Master of Science, University of Toledo, 2019, Engineering (Computer Science)

The world is on the brink of an era of Unmanned Aerial Vehicles (UAVs), widely known to public as drones, where we will get to experience multiple UAVs flying in the national airspace carrying out diverse tasks such as monitoring, surveillance, product deliveries, law enforcement, fertilizing crop fields, aerial photography, and transport. In such scenarios, where multiple UAVs are flying in a smaller airspace, there is a possibility of collisions, path overlaps, mix-ups, and uncertainties as far as their flying routes are concerned. These flying routes could be inside constructed air corridors where the UAVs would be allotted to fly, similar to the air corridors of commercial aircraft. There is a growing need to identify and construct these air corridors for UAVs to fly in their respective corridors to avoid such mishaps as is what is done with commercial airplanes. The airplanes fly in their designated air corridors from one location to another without any uncertainty. It would be really useful to devise and design such a system for multiple UAVs as well, that would be able to construct air corridors for them to fly through. This served as the primary motivation behind proposing a novel approach to estimate and visualize air corridors for autonomous multi-UAV flights in an airspace. In addition to it, we studied various popular uncertainty visualization techniques and came up with a cutting-edge way to incorporate uncertainty into the visualization of the air corridors. Furthermore, we provide a standalone web application with a user-friendly graphical user interface (GUI) developed using HTML5, CSS3, JavaScript and an open-source JavaScript library for visualizing world-class 3-D maps called CesiumJS. Subsequently, we present the estimation and visualization results and discuss possible application areas where the proposed technique could be put to use. Finally, we discuss the summarized research findings and future research directions.

Committee: Ahmad Javaid (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Devinder Kaur (Committee Member) Subjects: Computer Engineering; Computer Science

MS, University of Cincinnati, 2022, Engineering and Applied Science: Aerospace Engineering

The aim of this thesis is to expand the capabilities of Unmanned Aerial vehicles (UAV), by presenting an autonomous control algorithm and using a morphing box wing, which supports vertical and horizontal flight mode along with transition. This research work provides detailed description of the position and attitude controller for a hybrid morphing box wing UAV. Hybrid morphing wing UAV has ability to perform mission as both quadcopter and fixed-wing aircraft in efficient manner. Development of a single controller with ability to handle two different mode is discussed. Numerous work has been done to increase the application of conventional UAVs, but not very much work has been done in increasing the capability to efficiently combine and fly UAV in horizontal and vertical modes of flight. Hybrid morphing wing UAV is a structural enhancement of a conventional quadrotor that is attached with a wing structure which has morphing ability, and that helps to optimizes performance of UAV during flight. Maneuvering of a traditional quadcopter is controlled by varying the rotors speeds, where the rotation is clockwise for two diagonally opposite rotors and anti-clockwise for the other two rotors. The opposite rotation of rotors in quadcopter is used to control the yaw moment of the system. The quadcopters have two configurations in which they fly, namely '+' configuration and 'X' configuration. The '+' configuration have one rotating propeller on each side of the axes for rolling and pitching, whereas 'X' configuration have two counter rotating propeller on each side of the axes for rolling and pitching. The hybrid UAV proposed in this thesis, uses a quadcopter in 'X' configuration attached to the box wing. In order to improve the efficiency of the box wing during the whole flight regime, two approaches have been applied, first is the box wing in the shape of a nozzle and second is the morphing wing. Nozzle wing design (open full item for complete abstract)

Committee: Shaaban Abdallah Ph.D. (Committee Member); Rajnikant Sharma Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Aerospace Materials

PhD, University of Cincinnati, 2022, Engineering and Applied Science: Aerospace Engineering

