Master of Science in Electrical Engineering (MSEE), Wright State University, 2020, Electrical Engineering

Fixed-wing Unmanned Aerial Vehicles (UAVs) have been found highly useful in various environments, including military and law enforcement. With the increased use of fixed-wing UAVs, there becomes an increased need to optimize the resources available. One approach to resource management is to create multi-objective flights. This thesis presents the design, analysis, and experimental implementation of multi-objective resource management for the resource of Range, distance available to the UAV, from the viewpoint of Intelligence Surveillance and Reconnaissance (ISR). First, a Simulation Environment is created capable of tracking multiple fixed-wing UAVs and to allow for the UAVs' being controlled by an externally driven algorithm. Second, an Inference algorithm is developed with the objective of information seeking. Several algorithms are developed and used in conjunction with a Sequential Analysis test to allow for calculating Target Value, calculating Target Confidence, and validating the calculated Target Value. Third, a Control algorithm is developed with the objective of Target seeking. The Control algorithm uses several approaches to path generation, including Dubins path, Optimized Order path, and Closest Target path. Finally, a supervisor algorithm termed Joint Inference and Control (JIC) joins Inference and Control together. Monte Carlo simulated test flight results are shown to illustrate the effectiveness of the developed algorithms.

Committee: Luther Palmer III Ph.D. (Advisor); Xiaodong Zhang Ph.D. (Committee Member); Pradeep Misra Ph.D. (Committee Member); Trevor J. Bihl Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science (MS), Ohio University, 2007, Electrical Engineering & Computer Science (Engineering and Technology)

Spatial disorientation plays a large role in fatal accidents especially in General Aviation (GA). In the event of low visibility, maintenance of spatial awareness is a crucial factor, to the pilot, in keeping the aircraft at a level attitude. That said, current GA cockpit instrumentation provides no significant solution to alleviate the spatial disorientation problem encountered by pilots. Latest advancements in navigational technology and the development of Mircoelectromechanical System (MEMS) for precise attitude determination have led to the research and development of a prototype Synthetic and Peripheral Vision Display (SPVD) system at Ohio University. This thesis discusses the architecture and flight tests conducted that document the performance and viability of a prototype SPVD. Additionally, it also discusses the study of a series of Human Factors flight trials designed to test the efficacy of the system. Furthermore the thesis provides an insight into the utilization of SPVD's for remote piloting of Unmanned Air Vehicles (UAV's).

Committee: Michael Braasch (Advisor) Subjects:

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

Harmful algae blooms in surface waters are a global environmental concern and threaten both human and environmental health. By outcompeting aquatic diversity, causing dissolved oxygen levels in surface waters to fall, and secreting toxins, algae blooms stress water treatment infrastructure and result in large economic losses. To control and manage the impact of harmful algae blooms, timely detection and monitoring is critical. However, current monitoring methods, such as permanent monitoring stations or water sampling, can be very costly or time-intensive, and require direct water access. The methods become dangerous or impractical in areas surrounded by cliffs or wetlands. In this study, a flexible, efficient, and cost-effective approach for monitoring surface water quality was developed by integrating water quality sensors and unmanned aerial vehicles (UAV). The integration platform was designed, constructed, and deployed through the summer of 2023 to monitor chlorophyll, phycocyanin, and turbidity in William H. Harsha Lake of Clermont County, Ohio. The water quality parameters, used as an indicator of algae blooms, were then correlated to reflectance from Landsat 8 and 9 and Sentinel 2 satellites through single and multiple linear regressions. Multiple linear regressions using reflectance from Sentinel 2 satellites yielded the highest correlations between reflectance and water quality, with R2 values of 0.70, 0.86, and 0.97 for chlorophyll, phycocyanin and turbidity, respectively. From the regressions, visible, near infrared, and red-edge bands were identified as useful for algae detection, and commercially available multispectral cameras capable of integration with UAVs were identified for future improvement of the UAV monitoring platform.

