Master of Science, The Ohio State University, 2024, Food, Agricultural and Biological Engineering

Waste coal in abandoned mine lands poses significant environmental challenges, affecting nearby communities, rivers, and streams. Effective management of these piles is essential due to concerns such as acid mine drainage, soil and water contamination, coal fires, and methane emissions. Various strategies have been proposed for managing waste coal, including potential utilization for rare earth element recovery, soil amendment, construction aggregates, and energy generation. However, the implementation of these strategies remains uncertain due to the lack of precise location and volume data on waste coal piles. Traditional methods for gathering these data rely on field visits and Global Navigation Satellite System surveying, which are costly and labor-intensive. Advances in satellite technologies and the availability of digital elevation models (DEMs) offer an opportunity to estimate waste coal volume on a regional scale in a timely and cost-effective manner. Thus, the objective of this thesis was to develop a robust data analytical framework to locate and estimate the volume of waste coal piles on a regional scale, using the Muskingum River Basin (MRB) in Ohio as the study area. Initially, a prototype was developed to determine the most effective machine learning (ML) model to map waste coal piles in a historical coal mine site within the MRB. While all four ML models effectively identified dominant classes such as Grassland and Forest, the Random Forest (RF) model demonstrated superior performance in classifying the more complex waste coal class, with a precision of 86.15% and recall of 76.71%. Subsequently, the greatest disturbance and reclamation mapping of these waste coal piles were conducted using the LandTrendr algorithm to distinguish waste coal piles in abandoned mine lands from those in active mining areas. Moreover, this study utilized publicly available elevation models to estimate waste coal volume in the MRB. However, since historical terrain mo (open full item for complete abstract)

Committee: Ajay Shah (Advisor); Sami Khanal (Advisor); Tarunjit Singh Butalia (Committee Member) Subjects: Artificial Intelligence; Engineering; Geographic Information Science; Remote Sensing; Sustainability

Doctor of Philosophy, The Ohio State University, 2024, Computer Science and Engineering

Unmanned Aerial Systems (UAS) are increasingly important. Farmers monitor crops and apply pesticides with UAS. First responders use UAS in applications ranging from fire fighting to search and rescue operations. There is potential for rapid shopping delivery by UAS. In all these applications, UAS work closely alongside humans. Onboard firmware controls the behavior of UAS. This dissertation studies ways to improve the quality of firmware. We start by presenting the first large-scale analysis of software defects ("bugs") reported in open-source UAS firmware. We examine nearly 300 reported bugs in the two most popular open-source systems (ArduPilot and PX4) and categorize the defects. Motivated by our findings, we propose three technologies to automate the detection and repair of UAS bugs. First, Avis seeks to automatically diagnose sensor bugs caused by misusing onboard sensors. Second, SA4U identifies unit type errors caused by incorrectly mixing values with different physical unit types (e.g., meters and minutes) in a computation. Finally, Scalpel automatically repairs bugs found by SA4U. Deep learning is increasingly used to provide advanced autonomous behavior for UAS. To support higher quality deep learning systems we propose checkd. Checkd automates checkpoint/restore policy configurations. Underlying checkd's contribution is the thesis that better tuned models yield better behavior. Checkd helps practitioners fine-tune models by reducing the overall cost to train.

Committee: Feng Qin (Advisor); Michael Bond (Committee Member); Christopher Stewart (Committee Member) Subjects: Computer Science

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

UAS and UGS, combined with complex sensor suites, are creating opportunities for innovation in mapping and remote sensing. This thesis presents the development and integration of a multispectral suite for the augmentation of airport surface inspections. Multiple host platforms were investigated and a UAS and UGS were selected. A gimbal was used to house all the sensors and supporting hardware. The sensor suite has an integrated GNSS/INS, RTK enabled, navigation source producing high-accuracy PVTA. Specific focus is on the LIDAR sensor and processing of the generated point cloud data using high accuracy georeferencing techniques. The high-accuracy PVTA data from the GNSS/INS was used to generate a georeferenced point cloud using a custom MATLAB script. Additional post-processing including the concatenation of multiple georeferenced point clouds was also performed.

