An autonomous system consisting of an unmanned aerial vehicle (UAV) in cooperation with an unmanned ground vehicle (UGV) is of interest in applications for military reconnaissance, surveillance and target acquisition (RSTA). The basic idea of such a system is to take advantage of the vehicles strengths and counteract their weaknesses. The cooperation aspect suggests that the UAV is capable of autonomously landing on the UGV. A fundamental part of the landing is to localise the UAV with respect to the UGV. Traditional navigation systems utilise global navigation satellite system (GNSS) receivers for localisation. GNSS receivers have many advantages, but they are sensitive to interference and spoofing. Therefore, this thesis investigates the feasibility of autonomous landing in a GNSS-denied scenario. The proposed landing system is divided into a control and an estimation system. The control system uses a proportional navigation (PN) control law to approach the UGV. When sufficiently close, a proportional-integral-derivative (PID) controller is used to match the movements of the UGV and perform a controlled descent and landing. The estimation system comprises an extended Kalman filter that utilises measurements from a camera, an imu and ultra-wide band (UWB) impulse radios. The landing system is composed of various results from previous research. First, the sensors used by the landing system are evaluated experimentally to get an understanding of their characteristics. The results are then used to determine the optimal sensor placements, in the design of the EKF, as well as, to shape the simulation environment and make it realistic. The simulation environment is used to evaluate the proposed landing system. The combined system is able to land the UAV safely on the moving UGV, confirming a fully-functional landing system. Additionally, the estimation system is evaluated experimentally, with results comparable to those obtained in simulation. The overall results are promising for the possibility of using the landing system with the presented hardware platform to perform a successful landing.

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As the capabilities of autonomous systems have increased so has interest in utilizing teams of autonomous systems to accomplish tasks more efficiently. This dissertation takes steps toward enabling the cooperation of unmanned systems in scenarios that are challenging, such as GPS-denied or perceptually aliased environments. This work begins by developing a cooperative navigation framework that is scalable in the number of agents, robust against communication latency or dropout, and requires little a priori information. Additionally, this framework is designed to be easily adopted by existing single-agent systems with minimal changes to existing software and software architectures. All systems in the framework are validated through Monte Carlo simulations. The second part of this dissertation focuses on making cooperative navigation robust in challenging environments. This work first focuses on enabling a more robust version of pose graph SLAM, called cycle-based pose graph optimization, to be run in real-time by implementing and validating an algorithm to incrementally approximate a minimum cycle basis. A new algorithm is proposed that is tailored to multi-agent systems by approximating the cycle basis of two graphs that have been joined. These algorithms are validated through extensive simulation and hardware experiments. The last part of this dissertation focuses on scenarios where perceptual aliasing and incorrect or unknown data association are present. This work presents a unification of the framework of consistency maximization, and extends the concept of pairwise consistency to group consistency. This work shows that by using group consistency, low-degree-of-freedom measurements can be rejected in high-outlier regimes if the measurements do not fit the distribution of other measurements. The efficacy of this method is verified extensively using both simulation and hardware experiments.

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. / Doctor of Philosophy / Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.

This thesis presents a dedicated GPS-denied landing system designed for electric vertical takeoff and landing (eVTOL) aircraft. The system employs active fiducial light pattern localization (AFLPL), which provides highly accurate and reliable navigation during critical landing phases. AFLPL utilizes images of a constellation comprised of modulating infrared lights strategically positioned on the landing site, to determine the aircraft pose through the use of a perspective-n-point (PnP) solver. The AFLPL system underwent thorough development, enhancement, and implementation to address and demonstrate its potential in navigation and its inherent limitations. A proposed method addresses the limitations of AFLPL by using an extended Kalman filter (EKF) to fuse PnP camera pose estimates with sensor measurements from an inertial measurement unit (IMU), attitude heading reference system (AHRS), and optional global positioning system (GPS). The EKF estimation is reported to significantly enhance the accuracy, reliability, and update frequency of the aircraft state estimation. To refine and validate the AFLPL and EKF algorithms, a simulation was developed, consisting of an eVTOL executing a glideslope landing trajectory. Furthermore, a hardware system consisting of a multirotor and infrared light ground units was implemented to test these methods under real-world conditions. This research culminated in the successful demonstration of the AFLPL-based estimation system's efficacy through an autonomous, GPS-denied landing flight test, affirming its potential to improve the navigation and control of eVTOL aircraft lacking access to GPS information.

The aim of this work has been to investigate how it is that it sometimes can be difficult for special educators to gain access by teachers to the classroom/lesson to make observations. A partial order has been to determine whether the special educator can curb the negative response from the teacher in question, concerning access to the classroom/lesson.   The survey has a phenomenological perspective as the basis of the test procedures and the analyses made. Phenomenology has as its starting point that the human does not perceive the world in the same way, and that she has different motives and intentions for her actions. In order to understand why the human act like she does, you have to create an image of the personal meaning behind this action. To create this image, I have implemented conversational interviews with five teachers at a high school, in central Sweden.   The investigation revealed that it is missing that the school's teachers are clearly informed of which laws and regulations the special educator is subordinate to. None of the teachers had been informed by their headmaster on the importance of cooperation with the special educator. This means that when the special educator visits and because of confidentiality has been unable to tell us why, the special educator could be perceived as checking the teacher.   To inform before the visit about how the visit should be done, and what the purpose of the visit is are very important factors to make the teachers in the study to feel comfortable with having a special educator in the classroom. There were teachers in the study who demanded answers as to why the visit would take place, in order to let in special educator in the classroom. It was, however, none of the teachers who denied the special educator access to the classroom/lesson due to unwillingness, but there were underlying factors such as to protect students and provide them with an environment conducive to work.   Based on the descriptions, given by the teachers in the study, it appears as if the cooperation between all concerned to create good conditions for the pupils fails.