Committee: Dongmei Feng Ph.D. (Committee Chair); Richard Beck Ph.D. (Committee Member); Drew McAvoy Ph.D. (Committee Member) Subjects: Environmental 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 (M.S.), University of Dayton, 2023, Aerospace Engineering

There has been a recent surge in the need for unmanned aerial vehicles (UAVs), drones, and air taxis for a variety of commercial, entertainment, and military applications. New aircraft designs put forth by companies have shown to feature multiple lift producing surfaces and rotors acting in proximity to each other. These configuration choices are primarily informed by the “compactness” requirement in the design. For this reason, configurational choices are being considered that would otherwise not receive attention. Multi-wing configurations or distributed lift systems become a compelling choice in conceptual design of future UAVs and private air vehicles (PAVs) that complements the vertical takeoff and landing capabilities of the design. For multi-wing configurations to be considered in the early conceptual design process, the reliability of traditional lower order aerodynamic methods in predicting these aerodynamic effects must be determined. However, the nature of a highly distributed lift configuration, with 10 or more lifting surfaces in close proximity, does not lend itself to rapid or accurate viscous numerical solution. Moreover, highly distributed lift configurations drive individual lifting surface Reynolds numbers into a range where viscous interactions could have a profound effect on aerodynamic performance. As such, the degree of dependence of wing-wing interactions due to viscous effects could be determined in a first iteration through a reductionist approach. Focusing specifically on the three-dimensional viscous interactions and the aerodynamic forces on the upstream and downstream wings allows for a direct determination of the importance and isolated contribution of these effects. Proximity effects due to wing-wing interactions were experimentally quantified as a function of gap and stagger across a wide range of different relative angles of attack (decalage). The proximity effects and the zone of influence at different gap and stagger locations wer (open full item for complete abstract)

Committee: Sidaard Gunasekaran (Committee Chair); Aaron Altman (Committee Member); Michael Mongin (Committee Member); Markus Rumpfkeil (Committee Member) Subjects: Aerospace Engineering; Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2022, Environment and Natural Resources

Unmanned aerial vehicles (UAVs) have an increasingly relevant role in the field of hydrology and water resources management. Their affordability and ease of use in comparison to traditional field-based methods have made research on their applications increase rapidly in the past decade. One application of UAVs to the hydrology of river systems is the estimation of particle sizes within a channel. This project investigated the ability of UAV imagery and Structure-from-Motion (SfM) photogrammetry to estimate grain-size distributions within a reach along the Olentangy River. To do this, we selected a study reach within the Highbanks Metro Park that was approximately 250 m in length and 50 m in width. We flew a DJI Mavic 2 Pro quadcopter UAV and collected imagery of subaerially exposed grains throughout gravels bars within this study reach. These images were processed using a SfM workflow that yielded point clouds and orthomosaics from which we extracted multiple topography-based and image-based metrics to be used as proxies for grain sizes. We then calibrated statistical regression models to predict the D50 and D84 grain size percentiles from these grain size proxies. While previous literature has suggested that topographic roughness metrics outperform image textural metrics for statistical grain size estimation, our study showed that the statistical models that were calibrated based on image textural properties performed better than those that were calibrated based on point cloud roughness properties. This contradiction may reflect the unique nature of our study site where the grains were dominated by smaller particles in comparison to other studies. The smaller grain sizes in our study area would have likely produced less significant topographic signatures in comparison to larger grains, which makes topographic roughness difficult to accurately measure and apply to statistical grain size estimation techniques. The results of this study suggest that topography-based g (open full item for complete abstract)

Committee: Steve Lyon (Advisor); Sami Khanal (Committee Member); Kaiguang Zhao (Committee Member) Subjects: Environmental Science; Geology; Geomorphology; Hydrologic Sciences; Hydrology; Water Resource Management

