The last-mile delivery option has become a focal point of academic research and industrial development in recent years. Multiple factors such as increased demands on delivery flexibility, customer requirements, delivery urgency, and many others are enforcing to adopt this option. For fulfilling this paradigm shift in delivery and providing additional flexibility, drones can be considered as a viable option to use for last-mile delivery cases. Numerous drones are available in the market with varying capacities and functionalities, posing a significant challenge for decision-makers to select the most appropriate drone type for a specific application. For this purpose, this study proposes a comprehensive list of criteria that can be used to compare a set of available last-mile delivery drones. Additionally, we introduced a systematic multi-criterion, multi-personnel decision-making approach, referred to as the Interval Valued Inferential Fuzzy TOPSIS method. This method is robust and can handle the fuzziness in decision-making, thereby providing quality drone selection decisions under different applications. We then apply this method to a real-life test setting. Results suggest that smaller drones or quadcopters are considered viable to use in urban environments, while long-range drones are preferred for the last mile delivery needs in rural settings.

Drones are unmanned aerial vehicles (UAVs) operated remotely with the help of cameras, GPS, and on-device SD cards. These are used for many applications including civilian as well as military. On the other hand, drone swarms are a fleet of drones that work together to achieve a special goal through swarm intelligence approaches. These provide a lot of advantages such as better coverage, accuracy, increased safety, and improved flexibility when compared to a single drone. However, the deployment of such swarms in an adversarial environment poses significant challenges. This work provides an overview of the current state of research on drone swarms in adversarial environments including algorithms for swarming formation of robotic attack drones with their strengths and weaknesses as well as the attack strategies used by attackers. This work also outlines the common adversarial counter-attack methods to disrupt drone attacks consisting of detection and destruction of drone swarms along with their drawbacks, a counter UAV defense system, and splitting large-scale drones into unconnected clusters. After identifying several challenges, an optimized algorithm is proposed to split the large-scale drone swarms more efficiently.

A number of challenges in the development and application of flying qualities criteria for modern aircraft are addressed in this dissertation. The history of flying qualities is traced from its origins to modern day techniques as applied to piloted aircraft. Included in this historical review is the case that was made for the development of flying qualities criteria in the 1940's and 1950's when piloted aircraft became prevalent in the United States military. It is then argued that UAVs today are in the same context historically as piloted aircraft when flying qualities criteria were first developed. To aid in development of a flying qualities criterion for UAVs, a relevant classification system for UAVs. Two longitudinal flying qualities criteria are developed for application to autonomous UAVs. These criteria center on mission performance of the integrated aircraft and sensor system. The first criterion is based on a sensor platform's ability to reject aircraft disturbances in pitch attitude. The second criterion makes use of energy methods to create a metric to quantify the transmission of turbulence to the sensor platform. These criteria are evaluated with airframe models of different classes of air vehicles using the CASTLE 6 DOF simulation. Another topic in flying qualities is the evaluation of nonlinear control systems in piloted aircraft. A L1 adaptive controller was implemented and tested in a motion based, piloted flight simulator. This is the first time that the L1 controller has been evaluated for piloted handling qualities. Results showed that the adaptive controller was able to recover good flying qualities from a degraded aircraft. The final topic addresses a less direct, but extremely important challenge for flying qualities research and education: a capstone course in flight mechanics teaching flight test techniques and featuring a motion based flight simulator was implemented and evaluated. The course used a mixture of problem based learning and role based learning to create an environment where students could explore key flight mechanics concepts. Evaluation of the course's effectiveness to promote the understanding of key flight mechanics concepts is presented. / Ph. D.