The aim of this dissertation is to explore the flight and control characteristics of advanced multirotors. This dissertation mainly considers three advanced multirotor designs i) Tilt-rotor quadcopter ii) Re-configurable tilt-rotor quadcopter, and iii) Sliding-Arm Quadcopter system. The traditional multirotors have many structural and operational limitations such as under-actuation, fault-tolerance during flight, disturbance rejection, limited control bandwidth. This dissertation aims to identify and resolve these issues by different hardware and software enhancements. This research addresses the fundamental information gaps in the design of the flight controllers for the tilt-rotor quadcopter. It covers detailed information on the impact of propeller spin configurations on the dynamics and structure of the flight controller. The flying capabilities of the tilt-rotor quadcopter are explored for commanding the UAV to maintain a specified orientation while navigating across the way points. Linearization approach has been utilized to drive an accurate control allocation for the tilt-rotor system. This research also highlights the application of the quaternion state feedback in the attitude controller design for the tilt-rotor quadcopter by eliminating the conventional Euler angle based approach which could cause singularities in the system. In the conventional quadcopters, propeller or motor failure is a primary fault during flight. This research introduces the concept of structural and control reconfiguration in the system where the UAV can continue to fly and complete the flight mission after the failure of one propeller. The structural reconfiguration is achieved by extending the arm length of the quadcopter opposite to the failed rotor in a passive manner. It causes a shift in the center of gravity and the moment of inertia of the system and the tilt-rotor system is converted into a T-Copter. This methodology has shown that the UAV can continue to fly after a motor (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Member); Rajnikant Sharma Ph.D. (Committee Member); Ou Ma Ph.D. (Committee Member); Kelly Cohen Ph.D. (Committee Member) Subjects: Aerospace Materials

Doctor of Philosophy in Engineering, University of Toledo, 2021, Engineering

The applications of Unmanned Aerial Vehicles (UAVs) range from military to filming. Soon pizza and post-delivery services will utilize UAVs. Being airborne, UAVs can be a target of physical or cyber-attacks. UAVs depend on continuous communication with the ground control station (GCS), a global positioning system (GPS), and other UAVs within the UAV Network (UAVNet). UAVs connected in ad-hoc manner are called Flying Ad hoc Networks (FANETs). They depend on protocols and communication models quite similar to preexisting ad hoc networks such as MANETs, VANETs, etc. Recent cyberattacks have revealed severe loopholes and vulnerabilities in drone networks. Hence, a detailed study demands to recreate the attacking scenarios and improvise on the vulnerabilities for developing strong security measures- this is achievable by simulating accurate attacks and then employing a security model. This work considers the simulation and implementation of the security model in three stages: In Stage-I, we identify a comprehensive UAV simulator's characteristics and simulate attacks; In Stage II, we utilize game theory and Quantal Response Equilibrium (QRE) for the prevention of DDoS attack; and in the Stage-III we implement Bounded rationality for the security of delivery systems. The majority of the available drone simulators focus on the designing, gaming, or military aspects. But from a cybersecurity standpoint, an effective simulator demands the inclusion of accurate mathematical modeling, correct representation of path and terrains, fly zones, easy to handle user interface, and, most importantly, the communicative elements of the Flying ad hoc network (FANETs). Learning about UAVs as networking devices is essential from a security perspective because hackers aim to attack a communicating network's vulnerable aspects. Therefore, in Stage I of our work, we studied several application-specific UAV simulators and then proposed an ideal drone simulator's characteristics. Later (open full item for complete abstract)

Committee: Ahmad Y. Javaid (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Weiqing Sun (Committee Member); Devinder Kaur (Committee Member); Mohammed Y. Niamat (Committee Member) Subjects: Computer Engineering; Computer Science; Engineering; Information Science; Information Systems; Information Technology