Doctor of Philosophy, The Ohio State University, 2022, Computer Science and Engineering

Fully autonomous aerial systems (FAAS) combine edge and cloud hardware with UAVs and considerable software support to create self-governing systems. FAAS complete complicated missions with no human piloting by sensing and responding to their environment in real-time. FAAS require highly complex designs to function properly, including layers of on-board, edge, and cloud hardware and software. FAAS also necessitate complex software used for controlling low-level UAV actions, data collection and management, image processing, machine learning, mission planning, and high-level decision-making which must integrate across the compute hierarchy effectively to meet autonomy goals in real-time. The complexity of even a relatively simple FAAS makes efficiency difficult to guarantee. Efficiency, however, is paramount to the effectiveness of a FAAS. FAAS perform missions in resource-scarce environments like natural disaster areas, crop fields, and remote infrastructure installations. These areas have limited access to computational resources, network connectivity, and power. Furthermore, UAV battery lives are short, with flight times rarely exceeding 30 minutes. If FAAS are inefficiently designed, UAV may waste precious battery life awaiting further instructions from remote compute resources, delaying or precluding mission completion. For this reason, it is imperative that FAAS designers carefully choose or design edge hardware configurations, machine learning models, autonomy policies, and deployment models. FAAS have the capability to revolutionize a number of industries, but much research must be done to facilitate their usability and effectiveness. In this dissertation, I outline my efforts toward designing and implementing FAAS that are efficient and effective. This dissertation will focus on the following five topics encompassing design, implementation, and applications of FAAS: §1. Creation of new general and domain-specific machine learning algorithms a (open full item for complete abstract)

Committee: Christopher Stewart (Advisor); Sami Khanal (Committee Member); Anish Arora (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies. This drive stemmed from the developments in communication technologies for Connected and Autonomous Vehicles (CAV), such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. Coupled with automated driving capabilities of CAVs, a new and exciting era has started in the world of transportation as each transportation agent is becoming more and more connected. To keep up with the times, research in the academia and the industry has focused on utilizing vehicle connectivity for various purposes, one of the most significant being fuel savings. Motivated by this goal of fuel saving applications of Connected Vehicle (CV) technologies, the main focus and contribution of this dissertation is developing and evaluating a complete Eco-Driving strategy for CAVs. Eco-Driving is a term used to describe the energy efficient use of vehicles. In this dissertation, a complete and comprehensive Eco-Driving strategy for CAVs is studied, where multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. The first step in making a CAV achieve Eco-Driving is to develop a route-dependent speed profile called Eco-Cruise that is fuel optimal. The methods explored to achieve this optimally fuel economic speed profile are Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP). Using a generalized Matlab function that minimizes the fuel rate for a vehicle travelling on a certain route with route gradient, acceleration and deceleration limits, speed limits and traffic sign (traffic lights and STOP signs) locations as constraints, a DP based fuel optimal velocity profile is found. The ego CAV (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Mrinal Kumar (Committee Member); Bilin Aksun-Guvenc (Committee Member) Subjects: Automotive Engineering; Computer Science; Design; Energy; Engineering; Experiments; Mechanical Engineering; Systems Design; Technology; Transportation

Master of Science (MS), Ohio University, 2021, Mechanical Engineering (Engineering and Technology)

Potential energy savings for small unmanned multirotor copters inside Ground Effects (GE) could be used to increase flight time or mission payload. Operating Inside Ground Effects (IGE) presents non-linear thrust responses potentially introducing instabilities requiring more advanced control than currently present on small autopilot systems. While maximum energy savings are found for rotorcraft hover flight IGE, low altitude forward flight has been shown to offer partial energy saving for small forward velocities compared to hover. The aim of this research was to explore multirotor copter forward flight IGE using an aerodynamics model, such as Blade Element Momentum Theory (BEMT), and quadcopter simulation flights. An existing BEMT method designed to include GE was further modified to consider the impacts forward flight on rotor thrust output for sUAS sized propellers. Thrust results were then adapted to the rotor dynamics of the quadcopter model to simulate low altitude flight of a multirotor sUAS. Non-linear dynamic inversion was used to stabilize the rotorcraft dynamics IGE and maintain specific Height Ratios (HR) during forward flight. GE thrust boosts were compensated for using a GE strength determination method which predicted the rotor GE response by monitoring individual rotor altitudes. Rotor power data collected from quadcopter simulation flights both OGE and IGE were used to identify flight conditions with decreased rotor power and measure the control effort needed multirotor flight IGE. Simulation results found average rotor power to decrease with decreasing HR and forward flight velocity. Increasing forward flight velocity was found to decrease the range of HR where GE energy savings were still present. Flight conditions with decreased power requirements were identified and grouped within an increased rotor efficiency region ranging from HRs of 0.5 to 2 and a forward flight ratios of 0 to 1.5. The increased efficiency region included a range of flight c (open full item for complete abstract)

Committee: Jay Wilhelm (Advisor); Sergio Ulloa (Committee Member); Douglas Lawrence (Committee Member); Robert Williams (Committee Member) Subjects: Aerospace Engineering; Energy; Engineering

Doctor of Philosophy, The Ohio State University, 2021, Mechanical Engineering

The objective of this dissertation is to develop a framework for chance-constrained path planning in autonomous agents operating in evolving unstructured environments. Path Planning is an important problem in many fields such as robotic manipulators, mobile robotics, scheduling, flight planning, and autonomous cars and aircraft. Often, the presence of external disturbances, measurement errors and/or inadequately modeled processes in the environment can cause uncertainty in characterization of the obstacles' shape, size and location. Traditionally, such unstructured environments are typically modeled using conservative safety margins and posed as constraints or included in the cost function as a penalty. There exist no systematic methods to tune the margins or the cost function with disparate physical meaning, e.g. travel time and safety margin. In this work, the inherent uncertainty in the obstacles is posed as chance-constraints (CC) bounded by the risk of violation of those constraints in an optimal control problem for path planning. Pseudospectral discretization methods are used to transcribe the optimal control problem to a nonlinear program (NLP) which is solved using off-the-shelf optimization solvers. The constrained optimization problems are heavily dependent on a suitable initial guess provided to the solver, which affects both the computation time and optimality of the solution. Triangulation and grid based discrete optimization methods are studied for their merits and employed to generate the initial guesses. It is shown that by varying the risk of violation of obstacle boundaries, a family of solutions can be generated signifying the risk associated with each solution. This approach enables the decision maker to be `risk-aware' by providing the methodical approach to undertake missions based-on its `risk-appetite' in the given situation. This idea is then extended to recursive planning for evolving environments. An in-depth example for path plannin (open full item for complete abstract)

Committee: Mrinal Kumar Ph.D. (Advisor); James Gregory Ph.D. (Committee Member); Levent Guvenc Ph.D. (Committee Member); Vadim Utkin Ph.D. (Committee Member) Subjects: Aerospace Engineering; Engineering; Mechanical Engineering; Robotics; Systems Design

MS, University of Cincinnati, 2020, Engineering and Applied Science: Aerospace Engineering

There are innumerable applications of unmanned aerial vehicles (UAV) in today's world. The level of impact of its application depends primarily on the industry it is being applied to. One of the biggest challenges and a preeminent sector of using UAVs is in the field of healthcare. The number of cars today on the roads will only increase substantially with time. While the congestion on roads has several disadvantages, one of the major issues is that an ambulance could get stuck in traffic and be unable to reach the emergency scene on time. It's situations like these wherein the challenge lies. While there are air medical services that are currently being used to transport the patient to the hospital, the cost of using the same is too expensive and highly unaffordable for normal people [1]. Based on the statistics in different parts of the world, the congestion on roads has different levels of severity. And as technology further improves, more chaos on the roads will lead to more accidents and an increase in emergency situations. This thesis focuses on discussing a solution for times where an Emergency Medical Services (EMS) vehicle cannot reach the emergency situation in an optimal and efficient manner due to traffic jams on roads or inaccessibility issues such as rurality of the location of emergency cases. Here, we discuss the system requirements engineering of an emergency drone equipped with emergency medical services, which has the potential to help buy the patient some time while the ambulance arrives on the scene. Using drones allows the EMS to improve the response time by equipping the EMS vehicles with a drone which has a customized first aid kit that includes NARCAN, EpiPen, AED & Bleeder Kit. It is controlled by a remote Pilot-in-command that operates the drone to fly from over the top of the vehicle to the location of the emergency scene. This also involves a by-stander helping the patient with his medical needs while the ambulance is on its way by (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Shaaban Abdallah Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Anoop Sathyan PhD (Committee Member) Subjects: Aerospace Materials

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

In this dissertation, the main focus is on developing a low-cost, robust, and efficient solution for Cooperative Localization to aid navigation of Unmanned Autonomous Vehicles in GPS-denied or degraded conditions. Initially, we derive conditions for complete observability of fixed-wing Unmanned Aerial Vehicles (UAVs) and Multi-rotor UAVs. A Relative Position Measurement Graph (RPMG) is created where the nodes of the graph are the vehicles or known features (landmarks) and the edges between them represent the measurements. Using graph theory and concepts of linear algebra, conditions for the maximum rank of the observability matrix are derived and a relationship between the rank of the observability matrix and the measurements available in the system are developed. One of the drawbacks of the conditions from this analysis is the necessity to maintain a connected RPMG at all time instants. Hence, a discrete-time observability condition is developed where the union of the RPMGs over a time interval has to be connected. Next, we address a fundamental problem for close coordination and control of Unmanned Vehicles (UVs). For various applications, the inertial position of the vehicles is not important. Relative pose and orientation among vehicles are useful for developing controllers in such cases. It is known that an Extended Kalman Filter (EKF) performs extremely well provided it is initialized close to the truth and receives measurements. For vehicles traveling long distances without any GPS measurements or with severe network delays such that they need to re-initialize the filters, the assumption of known a-priori is not valid. To circumvent these problems, a Multi-Hypothesis EKF (MHEKF) is developed with range-only measurements where the EKF has no a-priori information during initialization which means that the uncertainty associated is very large. In the end, we solve a distributed cooperative localization problem for ground vehicles. Centralized CL is c (open full item for complete abstract)

Committee: Rajnikant Sharma Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Kevin Brink Ph.D. (Committee Member); Kelly Cohen Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Robots

MS, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

Unmanned Aerial Vehicles (UAV) are gaining attention in the civilian domain with their numerous potential applications. This has been demonstrated recently in light of developments around the pandemic, where UAVs were used by law enforcement departments of various countries of the world. Multinational Corporations such as Mercedes Benz partnered with Matternet for drone-based deliveries in Switzerland. Ford recently filed a patent for a drone system that can be integrated with a car that could provide emergency services. UAVs rely very much on positional signals for navigation. Positional signals such as a global positioning system (GPS) are susceptible to an outage for periods ranging from one second to a minute. This work provides a novel approach by introducing an Artificial Neural Network (ANN) in the cases where there are long gaps in positional signal received by a UAV. During our prior research, similar problems were manifesting during bridge inspection during flights flown by the drones. Even in our experiments with indoor localization systems using `Decawave', we faced similar problems. Decawave comprises Ultra-Wide-band modules that use Positioning and Networking Stack (PANS), a software library, that implements the Two-Way-Ranging method for localization. In the proposed work, an ANN is trained on drone dynamics for a pre-traveled path. Then this pre-trained network, during flight, uses back-propagation to update its weights/parameters in an online fashion, where-by it learns to “fill in” the GPS signal gaps by predicting the dynamics. In the event of a GPS Signal loss, this ANN, receiving the current state of the body as input, performs a forward propagation to predict the rigid body dynamics for the next state. The online learning capability ensures that this ANN's weights are updated to reflect changing dynamics arising from changes such as different payloads. The results highlight a comparative study between a drone that implements only Extended Kalm (open full item for complete abstract)

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

Master of Science in Electrical Engineering, University of Dayton, 2020, Electrical and Computer Engineering

The purpose of this thesis is to produce a closed-loop feedback mission planning tool that allows for the operator to control multiple Unmanned Aerial Vehicles (UAV) within a mission. Different styles of UAVs and mission planners that are available on the market were evaluated and selected for their cost, size, ability to customize, and fit for mission work. It was determined that commercially available mission planners do not provide the level of automation required, such as allowing for different algorithms for assigning UAV tasks and for planning UAV flight paths within a mission. Comparisons were made between different algorithms for path planning and tasking. From these comparisons, a bio-inspired machine-learning algorithm, Genetic Algorithm (GA), was chosen for assigning tasks to UAVs and Dubins path was chosen for modeling UAV flight paths within the mission simulation. Since market mission planners didn't allow for control of multiple UAVs, or wouldn't allow for the operator to add algorithms to increase usability and automation of the program, it was decided to create a Graphic User Interface (GUI) that would allow the operator to customize UAVs and the mission scenario. A test mission scenario was then designed, which included 9 Points of Interest (POI), 1 to 3 Targets of Interest (TOI), 3 to 5 UAVs, as well as simulation options that modeled failure of a task or a UAV crash. Operator feedback was incorporated into the simulation by allowing the operator to determine a course of action if a failure occurred, such as reprogramming the other UAVs to complete the tasks left by the crashed UAV or reassessing a failed task. Overall mission times decreased for reprogramming the UAVs versus running a separate mission to complete any tasks left by the crashed UAV. Additional code was added to the GA and Dubins path to increase speed without decreasing solution fitness.

Committee: Amy Neidhard-Doll Ph.D. (Advisor); Eric Balster Ph.D. (Committee Member); Bradley Ratliff Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Wright State University, 2019, Human Factors and Industrial/Organizational Psychology PhD

Intelligent agent technologies are increasing the potential capacity for systems to behave more autonomously and are enabling more advanced human-autonomy teaming. For instance, future applications of human-autonomy teaming for the command and control of unmanned vehicles are now under consideration. This would involve a shift from a supervisory control approach to a teaming structure. These two approaches, instantiated as the task division and relationship between a human operator and a teammate, were empirically examined. The team's composition, either human-human or human-autonomy, was also considered. A control station that supports single operator management of multiple simulated unmanned vehicles performing a base defense mission was employed along with a task management interface to support coordination and team cognition. A 2 x 2 x 2 mixed experimental design was used to evaluate operator-driven (supervisory control) and role-driven (teaming) team structures (within-subjects), across two levels of mission complexity (within-subjects), by both human-human teams and human-autonomy teams (between-subjects). Twenty-four participants completed four 30-minute trials, during which they worked with their teammate to complete a series of mission tasks. The role-driven team structure resulted in increased team performance on all measures with reduced workload. Team performance did not differ for Team Composition but the human-human teams resulted in a greater number of communications, and the teammate was rated higher in terms of trust and reliability. These results indicate that a teaming approach between human operators and autonomy can be beneficial, however, the interfaces need to support teammate interactions and provide transparency. Future research needs are also discussed.

