Unmanned Aerial Vehicles (UAVs) are quite successful in maintaining steady flight operations, but propeller failure that exists causes them to experience a possible crash. The objective of this thesis project is to propose a UAV failure detection model as part of the developing framework of an autonomous emergency landing system for UAVs. Health monitoring is integrated where the quadcopter is flown for three cases of propeller faults. Vibration signals are measured during each flight, where a hardware system is designed with Arduino Uno and an Inertial Measurement Unit (IMU) sensor that contains a 3-axis accelerometer and a 3-axis gyroscope, and vibration graphs are made. Once the data is extracted, different parameters (aX, aY, aZ, gX, gY, and gZ) are selected with dimension n ∈ {1,2,3,4,5,6}, and 750 data points are chosen for the K-Means Clustering algorithm. Quadcopter Failure Detection Cluster (QFDC) plots and confusion matrices are created, and three different health states are classified as clusters – normal state, faulty state, and failure state. The parameter set gZ-aZ has the best performance metrics with an accuracy of 92.1%, which is chosen for the decision-making step that involves a Light Emitting Diode (LED) subsystem. Boundary conditions are set from the gZ-aZ QFDC plot where three LEDs turn on based on the specified health state to validate the model. The accuracies of the LED system range between 89% and 95%. Successful failure detection for UAVs would make UAVs safer and more reliable to fly with less imposed restrictions.

This project presents a motion planning algorithmcapable of generating a quadrotor UAV trajectory between aninitial state and a goal state in an obstacle-cluttered environment.This trajectory is dynamically feasible, collision free and optimalby minimizing a cost function in jerk. The algorithm consistsof incrementally expanding a set of concatenated trajectoriesdefined as motion primitives, stored in a tree data structure, untila feasible high-level trajectory is found. TheA∗sorting algorithmis utilized to sort the tree by least cost, hence ensuring the optimaltrajectory is found. In case of narrow spaces requiring someangled UAV attitude, aggressive maneuvering can be attemptedto achieve a feasible trajectory. Two scenarios are introducedfor which feasible trajectories are calculated. These scenariosare also virtually simulated by coupling a UAV dynamic modelwith a feedback controller, for which the feasible trajectories areachieved despite introducing artificial disturbances in the controlinputs. Limitations of the implemented methods are mentioned,together with suggestions to areas of improvement, at the end ofthe report. / Detta projekt presenterar en rörelseplaneringsalgoritm som genererar en bana mellanett initialtillstånd och ett slutmålstillstånd för en fyrmotorigUAV i ett rum med hinder. Denna bana är dynamiskgenomförbar, kollisionsfri samt optimal genom att minimeraen kostnadsfunktion i ryck. Algoritmen består av att stegvis utöka en uppsättning av hoplänkade banor definierade sommotion primitives, som lagras i ett träd (datastruktur), tills engenomförbar bana hittas. SorteringsalgoritmenA∗användsför att sortera trädet efter minst kostnad, vilket säkerställer att den optimala banan hittas. I fallet med små utrymmen,som kräver en vinklad orientering hos UAV:n, kan aggressivamanövrar utföras för att försöka hitta en genomförbar bana.Två scenarion presenteras där genomförbara banor beräknasfram. Dessa scenarion simuleras också virtuellt genom attkoppla UAV:ns dynamiska modell med ettåterkopplingssystem,där banorna genomförs trots att artificiella störningar införsi kontrollsignalerna. Begränsningar i metoden och förslag på förbättringar diskuteras i slutet på rapporten. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Nowadays inspections of civil engineering structures are performed manually at close range to be able to assess damages. This requires specialized equipment that tends to be expensive and to produce closure of the bridge. Furthermore, manual inspections are time-consuming and can often be a source or risk for the inspectors. Moreover, manual inspections are subjective and highly dependent on the state of mind of the inspector which reduces the accuracy of this kind of inspections. Image-based inspections using cameras or unmanned aerial vehicles (UAV) combined with image processing have been used to overcome the challenges of traditional manual inspections. This type of inspection has also been studied with the use of machine learning algorithms to improve the detection of damages, in particular cracks. This master’s thesis presents an approach that combines different aspects of the inspection, from the data acquisition, through the crack detection to the quantification of essential parameters. To do this, both digital cameras and a UAV have been used for data acquisition. A convolutional neural network (CNN) for the identification of cracks is used and subsequently, different quantification methods are explored to determine the width and length of the cracks. The results are compared with control measures to determine the accuracy of the method. The results present low to no false negatives when using the CNN to identify cracks. The quantification of the identified cracks is performed obtaining the highest accuracy estimation for 0.2mm cracks.

