Trends in USV research will continue on the path toward a fully autonomous USV capable of troop transport or enemy engagement. Imaging sonar will be an integral part of this development. However, due in part to sonar's inherent physical limitations, as well as its sensitivity to environmental factors, sonar technology represents a bottleneck to the development of situationally aware USVs capable of high-speed maneuvers. The work presented in this thesis is intended to provide a platform to bridge this gap, which is the design, analysis, and field testing of a mount for an imaging sonar intended as a retrofit for an existing vessel. The result of this work represents a step toward the ultimate goal of a fully autonomous USV, and will enable the advancement of research in the use of imaging sonar on surface vehicles. This thesis examines the problem of mounting a sonar on a surface vehicle from a fundamental perspective. It describes the development of a list of customer needs, presents a prototype design, and presents the important analyses for the prototype. The prototype mount was built, and field testing for proof of concept was carried out on the Virginia Tech USV, which is a Rigid Hull Inflatable Boat (RHIB), and the Navy Special Operations Craft - Riverine (SOC-R) on the Pearl River at Stennis Space Center. Testing showed the mount to be highly effective at limiting risk to personnel and equipment while operating in difficult environments like swamps. However, it also exposed some limitations associated with the mount's breakaway device, and the mounting location at the side in 2012, and at the stern in 2013. Based on experience gained from testing, a new mount design is presented for use at the bow. The bow location offers better impact protection to the sonar as long as the sonar can be positioned above the boat's draft. Field tests also exposed the need for an omnidirectional breakaway device which limits impact loads on the sonar during collisions. The Ball and Socket Breakaway (BSB) device was designed to satisfy this need. The BSB is acts as a "mechanical fuse," which holds the sonar rigidly under normal operating conditions, but will slip and rotate when the sonar strikes an object. It is designed to respond to impact loads on the sonar from the front, sides, or back, resulting in improved sonar protection during the varied maneuvers necessary for operation in shallow, narrow passageways. The expected moment holding capacity of the BSB as it is currently designed is 300 N-m (2650 lb-in), which should allow for speeds up to 3 m/s (6 kt) before drag-induced breakaway. / Master of Science

Wind observations that are critical for understanding meteorological processes occurring inside of the Earth's atmospheric boundary layer (ABL) are sparse due to limitations of conventional atmospheric sensors. In this dissertation, dynamic systems and estimation theory are combined with experimental methods to exploit the flight envelope of multirotor UAS for wind sensing. The parameters of three quadrotor motion models, consisting of a kinematic particle, a dynamic particle, and a dynamic rigid body models are developed to measure wind velocity in hovering flight. Wind tunnel and steady level flight tests are used to characterize kinematic and dynamic particle models. System identification stepwise regression and output error algorithms are used to determine the model structure and parameter estimates of rigid body models. The comparison of all three models demonstrates the rigid body model to have higher performance resolving slow-varying winds based on a frequency response analysis and field experiments conducted next to a 3-D sonic anemometer. The dissertation also presents an extension of the rigid body wind estimation framework to profile the horizontal components of wind velocity in vertical steady ascending flight. The extension employed system identification to characterize five rigid body models for steady-ascending flight speeds increasing from 0 to 2 m/s in intervals of 0.5~m/s. State observers for wind profiling were synthesized using all five rigid body models. Performance assessments employing wind observations from in situ and remote sensors demonstrated model-based wind profiling results to be be in close agreement with ground-truth wind observations. Finally, the rigid body wind sensing framework developed in this dissertations for multirotor UAS is employed to support science objectives for the Advanced Lagrangian Predictions for Hazards Assessment Project. Quadrotor wind measurements sampled at 10 m above sea level were used to characterize the leeway of a person in water for search and rescue scenarios. Leeway values determined from quadrotor wind measurements were found to be in close to leeway parameters previous published in the literature. This results demonstrates the utility of model-based wind sensing for multirotor UAS for providing wind velocity observations in complex environments where conventional wind observations are not readily available. / Doctor of Philosophy / Wind observations that are critical for understanding meteorological processes occurring inside of the Earth's atmospheric boundary layer (ABL) are sparse due to limitations of conventional atmospheric sensors. In this dissertation, dynamic systems and estimation theory are combined with experimental methods to exploit the flight envelope of multirotor UAS for wind sensing. The parameters of three quadrotor motion models, consisting of a kinematic particle model, a dynamic particle model, and a dynamic rigid body model, are characterized to measure wind velocity in hovering flight. Parameter characterizations are realized using data from wind tunnel, steady level flight tests and system identification experiments. Model-based wind estimations algorithms are developed using the kinematic particle model directly and by synthesizing state observers for the dynamic particle and rigid body models separately. For comparison purposes, the frequency response characteristic of the dynamic particle and rigid body models is examined to determine the range of wind fluctuations that each model can resolve. Performance comparisons demonstrate that the rigid body model to resolve higher wind fluctuations and yield more accurate wind estimates. The dissertation extends the rigid body wind estimation algorithm to estimate wind velocity profiles of the horizontal wind vector. The rigid body wind estimation algorithms is used to answer science questions about about the drift of a person in water.

The usage of unmanned aerial systems (UAS), also called drones, has grown at an increasing rate, with expectations of the number of unmanned aircraft (UA) to triple between 2019 and 2023 as commercial and government usage of UAS increases as per the Federal Aviation Administration. As the usage of UA increases, the probability of a UA crash resulting in injuries of 3rd parties on the ground also increases. The goal of this research was to create a method and software tool that gives the user an accurate representation of the risk to 3rd parties on the ground associated with a given flight plan. The main area of focus was on large rotorcraft and fixed-wing aircraft that are used by the military and that have the potential to do large amounts of damage if a crash were to occur. How unique types of failures affect the ground area at risk and the UA crash characteristics and how these characteristics affect population on the ground were all considered. With this information, a probability of fatality value is calculated, which helps the user determine if the mission risk is acceptable. The ability to optimize this flight path to find the lowest risk flight path is also possible, based upon user specifications. / Master of Science / Understanding the likelihood of an undesired event occurring is vital for the use of any system in the real world. This is especially true in the case of aircraft, were an undesired event can likely cause loss of life. A new area of aircraft that require additional insight into the failure characteristics are unmanned aerial systems, often referred to as drones. Drones do not have a pilot inside the aircraft, who could correct for any failures that might occur. Due to this potential inability to correct for a failure, a method must be developed to gain a better understanding of the potential failures and risks involved in drone operations. The method developed during this work was turned into a software tool, which allows a mission for a drone to be mapped out and the risk to be determined. Due to the drones being unmanned the risk is taken as the expected number of fatalities to the 3rd party individuals on the ground. This expected number of fatalities is determined by the population density of the area the flight is occurring over, and the crash characteristics for the aircraft. These methods and accompanying assumptions are outlined in the body of this work.

This thesis compares three variations of the Bayesian network as an aid for decision-making using uncertain information. After reviewing the basic theory underlying probabilistic graphical models and Bayesian estimation, the thesis presents a user-defined static Bayesian network, a static Bayesian network in which the parameter values are learned from data, and a dynamic Bayesian network with learning. As a basis for the comparison, these models are used to provide a prior assessment of the safety of flight of a small unmanned aircraft, taking into consideration the state of the aircraft and weather. The results of the analysis indicate that the dynamic Bayesian network is more effective than the static networks at predicting safety of flight. / Master of Science / This thesis used probabilities to aid decision-making using uncertain information. This thesis presents three models in the form of networks that use probabilities to aid the assessment of flight safety for a small unmanned aircraft. All three methods are forms of Bayesian networks, graphs that map causal relationships between random variables. Each network models the flight conditions and state of the aircraft; two of the networks are static and one varies with time. The results of the analysis indicate that the dynamic Bayesian network is more effective than the static networks at predicting safety of flight.

This dissertation addresses model form uncertainty quantification, non-deterministic simulations, and sensitivity analysis of the results of these simulations, with a focus on application to analysis of unmanned aircraft systems. The model form uncertainty quantification utilizes equation error to estimate the error between an identified model and flight test results. The errors are then related to aircraft states, and prediction intervals are calculated. This method for model form uncertainty quantification results in uncertainty bounds that vary with the aircraft state, narrower where consistent information has been collected and wider where data are not available. Non-deterministic simulations can then be performed to provide uncertainty quantified estimates of the system performance. The model form uncertainties could be time varying, so multiple sampling methods were considered. The two methods utilized were a fixed uncertainty level and a rate bounded variation in the uncertainty level. For analysis using fixed uncertainty level, the corner points of the model form uncertainty were sampled, providing reduced computational time. The second model better represents the uncertainty but requires significantly more simulations to sample the uncertainty. The uncertainty quantified performance estimates are compared to estimates based on flight tests to check the accuracy of the results. Sensitivity analysis is performed on the uncertainty quantified performance estimates to provide information on which of the model form uncertainties contribute most to the uncertainty in the performance estimates. The proposed method uses the results from the fixed uncertainty level analysis that utilizes the corner points of the model form uncertainties. The sensitivity of each parameter is estimated based on corner values of all the other uncertain parameters. This results in a range of possible sensitivities for each parameter dependent on the true value of the other parameters. / Ph. D. / This dissertation examines a process that can be utilized to quantify the uncertainty associated with an identified model, the performance of the system accounting for the uncertainty, and the sensitivity of the performance estimates to the various uncertainties. This uncertainty is present in the identified model because of modeling errors and will tend to increase as the states move away from locations where data has been collected. The method used in this paper to quantify the uncertainty attempts to represent this in a qualitatively correct sense. The uncertainties provide information that is used to predict the performance of the aircraft. A number of simulations are performed, with different values for the uncertain terms chosen for each simulation. This provides a family of possible results to be produced. The uncertainties can be sampled in various manners, and in this study were sampled at fixed levels and at time varying levels. The sampling of fixed uncertainty level required fewer samples, improving computational requirements. Sampling with time varying uncertainty better captures the nature of the uncertainty but requires significantly more simulations. The results provide a range of the expected performance based on the uncertainty. Sensitivity analysis is performed to determine which of the input uncertainties produce the greatest uncertainty in the performance estimates. To account for the uncertainty in the true parameter values, the sensitivity is predicted for a number of possible values of the uncertain parameters. This results in a range of possible sensitivities for each parameter dependent on the true value of the other parameters. The range of sensitivities can be utilized to determine the future testing to be performed.

After many years of successful operation in military domains, Unmanned Aerial Systems (UASs) are generating significant interest amongst civilian operators in sectors such as law enforcement, search and rescue, aerial photography and mapping. To maximise the benefits brought by UASs to sectors such as these, a high level of autonomy is desirable to reduce the need for highly skilled operators. Highly autonomous UASs require a high level of situation awareness in order to make appropriate decisions. This is of particular importance to civilian UASs where transparency and equivalence of operation to current manned aircraft is a requirement, particularly in the terminal area immediately surrounding an airfield. This thesis presents an artificial situation awareness system for an autonomous UAS capable of comprehending both the current continuous and discrete states of traffic vehicles. This estimate forms the basis of the projection element of situation awareness, predicting the future states of traffic. Projection is subject to a large degree of uncertainty in both continuous state variables and in the execution of intent information by the pilot. Both of these sources of uncertainty are captured to fully quantify the future positions of traffic. Based upon the projection of future traffic positions a self separation system is designed which allows an UAS to quantify its separation to traffic vehicles up to some future time and manoeuvre appropriately to minimise the potential for conflict. A high fidelity simulation environment has been developed to test the performance of the artificial situation awareness and self separation system. The system has demonstrated good performance under all situations, with an equivalent level of safety to that of a human pilot.

We present real-time autonomous implementations of a practical distributed formation control scheme for a multi-quadrotor system for two different cases: parameters of linearized dynamics are exactly known, and uncertain system parameters. For first case, we design a hierarchical, decentralized controller based on the leader-follower formation approach to control a multi-quadrotor swarm in rigid formation motion. The proposed control approach has a two-level structure: high-level and low-level. At the high level, a distributed control scheme is designed with respect to the relative and global position information of the quadrotor vehicles. In the low-level, we analyze each single quadrotor control design in three parts. The first is a linear quadratic controller for the pitch and roll dynamics of quadrotors. The second is proportional controller for the yaw motion. The third is proportional-integral-derivative controller in altitude model. For the second case, where inertial uncertainties in the pitch and roll dynamics of quadrotors are considered, we design an on-line parameter estimation with the least squares approach, keeping the yaw, altitude and the high-level controllers the same as the first case. An adaptive linear quadratic controller is then designed to be used with lookup table based on the estimation of uncertain parameters. Additionally, we study on enhancement of self and inter-agent relative localization of the quadrotor agents using a single-view distance-estimation based localization methodology as a practical and inexpensive tool to be used in indoor environments for future works. Throughout the formation control implementations, the controllers successfully satisfy the objective of formation maintenance for non-adaptive and adaptive cases. Simulations and experimental results are presented considering various scenarios, and positive results obtained for the effectiveness of our algorithm.

<p>Due to the rapid development of technologies for unmanned aircraft systems (UAS's), the supply and demand market for UAS's is expanding globally. With the great number of UAS's ready to fly in civilian airspace, an UAS aircraft traffic management system that can guarantee the safe, resilient and efficient operation of UAS's is absent. The vast majority of existing literature on UAS traffic lacks of the attention to the fundamental characteristics of UAS operation, which leads to models and methods that are difficult to implement or lacks scalability. Motivated by these challenges, this research aims at achieving three objectives: 1) the proper frameworks that scale well with high-frequency, high-density UAS operations, 2) the models that captures the fundamental characteristics of UAS operations, 3) the methods that can be implemented in practice with guarantees of efficiency, safety, and resilience. In particular, the objectives are studied at low-level UAS traffic congestion control, agent-level UAS configuration control and unknown agent prediction. The proposed frameworks and obtained results offer comprehensive and practical guidelines of real world UAS operations at different levels.</p>

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