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

Imagine an aircraft wing with better performance, less fuel consumption, and a smaller span than the conventional wing. The concept of a ring wing, with its unique circular wing configuration, makes it possible. This thesis explores the concept of a ring wing, which has fascinated aviation enthusiasts for decades. Despite their intriguing potential, ring-wing planes face numerous design challenges, and little comprehensive research has been conducted to explore the various configurations and their performance in different flight conditions. This thesis aims to fill that gap by comprehensively analyzing the aerodynamic characteristics of ring-wing configurations and their performance in various flight conditions. To do this, we start by validating the conventional wing and then modifying it to create different configurations of ring and elliptical wings. These configurations are further modified by changing the sweep angle. Finally, we add a vertical structure to push the wing backward for aerodynamic reasons. We compare these wings with straight box wings with the same dimensional parameters to observe the difference in aerodynamic parameters. Additionally, we compare the performance of the wing configuration with maximum aerodynamic performance (among all the wing configurations in this thesis) with the performance of a combination of multiple small wings that add up to the same area as the big wing. The idea is to replace the one big wing configuration with two or more small wing configurations that add up to the same area to reduce the diameter. The results of this study will provide valuable insights into the feasibility of ring-wing aircraft. Specifically, the results will help determine whether ring-wing aircraft are a viable alternative to conventional aircraft.

Committee: Shaaban Abdallah Ph.D. (Committee Chair); Peter Disimile Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Aerospace Engineering

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The Unmanned Aerial Vehicle (UAV) navigation for last-mile delivery solutions in the Advanced Air Mobility (AAM) framework relies heavily on the Global Positioning System (GPS). However, in urban areas, unreliable GPS data necessitates the exploration of alternate means of navigation. This thesis proposes one such approach that utilizes known features i.e., landmarks that the UAV can activate on-demand to facilitate localization. Precise navigation requires accurate localization (knowledge of the UAV's current position with respect to a common reference frame or map) which can be achieved by measuring range to these landmarks with the help of onboard sensors. Activating these landmarks is associated with practical challenges like operational costs and communication expenses. Consequently, this work delves into a method for optimizing on-demand landmark activation while ensuring that the localization uncertainty remains within predefined limits. The primary objective is to predict the landmarks needed to travel safely along a given path. Employing a moving horizon strategy, we predict the UAV's position uncertainty based on available measurements from landmarks. This allows for proactive control of estimation certainty by selecting the most suitable landmarks. The landmark activation algorithm is formulated as a Mixed Integer Non-Linear Programming (MINLP) optimization problem. The proposed landmark optimization algorithm is further extended to a Cooperative Localization setup, wherein UAVs share sensor information for collective state estimation, in addition to the range measurements from the landmarks, enhancing the overall localization accuracy. The solution, applicable to both Single UAV and cooperative scenarios, is designed to satisfy uncertainty conditions while activating the minimal number of landmarks. Finally, the effectiveness and performance of the proposed algorithm are validated through simulations.

Committee: Rajnikant Sharma Ph.D. (Committee Chair); Janet Jiaxiang Dong Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Robotics

Master of Science (MS), Ohio University, 2024, Computer Science (Engineering and Technology)

Larger, commercial, towered airports are highly equipped to provide pilots with real-time weather related data before flying the aircraft. In the case of small airports, there is a weather data gap for the aircraft flying at a lower altitude. Accurate cloud information (cloud type and its height from the ground) is crucial data for pilots flying at low altitudes because it affects both visibility and safety. A ceilometer is a device that estimates cloud height from the ground, but it is often costly and lacks portability. This thesis proposes an innovative, cost-effective approach using computer vision and deep learning to address these limitations. One of the primary challenges for these methods is the need for extensive datasets for training and evaluation, as real-world data collection of cloud height and type is time-consuming and resource-intensive. To overcome this, we generated synthetic cloud data using a stereo camera setup with ground truth height information in a virtual environment. In this thesis, cloud information involves cloud-base height estimation and classification of the type of cloud. We proposed methods that can provide better efficiency in predicting the cloud-base height than state-of-the-art methods when applied to the real-world dataset in the future. We have incorporated synthetic data to evaluate the performance of our method. These synthetic data, created by simulating VDB clouds, enable the testing and validation of cloud detection models and calibrating height predictions. We rendered the 3D scene and created ground truth bounding box and cloud-type datasets, such as Altocumulus, Altostratus, Cirrocumulus, Cumulonimbus, Cumulus, Cirrostratus, Cirrus, Stratocumulus, and Stratus. We trained the YOLO-v8 model with the cloud detection dataset and employed unseen synthetic data to assess its robustness and accuracy. Once vetted, we generated disparity images from the stereo pairs. We trained several CNN-based regression models using this di (open full item for complete abstract)