Committee: Chris Bartone (Advisor); David Ingram (Committee Member); Jay Wilhelm (Committee Member); Jundong Liu (Committee Member) Subjects: Electrical 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

Doctor of Philosophy, Miami University, 2022, Biology

Scientists have long been fascinated by morphological novelties, which at times seem to spring out of the ancestral form from no pre-existing structure (or structures, i.e. a complex novel trait). With molecular biology, it is relatively straightforward to work out the genes and regulatory interactions responsible for the proper development of these structures in extant species. However, what is more important from an evolutionary perspective (and more difficult to determine) is how a developmental gene regulatory network (GRN) is first pieced-together to create a novel structure. An intriguing possibility is that two or more ancestral GRNs may become cross-wired to drive the formation of a complex novel structure that is radically different from its origin tissues. The objective of this dissertation is to better understand how pre-existing GRNs from more than one origin tissue may combine to spark the origin of a complex morphological novelty, focusing on the origin of the insect wing. This chapter will provide background knowledge on the evolutionary impact of wing origin on the insects (Section 1.2), as well as relevant information on the development and morphology of the wings and proposed origin tissues (Section 1.3). This is followed by a brief history of the wing origin debate utilizing traditional comparative morphology (Section 1.4), a review of the major components of the wing GRN in the fruit fly (Section 1.5), and finally, a discussion on what evo-devo studies have discovered regarding the origin of the wing GRN (Section 1.6).

Committee: Yoshinori Tomoyasu (Advisor); Xin Wang (Committee Member); Michael Robinson (Committee Member); Jennifer Schumacher (Committee Member); Paul James (Committee Member) Subjects: Biology; Developmental Biology; Entomology; Evolution and Development

MS, University of Cincinnati, 2021, Engineering and Applied Science: Aerospace Engineering

As the use of Unmanned Aerial Systems (UAS) in large-scale operations becomes increasingly more common, a method which efficiently assigns tasks to a fleet of UAS becomes critical. In a disaster response scenario, effectively assigning tasks can minimize the amount of time to accomplish the mission, potentially reducing economic damage and the loss of live. To aid with ease of implementation, tasks are decomposed to a set of waypoints that must all be visited for the task to be considered accomplished. Optimizing to accomplish all tasks in the minimum time forms a version of a version of the multiple-depot multiple travelling salesman problem (MDMTSP) with a MinMax objective function. In this research, an iterative market-based algorithm was developed to solve the task assignment problem. It was selected not only for its ability to solve the MDMTSP, but also for its ability to handle additional constraints. The market algorithm performs well in benchmarks, finding a solution within a few percent of a known optimal value for several single-depot multiple travelling salesman problems. The algorithm also compares favorably against other solvers for the MDMTSP which approximate the optimum, finding solutions that are comparable, if not slightly better than other methods, but at a greatly reduced time. The ability of the algorithm to quickly find new task assignments when necessary is also showcased, dynamically reallocating tasks if a change is made to either the set of tasks or the fleet. The market-based algorithm is also used to generate task assignments when constraints are placed on vehicle endurance and vehicle dynamics, while tasks may also be heterogeneous. Finally, a distributed version of the market is created with the goal of an eventual integration with existing distributed consensus algorithms and transitioning to a real-world flight test.

Committee: Kelly Cohen Ph.D. (Committee Chair); Elad Kivelevitch Ph.D. (Committee Member); George T. Black (Committee Member); Anoop Sathyan PhD (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Aerospace Materials

Master of Science, The Ohio State University, 2021, Food, Agricultural and Biological Engineering