The localization of autonomous ground vehicles in dense urban environments poses a challenge. Applications in classical outdoor robotics rely on the availability of GPS systems in order to estimate the position. However, the presence of complex building structures in dense urban environments hampers a reliable localization based on GPS. Alternative approaches have to be applied In order to tackle this problem. This thesis proposes an approach which combines observations of a single perspective camera and odometry in a probabilistic framework. In particular, the localization in the space of appearance is addressed. First, a topological map of reference places in the environment is built. Each reference place is associated with a set of visual features. A feature selection is carried out in order to obtain distinctive reference places. The topological map is extended to a hybrid representation by the use of metric information from Geographic Information Systems (GIS) and satellite images. The localization is solved in terms of the recognition of reference places. A particle lter implementation incorporating this and the vehicle's odometry is presented. The proposed system is evaluated based on multiple experiments in exemplary urban environments characterized by high building structures and a multitude of dynamic objects.

Submitted by Ana Carla Almeida (ana.almeida@ucsal.br) on 2016-09-14T19:49:52Z No. of bitstreams: 1 dissertacao lorena brandao portella 2013 familia.pdf: 1149865 bytes, checksum: 02043478d877388a9d31e7b9692f8620 (MD5) / Approved for entry into archive by Maria Emília Carvalho Ribeiro (maria.ribeiro@ucsal.br) on 2016-09-21T20:02:25Z (GMT) No. of bitstreams: 1 dissertacao lorena brandao portella 2013 familia.pdf: 1149865 bytes, checksum: 02043478d877388a9d31e7b9692f8620 (MD5) / Made available in DSpace on 2016-09-21T20:02:25Z (GMT). No. of bitstreams: 1 dissertacao lorena brandao portella 2013 familia.pdf: 1149865 bytes, checksum: 02043478d877388a9d31e7b9692f8620 (MD5) Previous issue date: 2013-01-31 / O objetivo principal deste trabalho foi compreender a entrega do filho recém-nascido a partir da motivação e dos sentimentos da mãe que a realiza. Denominada mãe abandonante, essa mulher que não deseja maternar pode deixar seu filho em condições perigosas ou entrega-lo para adoção, e este trabalho diferencia abandono de entrega. Seguindo uma abordagem qualitativa foram entrevistadas duas gerações de mães abandonantes [de quarenta e três anos de idade e vinte anos de idade] que já abandonaram alguns filhos entre zero e três anos [já tiveram sete e dois filhos respectivamente], e através de entrevista buscou-se motivações e sentimentos que se acercaram do enjeitamento. Para tanto, foi necessário avaliar e descrever o cotidiano de cada figura feminina sob diferentes etapas do ciclo de vida. As categorias utilizadas para analisar as aproximações e distanciamentos das duas jornadas foram infância, adolescência/juventude, iniciação sexual, maternidade, filhos e relação com parceiros, entrega de filhos. O que fica claro com o estudo é que essa entrega reflete a história de vida de cada uma e que os sentimentos e motivações são complexos e só podem ser analisados de per si. O estudo conclui que abandono gera abandono e, que nessa perspectiva, quem não teve um modelo de apego seguro com seus genitores, ou cuidador, estará mais propenso ao enjeitamento. / This study discusses the process of child abandonment in maternal perspective, identifying the birthmother’s profile and the relationship of her life story with this denial. In light of the literature this phenomenon is surrounded by factors of personal, family, social or economic issues that contribute for a mother to give up her son for adoption and is related in this research. The birthmother is the one who abandons leaving or delivering the child to be cared for by others and this research seeks to differentiate abandonment from giving to adoption. We interviewed two generations of birthmothers [forty-three and twenty years of age] who have already left some children between zero and three years [seven and two children respectively] through the interview and looked up motivations and feelings that have approached the abandonment. Therefore, it was necessary to evaluate and describe the daily life of each female figure under different stages of the life cycle. The categories used to analyze the similarities and differences of the two women were: childhood, adolescence/ youth, sexual initiation, maternity, children and relationship with partners, delivering children. What is clear from the study is that this delivery or abandonment reflects the life history of each one, and that the feelings and motivations are complex and can only be analyzed in each case. The study concludes that negligence generates abandonment, which in this perspective, those who did not have a model of secure attachment with their parent or caregiver, will be more prone to abandonment.

The localization of autonomous ground vehicles in dense urban environments poses a challenge. Applications in classical outdoor robotics rely on the availability of GPS systems in order to estimate the position. However, the presence of complex building structures in dense urban environments hampers a reliable localization based on GPS. Alternative approaches have to be applied In order to tackle this problem. This thesis proposes an approach which combines observations of a single perspective camera and odometry in a probabilistic framework. In particular, the localization in the space of appearance is addressed. First, a topological map of reference places in the environment is built. Each reference place is associated with a set of visual features. A feature selection is carried out in order to obtain distinctive reference places. The topological map is extended to a hybrid representation by the use of metric information from Geographic Information Systems (GIS) and satellite images. The localization is solved in terms of the recognition of reference places. A particle lter implementation incorporating this and the vehicle's odometry is presented. The proposed system is evaluated based on multiple experiments in exemplary urban environments characterized by high building structures and a multitude of dynamic objects.

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