Committee: Chad Mourning (Advisor); Zhewei Wang (Committee Member); Jundong Liu (Committee Member); Bhaven Naik (Committee Member) Subjects: Computer Science

Master of Science, Miami University, 2024, Electrical and Computer Engineering

In this thesis, a thorough investigation into the optimization of user connectivity in ad hoc communication networks using robust policy creation and intelligent UAV location in stochastic environments is presented. In order to handle the dynamic and decentralized character of ad hoc networks, we identified the optimal UAV positions by applying a multi-agent deep Q-learning technique. To train stochastic environment-adaptive policies, a novel simple algorithm was devised with an emphasis on the usefulness of these policies under different scenarios. Through an empirical investigation, the study offered information on the generalizability and adaptability of learnt behaviors by examining how well policies based on one distribution of settings performed when applied to different, unseen distributions. In this thesis, we also explored the resilience of UAV networks against jamming attempts and propose a method for unaffected UAVs to self-adjust their placements. This approach ensured optimal user coverage even in adversarial situations. By demonstrating the potential of machine learning techniques to maximize network performance and enhance user connectivity in the face of environmental uncertainties and security risks, these contributions will collectively advance the field of UAV-assisted communication.

Committee: Dr. Bryan Van Scoy (Advisor); Dr. Mark Scott (Committee Member); Dr. Veena Chidurala (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Master of Science, Miami University, 2024, Electrical and Computer Engineering

Optimizing user throughput is crucial for practical deployment in the developing field of Unmanned Aerial Vehicle (UAV) -assisted vehicular communications. This thesis introduces UAV-assisted vehicle-to-infrastructure (V2I) communication using UAVs as base stations (BS) to maximize system throughput while minimizing energy consumption. We investigate UAV positioning on a highway, considering vehicle mobility. Our approach combines discrete user association and subchannel allocation with continuous UAV trajectory and power control, addressing challenges in mixed-integer non-linear programming. The optimization ensures UAV trajectories adhere to realistic flight dynamics, incorporating acceleration and enforcing collision avoidance and operational bounds. Additionally, it takes into account the energy dynamics of UAVs, including both flying power and communication power. The complex optimization problem is resolved using a genetic algorithm to achieve a solution of UAV trajectory, subchannel allocation, power allocation, and vehicle association. A baseline state is configured considering initial UAV position, speed, subchannel allocation, initial association, and vehicle speed. A trade-off in the weighting factor of the optimization problem objective function shows a 37-65% increase in total data rate and a reduction of 11-19% in energy consumption.

Committee: Mark J Scott (Advisor); Bryan Van Scoy (Committee Member); Miao Wang (Committee Member) Subjects: Electrical Engineering

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The path planning and power management of hybrid fuel UAVs under presence of noise-restrictions is studied here. This problem is motivated by two scenarios: i) widespread use of UAVs in congested, urban environment; and ii) Noise-sensitive surveillance missions. In either case, it is envisioned that noise-restrictions are in place in subsets of the environment, such that ground-level noise produced by the UAV at hand must be under a certain intensity. In the case of urban usage, we consider it likely that such restrictions are eventually put in place near residential and business areas. In the case of a hybrid-fuel UAV, where energy sources include a battery-pack and combustion engine, the noise produced by the engine is intense relative to the propeller noise. In this scenario, the path planning and power planning is a coupled problem: given a path, certain power plans are infeasible, and given an energy plan certain paths are infeasible. Thus, the path of the UAV must be found in tandem with the power plan. This results in a novel problem, which we study here. The single-agent problem is studied first within a discrete framework, as is standard for vehicle motion planning. An environment is discretized into a graph, such that nodes represent locations in the configuration space and edges between the nodes are flight legs the UAV travels along between nodes. Edges are parameterized by cost and energy values. The objective is to find a feasible sequence of nodes of lowest cost without violating the power and noise constraints. We develop a fast, exact algorithm to solve this planning problem quickly on graphs of tens of thousands of nodes. The problem is approached in an optimal control framework, with only an initial approach presented in this dissertation. Battery modeling in the context of this problem is also studied briefly. The final piece of work is returning to the discrete problem in the context of multi-agent path finding (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); David Casbeer Ph.D M.A B.A. (Committee Member); Kenny Chour Ph.D M.A B.A. (Committee Member); Michael Alexander-Ramos Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member); Satyanarayana Gupta Manyam Ph.D M.A B.A. (Committee Member) Subjects: Operations Research