According to the United States Department of Agriculture, projections show that food production needs to increase by 70-100% from 2010 – 2050 due to population growth in addition to other socioeconomic pressures. New methods are needed to increase the productivity and efficiency of agricultural systems and are critical for mitigating climate change and ensuring food security. Remote sensing (RS) and Unmanned Aerial Systems (UAS) have the potential to allow agricultural researchers to better manage and monitor these complex systems. Foliage temperature is a key variable in biophysical vegetative modeling and has been well documented to be an indicator of crop water stress. The ability to monitor subtle changes in foliage temperature using a calibrated thermal infrared (TIR) camera mounted on a UAS would open avenues for field-based stress monitoring at scales not possible without using airborne systems. However, current approaches to process thermal image data are time-consuming, inaccurate, or not well suited for foliage in field environments. And importantly, methods to extract foliage pixels from the background (i.e. soil, weeds, etc) are needed to remove the influence of background elements that can have dramatically different temperatures from the surrounding plant tissue. This study aims to train and validate a Supervised Machine Learning (SML) algorithm using a dual-camera system to extract foliage temperature in a complex field environment. A UAS campaign focused on a set of maize treatments was conducted at Waterman Farm throughout the summer of 2020, spanning diurnal acquisitions across the growing season. In-situ tower-based sensors were deployed to provide validation of the airborne data. Remotely sensed images, which included red, green, blue, and thermal infrared bands, were used to train an SML algorithm. Our results show that the combination of these four bands can be used to accurately identify foliage pixels within complex field scenes with an accu (open full item for complete abstract)

Committee: Darren Drewry (Advisor); Scott Shearer (Committee Member) Subjects: Agricultural Engineering; Computer Science

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Aerospace Engineering

In the coming years, the number of small Unmanned Aerial Systems (sUAS) operating in low-altitude, uncontrolled airspace is expected to vastly increase. These vehicles must be able to successfully navigate their assigned missions, while ensuring the safety of people and property below. Therefore, finding solutions to managing sUAS in highly congested airspace is vital. In this dissertation an intelligent system is used to help identify, track, and manage large-scale sUAS operations using a novel sUAS Traffic Management (UTM) system. Due to the size and weight of sUAS, these vehicles pose many unique challenges that are not addressed by existing solutions, including high maneuverability and onboard instrument/sensor limitations. To develop and test the various aspects of this work a realistic fast-time simulation (FTS) was created. This FTS can serve as a tool for future researchers to explore other policies and concepts surrounding UTM in a close-to-reality environment. To identify and track the sUAS throughout their missions a novel tracking system was developed, which fuses data from three different sensor platforms to estimate the state of each vehicle. The tracking system within was able to successfully identify all sUAS, perfectly associate all data points to the correct tracks, and provide an accurate position and velocity estimation at all time steps. The conflict detection and resolution (CD&R) system within provides multi-layered tactical conflict detection and resolution services. This system is referred to as the Tactical Intelligent Detect and Avoid System for Drones (TIDAS-4D). Each layer uses a unique set of high-level heuristics to determine the appropriate action to resolve the conflict and a low-level fuzzy logic system to perform the desired action. TIDAS-4D was evaluated for its effectiveness at mitigating the risk of losses of separation (LOSs), near mid-air collisions (NMACs), and collisions between aircraft. Using only current-state info (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); George T. Black M.S. (Committee Member); Min Xue Ph.D. (Committee Member) Subjects: Aerospace Materials

Master of Science (MS), Ohio University, 2020, Electrical Engineering (Engineering and Technology)

With a growing demand for large unmanned aircraft system operations in the national airspace system, a method to safely and automatically land unmanned aircraft at a wide range of airports with varying levels of equipage is still needed. Currently no navigation system is capable of a fully coupled precision approach and landing without the use of ground based navigational aids. To enable widescale adoption and usage of unmanned aircraft systems, an aircraft based augmentation system that provides precision approach and landing service without sacrificing safety is required to land the aircraft at all runways. This thesis proposes an aircraft based GPS augmentation system using an on-board downward facing radar altimeter for precision approach and landing of unmanned aircraft systems. The proposed architecture is initially evaluated using a simulation environment designed to test multiple different GNSS, radar altimeter, and terrain elevation database configurations. Following the offline simulation, a flight test analysis is completed testing the proposed architecture using pre-recorded flight test data at the Ohio University Airport (OH) and Reno-Tahoe International Airport (NV). Furthermore, this thesis provides a sensitivity study on the systematic errors in the augmentation system to better characterize and account for the inherent errors of the architecture's subsystems. This thesis then discusses modifications to the previously developed terrain database spot algorithm to better account for the characteristics of the selected radar altimeter. Finally, an approach for future certification is proposed followed by recommendations for further research on the topic.