Aerobiological sampling using unmanned aerial vehicles (UAVs) is an exciting research field blending various scientific and engineering disciplines. The biological data collected using UAVs helps to better understand the atmospheric transport of microorganisms. Autopilot-equipped UAVs can accurately sample along pre-defined flight plans and precisely regulated altitudes. They can provide even greater utility when they are networked together in coordinated sampling missions: such measurements can yield further information about the aerial transport process. In this work flight vehicle path planning, control and coordination strategies are considered for unmanned autonomous aerial vehicles. A time-optimal path planning algorithm, that is simple enough to be solved in real time, is derived based on geometric concepts. The method yields closed-form solution for an important subset of candidate extremal paths; the rest of the paths are found using a simple numerical root-finding algorithm. A multi-UAV coordination framework is applied to a specific control-volume sampling problem that supports aerobiological data-collection efforts conducted in the lower atmosphere. The work is part of a larger effort that focuses on the validation of atmospheric dispersion models developed to predict the spread of plant diseases in the lower atmosphere. The developed concepts and methods are demonstrated by field experiments focusing on the spread of the plant pathogen <i>Phytophthora infestans</i>. / Ph. D.

Through this research, a robust aircraft design methodology is developed for analysis and optimization of the Air Vehicle (AV) segment of Unmanned Aerial Vehicle (UAV) systems. The analysis functionality of the AV design is integrated with a Genetic Algorithm (GA) to form an integrated Multi-disciplinary Design Optimization (MDO) methodology for optimal AV design synthesis. This research fills the gap in integrated subsonic fixed-wing UAV AV MDO methods. No known single methodology captures all of the phenomena of interest over the wide range of UAV families considered here. Key advancements include: 1) parametric Low Reynolds Number (LRN) airfoil aerodynamics formulation, 2) UAV systems mass properties definition, 3) wing structural weight methods, 4) self-optimizing flight performance model, 5) automated geometry algorithms, and 6) optimizer integration. Multiple methods are provided for many disciplines to enable flexibility in functionality, level of detail, computational expediency, and accuracy. The AV design methods are calibrated against the High-Altitude Long-Endurance (HALE) Global Hawk, Medium-Altitude Endurance (MAE) Predator, and Tactical Shadow 200 classes, which exhibit significant variations in mission performance requirements and scale from one another. Technology impacts on the design of the three UAV classes are evaluated from a representative system technology year through 2025. Avionics, subsystems, aerodynamics, design, payloads, propulsion, and structures technology trends are assembled or derived from a variety of sources. The technology investigation serves the purposes of validating the effectiveness of the integrated AV design methods and to highlight design implications of technology insertion through future years. Flight performance, payload performance, and other attributes within a vehicle family are fixed such that the changes in the AV designs represent technology differences alone, and not requirements evolution. The optimizer seeks to minimize AV design gross weight for a given mission requirement and technology set. All three UAV families show significant design gross weight reductions as technology improves. The predicted design gross weight in 2025 for each class is: 1) 12.9% relative to the 1994 Global Hawk, 2) 6.26% relative to the 1994 Predator, and 3) 26.3% relative to the 2000 Shadow 200. The degree of technology improvement and ranking of contributing technologies differs among the vehicle families. The design gross weight is sensitive to technologies that directly affect the non-varying weights for all cases, especially payload and avionics/subsystems technologies. Additionally, the propulsion technology strongly affects the high performance Global Hawk and Predator families, which have high fuel mass fractions relative to the Tactical Shadow 200 family. The overall technology synergy experienced 10-11 years after the initial technology year is 6.68% for Global Hawk, 7.09% for Predator, and 4.22% for the Shadow 200, which means that the technology trends interact favorably in all cases. The Global Hawk and Shadow 200 families exhibited niche behavior, where some vehicles attained higher aerodynamic performance while others attained lower structural mass fractions. The high aerodynamic performance Global Hawk vehicles had high aspect ratio wings with sweep, while the low structural mass fraction vehicles had straight, relatively low aspect ratios and smaller wing spans. The high aerodynamic performance Shadow 200 vehicles had relatively low wing loadings and large wing spans, while the lower structural mass fraction counterparts sought to minimize physical size. / Ph. D.