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

Drones with autonomous capability have found several use cases in delivery, surveillance, etc. However, in the event when a UAV needs to perform an emergency landing due to connection lost or technical difficulties, it is important to include an automated landing zone detection for a safe landing to prevent loss of life and damage to property. This work focuses on developing a solution using image processing algorithms and segmentation models to identify safe landing zones for a UAV in an indoor cluttered environment on a frame-to frame basis. A custom cluttered environment dataset is collected using the downward facing camera of a UAV in indoor conditions. The ideal landing zone in this work has to be larger than at least minimum dimensional limits, undisturbed by objects and should not be marked in any way or have any distinguishable features. The proposed solution finds optimal landing areas by passing the input image through a segmentation model to first detect all objects in the image and then the landing algorithm uses the objects coordinates from the segmentation results to find safe landing areas in the image search space. Two deep neural architectures have been selected and compared to segment objects – U-Net and YOLOv8. Experiments on test data show YOLOv8 outperforming the U-Net model with IoU scores of 0.89 and 0.77 respectively. Yolov8 also scores a higher F1 score of 0.94 than U-Net, which scored 0.86. Pareto Optimal graphs are plotted to find optimal landing areas that minimize the distance to travel and maximize the area found. Using the described technique, optimal safe landing areas are detected under 0.25 seconds. This research proposes one possible solution for emergency UAV landing problem, together with its limitations.

Committee: Manish Kumar Ph.D. (Committee Chair); Oya Aran Ph.D. (Committee Member); Janet Jiaxiang Dong Ph.D. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

Unmanned Aerial Vehicles (UAVs) have seen a rise in applications to various fields. With plenty of algorithms to support automation in UAV flights, Global Positioning System (GPS) is still the major source of position estimation. This has limited the application of UAVs to areas where GPS signal is available and strong. Thus, some other method of position estimation for the UAV is required to expand the UAV application to GPS-denied areas. Moreover, when an operator is piloting a UAV from a remote location, the operator is solely relying on the camera feed coming from the UAV to move the UAV. This camera feed gives a limited field of view of the environment, and the human operator may accidentally run the UAV into an obstacle. In this research, a method of using Hector SLAM for performing position estimation of the UAV in a GPS-denied indoor environment is presented. The Hector SLAM uses a 2D LiDAR mounted on top of the quadcopter to scan the unknown environment. Furthermore, to empower the UAV to autonomously avoid obstacles, an algorithm using Artificial Potential Field method is developed in this thesis which maneuvers the UAV away from obstacles while being piloted by a human operator. The system is developed using Robot Operating System (ROS) and PX4 autopilot. Two different ways, setpoints and attitude commands, of operating the UAV using a keyboard are implemented and compared. The algorithm has been tested in Gazebo Classic simulator and its performance is evaluated.