Committee: Maarten Uijt de Haag Ph.D. (Advisor); Frank van Graas Ph.D. (Committee Member); Sabrina Ugazio Ph.D. (Committee Member); Justin Frantz Ph.D. (Committee Member) Subjects: Electrical Engineering; Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2019, Electrical Engineering

This research aims to accomplish three main tasks for a quadrotor UAV with mapping and navigation capabilities. Firstly, a Simultaneous Localization and Mapping (SLAM) system is developed utilizing a laser rangefinder an open source SLAM algorithm called GMapping. This system allows for mapping of the surrounding environment as well as localizing the position of the quadrotor, enabling position control. Secondly, several path planning algorithms were implemented and evaluated. This allows the quadrotor to navigate through the environment even in the presence of obstacles. Lastly, to compensate for possible faults in the SLAM measurements, a fault-tolerant control method is developed. Real-time experimental results have shown the effectiveness of the algorithms.

Committee: Xiaodong Zhang Ph.D. (Advisor); Luther Palmer Ph.D. (Committee Member); Kuldip Rattan Ph.D. (Committee Member); Jonathan Muse Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

Collaboration is an important aspect of many successful natural systems, but it is rare to find in transportation systems. However, recent advances in the standardization of communication technologies, improvements in unmanned aerial systems, and deployments of large autonomous vehicle fleets could be used to collaboratively optimize entire traffic networks and improve the safety of self-driving cars. This thesis considers how unmanned aerial systems could use communication to provide useful information to self-driving cars and transportation systems. A custom unmanned aerial system is designed and built to study dedicated short-range communication (DSRC) technology. The physical layer of DSRC is studied extensively using the unmanned aerial system and concerns are given for antenna design. Then a simulation environment is built to study large scale implementation of communication and unmanned aerial systems in traffic networks. This simulation environment is shown to be useful for a wide array of traffic studies. Finally, considerations are given for future work.

Committee: Levent Guvenc (Advisor); Bilin Aksun-Guvenc (Committee Member) Subjects: Civil Engineering; Computer Engineering; Computer Science; Electrical Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 2018, Electrical Engineering (Engineering and Technology)

Many mobile devices (phones, tablets, smartwatches, etc.) have incorporated embedded GNSS receivers into their designs allowing for wide-spread on-demand positioning. These receivers are typically less capable than dedicated receivers and can have an error of 8-20m. However, future application, such as UAS package delivery, will require higher accuracy positioning. Recently, the raw GPS measurements from these receivers have been made accessible to developers on select mobile devices. This allows GPS augmentation techniques usually reserved for expensive precision-grade receivers to be applied to these low cost embedded receivers. This thesis will explore the effects of various GPS augmentation techniques on these receivers.

Committee: Maarten Uijt de Haag (Advisor); Chris Bartone (Committee Member); Michael Braasch (Committee Member); Viorel Popescu (Committee Member) Subjects: Electrical Engineering

Master of Science (MS), Ohio University, 0, Electrical Engineering & Computer Science (Engineering and Technology)