The use of visual sensors in providing the necessary information for the autonomous guidance and navigation of the unmanned-air vehicles (UAV) or micro-air vehicles (MAV) applications is inspired by biological systems and is motivated first of all by the reduction of the navigational sensor cost. Also, visual sensors can be more advantageous in military operations since they are difficult to detect. However, the design of a reliable guidance, navigation and control system for aerial vehicles based only on visual information has many unsolved problems, ranging from hardware/software development to pure control-theoretical issues, which are even more complicated when applied to the tracking of maneuvering unknown targets. This dissertation describes guidance law design and implementation algorithms for autonomous tracking of a flying target, when the information about the target's current position is obtained via a monocular camera mounted on the tracking UAV (follower). The visual information is related to the target's relative position in the follower's body frame via the target's apparent size, which is assumed to be constant, but otherwise unknown to the follower. The formulation of the relative dynamics in the inertial frame requires the knowledge of the follower's orientation angles, which are assumed to be known. No information is assumed to be available about the target's dynamics. The follower's objective is to maintain a desired relative position irrespective of the target's motion. Two types of guidance laws are designed and implemented in the dissertation. The first one is a smooth guidance law that guarantees asymptotic tracking of a target, the velocity of which is viewed as a time-varying disturbance, the change in magnitude of which has a bounded integral. The second one is a smooth approximation of a discontinuous guidance law that guarantees bounded tracking with adjustable bounds when the target's acceleration is viewed as a bounded but otherwise unknown time-varying disturbance. In both cases, in order to meet the objective, an intelligent excitation signal is added to the reference commands. These guidance laws are modified to accommodate measurement noise, which is inherently available when using visual sensors and image processing algorithms associated with them. They are implemented on a full scale non-linear aircraft model using conventional block backstepping technique augmented with a neural network for approximation of modeling uncertainties and atmospheric turbulence resulting from the closed-coupled flight of two aerial vehicles. / Ph. D.

Next-generation cellular systems will rely on millimeter wave (mmWave) bands to meet the increasing demand for wireless connectivity from end user equipment. Given large available bandwidth and small-sized antenna elements, mmWave frequencies can support high communication rates and facilitate the use of multiple-input-multiple-output (MIMO) techniques to increase the wireless capacity. However, the small wavelength of mmWave yields severe path loss and high channel uncertainty. Meanwhile, using a large number of antenna elements requires a high energy consumption and heavy communication overhead for MIMO transmissions and channel measurement. To facilitate efficient mmWave communications, in this dissertation, the challenges of energy efficiency and communication overhead are addressed. First, the use of unmanned aerial vehicle (UAV), intelligent signal reflector, and device-to-device (D2D) communications are investigated to improve the reliability and energy efficiency of mmWave communications in face of blockage. Next, to reduce the communication overhead, new channel modeling and user localization approaches are developed to facilitate MIMO channel estimation by providing prior knowledge of mmWave links. Using advance mathematical tools from machine learning (ML), game theory, and communication theory, this dissertation develops a suite of novel frameworks using which mmWave communication networks can be reliably deployed and operated in wireless cellular systems, UAV networks, and wearable device networks. For UAV-based wireless communications, a learning framework is developed to predict the cellular data traffic during congestion events, and a new framework for the on-demand deployment of UAVs is proposed to offload the excessive traffic from the ground base stations (BSs) to the UAVs. The results show that the proposed approach enables a dynamical and optimal deployment of UAVs that alleviates the cellular traffic congestion. Subsequently, a novel energy-efficient framework is developed to reflect mmWave signals from a BS towards mobile users using a UAV-carried intelligent reflector (IR). To optimize the location and reflection coefficient of the UAV-carried IR, a deep reinforcement learning (RL) approach is proposed to maximize the downlink transmission capacity. The results show that the RL-based approach significantly improves the downlink line-of-sight probability and increases the achievable data rate. Moreover, the channel estimation challenge for MIMO communications is addressed using a distributional RL approach, while optimizing an IR-aided downlink multi-user communication. The results show that the proposed method captures the statistic feature of MIMO channels, and significantly increases the downlink sum-rate. Moreover, in order to capture the characteristics of air-to-ground channels, a data-driven approach is developed, based on a distributed framework of generative adversarial networks, so that each UAV collects and shares mmWave channel state information (CSI) for cooperative channel modeling. The results show that the proposed algorithm enables an accurate channel