SSC into the DJI Matrice 600 Pro done as an UAV use case. The efficiency of the integrated system is assessed through various physical calculations. Subsequently, a multi-body simulation using the finite element method is conducted on the chosen UAV model. Furthermore, representative volume elements are defined within the structural supercapacitor, and simulations are performed to comprehend the underlying processes. During the exploration of multiphysical coupling effects between mechanical stresses and electrochemical behaviors, certain geometric parameters are identified as influential factors. Regression analysis is employed to formulate mathematical equations representing these dependencies for simulation purposes. A multiphysical simulation is executed, considering compression as a representative load case. The results are evaluated using cyclic voltammetry. The study concludes that mechanical compression loads have an adverse effect on the electrochemical properties of the structural supercapacitor:Symbol- und Abkürzungsverzeichnis Abbildungsverzeichnis Tabellenverzeichnis 1 Einleitung 2 Grundlagen 2.1 Strukturelle elektrische Energiespeicherungssysteme 2.2 Superkondensatoren – Aufbau und Funktionsweise 2.3 Berechnungsgrößen am strukturellen Superkondensator 3 Stand der Forschung 3.1 Literaturrecherche – Strukturelle Superkondensatoren 3.2 Festlegung von Parametern und Auswahl des SSC 4 Anwendungsfall: DJI Matrice 600 Pro 4.1 Produktanalyse DJI Matrice 600 Pro 4.2 Integration des strukturellen Superkondensators in die Struktur 4.3 Berechnung elektrischer Eigenschaften 4.4 Analyse und Berechnung der wirkenden Kräfte 4.5 FEM-Mehrkörpersimulation am UAV-Anwendungsfall 5 Strukturmechanische Simulation am SSC 5.1 SSC-Bereichsanalyse und Simulationsaufgabe 5.2 Repräsentative Volumenelemente und Einheitszelle 5.3 Simulation Bereich 1: Poröse Faser in der Matrix 5.4 Simulation Bereich 2: Fasern in der Matrix 5.5 Simulation Bereich 3: Poröser Elektrolyt 6 Multiphysikalische SSC-Simulation 6.1 Multiphysikalischer Kopplungseffekt 6.2 Analyse der geometrischen Größen Porosität und Tortuosität 6.3 Multiphysikalische Simulation mit COMSOL Multiphysics Zusammenfassung und Ausblick Literatur

This study aimed to develop and evaluate a method to obtain a proportional-integral-derivative (PID) controller. The controller is for a control surface that controls pitch motion, by using data from flight tests with an unmanned aerial vehicle (UAV). Finding a suitable method to develop the controllers is essential to make the UAV autonomous, whilst being stable and controllable. Before developing the PID, data from test flights were used to model a transfer function for the control surface with MATLAB's toolbox for system identification. Thereafter, using the transfer function, the PID was developed by using MATLAB’s toolbox for control systems. The whole method was evaluated by studying the rise time, settling time, and overshoot for the PID, and studying how well the transfer function fits with the flight data. The method of modeling the pitch motion with system identification and finding the PID gains has good potential to simplify the process of finding a PID controller. However, to acquire an accurate model for the pitch motion, which in turn can give a well-performing PID, an improved data sampling was suggested. Additionally, flight tests conducted before and after PID tuning, and in different conditions are recommended to be done in future studies. The flight test would work as a validation for the model to acquire a robust PID that performs as expected. / Syftet med denna studie var att utveckla och utvärdera en metod för att hitta en proportionerlig integrerande deriverande (PID) regulator. Regulatorn är för en kontrollyta som kontrollerar tipprörelsen genom att använda data från flygtester med en drönare. Att hitta en lämplig metod för att utveckla regulatorer är nödvändigt för att göra drönaren autonom, samtidigt som den är stabil och kontrollerbar. Innan PID:n utvecklades användes data från flygtester för att modellera överföringsfunktionen för kontrollytan med MATLAB:s programvara för systemidentifiering. Därefter, genom att använda överföringsfunktionen, utvecklades PID:n med MATLAB:s programvara för reglersystem. Hela metoden utvärderades genom att studera stigtid, insvängningstid och översläng för PID regulatorn, samt studera hur väl överföringsfunktionen modellerar flygdata. Metoden för att modellera tipprörelsen och att hitta PID förstärkningarna har en god potential att förenkla processen av att hitta en PID regulator. Däremot för att få en precis modell för tipprörelsen, vilket i sin tur kan ge en välpresterande PID, föreslogs det att förbättra datainsamlingen. Dessutom rekommenderades det i framtida studier att flygtester genomförs i olika förhållande, både före och efter att PID regulatorn har hittats. Flygtesterna skulle fungera som en bekräftelse för modellen för att få en robust PID som presterar som väntat. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