Unmanned Aircraft System (UAS) are proving to be increasingly favorable in military and commercial applications. The range of applications include surveillance, aerial photography, environmental observations, search and rescue, mapping, forestry, agricultural survey, law enforcement among many. The small size unmanned multi-copters are highly capable and cost effective for low altitude operations and have extended access to dangerous and hazardous environments which were previously unavailable. Irrespective of the applications, a position and navigation solution are necessary to fly the UAS completely autonomous or even to manually control it easily. The Global Navigation Satellite System (GNSS) has become one of the most dependable solution for position and navigation outdoors but does not perform well in the indoor environment as the signal is obstructed by the roof and the walls. Hence, there is a need for non-Global Positioning System (GPS) position and navigation solution methods for indoors. Simultaneous Localization and Mapping (SLAM) and feature-based integrated navigation are two methods that can be used for this purpose, using various types of sensors like ranging sensors, cameras, and Inertial Measurement Unit (IMU). This thesis will focus on integrating depth imagery, Short Wave Infrared (SWIR) imagery and Long Wave Infrared (LWIR) imagery with an IMU to obtain and estimate of both the position and the map of the environment. In this discussion, the region of operation is restricted to structured environments and would be extended to unstructured environments in the future. This work will include preliminary flight test results from a small-size Blackout quadcopter operated in a structured indoor environment for maintenance purposes. The quadcopter has been equipped with a 3DR Pixhawk flight controller and an Odroid XU4 onboard computer running Ubuntu. The Robotics Operating System (ROS) is used to interface with and integrate all the sensors and control (open full item for complete abstract)

Committee: Maarten Uijt de Haag (Advisor); Frank Van Graas (Committee Member); Jim Zhu (Committee Member); Martin J Mohlenkamp (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2017, Civil Engineering

Unmanned aerial systems are developing into increasingly competitive platforms for aerial image surveying in a variety of applications. Easy-to-use and relatively inexpensive, their utility is however reliant on georeferencing their imagery with respect to an earth-based coordinate system. Traditionally this requires the time-consuming and potentially cost-increasing introduction of ground control points into the scene of interest. Other techniques, such as direct georeferencing using integrated Global Navigation Satellite System receivers and inertial navigations systems, struggle to achieve comparable accuracy due to weight and cost limitations faced with highest-accuracy instrumentation. In this work, unmanned aerial system imagery was georeferenced via registration with satellite imagery downloaded from online geobrowser image databases, specifically Environmental Systems Research Institute World Imagery. This method allows the potential elimination of all fieldwork related to ground control point distribution and surveying, taking advantage instead of instantaneous access daytime, cloud-free satellite imagery provided by geobrowsers. Registration was performed both using pixel-based template matching, and successive application of pixel-based and feature-based keypoint detection and matching techniques. Test imagery was collected by unmanned aerial systems over a parking lot and surrounding area in central Ohio. Root mean square error results were calculated for both pixel-based and successive pixel-based and feature-based registration with respect to 8 ground control points measured independently by Global Navigation Satellite System survey. Using 0.3 meter reference imagery, sub-pixel accuracy was achieved with successive pixel-based and feature-based registration. Possible applications include unmanned aerial systems mapping for endeavors such as precision agriculture, and unmanned aerial systems navigation.

Committee: Dorota Grejner-Brzezinska Ph.D. (Advisor); Charles Toth Ph.D. (Committee Member); Rongjun Qin Ph.D. (Committee Member) Subjects: Civil Engineering

Master of Science (MS), Ohio University, 2017, Electrical Engineering & Computer Science (Engineering and Technology)

With approval of UAS for civilian use in the National Airspace System, comes the need for formal integration. Manned and unmanned aircraft will share the same volumes of airspace, for which the safety standards must be upheld. Under manned aircraft operations, certain implicit assumptions exist that must be made explicit and translatable to the unmanned aircraft context. A formal system safety assessment approach through a fault tree analysis was used to identify assumptions contingent on a pilot's presence inside the fuselage and areas of weakness in operational equivalency of UAS. The UAS fault tree framework developed is applicable to unmanned aircraft systems of different sizes and complexity, while maintaining a semblance to the framework accepted within the manned aircraft community. In addition, a database of UAS incidents and accidents occurring internationally 2001-2016 was developed from published materials and databases of various sources. Database events were categorized according to the UAS Fault Tree Framework Level 1 Subsystems, the International Civil Aviation Organization (ICAO) Aviation Occurrence Categories, and the Human Factors Analysis and Classification System (HFACS). ICAO Aviation Occurrence Category specific fault trees were constructed for the three most commonly occurring categories in the database results. Significant sources of risk for UAS operations lie in Aircraft/System and Flight Crew/Human Factors failures. Commonly occurring Occurrence Categories in the results of the UAS database were different than those identified for fatal accidents occurring in manned commercial aviation operations. Increased system reliability and standardization is needed to ensure equivalent levels of safety for UAS operations in the NAS. Additionally, needs of UAS pilots are different than those for manned and model aircraft. Training requirements must be approached independently and formally evaluated for their effectiveness in risk mitigation.