modeling for mmWave MIMO communications over a large temporal-spatial domain. Furthermore, the CSI pattern is analyzed via semi-supervised ML tools to localize the wireless devices in the mmWave networks. Finally, to support D2D communications, a novel framework for mmWave multi-hop transmissions is investigated to improve the performance of the high-rate low-latency transmissions between wearable devices. In a nutshell, this dissertation provides analytical foundations on the ML-based performance optimization of mmWave communication systems, and the anticipated results provide rigorous guidelines for effective deployment of mmWave frequency bands into next-generation wireless systems (e.g., 6G). / Doctor of Philosophy / Different kinds of new smart devices are invented and deployed every year. Emerging smart city applications, including autonomous vehicles, virtual reality, drones, and Internet-of-things, will require the wireless communication system to support more data transmissions and connectivity. However, existing wireless network (e.g., 5G and Wi-Fi) operates at congested microwave frequency bands and cannot satisfy needs of these applications due to limited resources. Therefore, a different, very high frequency band at the millimeter wave (mmWave) spectrum becomes an inevitable choice to manage the exponential growth in wireless traffic for next-generation communication systems. With abundant bandwidth resources, mmWave frequencies can provide the high transmission rate and support the wireless connectivity for the massive number of devices in a smart city. Despite the advantages of communications at the mmWave bands, it is necessary to address the challenges related to high-frequency transmissions, such as low energy efficiency and unpredictable link states. To this end, this dissertation develops a set of novel network frameworks to facilitate the service deployment, performance analysis, and network optimization for mmWave communications. In particular, the proposed frameworks and efficient algorithms are tailored to the characteristics of mmWave propagation and satisfy the communication requirements of emerging smart city applications. Using advanced mathematical tools from machine learning, game theory, and wireless communications, this dissertation provides a comprehensive understanding of the communication performance over mmWave frequencies in the cellular systems, wireless local area networks, and drone networks. The anticipated results will promote the deployment of mmWave frequencies in next-generation communication systems.

Emerging integrated satellite-drone-terrestrial communication (ISDTC) technologies are expected to contribute to our life by bringing forth high speed wireless connectivity to every corner of the world. On the one hand, the Internet of Things (IoT) provides connectivity to various physical objects by enabling them to share information and to coordinate decisions. On the other hand, the non-terrestrial components of an ISDTC system, i.e. unmanned aerial vehicles (UAVs), and satellites, can boost the capacity of wireless networks by providing services to hotspots, disaster affected, and rural areas. Despite the several benefits and practical applications of ISDTC technologies, one must address many technical challenges such as, resource management, trajectory design, device cooperation, data routing, and security. The key goal of this dissertation is to develop analytical foundations for the optimization of ISDTC operations, and the deployment of non-terrestrial networks (NTNs). First, the problem of resource management within ISDTC systems is investigated for service-effective cooperation among the terrestrial networks and NTNs. The performance of a multi-layer ISDTC system is analyzed within a competitive market setting.Using a novel decentralized algorithm, spectrum resources are allocated to each one of the communication links, considering the fairness among devices. The proposed algorithm is proved to reach a Walrasian equilibrium, at which the sum-rate of the network is maximized. The results also show that the proposed algorithm can reach the equilibrium with a practical convergence speed. Then, the effective deployment of NTNs under environmental dynamics is investigated using machine learning solutions with meta training capabilities. First, the use of satellites for on-demand coverage to unforeseeable radio access needs is investigated using game theory. The optimal data routing strategies are learned by the satellite system, using a novel reinforcement learning approach with distribution-robust meta training capability. The results show that, the proposed meta training mechanism significantly reduces the learning cost on the satellites, and is guaranteed to reach the maximal service coverage in the system. Next, the problem of control of UAV-carried radio access points under energy constraints is studied. In particular, novel frameworks are proposed to design trajectories for UAVs that seek to deliver data service to distributed, dynamic, and unforeseeable wireless access requests. The results show that the proposed approaches are guaranteed to converge to an optimal trajectory, and can get a faster convergence speed and lower computation cost using decomposition, cross validation and meta learning. Finally, this dissertation looks at the security of an IoT system. In particular, the impact of human intervention on the system security is analyzed under specific resource constraints. Psychological game theory frameworks are proposed to analyze the human psychology and its impact on the security of the system. The results show that the proposed solution can help the defender optimize its connectivity within the IoT system by estimating the attacker's behavior. In summary, the outcomes of this dissertation provide key guidelines for the effective deployment of ISDTC systems. / Doctor of Philosophy / In the past decade, the goal of providing wireless connectivity to all individuals and communities, including the most disadvantaged ones, has become a national priority both in the US and globally. Yet, remarkably, until today, there is still a great portion of the Earth's population who falls out of today's wireless broadband coverage. While people who live in under-developed or rural areas are still in "wireless darkness", communities in megacities often experience below-par wireless service due to their overloaded communication systems. To provide high-speed, reliable wireless connectivity to those on the less-served side of the digital divide, an integrated space-air-ground communication system can be designed. Indeed, airborne and space-based non terrestrial networks (NTNs) can enhance the capacity and coverage of existing wireless cellular networks (e.g., 5G and beyond) by providing supplemental, affordable, flexible, and reliable service to users in rural, disaster affected, and over-crowded areas. In order to fill the coverage holes and bridge the digital divide, seamless integration among NTNs and terrestrial networks is needed. In particular, when deploying an integrated communication system, one must consider the problems of spectrum management, device cooperation, trajectory design, and data routing within the system. Meanwhile, with the increased exposure to malicious attacks on high altitude platforms and vulnerable IoT devices, the security within the integrated system must be analyzed and optimized for reliable data service. To overcome all the technological challenges that hinder the realization of global digital inclusion, this dissertation uses techniques from the fields of game theory, meta learning, and optimization theory to deploy, control, coordinate, and manage terrestrial networks and NTNs. The anticipated results show that a properly integrated satellite-drone-terrestrial communication (ISDTC) system can deliver cost-effective, high speed, seamless wireless service to our world.

One of the major application areas for UAVs is the automated traversing and inspection of infrastructure. Much of this infrastructure is linear, such as roads, pipelines, rivers, and railroads. Rather than hard coding all of the GPS coordinates along these linear components into a flight plan for the UAV to follow, one could take advantage of computer vision and machine learning techniques to detect and travel along them. With regards to roads and railroads, two separate algorithms were developed to detect the angle and distance offset of the UAV from these linear infrastructure components to serve as control inputs for a flight controller. The road algorithm relied on applying a Gaussian SVM to segment road pixels from rural farmland using color plane and texture data. This resulted in a classification accuracy of 96.6% across a 62 image dataset collected at Kentland Farm. A trajectory can then be generated by fitting the classified road pixels to polynomial curves. These trajectories can even be used to take specific turns at intersections based on a user defined turn direction and have been proven through hardware-in-the-loop simulation to produce a mean cross track error of only one road width. The combined segmentation and trajectory algorithm was then implemented on a PC (i7-4720HQ 2.6 GHz, 16 GB RAM) at 6.25 Hz and a myRIO 1900 at 1.5 Hz proving its capability for real time UAV control. As for the railroad algorithm, template matching was first used to detect railroad patterns. Upon detection, a region of interest around the matched pattern was used to guide a custom edge detector and Hough transform to detect the straight lines on the rails. This algorithm has been shown to detect rails correctly, and thus the angle and distance offset error, on all images related to the railroad pattern template and can run at 10 Hz on the aforementioned PC. / Master of Science

The rapid, accurate determination of the positions and strengths of sources of dangerous radioactivity takes high priority after a catastrophic event to ensure the safety of personnel, civilians, and emergency responders. This thesis presents approaches and algorithms to autonomously investigate radioactive material using an unmanned aerial vehicle. Performing this autonomous analysis comprises five major steps: ingress from a base of operations to the danger zone, initial detection of radioactive material, measurement of the strength of radioactive emissions, analysis of the data to provide position and intensity estimates, and finally egress from the area of interest back to the launch site. In all five steps, time is of critical importance: faster responses promise potentially saved lives. A time-optimal ingress and egress path planning method solves the first and last steps. Vehicle capabilities and instrument sensitivity inform the development of an efficient search path within the area of interest. Two algorithms—a grid-based recursive Bayesian estimator and a novel radiation contour analysis method—are presented to estimate the position of radioactive sources using simple gross gamma ray event count data from a nondirectional radiation detector. The latter procedure also correctly estimates the number of sources present and their intensities. Ultimately, a complete unsupervised mission is developed, requiring minimal initial operator interaction, that provides accurate characterization of the radiation environment of an area of interest as quickly as reasonably possible. / Master of Science