Committee: Manish Kumar Ph.D. (Committee Chair); David Thompson Ph.D. (Committee Member); Janet Jiaxiang Dong Ph.D. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

Real world operational environments for uncrewed aerial systems are inherently dynamic and full of uncertainties. This presents significant challenges to the safety and reliability of autonomous precision landing systems for UAVs. This study focused on evaluating and improving upon the existing ArduPilot precision landing system. ATARI testing identified brittle areas within the precision landing architecture. The precision landing failsafe interacts with other failsafes, including the critical battery failsafe, which created a state where the ground station could not land the UAV. Motion capture testing demonstrated a testing method to obtain a measure of ground truth and assess UAV performance. Analysis showed precision landing performed within requirements and much better than GPS. Results also identified room for improvement in the relative position estimation. In response to the identified brittle behavior, new flight modes were created to successfully decouple precision landing from the LAND and RTL flight modes. Now there is the option to normal RTL or LAND while precision landing is enabled, preserving precision landing strictness. GCS can force the UAV to land in the event of precision landing failsafe. A fuzzy logic based risk assessment algorithm was developed and successfully tested with logged data from a real precision landing flight. This will be valuable in future integration with a new failsafe which to make real time go/no-go decisions to continue precision landing. This research not only underscores the high performance of ArduPilot's precision landing system, but added critical safety improvements and laid the foundation for further enhancements to UAV precision landing and operational reliability.

Committee: Kelly Cohen Ph.D. (Committee Chair); Jon Ander Martin Ph.D. (Committee Member); Anoop Sathyan Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Aerospace Engineering

Master of Science, The Ohio State University, 2023, Aero/Astro Engineering

The Parametric Flutter Margin (PFM) method is utilized to identify the aeroelastic characteristics of an Unmanned Aerial Vehicle's wing in a series of Ground Vibration Tests (GVTs) and initial flight-testing study. A self-contained electromechanical excitation system developed to excite and measure the resultant wing dynamics such that Frequency Response Functions (FRF) can be identified from recorded inertial data is detailed. The results of bench-top testing performed to characterize the purpose-built excitation pods is discussed. Specifically, ground-testing indicates that the excitation pod's instrumentation could accurately measure a perturbation force generated by a moving mass within 3% error of directly measured values. Further testing reveals that the excitation pod's instrumentation could measure the resultant wing acceleration response within 1 ms-2 error. A series of GVTs performed on various wing configurations demonstrate that the self-contained excitation pods generate a sufficiently powerful perturbation force to energize an elastic response. The structural modes experimentally identified through the excitation system and PFM estimation method closely match corresponding Finite Element Analysis (FEA) predictions. A flight-testing campaign was conducted to demonstrate the excitation system and PFM method in a free-flight environment. The results of this preliminary flight-test study indicate that the frequency characteristics associated with the flexible wing's symmetric and anti-symmetric modes can be identified. Initial flight-testing results reveal the importance of properly defining the excitation signals such that the symmetric and anti-symmetric forcing functions are of comparable magnitude. After identifying the phase-crossover frequencies from FRFs measured through flight-testing, a flutter margin versus airspeed curve is developed for both the wing's symmetric and anti-symmetric modes. From these curves, an anti-symmetric flutter mechanism wi (open full item for complete abstract)

Committee: Matthew McCrink (Advisor); Moti Karpel (Committee Member); Jack McNamara (Committee Member) Subjects: Aerospace Engineering; Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 2023, Mechanical Engineering (Engineering and Technology)

Autonomous uncrewed aircraft will require collision avoidance systems designed with autonomy in mind as they integrate into the increasingly crowded national airspace system. Current uncrewed aircraft collision avoidance systems typically require a remote pilot to execute avoidance or provide poorly defined guidance that does not benefit autonomous systems. Path Recovery Automated Collision Avoidance System re-plans flight paths to adjust to collisions autonomously using path planners and keep out zones but does not currently detect or mitigate overtaking collisions. This work investigates the effect of geometric keep out zones on the overtaking scenario for autonomous uncrewed aircraft. Keep out zone shapes were developed by relating relative velocities and turn rates of the aircraft in the overtaking scenario and tested using the Path Recovery Automated Collision Avoidance System. Operational ranges for approach heading, relative velocity, and look-ahead time were then determined. The developed set of keep out zones prevented intruder aircraft from entering the minimum separation distance of one wingspan of the mission aircraft in the overtaking scenario for scenarios with look-ahead times between five and twelve seconds, relative velocities of two to twenty, and approach angles between 110◦ and -110◦ measured from the heading of the main UAS. Minimum separation was maintained for low speed encounters with relative velocities between 1.1 and 2.0 for look-ahead times between two and eight seconds for all approach angles. With a look-ahead time range of five to eight seconds, overtaking collisions of all tested approach angles and relative speeds are handled with more than twice the separation required for success, showing that the developed keep out zones are feasible for implementation on possible autonomous collision avoidance systems.