During their growth, crops may experience a variety of health issues, which often lead to a reduction in crop yield. In order to avoid financial loss and sustain crop survival, it is imperative for farmers to detect and treat crop health issues. Interest in the use of unmanned aerial vehicles (UAVs) for precision agriculture has continued to grow as the cost of these platforms and sensing payloads has decreased. The increase in availability of this technology may enable farmers to scout their fields and react to issues more quickly and inexpensively than current satellite and other airborne methods. In the work of this thesis, methods have been developed for applications of UAV remote sensing using visible spectrum and multispectral imagery. An algorithm has been developed to work on a server for the remote processing of images acquired of a crop field with a UAV. This algorithm first enhances the images to adjust the contrast and then classifies areas of the image based upon the vigor and greenness of the crop. The classification is performed using a support vector machine with a Gaussian kernel, which achieved a classification accuracy of 86.4%. Additionally, an analysis of multispectral imagery was performed to determine indices which correlate with the health of corn crops. Through this process, a method for correcting hyperspectral images for lighting issues was developed. The Normalized Difference Vegetation Index values did not show a significant correlation with the health, but several indices were created from the hyperspectral data. Optimal correlation was achieved by using the reflectance values for 740 nm and 760 nm wavelengths, which produced a correlation coefficient of 0.84 with the yield of corn. In addition to this, two algorithms were created to detect stink bugs on crops with aerial visible spectrum images. The first method used a superpixel segmentation approach and achieved a recognition rate of 93.9%, although the processing time was high. The second method used an approach based upon texture and color and achieved a recognition rate of 95.2% while improving upon the processing speed of the first method. While both methods achieved similar accuracy, the superpixel approach allows for detection from higher altitudes, but this comes at the cost of extra processing time. / Master of Science

The military's armed surveillance drones are the most elite modernized weaponry in the twenty-first century. They have introduced a new way to see without being seen. In this paper, I investigate the US military's use of drones in warfare, specifically in terms of distance and what that entails for the operator physically, mentally, and emotionally. My analysis will address the question: how are remotely piloted aircrafts connecting distance and humanity in asymmetric warfare? I argue that drones are unlike any other weapon produced thus far because they introduce a completely new way to fight wars at a distance; therefore, a new understanding of humanity and warfare needs to be established. Warfare by remote control in a post-panoptic society has ended the era of mutual engagement and created one of extensive asymmetry. This thesis also examines the militaries historical motives for pursuing weapons that make the enemy into an objective target below. The data that I use to explore these implications is second-hand anecdotes and interviews of former RPA operators, and various media accounts. Based on this data, I find that drones have made it more difficult to kill a target due to the added surveillance technology that allows the operator to see the effects of his or her weapon in real time. This visualization then has the effect of creating intimacy/reducing emotional distance between the operator and the target. / Master of Arts

Visual perception has been a significant subject matter of robotics research for decades but has accelerated in recent years as both technology and community are more prepared to take on new challenges with autonomous systems. In this thesis, a framework for 3D reconstruction using a stereo camera for the purpose of obstacle detection and mapping is presented. In this application, a UAV works collaboratively with a UGV to provide high level information of the environment by using a downward facing stereo camera. The approach uses frame to frame SURF feature matching to detect candidate points within the camera image. These feature points are projected into a sparse cloud of 3D points using stereophotogrammetry for ICP registration to estimate the rigid transformation between frames. The RTK-GPS constrained pose estimate from the UAV is fused with the feature matched estimate to align the reconstruction and eliminate drift. The reconstruction was tested on both simulated and real data. The results indicate that this approach improves frame to frame registration and produces a well aligned reconstruction for a single pass compared to using the raw UAV position estimate alone. However, multi-pass registration errors occur on the order of about 0.6 meters between parallel passes, and approximately 2 degrees of local rotation error when compared to a reconstruction produced with Agisoft Metashape. However, the proposed system performed at an average frame rate of about 1.3 Hz compared to Agisoft at 0.03 Hz. Overall, the system improved obstacle registration and can perform online within existing ROS frameworks. / Master of Science / Visual perception has been a significant subject matter of robotics research for decades but has accelerated in recent years as both technology and community are more prepared to take on new challenges with autonomous systems. In this thesis, a framework for 3D reconstruction using cameras for the purpose of obstacle detection and mapping is presented. In this application, a UAV works collaboratively with a UGV to provide high level information of the environment by using a downward facing stereo camera. The approach uses features extracted from camera images to detect candidate points to be aligned. These feature points are projected into a sparse cloud of 3D points using stereo triangulation techniques. The 3D points are aligned using an iterative solver to estimate the translation and rotation between frames. The RTK (Real Time Kinematic) GPS constrained position and orientation estimate from the UAV is combined with the feature matched estimate to align the reconstruction and eliminate accumulated errors. The reconstruction was tested on both simulated and real data. The results indicate that this approach improves frame to frame registration and produces a well aligned reconstruction for a single pass compared to using the raw UAV position estimate alone. However, multi-pass registration errors occur on the order of about 0.6 meters between parallel passes that overlap, and approximately 2 degrees of local rotation error when compared to a reconstruction produced with the commercial product, Agisoft. However, the proposed system performed at an average frame rate of about 1.3 Hz compared to Agisoft at 0.03 Hz. Overall, the system improved obstacle registration and can perform online within existing Robot Operating System frameworks.