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Cyber-physical systems (CPSs) are systems that integrate physical elements with a cyber layer that enables sensing, monitoring, and processing the data from the physical components. Examples of CPSs include autonomous vehicles, unmanned aerial vehicles (UAVs), smart grids, and the Internet of Things (IoT). In particular, many critical infrastructure (CI) that are vital to our modern day cities and communities, are CPSs. This wide range of CPSs domains represents a cornerstone of smart cities in which various CPSs are connected to provide efficient services. However, this level of connectivity has brought forward new security challenges and has left CPSs vulnerable to many cyber-physical attacks and disruptive events that can utilize the cyber layer to cause damage to both cyber and physical components. Addressing these security and operation challenges requires developing new security solutions to prevent and mitigate the effects of cyber and physical attacks as well as improving the CPSs response in face of disruptive events, which is known as the CPS resilience. To this end, the primary goal of this dissertation is to develop novel analytical tools that can be used to study, analyze, and optimize the resilience and security of critical CPSs. In particular, this dissertation presents a number of key contributions that pertain to the security and the resilience of multiple CPSs that include power systems, the Internet of Things (IoT), UAVs, and transportation networks. First, a mathematical framework is proposed to analyze and mitigate the effects of GPS spoofing attacks against UAVs. The proposed framework uses system dynamics to model the optimal routes which UAVs can follow in normal operations and under GPS spoofing attacks. A countermeasure mechanism, built on the premise of cooperative localization, is then developed to mitigate the effects of these GPS spoofing attacks. To practically deploy the proposed defense mechanism, a dynamic Stackelberg game is formulated to model the interactions between a GPS spoofer and a drone operator. The equilibrium strategies of the game are analytically characterized and studied through a novel, computationally efficient algorithm. Simulation results show that, when combined with the Stackelberg strategies, the proposed defense mechanism will outperform baseline strategy selection techniques in terms of reducing the possibility of UAV capture. Next, a game-theoretic framework is developed to model a novel moving target defense (MTD) mechanism that enables CPSs to randomize their configurations to proactive deter impending attacks. By adopting an MTD approach, a CPS can enhance its security against potential attacks by increasing the uncertainty on the attacker. The equilibrium of the developed single-controller, stochastic MTD game is then analyzed. Simulation results show that the proposed framework can significantly improve the overall utility of the defender. Third, the concept of MTD is coupled with new cryptographic algorithms for enhancing the security of an mHealth Internet of Things (IoT) system. In particular, using a combination of theory and implementation, a framework is introduced to enable the IoT devices to update their cryptographic keys locally to eliminate the risk of being revealed while they are shared. Considering the resilience of CPSs, a novel framework for analyzing the component- and system-level resilience of CIs is proposed. This framework brings together new ideas from Bayesian networks and contract theory – a Nobel prize winning theory – to define a concrete system-level resilience index for CIs and to optimize the allocation of resources, such as redundant components, monitoring devices, or UAVs to help those CIs improve their resilience. In particular, the developed resilience index is able to account for the effect of CI components on the its probability of failure. Meanwhile, using contract theory, a comprehensive resource allocation framework is proposed enabling the system operator to optimally allocate resources to each individual CI based on its economic contribution to the entire system. Simulation results show that the system operator can economically benefit from allocating the resources while dams can have a significant improvement in their resilience indices. Subsequently, the developed contract-theoretic framework is extended to account for cases of asymmetric information in which the system operator has only partial information about the CIs being in some vulnerability and criticality levels. Under such asymmetry, it is shown that the proposed approach maximizes the system operator's utility while ensuring that no CI has an incentive to ask for another contract. Next, a proof-of-concept framework is introduced to analyze and improve the resilience of transportation networks against flooding. The effect of flooding on road capacities and on the free-flow travel time, is considered for different rain intensities and roads preparedness. Meanwhile, the total system's travel time before and after flooding is evaluated