Committee: Jay Wilhelm (Advisor); David Drabold (Committee Member); Yahya Al-Majali (Committee Member); Brian Wisner (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering; Mechanical Engineering; Robotics

Master of Science, The Ohio State University, 2023, Aerospace Engineering

The ingestion of birds or unmanned aerial vehicles (UAVs) into a jet engine is a significant hazard to the safety of aircraft. While bird ingestions have been extensively researched, the threat posed by UAVs is a more recent concern due to their rise in popularity. To gain a better understanding of the dangers of UAV ingestions, it is useful to compare to ingestions of birds of similar size. This analysis is an important initial step in determining how previous knowledge regarding soft body impacts (i.e., bird impacts) relates to hard body impacts (i.e., UAVs). To properly analyze these ingestions, the use of a representative fan model that would be certified to be airworthy is used. This model includes a fan with representative boundary conditions for ingestion, including blade retention systems, nose cone, casing, and shaft. Additionally, the bird models and UAV models should be experimentally validated to have credible results. Ingestion simulations with these models will provide a better understanding of how different sizes of soft and hard bodies affect the fan during take-off, better-preparing manufacturers and operators alike for the unfortunate event.

Committee: Randall Mathison (Committee Member); Dr. Kiran D'Souza (Advisor) Subjects: Aerospace Engineering; Mechanical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2023, Electrical Engineering

Simultaneous Localization and Mapping (SLAM) is the process of using distance measurements to points in the surrounding environment to build a digital map and perform localization. It has been observed that featureless environments like tunnels or straight hallways will cause positioning faults in SLAM. This research investigates the fault diagnosis and accommodation problem for a laser-rangefinder-based SLAM systems on a quadrotor. A potential solution of using optical flow as velocity estimate and an extended Kalman filter (EKF) to perform position estimation is proposed. A fault diagnosis method for detecting faults in positional SLAM data or optical flow velocity data is developed by using two parallel EKFs. When a fault in the SLAM position or optical flow velocity is detected, the EKF adapts to provide a robust position estimate to ensure the safety of the flight control system.

Committee: Xiaodong Zhang Ph.D. (Advisor); Zhiqiang Wu Ph.D. (Committee Member); Weisong Wang Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science (M.S.), University of Dayton, 2023, Aerospace Engineering

In recent years there has been a rapid expansion of UAVs being used for agricultural spraying. UAVs have many advantages, and their use has the potential to greatly benefit the agricultural industry through reduction in cost, labor, and potentially, off target contamination. There are currently conflicting claims within the literature regarding various factors involved in spraying with UAVs and the potential for drift from sprays emitted in their wake. This inhibits implementation of rules and regulations and can misinform operators of the best practices to ensure minimal drift of chemicals being applied. This is in part due to limitations in the robustness of testing practices being implemented, whether in field or laboratory settings. To explore this problem several experimental methods were employed to examine the effect propeller wake, nozzle location, and number of propellers have on agricultural spray droplet distributions. Experiments were conducted using standard agricultural nozzles sprayed at varying transverse and downwind distances from one of two APC 17x7 propellers at different RPMs. Traditional droplet size testing was performed by traversing the nozzle and propeller across a laser diffraction instrument to determine droplet size distributions. Results indicated a shift to larger droplet sizes with the introduction of propeller wake. However, flow visualization, volume collection, and shadowgraph images coupled with an extinction based statistical algorithm reveal a plume widening effect and significant oscillations induced by the propeller wake leading to a loss in total volume and significant reduction in probability of droplets existing downwind of the propeller due to finer droplets being carried away by the wake before reaching the measurement 4 plane of the laser diffraction instrument. The results clearly show that placing a nozzle directly beneath the propeller hub for both 1 and 2 propellers is the worst (open full item for complete abstract)

