Society today brings a high pace development and demand of Artificial intelligence systems as well as robotics. To further expand and to take one step closer to have Unmanned Aerial Vehicles (UAVs) working in the cities, the European Union Aviation Safety Agency launched a project that introduces U-space airspace, an airspace where UAVs, for instance, are allowed to operate for commercial services.The problems defined for U-space airspace resemble problems defined in the area of multi-agent path finding, such as scaling and traffic etc., resulting an interest to research whether MAPF-solutions can be applied to U-space scenarios. The following thesis extends the state-of-the-art MAPF-algorithm Continuous-time Conflict based search (CCBS) to handle simplified U-space scenarios, as well as extend other A*-based algorithms, such as a version of the Receding Horizon Lattice-based Motion Planning named Extended Multi-agent A* algorithm with Wait-Time (EMAWT) and an extended A* named Extended Multi-agent A* algorithm (EMA) to handle them. Comparisons of the three algorithms resulted in the EMAWT being the most reliable and stable solution throughout all tests, whilst for fewer agents, the CCBS being the clear best solution.

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京都大学 / 新制・課程博士 / 博士(生命科学) / 甲第24758号 / 生博第499号 / 新制||生||66(附属図書館) / 京都大学大学院生命科学研究科高次生命科学専攻 / (主査)教授 今吉 格, 教授 鈴木 淳, 教授 谷口 雄一 / 学位規則第4条第1項該当 / Doctor of Philosophy in Life Sciences / Kyoto University / DGAM

Thermokarst is a typical process that indicates widespread permafrost degradation in yedoma landscapes. The Lena-Aldan interfluvial area in Central Yakutia in eastern Siberia is now facing extensive landscape changes with surface subsidence due to thermokarst development during the past few decades. To clarify the spatial extent and rate of subsidence, multiple spatial datasets, including GIS and remote sensing observations, were used to analyze the Churapcha rural locality, which has a typical yedoma landscape in Central Yakutia. Land cover classification maps for 1945 and 2009 provide basic information on anthropogenic disturbance to the natural landscape of boreal forest and dry grassland. Interferometric synthetic aperture radar (InSAR) with ALOS-2/PALSAR-2 data revealed activated surface subsidence of 2 cm/yr in the disturbed area, comprising mainly abandoned agricultural fields. Remote sensing with an unmanned aerial system also provided high-resolution information on polygonal relief formed by thermokarst development at a disused airfield where InSAR analysis exhibited extensive subsidence. It is worth noting that some historically deforested areas have likely recovered to the original landscape without further thermokarst development. Spatial information on historical land-use change is helpful because most areas with thermokarst development correspond to locations where land was used by humans in the past. Going forward, the integrated analysis of geospatial information will be essential for assessing permafrost degradation.

Root-zone soil moisture (RZSM) is an important variable when forecasting plant growth, determining water availability during drought, and understanding evapotranspiration as a flux. However, current methods indirectly estimate RZSM using data assimilation, which requires time-series data to make model-based predictions. This is because direct measurement requires a lower frequency signal, typically P-band and below (<500MHz), to reach root zone depths and, in turn, necessitates a larger antenna to be deployed in space, which is often unfeasible. A new remote sensing technique known as Signals of Opportunity (SoOp) reutilizes transmitted communication signals to perform microwave remote sensing. This means that SoOp platforms need not include a transmitter, but rather rely on passive radar technology to make measurements. This thesis details the development of a UAS-based P-band SoOp receiver instrument. This platform will be used to progress the state-of-art in techniques for direct measurement of RZSM.