Committee: Frank van Graas Ph.D. (Advisor); Maarten Uijt de Haag Ph.D. (Committee Member); Jeffrey Dill Ph.D. (Committee Member); Robert Stewart Ph.D. (Committee Member) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 2016, Geography

The glaciers of the Cordillera Blanca Peru are rapidly retreating as a result of climate change, altering the timing, quantity and quality of water available to downstream users. Changes in water availability have serious implications for ecosystems, human livelihoods and regional economies. This dissertation investigates spatiotemporal changes in the glacier hydrologic system of the Cordillera Blanca Peru. It includes three major components. First, I develop multispectral unmanned aerial vehicles (UAV) and kite platforms capable of operating at over 5000m in mountain regions. Secondly, I deploy these platforms to investigate processes of glacier change and surface/subsurface hydrology within the glacial valleys of the Cordillera Blanca. Finally, I integrate UAV datasets with traditional field hydrology to improve our understanding of the spatiotemporal variability in soil moisture and its role in moderating groundwater storage within the Cordillera Blanca. I designed and deployed UAVs on multiple missions at over 5000masl in the Cordillera Blanca, Peru. After describing the UAV design in Chapter 2, this dissertation reports on results of four studies that utilise the UAV to address research questions within the region. Chapter 3 comprehensively assesses the accuracy of photogrammetrically derived structure from motion (SfM) digital elevation models (DEMs), by quantitatively and qualitatively comparing the data against surveyed GPS positions and LiDAR DEMs. Finding that accuracy is as good if not superior to low density LiDAR, with the high density SfM point clouds retaining unique surface details. Chapter 4 investigates the dynamics of glacier change over the debris covered Llaca glacier. I document the importance of debris cover and surface features such as ice cliffs in controlling melt rates. Average glacier downwasting is 0.75m over one year but is highly heterogeneous. Ice cliff horizontal recession rates of up to 25m annual were measured illustrating the i (open full item for complete abstract)

Committee: Bryan Mark PhD (Advisor); Darla Munroe PhD (Committee Member); Michael Durand PhD (Committee Member); Liu Desheng PhD (Committee Member) Subjects: Geography; Geomorphology; Hydrologic Sciences; Hydrology; Physical Geography; Remote Sensing; Robotics; Soil Sciences; Technology; Water Resource Management

Doctor of Philosophy, The Ohio State University, 2016, Industrial and Systems Engineering

This dissertation examines the challenges inherent in designing and regulating to support human-automation interaction for new technologies that will be deployed into complex systems. A key question for new technologies with increasingly capable automation, is how work will be accomplished by human and machine agents. This question has traditionally been framed as how functions should be allocated between humans and machines. Such framing misses the coordination and synchronization that is needed for the different human and machine roles in the system to accomplish their goals. Coordination and synchronization demands are driven by the underlying human-automation architecture of the new technology, which are typically not specified explicitly by designers. The human machine interface (HMI), which is intended to facilitate human-machine interaction and cooperation, typically is defined explicitly and therefore serves as a proxy for human-automation cooperation requirements with respect to technical standards for technologies. Unfortunately, mismatches between the HMI and the coordination and synchronization demands of the underlying human-automation architecture can lead to system breakdowns. A methodology is needed that both designers and regulators can utilize to evaluate the predicted performance of a new technology given potential human-automation architectures. Three experiments were conducted to inform the minimum HMI requirements for a detect and avoid (DAA) system for unmanned aircraft systems (UAS). The results of the experiments provided empirical input to specific minimum operational performance standards that UAS manufacturers will have to meet in order to operate UAS in the National Airspace System (NAS). These studies represent a success story for how to objectively and systematically evaluate prototype technologies as part of the process for developing regulatory requirements. They also provide an opportunity to reflect on the lessons learned in order (open full item for complete abstract)