Committee: Sidaard Gunasekaran (Committee Chair); Timothy Anderson (Committee Member); Kyle Butz (Committee Member); C. Taber Wanstall (Committee Member) Subjects: Aerospace Engineering; Agriculture

PHD, Kent State University, 2023, College of Arts and Sciences / Department of Geography

Unmanned Aerial Vehicles (UAVs) have been a popular tool for close-range remote sensing of vegetation for the past decade. They are often equipped with multispectral cameras, making them a cost-effective option for data collection in precision agriculture, forestry, and other research fields. These UAVs typically process imagery data using vegetation indices to detect plant stress and estimate vegetation coverage. Recently, consumer-level UAVs with RGB cameras have become more common for vegetation detection and extraction. A growing number of studies using these UAVs have successfully applied RGB vegetation indices for vegetation detection and extraction. However, the effectiveness of these indices still has room for improvement. This study aims to evaluate the performance of seven commonly used RGB vegetation indices in three different environmental settings. A consumer-level UAV was used to conduct three data collection sessions. The study applied two image-thresholding methods, Iterative Thresholding and Otsu's Method, to analyze the index maps obtained from each RGB-based vegetation index and assess their effectiveness for vegetation extraction.

Committee: Jay Lee (Committee Chair); Ye Zhao (Committee Member); Timothy Assal (Committee Member); He Yin (Committee Member) Subjects: Environmental Studies; Geographic Information Science; Geography; Remote Sensing

Master of Science, University of Toledo, 2022, Geology

Wetlands are referred to as the kidney of the catchment due to their ability to reduce nutrient loads adjoining water bodies, hence mitigating eutrophication. The Ohio government has employed this beneficial mechanism as part of the H2Ohio program to abate the immediate release of nutrients in the water body by reconstructing wetlands at a number of locations in the Maumee watershed. We are using a combination of Unmanned Aerial Vehicles (UAVs), machine learning, and field mapping to generate maps of wetland vegetation communities which assist in establishing the effectiveness of these restored sites in nutrient removal. The vegetation in most of these wetlands can take in and recycles most of the nutrients (especially Nitrogen and Phosphorus) from the incoming runoff, thereby reducing the nutrient loads into the waterbodies. Out of 82 wetlands restoration projects in northwest Ohio, this research focuses on two (Forder Bridge and Oakwoods Nature Preserve). High-resolution near-infra-red (NIR) and visible images acquired with UAV were corrected geometrically and radiometrically in Agisoft Metshape to generate Orthomosaic and Digital Elevation Model (DEM). These products are combined and then segmented into homogenous units (objects) based on the similarities in shape, scale, color, smoothness, texture, etc. Then, object-based classifiers, including Support Vector Machine (SVM) and Random Forest (RF), were used to classify the landcover into desired classes based on field sampling. Each class represents a vegetation type or other landcover (like waterbody, bare ground, etc.) present on the wetland. The accuracy of the classification model was tested with held-back field validation data using a confusion matrix. The entire process was compared across varying flight altitudes (100ft, 200ft, and 300ft), two Object based image analysis (OBIA) software (eCognition and Orfeo Toolbox(OTB)), and two wetland types (Forder Bridge and Oakwoods). At Forder Bridge, the kappa coe (open full item for complete abstract)

Committee: Richard Becker (Committee Chair); Kennedy Doro (Committee Member); Kevin Czajkowski (Committee Member) Subjects: Environmental Science; Remote Sensing