3D GEOSPATIAL INDOOR NAVIGATION FOR DISASTER RISK REDUCTION AND RESPONSE IN URBAN ENVIRONMENT

Disaster management for urban environments with complex structures requires 3D extensions of indoor applications to support better risk reduction and response strategies. The paper highlights the need for assessment and explores the role of 3D geospatial information and modeling regarding the indoor structure and navigational routes which can be utilized as disaster risk reduction and response strategy. The reviewed models or methods are analysed testing parameters in the context of indoor risk and disaster management. These parameters are level of detail, connection to outdoor, spatial model and network, handling constraints. 3D reconstruction of indoors requires the structural data to be collected in a feasible manner with sufficient details. Defining the indoor space along with obstacles is important for navigation. Readily available technologies embedded in smartphones allow development of mobile applications for data collection, visualization and navigation enabling access by masses at low cost. The paper concludes with recommendations for 3D modeling, navigation and visualization of data using readily available smartphone technologies, drones as well as advanced robotics for Disaster Management

Development of a Versatile Modular Platform for Aerial Manipulators

The scope of this chapter is the development of an aerial manipulator platform using an octarotor drone with an attached manipulator. An on-board spherical camera provides visual information for the drone’s surroundings, while a Pan-Tilt-Zoom camera system is used to track targets. A powerful computer with a GPU offers significant on-board computational power for the visual servoing of the aerial manipulator system. This vision system, along with the Inertial Management Unit based controller provides exemplary guidance in confined and outdoor spaces. Coupled with the manipulator’s force sensing capabilities the system can interact with the environment. This aerial manipulation system is modular as far as attaching various payloads depending on the application (i.e., environmental sensing, facade cleaning and others, aerial netting for evader-drone geofencing, and others). Experimental studies using a motion capture system are offered to validate the system’s efficiency

Навігація БПЛА в приміщенні на основі TDOA методу

Робота публікується згідно наказу ректора від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник дипломної роботи: професор сумісник кафедри авіоніки, Сібрук Леонід ВікторовичThe popularity of UAV’s during last years is greatly increasing. Drones are getting more broad use in various commercial applications. They are used for mapping, monitoring, logistics, media, search and rescue operations and many more possible use cases. One of the recently emerged UAV’s type are indoor drones. Such drones are mostly used for inspections, security monitoring, warehouse operations and public safety. On this basis, a demand for indoor navigation system arises. The specifics of indoor operations of drones, creates unique technical challenges. Development of reliable and precise navigational systems, will allow to implement autonomous UAV system, which will vastly increase efficiency of indoor drone operations. Studies on this topic are sparse and require further investigations and development. For development of navigation systems, it is possible to rely on existing technologies from different areas, such as indoor positioning for pedestrian navigation, or positioning algorithms, used in aviation. Estimation of theoretical performance and accuracy of indoor navigational algorithms and technologies can allow further improvements and implementation of new technologies for practical use. The developed mathematical model is used for analysis of TDOA-based positioning algorithm, which can be used in such positioning systems.Популярность БПЛА в последние годы значительно возрастает. Дроны получают все более широкое применение в различных коммерческих приложениях. Они используются для картирования, мониторинг, логистика, средства массовой информации, поисково-спасательные операции и многое другое возможное использование случаи. Одним из недавно появившихся типов БПЛА являются внутренние дроны. Такие дроны чаще всего используется для инспекций, мониторинга безопасности, складских операций и общественной безопасности. На этом основе возникает спрос на внутреннюю навигационную систему. Специфика работы внутри помещений дронов, создает уникальные технические проблемы. Разработка надежных и точных навигационных систем, позволит реализовать автономную систему БПЛА, что значительно повысить эффективность работы дронов внутри помещений. Исследования по этой теме немногочисленны и требуют дальнейших исследований и разработок. Для разработки навигационных систем можно полагаться на существующие технологии от различные области, такие как позиционирование в помещении для пешеходной навигации или позиционирование алгоритмы, используемые в авиации. Оценка теоретической производительности и точности алгоритмов внутренней навигации и технологии могут позволить дальнейшие улучшения и внедрение новых технологий для практического использования. Разработанная математическая модель используется для анализа алгоритм позиционирования, который можно использовать в таких системах позиционирования

Point cloud data compression

The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a substantial research effort dedicated to developing efficient compression algorithms for point cloud data. This Master's thesis aims to investigate the current state-of-the-art lossless point cloud geometry compression techniques, explore some of these techniques in more detail and then propose improvements and/or extensions to enhance them and provide directions for future work on this topic

Mapping and Real-Time Navigation With Application to Small UAS Urgent Landing

Small Unmanned Aircraft Systems (sUAS) operating in low-altitude airspace require flight near buildings and over people. Robust urgent landing capabilities including landing site selection are needed. However, conventional fixed-wing emergency landing sites such as open fields and empty roadways are rare in cities. This motivates our work to uniquely consider unoccupied flat rooftops as possible nearby landing sites. We propose novel methods to identify flat rooftop buildings, isolate their flat surfaces, and find touchdown points that maximize distance to obstacles. We model flat rooftop surfaces as polygons that capture their boundaries and possible obstructions on them. This thesis offers five specific contributions to support urgent rooftop landing. First, the Polylidar algorithm is developed which enables efficient non-convex polygon extraction with interior holes from 2D point sets. A key insight of this work is a novel boundary following method that contrasts computationally expensive geometric unions of triangles. Results from real-world and synthetic benchmarks show comparable accuracy and more than four times speedup compared to other state-of-the-art methods. Second, we extend polygon extraction from 2D to 3D data where polygons represent flat surfaces and interior holes representing obstacles. Our Polylidar3D algorithm transforms point clouds into a triangular mesh where dominant plane normals are identified and used to parallelize and regularize planar segmentation and polygon extraction. The result is a versatile and extremely fast algorithm for non-convex polygon extraction of 3D data. Third, we propose a framework for classifying roof shape (e.g., flat) within a city. We process satellite images, airborne LiDAR point clouds, and building outlines to generate both a satellite and depth image of each building. Convolutional neural networks are trained for each modality to extract high level features and sent to a random forest classifier for roof shape prediction. This research contributes the largest multi-city annotated dataset with over 4,500 rooftops used to train and test models. Our results show flat-like rooftops are identified with > 90% precision and recall. Fourth, we integrate Polylidar3D and our roof shape prediction model to extract flat rooftop surfaces from archived data sources. We uniquely identify optimal touchdown points for all landing sites. We model risk as an innovative combination of landing site and path risk metrics and conduct a multi-objective Pareto front analysis for sUAS urgent landing in cities. Our proposed emergency planning framework guarantees a risk-optimal landing site and flight plan is selected. Fifth, we verify a chosen rooftop landing site on real-time vertical approach with on-board LiDAR and camera sensors. Our method contributes an innovative fusion of semantic segmentation using neural networks with computational geometry that is robust to individual sensor and method failure. We construct a high-fidelity simulated city in the Unreal game engine with a statistically-accurate representation of rooftop obstacles. We show our method leads to greater than 4% improvement in accuracy for landing site identification compared to using LiDAR only. This work has broad impact for the safety of sUAS in cities as well as Urban Air Mobility (UAM). Our methods identify thousands of additional rooftop landing sites in cities which can provide safe landing zones in the event of emergencies. However, the maps we create are limited by the availability, accuracy, and resolution of archived data. Methods for quantifying data uncertainty or performing real-time map updates from a fleet of sUAS are left for future work.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170026/1/jdcasta_1.pd

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov