<p> In this thesis, we address the problem of sensor management with particular application to using unmanned aerial vehicles (U AV s) for multi target tracking. Also, we present a decision based approach for controlling information flow in decentralized multi-target multi-sensor data fusion.</p> <p> Considering the problem of sensor management for multitarget tracking, we study the problem of decision based control of a group of UAVs carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. We propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov Decision Processes (MDPs) based on Dynamic Element Matching (DEM) algorithms. DEM algorithms, widely used for reducing harmonic distortion in Digital-to-Analog converters, are used as a core element in the modified algorithm. We introduce and demonstrate a number of new performance metrics, to verify the effectiveness of an MDP policy, especially useful for quantifying the impact of the modified DEM-based Value Iteration algorithm on an MDP policy. Also, we introduce a multi-level hierarchy of MDPs controlling each of the UAV s. Each level in the hierarchy solves a problem at a different level of abstraction. Simulation results are presented on a representative multisensor-multitarget tracking problem showing a significant improvement in performance compared to the classical algorithm. The proposed method demonstrated robust performance while guaranteeing polynomial computational complexity.</p> <p> Decentralized multisensor-multitarget tracking has numerous advantages over singlesensor or single-platform tracking. In this thesis, we present a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. We present a decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on Markov Decision Processes with introduced hierarchial structure will control the information exchange and data fusion process. The information based objective function is based on the Posterior Cramer-Rao lower bound and constitutes the basis of a reward structure for Markov decision processes which are used, together with decentralized lookup substrate, to control the data fusion process. We analyze three distributed data fusion algorithms - associated measurement fusion, tracklet fusion and track-to-track fusion. The thesis also provides a detailed analysis of communication and computational load in distributed tracking algorithms. Simulation examples demonstrate the operation and the performance results of the system.</p> <p> In this thesis, we also present the development of a multisensor-multitarget tracking testbed for simulating large-scale distributed scenarios, capable of handling multiple, heterogeneous sensors, targets and data fusion methods</p>. / Thesis / Doctor of Philosophy (PhD)

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Energy harvesting technology has the ability to create autonomous, self-powered electronic systems that do not rely on battery power for their operation. The term energy harvesting describes the process of converting ambient energy surrounding a system into useful electrical energy through the use of a specific material or transducer. A widely studied form of energy harvesting involves the conversion of mechanical vibration energy into electrical energy using piezoelectric materials, which exhibit electromechanical coupling between the electrical and mechanical domains. Typical piezoelectric energy harvesting systems are designed as add-on systems to a host structure located in a vibration rich environment. The added mass and volume of conventional vibration energy harvesting designs can hinder to the operation of the host system. The work presented in this dissertation focuses on advancing piezoelectric energy harvesting concepts through the introduction of multifunctionality in order to alleviate some of the challenges associated with conventional piezoelectric harvesting designs. The concept of multifunctional piezoelectric self-charging structures is explored throughout this work. The operational principle behind the concept is first described in which piezoelectric layers are directly bonded to thin-film battery layers resulting in a single device capable of simultaneously harvesting and storing electrical energy when excited mechanically. Additionally, it is proposed that self-charging structures be embedded into host structures such that they support structural load during operation. An electromechanical assumed modes model used to predict the coupled electrical and mechanical response of a cantilever self-charging structure subjected to harmonic base excitation is described. Experimental evaluation of a prototype self-charging structure is then performed in order to validate the electromechanical model and to confirm the ability of the device to operate in a self-charging manner. Detailed strength testing is also performed on the prototype device in order to assess its strength properties. Static three-point bend testing as well as dynamic harmonic base excitation testing is performed such that the static bending strength and dynamic strength under vibration excitation is assessed. Three-point bend testing is also performed on a variety of common piezoelectric materials and results of the testing provide a basis for the design of self-charging structures for various applications. Multifunctional vibration energy harvesting in unmanned aerial vehicles (UAVs) is also investigated as a case study in this dissertation. A flight endurance model recently developed in the literature is applied to model the effects of adding piezoelectric energy harvesting to an electric UAV. A remote control foam glider aircraft is chosen as the test platform for this work and the formulation is used to predict the effects of integrating self-charging structures into the wing spar of the aircraft. An electromechanical model based on the assumed modes method is then developed to predict the electrical and mechanical behavior of a UAV wing spar with embedded piezoelectric and thin-film battery layers. Experimental testing is performed on a representative aluminum wing spar with embedded self-charging structures in order to validate the electromechanical model. Finally, fabrication of a realistic fiberglass wing spar with integrated piezoelectric and thin-film battery layers is described. Experimental testing is performed in the laboratory to evaluate the energy harvesting ability of the spar and to confirm its self-charging operation. Flight testing is also performed where the fiberglass spar is used in the remote control aircraft test platform and the energy harvesting performance of the device is measured during flight. / Ph. D.

A numerical method is presented for determining the natural vibration frequencies, and the corresponding mode shapes, of a rotating cantilever beam which has a nonuniform, unsymmetrical cross section. Two coupled fourth-order differential equations of motion with variable coefficients are derived which govern the motion of such a beam having deformations in two directions. Through the development and utilization of the integrating matrix, the solution of the differential equations is obtained in the form of an eigenvalue problem. The solutions to the eigenvalue problem are determined by an iteration method based upon a special orthogonality relationship which is derived. Numerical examples, including an application to a twisted propeller blade, are presented with the results of the integrating matrix solutions being compared to exact solutions and experimental data. / Master of Science

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 discusses the use of aerial photography to construct a workable model capable of predicting the maximum carrying capacity of cattle on a given farm to a reasonably high degree of accuracy. The thesis then discusses how this model can be expanded to reach the goal of predicting actual carrying capacity, the value of farm production, or other such specific indicators of land use intensity. / Thesis / Candidate in Philosophy

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.

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.

Unmanned aerial vehicles (UAVs), when paired with an onboard camera, have proven to be useful tools in many applications, including aerial photography, precision agriculture, and search and rescue operations. Likewise, UAVs capable of physically interacting with the environment have shown great potential to help people perform dangerous, or time-consuming tasks more safely and efficiently than they could on their own. However, due to onboard computation and battery life limitations and complex flight dynamics, using UAVs to physically interact with the environment is still a developing area of research. Considering these limitations, the primary goals of this work are to (1) develop a new UAV platform for aerial manipulation, (2) develop modular hardware and software for the platform to enable specific tasks to be performed autonomously, and (3) develop a visual target tracking method to enable robust performance of autonomous aerial manipulation tasks in unstructured, real-world environments. To that end, the design of the Interacting-BoomCopter UAV (I-BC) is presented here as a new platform for aerial manipulation. With a simple tricopter frame, a single additional actuator for generating horizontal forces, and lightweight, modular end-effectors, the I-BC aims to balance efficiency and functionality in performing aerial manipulation tasks, and is able to perform various tasks such as mounting sensors in hard-to-reach places, and opening small doors or panels. An onboard camera, force and distance sensors, and a powerful single board computer (SBC) enable the I-BC to operate autonomously in unstructured environments, with potential applications in areas such as large-scale infrastructure inspection, industrial inspection and maintenance, and nuclear decontamination efforts.