This thesis aims to tune the control parameters of a bio-inspired guidance system designed to confer a tactical behavior to unmanned aerial vehicles (UAVs). This bio-inspired guidance system is capable of reducing exposure to threats, while traversing previously uncharted, and potentially hostile territories. UAVs employing this guidance system may exhibit a more or less tactical behavior by tuning 9 user-defined parameters within specified intervals. Although the UAV's behavior can be easily forecasted whenever all parameters are set to exhibit the most cautious behavior or the most reckless behavior, it is difficult to devise a taxonomy of flight behavior whenever these parameters are not set at the boundaries of their admissible intervals. The scope of this thesis is to analyze and forecast the UAV's behavior as a function of these user-defined parameters. To this goal, the Taguchi analysis method is employed to deduce those parameters that affect the UAV's behavior more than others. Successively, 81 software-in-the-loop simulations have been performed to analyze the UAV's behavior as a function of the most influential user-defined parameters. Finally, 10 flight tests were performed to validate the numerical results. / Master of Science / This thesis aims to tune the control parameters of a bio-inspired guidance system designed to confer a tactical behavior to unmanned aerial vehicles (UAVs). This bio-inspired guidance system is capable of reducing exposure to threats, while traversing previously uncharted, and potentially hostile territories. UAVs employing this guidance system may exhibit a more or less tactical behavior by tuning 9 user-defined parameters within specified intervals. Although the UAV's behavior can be easily forecasted whenever all parameters are set to exhibit the most cautious behavior or the most reckless behavior, it is difficult to devise a taxonomy of flight behavior whenever these parameters are not set at the boundaries of their admissible intervals. The scope of this thesis is to analyze and forecast the UAV's behavior as a function of these user-defined parameters. To this goal, the Taguchi analysis method is employed to deduce those parameters that affect the UAV's behavior more than others. Successively, 81 software-in-the-loop simulations have been performed to analyze the UAV's behavior as a function of the most influential user-defined parameters. Finally, 10 flight tests were performed to validate the numerical results.

Unmanned vehicles are often used in time-critical missions such as reconnaissance or search and rescue. To this end, this thesis provides autonomous localization and mapping tools for 1/R² sources. A "1/R²" source is one in which the received intensity of the source is inversely proportional to the square of the distance from the source. An autonomous localization algorithm is developed which utilizes a particle swarm particle ltering method to recursively estimate the location of a source. To implement the localization algorithm experimentally, a command interface with Virginia Tech's autonomous helicopter was developed. The interface accepts state information from the helicopter, and returns command inputs to drive the helicopter autonomously to the source. To make the use of the system more intuitive, a graphical user interface was developed which provides localization functionality as well as a waypoint navigation outer-loop controller for the helicopter. This assists in positioning the helicopter and returning it home after the the algorithm is completed. An autonomous mapping mission with a radioactive source is presented, along with a localization experiment utilizing simulated sensor readings. This work is the rst phase of an on-going project at the Unmanned Systems Lab. Accordingly, this thesis also provides a framework for its continuation in the next phase of the project. / Master of Science

Today, drones have been receiving a lot of notice from commercial businesses. Businesses (mainly companies that have delivery services) are trying to expand their productivity in order bring more satisfaction for their loyal customers. One-way companies can expand their delivery services are through the use of delivery drones. Drones are very powerful devices that are going through many evolutionary changes for their uses throughout the years. For many years, researchers in academia have been examining how drones can plan their paths along with avoiding collisions of other drones and certain obstacles in the civil airspace. However, researchers have not considered how the motion path planning can a ect the overall scheduling aspect of civilian drones. In this thesis, we propose an algorithm for a collision-free scheduling motion path planning of a set drones such that they avoid certain obstacles as well as maintaining a safety distance from each other. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Submitted by SBI Biblioteca Digital (sbi.bibliotecadigital@puc-campinas.edu.br) on 2018-06-13T12:23:13Z No. of bitstreams: 1 JOSE RICARDO ALVES.pdf: 4604947 bytes, checksum: f43a40d3029436f7ad7bf03fe51c7e87 (MD5) / Made available in DSpace on 2018-06-13T12:23:14Z (GMT). No. of bitstreams: 1 JOSE RICARDO ALVES.pdf: 4604947 bytes, checksum: f43a40d3029436f7ad7bf03fe51c7e87 (MD5) Previous issue date: 2017-12-18 / Pontif?cia Universidade Cat?lica de Campinas - PUC - Campinas / The aim of this dissertation was to use current technology to collect environmental quantities of forests to perform a periodic monitoring that would aid in preventing the advance of degradation and deforestation of the urban forest remnants of Campinas, more specifically Mata do Quilombo, located in the Bar?o Geraldo district. The use of Unmanned Aerial Vehicles (UAV) in the diagnosis and preservation of this urban forest remnant provided a low-cost method and a non-invasive monitoring. The strategy was to use the UAV with embedded remotely-activated electronic sensors in order to collect units of environmental quantities such as the location of clearings within the forest created by deforestation, air temperature and humidity, luminosity and the georeferenced location of the study site. In addition to the UAV technology, wireless technology was also used to communicate with various frequency bands, thus validating the simultaneous operation of the embedded sensors without any interference between them or external sources. After the data was collected, the objective was to submit it to an outlier verification method and make them available in a monitoring center, so that the user could later save this information in a remote access system known as supervisory system. / Esse trabalho teve o intuito de utilizar tecnologias atuais para coleta das grandezas ambientais das matas para realizar um acompanhamento peri?dico dos remanescentes florestais urbanos de Campinas, mais especificamente a Mata do Quilombo, localizada no distrito de Bar?o Geraldo, que auxiliasse na preven??o do avan?o da degrada??o frequente e do desmatamento. A utiliza??o do VANT (Ve?culo A?reo N?o Tripulado) no diagn?stico e preserva??o desse remanescente florestal urbano proporcionou um m?todo de baixo custo e uma avalia??o de forma n?o invasiva. A estrat?gia visou utilizar o VANT com sensores eletr?nicos embarcados acionados remotamente afim de capturar unidades de grandezas ambientais como a localiza??o de clareiras no interior da mata provindas do desmatamento, temperatura e umidade do ar, luminosidade e localiza??o georreferenciada do local de estudo. Aliada ? tecnologia do VANT, tamb?m foi utilizada a tecnologia de comunica??o sem fio com diversas faixas de frequ?ncias, validando assim o funcionamento desses sensores embarcados em atividade simult?nea sem que houvesse interfer?ncias entre os mesmos e de meios externos. Ap?s a coleta, o intuito foi submeter os dados coletados a um tratamento de verifica??o de outlier (m?todo de descarte de dados que se distanciam dos demais) e disponibiliz?-los numa central de monitoramento, de forma que o usu?rio pudesse salvar essas informa??es posteriormente num servidor de acesso remoto conhecido como sistema supervis?rio.