Committee: Kevin Bennett Ph.D. (Advisor); Gary N. Burns Ph.D. (Committee Member); Mark Draper Ph.D. (Committee Member); Ion Juvina Ph.D. (Committee Member) Subjects: Psychology

MS, University of Cincinnati, 2019, Engineering and Applied Science: Computer Science

As the applications of Unmanned Aerial Vehicles (UAVs) are becoming more and more common, it is necessary to address their inherent technological challenges so as to make them safe and more useful. Designing an eective UAV path planning algorithm is essential in all UAV missions. The UAV path planning strategy depends on its application eld. The application specic constraints also need to be satised along with UAV mobility aspects for a successful UAV mission. This thesis aims to solve the UAV Path planning problem for the scenarios when the UAVs are used for remote sensing and data communication applications. The thesis consists of two pieces of work. The rst piece of work addresses and solves a UAV path planning problem when time windows and data ooading constraints are involved, which is very typical in a data communication application scenario. A Genetic Algorithm based approach is used to solve the problem in realistic time limits. In the second piece of work, an evaluation of the capabilities of currently available robotics and network simulators is conducted to determine their suitability to be used as a simulator for multimedia data communication over UAV networks. An ideal simulator for this purpose should have simulation capabilities for image/video capture, image processing, encoding/decoding and quality measurement along with flight and network simulation. A new simulation framework is proposed and tested by combining X-Plane, M3WSN and EvalVid simulator platforms to achieve an end to end simulation of a UAV multimedia data communication scenario.

Committee: Rui Dai Ph.D. (Committee Chair); Dharma Agrawal D.Sc. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2019, Electrical and Computer Engineering

Today's control systems that are implemented on commercial vehicles are designed to operate under nominal conditions with drivers, or pilots, typically handling off-nominal situations and scenarios. In operations requiring high levels of autonomy, which will possibly be the norm in the future, functions that have the capabilities to mitigate off-nominal conditions need to be incorporated in control system designs. Before such complex functions can be integrated into the civilian domain, it is imperative to be able to understand and predict system wide safety concerns by accurately identifying potential incidents or accidents. Such an identification process involves two main challenges. The first is the identification and the ranking of all possible hazards and accidents a system is prone to encounter. The second is the identification of sequences of events, or scenarios leading to the hazards of interest. This dissertation targets the second challenge. A generic Backtracking Process Algorithm (BPA) based on the deductive implementation of Markov Cell-to-Cell Mapping Technique is proposed for risk-informed identification of critical scenarios involving control systems of autonomous vehicles (a class of cyber-physical systems). A hybrid state system structure is used for the representation of autonomous vehicle systems, and a risk assessment framework is proposed for the quantitative and risk-based assurance of autonomous vehicle systems. An Unmanned Aircraft System (UAS) operating under a potential loss of link is used as a case study to demonstrate the capability of BPA in identifying critical scenarios leading to mission failures. An Autonomous Ground Vehicle operating in an urban setting is used as a case study to demonstrate the effectiveness of the proposed risk assessment framework and its usefulness in risk-informed control system design. A Sequential BPA (SBPA) is proposed for the risk assessment of autonomous systems across multiple phases of operation tha (open full item for complete abstract)

Committee: Umit Ozguner (Advisor); Tunc Aldemir (Advisor); Andrea Serrani (Committee Member); Carol Smidts (Committee Member); Keith Redmill (Committee Member); Adrian Lam (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, University of Toledo, 2018, Engineering (Computer Science)

