Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results.

This thesis focuses on the theory and implementation of visual navigation techniques for Autonomous Air Vehicles in outdoor environments. The target of this study is to fuse and cooperatively develop an incremental map for multiple air vehicles under the application of Simultaneous Location and Mapping (SLAM). Without loss of generality, two unmanned air vehicles (UAVs) are investigated for the generation of ground maps from current and a priori data. Each individual UAV is equipped with inertial navigation systems and external sensitive elements which can provide the possible mixture of visible, thermal infrared (IR) image sensors, with a special emphasis on the stereo digital cameras. The corresponding stereopsis is able to provide the crucial three-dimensional (3-D) measurements. Therefore, the visual aerial navigation problems tacked here are interpreted as stereo vision based SLAM (vSLAM) for both single and multiple UAVs applications. To begin with, the investigation is devoted to the methodologies of feature extraction. Potential landmarks are selected from airborne camera images as distinctive points identified in the images are the prerequisite for the rest. Feasible feature extraction algorithms have large influence over feature matching/association in 3-D mapping. To this end, effective variants of scale-invariant feature transform (SIFT) algorithms are employed to conduct comprehensive experiments on feature extraction for both visible and infrared aerial images. As the UAV is quite often in an uncertain location within complex and cluttered environments, dense and blurred images are practically inevitable. Thus, it becomes a challenge to find feature correspondences, which involves feature matching between 1st and 2nd image in the same frame, and data association of mapped landmarks and camera measurements. A number of tests with different techniques are conducted by incorporating the idea of graph theory and graph matching. The novel approaches, which could be tagged as classification and hypergraph transformation (HGTM) based respectively, have been proposed to solve the data association in stereo vision based navigation. These strategies are then utilised and investigated for UAV application within SLAM so as to achieve robust matching/association in highly cluttered environments. The unknown nonlinearities in the system model, including noise would introduce undesirable INS drift and errors. Therefore, appropriate appraisals on the pros and cons of various potential data filtering algorithms to resolve this issue are undertaken in order to meet the specific requirements of the applications. These filters within visual SLAM were put under investigation for data filtering and fusion of both single and cooperative navigation. Hence updated information required for construction and maintenance of a globally consistent map can be provided by using a suitable algorithm with the compromise between computational accuracy and intensity imposed by the increasing map size. The research provides an overview of the feasible filters, such as extended Kalman Filter, extended Information Filter, unscented Kalman Filter and unscented H Infinity Filter. As visual intuition always plays an important role for humans to recognise objects, research on 3-D mapping in textures is conducted in order to fulfil the purpose of both statistical and visual analysis for aerial navigation. Various techniques are proposed to smooth texture and minimise mosaicing errors during the reconstruction of 3-D textured maps with vSLAM for UAVs. Finally, with covariance intersection (CI) techniques adopted on multiple sensors, various cooperative and data fusion strategies are introduced for the distributed and decentralised UAVs for Cooperative vSLAM (C-vSLAM). Together with the complex structure of high nonlinear system models that reside in cooperative platforms, the robustness and accuracy of the estimations in collaborative mapping and location are achieved through HGTM association and communication strategies. Data fusion among UAVs and estimation for visual navigation via SLAM were impressively verified and validated in conditions of both simulation and real data sets. / © Cranfield University, 2013

Submitted in partial fulfilment of the academic requirements for the Degree of Master of Technology: Mechanical Engineering, Durban Institute of Technology, 2004. / The study presented here addresses the design of the composite wing and canard structures for an -un-inh-ab-it-ed-combat air vehicle. The desian philosophy is based on a ~- combination of finite element analysis and mathematical programming. The wings and canards were manufactured using advanced composite materials. the manufacturing methodology was based on a rapid protoryping approach using 3D computer models and eNe machining. The theory of composite materials is covered in detail, attention IS given to the properties of the separate constituents, composite material properties and manufacturing methods that are relevant to the project. The finite element method and sequential linear programming are discussed in the context of structural analysis and optimisation. An overview of the methodology and how it is implemented is presented. Numerical optimisation techniques are discussed with particular emphasis being placed on sequential linear programming. The optimisation problem formulation is presented in detail with attention paid to elements and their formulation as well as design variables, constraints and sensitivity analysis. Two design concepts were considered for the wing and canard structures, the first being a conventional configuration and the second being a novel radial design. The development and evaluation of these structural concepts are presented in detail. The optimisation study done on the canard is also presented as well as the manufacture thereof. Details regarding the manufacturing methodology used in the construction of the canard for the uninhabited combat air vehicle are presented in detail with particular / M

Vegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for various applications as it provides high-resolution imagery at lower altitudes. In this study, spectral reflectance values for each vegetation species were collected using a spectroradiometer instrument. Those values were correlated with five band UAS Image values to understand the sensor's performance, also added with reflectance’s similarities and divergence for vegetation species. Pixel and Object-based classification methods were performed using 0.15 ft Multispectral Imagery to identify the vegetation classes. Supervised Machine Learning Support Vector Machine (SVM) and Random Forest (RF) algorithms with topographical information were used to produce thematic vegetation maps. The Pixel-based procedure using the SVM algorithm generated an overall accuracy and kappa coefficient of above 90 percent. Both classification approaches have provided aesthetic vegetation thematic maps. According to statistical cross-validation findings and visual interpretation of vegetation communities, the pixel classification method outperformed object-based classification. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