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Common Objects in Context for bridge inspection (COCO-Bridge) was introduced for use by unmanned aircraft systems (UAS) to assist in GPS denied environments, flight-planning, and detail identification and contextualization, but has far-reaching applications such as augmented reality (AR) and other artificial intelligence (AI) platforms. COCO-Bridge is an annotated dataset which can be trained using a convolutional neural network (CNN) to identify specific structural details. Many annotated datasets have been developed to detect regions of interest in images for a wide variety of applications and industries. While some annotated datasets of structural defects (primarily cracks) have been developed, most efforts are individualized and focus on a small niche of the industry. This effort initiated a benchmark dataset with a focus on structural details. This research investigated the required parameters for detail identification and evaluated performance enhancements on the annotation process. The image dataset consisted of four structural details which are commonly reviewed and rated during bridge inspections: bearings, cover plate terminations, gusset plate connections, and out of plane stiffeners. This initial version of COCO-Bridge includes a total of 774 images; 10% for evaluation and 90% for training. Several models were used with the dataset to evaluate model overfitting and performance enhancements from augmentation and number of iteration steps. Methods to economize the predictive capabilities of the model without the addition of unique data were investigated to reduce the required number of training images. Results from model tests indicated the following: additional images, mirrored along the vertical-axis, provided precision and accuracy enhancements; increasing computational step iterations improved predictive precision and accuracy, and the optimal confidence threshold for operation was 25%. Annotation recommendations and improvements were also discovered and documented as a result of the research. / MS / Common Objects in Context for bridge inspection (COCO-Bridge) was introduced to improve a drone-conducted bridge inspection process. Drones are a great tool for bridge inspectors because they bring flexibility and access to the inspection. However, drones have a notoriously difficult time operating near bridges, because the signal can be lost between the operator and the drone. COCO-Bridge is an imagebased dataset that uses Artificial Intelligence (AI) as a solution to this particular problem, but has applications in other facets of the inspection as well. This effort initiated a dataset with a focus on identifying specific parts of a bridge or structural bridge elements. This would allow a drone to fly without explicit direction if the signal was lost, and also has the potential to extend its flight time. Extending flight time and operating autonomously are great advantagesfor drone operators and bridge inspectors. The output from COCO-Bridge would also help the inspectors identify areas that are prone to defects by highlighting regions that require inspection. The image dataset consisted of 774 images to detect four structural bridge elements which are commonly reviewed and rated during bridge inspections. The goal is to continue to increase the number of images and encompass more structural bridge elements in the dataset so that it may be used for all types of bridges. Methods to reduce the required number of images were investigated, because gathering images of structural bridge elements is challenging,. The results from model tests helped build a roadmap for the expansion and best-practices for developing a dataset of this type.

With the popularity of unmanned aircraft systems (UAS), there is a growing need to assess airworthiness for safe operations in shared airspace. In the context of this thesis, shared airspace implies the introduction of UAS into airspace designated for manned aircraft. Airworthiness guidelines are generally statements that state safety requirements to prevent unwanted consequences, such as aircraft accidents. Many governmental agencies such as the U.S. Federal Aviation Administration (FAA) analyzed the risks of UAS to third-parties, all personnel and properties exterior to the aircraft. This thesis concerns the adaptation of existing airworthiness documents written for manned aircraft to UAS. The proposed method has three stages that are applied in sequence to identify relevant and irrelevant airworthiness statements, the building blocks of an airworthiness document, with regard to UAS. This method is applied to MIL-HDBK-516C, used as a case study; however, the proposed methodology can be applied to any airworthiness document developed for manned aircraft. This thesis presents a list of all MIL-HDBK-516C airworthiness statements that are directly relevant, indirectly relevant, and irrelevant to UAS; additionally, the indirectly relevant airworthiness statements to UAS are provided along with suggested modification. / M.S. / With the popularity of unmanned aircraft systems (UAS), there is a growing need to assess airworthiness for safe operations in shared airspace. Airspace is the available air for aircraft to fly in; most airspaces are regulated and are subject to the jurisdiction of a particular country. In the context of this thesis, shared airspace implies the introduction of UAS into airspace designated for manned aircraft. Airworthiness guidelines are generally statements that state safety requirements to prevent unwanted consequences, such as aircraft accidents. Many governmental agencies such as the U.S. Federal Aviation Administration (FAA) analyzed the risks of UAS to third-parties, all personnel and properties exterior to the aircraft. This thesis concerns the adaptation of existing airworthiness documents written for manned aircraft to UAS. The proposed method has three stages that are applied in sequence to identify relevant and irrelevant airworthiness statements, the building blocks of an airworthiness document, with regard to UAS. This method is applied to MIL-HDBK-516C, Department of Defense Handbook: Airworthiness Certification Criteria, used as a case study. MIL-HDBK-516C is a military handbook used for airworthiness guidance. However, the proposed methodology can be applied to any airworthiness document developed for manned aircraft. This thesis presents a list of all MIL-HDBK-516C airworthiness statements that are directly relevant, indirectly relevant, and irrelevant to UAS; additionally, the indirectly relevant airworthiness statements to UAS are provided along with suggested modification.

The development and implementation of a flight test program for an unmanned aircraft is a multidisciplinary challenge. This thesis presents the development and implementation of a rigorous test program for the flight test of a Geometrically Scaled Joined Wing SensorCraft Remotely Piloted Vehicle from concept through successful flight test. The design methodology utilized in the development of the test program is presented, along with the extensive formal review process required for the approval of the test plan by the Air Force Research Laboratory. The design, development and calibration of a custom instrumentation package is also presented along with the setup, procedure and results from all testing. Results are presented for a wind tunnel test for air data boom calibration, propulsion system static thrust testing, a bifilar pendulum test for experimental calculation of mass moments of inertia, a static structural loading test for structural design validation, a full taxi test and a successful first flight. / Master of Science