Committee: David Woods (Advisor); Philip Smith (Committee Member); Jozef Raadschelders (Committee Member) Subjects: Aerospace Engineering; Engineering; Experiments; Industrial Engineering; Psychology; Systems Design; Technology

Doctor of Philosophy, The Ohio State University, 2015, Aero/Astro Engineering

This dissertation provides a flight-testing framework for assessing the performance of fixed-wing, small-scale unmanned aerial systems (sUAS) by leveraging sub-system models of components unique to these vehicles. The development of the sub-system models, and their links to broader impacts on sUAS performance, is the key contribution of this work. The sub-system modeling and analysis focuses on the vehicle's propulsion, navigation and guidance, and airframe components. Quantification of the uncertainty in the vehicle's power available and control states is essential for assessing the validity of both the methods and results obtained from flight-tests. Therefore, detailed propulsion and navigation system analyses are presented to validate the flight testing methodology. Propulsion system analysis required the development of an analytic model of the propeller in order to predict the power available over a range of flight conditions. The model is based on the blade element momentum (BEM) method. Additional corrections are added to the basic model in order to capture the Reynolds-dependent scale effects unique to sUAS. The model was experimentally validated using a ground based testing apparatus. The BEM predictions and experimental analysis allow for a parameterized model relating the electrical power, measurable during flight, to the power available required for vehicle performance analysis. Navigation system details are presented with a specific focus on the sensors used for state estimation, and the resulting uncertainty in vehicle state. Uncertainty quantification is provided by detailed calibration techniques validated using quasi-static and hardware-in-the-loop (HIL) ground based testing. The HIL methods introduced use a soft real-time flight simulator to provide inertial quality data for assessing overall system performance. Using this tool, the uncertainty in vehicle state estimation based on a range of sensors, and vehicle operational environments is pre (open full item for complete abstract)

Committee: James W. Gregory (Advisor); Charles Toth (Committee Member); Cliff Whitfield (Committee Member); Jeffery P. Bons (Committee Member) Subjects: Aerospace Engineering

MS, University of Cincinnati, 2015, Engineering and Applied Science: Aerospace Engineering

In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of Unmanned Aerial Systems (UAS) continue to multiply. Therefore, solutions to managing vehicles in highly congested airspace must be explored. In this study, an intelligent systems approach was used to help mitigate the risk of collision between aircraft in uncontrolled airspace using a UAS Traffic Management (UTM) System. To test the effectiveness of this system, a three-dimensional environment was created using MATLAB to simulate a fully autonomous heterogeneous fleet of UAS attempting to accomplish a variety of realistic missions, including precision agriculture, package delivery services, natural resource monitoring, and disaster management. Main research challenges include situational awareness, decision making, and multi-agent control in an uncertain, time-critical, spatio-temporal environment. To gain the knowledge, experience, and expertise necessary to solve this large-scale real-world problem, two preliminary research efforts were conducted. First, a simulated gaming platform known as Pong, originally created by ATARI, was used to demonstrate the effectiveness of a fully autonomous team to accomplish a desired task using a cascading Fuzzy system. With this knowledge, a simplified UTM system was developed to test a preliminary design of a fuzzy collision avoidance system. Once complete, this knowledge was used to develop the final UTM system platform capable of using intelligent separation assurance and collision avoidance techniques to mitigate the risk for Near Mid-Air Collisions between aircraft. This fuzzy solution utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. The collision avoidance system was tested in extreme conditions, including close proximity, high closure rates, and conservative maximum turn rates. In the preliminary homogenous case, the collision avoidance techniq (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Grant Schaffner Ph.D. (Committee Member); Gary Slater Ph.D. (Committee Member) Subjects: Aerospace Materials