using the concept of a Wardrop equilibrium. To this end, a proactive mechanism is developed to reduce the system's travel time, after flooding, by shifting capacities (available lanes) between same road sides. In a nutshell, this dissertation provides a suite of analytical techniques that allow the optimization of security and resilience across multiple CPSs. / Doctor of Philosophy / Cyber-physical systems (CPSs) have recently been used in many application domains because of their ability to integrate physical elements with a cyber layer allowing for sensing, monitoring, and remote controlling. This pervasive use of CPSs in different applications has brought forward new security challenges and threats. Malicious attacks can now leverage the connectivity of the cyber layer to launch remote attacks and cause damage to the physical components. Taking these threats into consideration, it became imperative to ensure the security of CPSs. Given that many CPSs provide critical services, for instance many critical infrastructure (CI) are CPSs such as smart girds and nuclear reactors; it is then inevitable to ensure that these critical CPSs can maintain proper operation. One key measure of the CPS’s functionality, is resilience which evaluates the ability of a CPS to deliver its designated service under potentially disruptive situations. In general, resilience measures a CPS’s ability to adapt or rapidly recover from disruptive events. Therefore, it is crucial for CPSs to be resilient in face of potential failures. To this end, the central goal of this dissertation is to develop novel analytical frameworks that can evaluate and improve security and resilience of CPSs. In these frameworks, cross-disciplinary tools are used from game theory, contract theory, and optimization to develop robust analytical solutions for security and resilience problems. In particular, these frameworks led to the following key contributions in cyber security: developing an analytical framework to mitigate the effects of GPS spoofing attacks against UAVs, introducing a game-theoretic moving target defense (MTD) framework to improve the cyber security, and securing data privacy in m-health Internet of Things (IoT) networks using a MTD cryptographic framework. In addition, the dissertation led to the following contributions in CI resilience: developing a general framework using Bayesian Networks to evaluate and improve the resilience of CIs against their components failure, introducing a contract-theoretic model to allocate resources to multiple connected CIs under complete and asymmetric information scenarios, providing a proactive plan to improve the resilience of transportation networks against flooding, and, finally, developing an environment-aware framework to deploy UAVs in disaster-areas.

We propose a Model-Based Deep Reinforcement Learning (MBDRL) framework for collaborative paylaod transportation using Unmanned Aerial Vehicles (UAVs) in Search and Rescue (SAR) missions, enabling heavier payload conveyance while maintaining vehicle agility. Our approach extends the single-drone application to a novel multi-drone one, using the Probabilistic Ensembles with Trajectory Sampling (PETS) algorithm to model the unknown stochastic system dynamics and uncertainty. We use the Multi-Agent Reinforcement Learning (MARL) framework via a centralized controller in a leader-follower configuration. The agents utilize the approximated transition function in a Model Predictive Controller (MPC) configured to maximize the reward function for waypoint navigation, while a position-based formation controller ensures stable flights of these physically linked UAVs. We also developed an Unreal Engine (UE) simulation connected to an offboard planner and controller via a Robot Operating System (ROS) framework that is transferable to real robots. This work achieves stable waypoint navigation in a stochastic environment with a sample efficiency following that seen in single UAV work. This work has been funded by the National Science Foundation (NSF) under Award No. 2046770. / Master of Science / We apply the Model-Based Deep Reinforcement Learning (MBDRL) framework to the novel application of a UAV team transporting a suspended payload during Search and Rescue missions. Collaborating UAVs can transport heavier payloads while staying agile, reducing the need for human involvement. We use the Probabilistic Ensemble with Trajectory Sampling (PETS) algorithm to model uncertainties and build on the previously used single UAVpayload system. By utilizing the Multi-Agent Reinforcement Learning (MARL) framework via a centralized controller, our UAVs learn to transport the payload to a desired position while maintaining stable flight through effective cooperation. We also develop a simulation in Unreal Engine (UE) connected to a controller using a Robot Operating System (ROS) architecture, which can be transferred to real robots. Our method achieves stable navigation in unpredictable environments while maintaining the sample efficiency observed in single UAV scenarios.