A strong emphasis on safety in commercial and military aviation is as old and as significant as the field of aviation itself. With the growing role of autonomy in aviation, the future of flight comprises of two general directions: manned and unmanned. Manned aircraft is the more established area, in which a human flight crew serves as the main driving force in ensuring an aircraft's safety and success. Within this time-tested concept, the most significant bottleneck of safety lies within a crew managing tasks of high mental workload. In recent years, autonomy has aided in easing cognitive workload. From there, the challenge lies within applying a seamless blend of human and autonomous control based on the needs of one's mental load. Meanwhile, the field of unmanned aerial vehicles (UAVs) poses its own unique challenges of integrating into a shared airspace and transitioning from remote human-centric control to fully autonomous control. In such a case, minimizing discrepancies between predicted UAV behavior and actual outcomes is an ongoing task to ensure a safe and reliable flight. While manned and unmanned flight safety may seem distinctly different in these regards, this dissertation proposes an overarching common theme that lies within the ability to effectively model inputs and outputs through machine learning to predict potential safety hazards and thereby improve the overall flight experience. This process is conducted by 1) evaluating different machine learning techniques on assessing cognitive workload, 2) predicting trajectories for autonomous UAVs, and 3) developing adaptive systems that dynamically select appropriate algorithms to ensure optimal prediction accuracy at any given time. The first phase of the research involves the manned side of flight safety and does so by examining effects of different machine learning techniques used for assessing cognitive workload. This begins by comparing the different algorithms on four different datasets i (open full item for complete abstract)

Committee: Vijay Devabhaktuni PhD (Committee Chair); Mansoor Alam PhD (Committee Member); Ahmad Javaid PhD (Committee Member); Devinder Kaur PhD (Committee Member); Weiqing Sun PhD (Committee Member); Lawrence Thomas PhD (Committee Member) Subjects: Computer Engineering; Computer Science

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

Unmanned Aerial Vehicles (UAVs) conventionally navigate by following a series of pre-planned waypoints. When encountering an obstacle in flight, such as no-fly zones or other aircraft, the vehicle's path or waypoints may need to be re-planned. Waypoint guidance can be used to avoid obstacles, which are typically generated off-line and relayed to the UAV requiring active communications. Vector Fields (VFs) that are generated based on a desired path can provide guidance around a newly discovered obstacle without the need for a re-plan. VF convergence and circulation components were optimized to minimize deviation from a desired path when guiding around a circular obstacle. Results indicated that the developed VF obstacle avoidance optimizer provides similar path deviation performance as waypoints without the need for re-planning. Lookup tables for GVF circulation and decay radius were constructed allowing for real time obstacle avoidance without the need to re-plan mission waypoints. Experimental flight tests were conducted using a indoor quadcopter with imposed turn rate constraints, emulating a fixed wing UAV. Deviation from the planned path for simulation and experimentation were compared.

Committee: Jay Wilhelm (Committee Chair) Subjects: Engineering; Mechanical Engineering; Robotics; Robots

Doctor of Engineering, Cleveland State University, 2017, Washkewicz College of Engineering

High Intensity Laser Power Beaming is a wireless power transmission technology developed at the Industrial Space Systems Laboratory from 2005 through 2010, in collaboration with the Air Force Research Laboratory to enable remote optical `refueling' of airborne electric micro unmanned air vehicles. Continuous tracking of these air vehicles with high intensity lasers while in-flight for tens of minutes to recharge the on-board battery system is not operationally practical; hence the recharge time must be minimized. This dissertation presents the development and system design optimization of a hybrid electrical energy storage system as a solution to this practical limitation. The solution is based on the development of a high energy density integrated system to capture and store pulsed energy. The system makes use of ultracapacitors to capture the energy at rapid charge rates, while lithium-ion batteries provide the long-term energy density, in order to maximize the duration of operations and minimize the mass requirements. A design tool employing a genetic algorithm global optimizer was developed to select the front-end ultracapacitor elements. The simulation model and results demonstrate the feasibility of the solution. The hybrid energy storage system is also optimized at the system-level for maximum end-to-end power transfer efficiency. System response optimization results and corresponding sensitivity analysis results are presented. Lastly, the ultrafast supply/extended energy storage system is generalized for other applications such as high-power commercial, industrial, and aerospace applications.

Committee: Hanz Richter Ph.D. (Committee Chair); Taysir Nayfeh Ph.D. (Committee Member); Lili Dong Ph.D. (Committee Member); Majid Rashidi Ph.D. (Committee Member); Petru Fodor Ph.D. (Committee Member) Subjects: Electrical Engineering