A mathematical model of a small fixed-wing aircraft was developed through application of parameter estimation techniques to simulated flight test data. Multiple controllers were devised based on this model for path following, including a self-scheduled linear parameter-varying (LPV) controller with path curvature as a scheduling parameter. The robustness and performance of these controllers were tested in a rigorous MATLAB simulation environment that included steady winds and gusts, measurement noise, delays, and model uncertainties. The linear controllers designed within were found to be robust to the disturbances and uncertainties in the simulation environment, and had similar or better performance in comparison to a nonlinear control law operating in an inner-outer loop structure. Steps are being taken to implement the resulting controllers on the unmanned aerial vehicle (UAV) testbed in the Nonlinear Systems Laboratory at Virginia Tech. / Master of Science

Fusarium virguliforme is a soil-borne pathogen that is the causal agent of sudden death syndrome (SDS). This disease is one of the top contributors to major yield losses in soybean across the United States. Characteristic symptoms of the disease include interveinal chlorosis and/or necrosis of trifoliate leaves and defoliation. In some cases, the foliar symptoms may not be present, but yield loss still occurs. This disease is evaluated using an incidence rating, the percent of plants in the plot that are expressing symptoms, and a severity rating, using a one to nine scale based on varying levels of chlorosis, necrosis, and defoliation. Using remote sensing provides an alternate approach to identify and evaluate plant diseases. It provides a non-destructive method to assess the severity of foliar symptoms and their distribution across production fields. SDS was chosen as the disease to use for this system due to the unique disease symptomology and yield loss. In 2019 and 2020, SDS trials were established in a production field location that has a history of SDS in Valmeyer, IL. This seed treatment study had different chemicals with varying levels of efficacy against SDS. Disease ratings were collected at the first sign of symptoms, and aerial imagery was collected on the same day. There were multiple dates across both years when this data was collected. ArcGIS was used to analyze the multispectral imagery and do a plot by plot analysis for each of the plots. A regression analysis was performed to test the relationship between the foliar disease ratings and the plot data collected from the multispectral imagery. Multiple vegetation indices were tested, and the results showed that overall, in 2019, GNDVI had the strongest relationship with foliar ratings. In 2020, NDRE had the strongest overall relationship with foliar ratings. The relationship between NDVI and the ratings was the most consistent at the last rating of the season.

Unmanned Aerial Vehicles (UAVs) are increasingly becoming economical platforms for carrying a variety of sensors. Building flight plans that place sensors properly, temporally and spatially, is difficult. The goal of sensor-driven planning is to automatically generate flight plans based on desired sensor placement and temporal constraints. We propose a simple taxonomy of UAV-enabled sensors, identify a set of generic sensor tasks, and argue that many real-world tasks can be represented by the taxonomy. We present a hierarchical sensor-driven flight planning system capable of generating 2D flights that satisfy desired sensor placement and complex timing and dependency constraints. The system makes use of several well-known planning algorithms and includes a user interface. We conducted a user study to show that sensor-driven planning can be used by non-experts, that it is easier for non-experts than traditional waypoint-based planning, and that it produces better flights than waypoint-based planning. The results of our user study experiment support the claims that sensor-driven planning is usable and that it produces better flights.

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Yes / Unmanned Aerial Vehicles (UAVs), commercially known as drones, have received great attention. This is due to their versatility and applicability to a large number of domains such as surveillance system, aerial photography, traffic control, flyable base stations to provide a broadband coverage and even for future urban transportation services. In this paper, the optimal distance between multiple aerial base stations has analytically been derived, based on an aerial coverage area computation. This is a fundamental wireless metric that can significantly minimise the intra-overlapped coverage and also can enhance wireless coverage connectivity and performance of aerial wireless networks. The novelty of our approach brings a better aerial optimal design understanding for UAVs communications performance without the need for establishing an aerial deployment setup.

Unmanned Aerial Vehicles (UAV) are expected to be deployed both by government and industry. Rules for integrating commercial UAVs into a nation's airspace still need to be defined, safety being the main concern. As part of this thesis, the communication needs of UAVs as important requirement for UAV integration into the national airspace is considered. Motivated by recent prediction of UAV quantities, revealing the importance of Micro UAVs (MAV) and Small UAVs (SUAV), the thesis determines spectral requirements for control and non-payload communication (CNPC). We show that spectral efficiency, particularly in the downlink, is critical to the large-scale deployment of UAVs. Due to the limited range of small SUAV and MAV systems, communication between air and ground elements of these UAVs is established through radio Line-of-Sight (LoS) links. Ultimately, efficient LoS UAV systems are based on a better understanding of channels in the downlink, i.e. air-to-ground (A2G) channels, and also on efficient waveform as well as spectrum management implementation. Because of limited research in wideband aeronautical channel modeling, we have derived an A2G channel prototype applicable to SUAV and MAV. As part of the research at Wire- less@VT in designing and prototyping Orthogonal Frequency Division Multiplexing (OFDM) waveforms, this thesis derives the optimal parameters for SUAV and MAV A2G channels. Finally, the thesis discusses concepts that relate flight route with spectrum management as well as opportunities for a more dynamic spectrum allocation for UAV communication systems. / Master of Science