<p>Modern mobile mapping systems (MMSs)  -- such as Uncrewed Aerial Vehicles (UAVs), backpack systems, Unmanned Ground Vehicles (UGVs), and wheel-based systems -- equipped with imaging/ranging modalities and navigation units -- i.e., integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) -- have emerged as promising platforms due to their ability to conduct fine spatial/temporal resolution mapping at a reasonable cost. The integration of camera and LiDAR data acquired by these MMSs can result in an accurate and comprehensive description of the object space, due to their complementary characteristics. Meaningful integration of multi-temporal data/products from different modalities onboard single or multiple systems is contingent on their positional quality. The objective of this dissertation is to develop strategies that enable the derivation of accurately georeferenced data from LiDAR and camera units onboard UAVs and backpack systems across diverse mapping environments. To do so, accurate system calibration parameters -- including the sensor's interior orientation parameters (IOP) and mounting parameters relating the sensors to the INS's Inertial Measurement Unit (IMU) body frame -- and trajectory information need to be derived.</p> <p><br></p> <p>In this dissertation, to resolve the issues that arose from unstable IOP of consumer-grade camera onboard a GNSS/INS-assisted UAV, a LiDAR-aided camera IOP refinement strategy is first proposed. Additionally, in a more general case where system calibration is required for both camera and LiDAR units onboard single or multiple GNSS/INS-assisted UAV(s), an automated, tightly-coupled camera/LiDAR integration workflow through simultaneous system calibration and trajectory refinement is developed. While UAVs typically operate in open sky conditions, conducting in-canopy mapping using backpack systems for forest inventory applications is significantly affected by GNSS signal outages induced by the canopy cover. To derive accurate trajectory information in such scenarios, a system-driven strategy for trajectory enhancement and mounting parameters refinement of UAV and backpack LiDAR systems in forest applications is developed. Furthermore, considering that this approach requires an initial trajectory with limited drift errors, the Simultaneous Localization and Mapping (SLAM) technique is adopted to directly derive the trajectory information. Specifically, a comprehensive forest feature-based (i.e., tree trunks and ground) LiDAR SLAM framework using 3D LiDAR mounted on backpack systems is developed. These proposed strategies are tested using multiple datasets from UAV and backpack mobile mapping systems. Experimental results verify that the proposed approaches successfully derive accurate system calibration parameters and trajectory information, and consequently well-aligned multi-system, multi-temporal, multi-sensor data with high relative/absolute accuracy.</p>

It is a well-recognized fact that unmanned aerial vehicles are an essential element in today’s network-centric integrated battlefield environment. Compared to solo UAV missions, multiple unmanned aerial vehicles deployed in co-operative mode, offer many advantages that has motivated UAV researchers all over the world to evolve concept of operations that aims in achieving a paradigm shift from traditional ”dull” missions to perform ”dirty” and ”dangerous” missions. In future success of a mission will depend on interaction among UAV groups with no interaction with any ground entity. To reach this capability level, it is necessary for researchers, to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. The thesis is in four parts: (i) Development of an organized framework to realize the goal of achieving fully autonomous systems. (ii) Design of UAV grouping algorithm and coordination tactics for ground attack missions. (iii) Cooperative network management in GPS denied environments. (iv) UAV group tactical path and goal re-plan in GPS denied wide area urban environments. This research thesis represents many first steps taken in the study of autonomous UAV systems and in particular group autonomy. An organized framework for autonomous mission control level by defining various sublevels, classifying the existing solutions and highlighting the various research opportunities available at each level is discussed. Significant contribution to group autonomy research, by providing first of its kind solution for UAV grouping based on Dubins’ path, establishing GPS protected wireless network capable of operating in GPS denied environment and demonstration of group tactical path and goal re-plan in a layered persistent ISR mission is presented. Algorithms discussed in this thesis are generic in nature and can be applied to higher autonomous mission control levels, involving strategic decisions among UAVs, satellites and ground forces in a network centric environment.

Advances in mobile robot technology allow an increasing variety of applications to be imagined, including: search and rescue, exploration of unknown areas and working with hazardous materials. State of the art robots are able to behave autonomously and without direct human control, using on-board devices to perceive, navigate and reason about the world. Unmanned Aerial Vehicles (UAVs) are particularly well suited to performing advanced sensing tasks by moving rapidly through the environment irrespective of the terrain. Deploying groups of mobile robots offers advantages, such as robustness to individual failures and a reduction in task completion time. However, to operate efficiently these teams require specific approaches to enable the individual agents to cooperate. This thesis proposes coordinated approaches to search scenarios for teams of UAVs. The primary application considered is Wilderness Search and Rescue (WiSaR), although the techniques developed are applicable elsewhere. A novel frontier-based search approach is developed for rotor-craft UAVs, taking advantage of available terrain information to minimise altitude changes during flight. This is accompanied by a lightweight coordination mechanism to enable cooperative behaviour with minimal additional overhead. The concept of a team rendezvous is introduced, at which all team members attend to exchange data. This also provides an ideal opportunity to create a comprehensive team solution to relay newly gathered data to a base station. Furthermore, the delay between sensing and the acquired data becoming available to mission commanders is analysed and a technique proposed for adapting the team to meet a latency requirement. These approaches are evaluated and characterised experimentally through simulation. Coordinated frontier search is shown to outperform greedy walk methods, reducing redundant sensing coverage using only a minimal coordination protocol. Combining the search, rendezvous and relay techniques provides a holistic approach to the deployment of UAV teams, meeting mission objectives without extensive pre-configuration.