Soil erosion by water is a main form of land degradation worldwide. The problem has been addressed, among others, in the United Nations Sustainability Goals. However, for mitigation of erosion consequences and adequate management of affected areas, reliable information on the magnitude and spatial patterns of erosion is needed. Although such need is often addressed by erosion modelling, precise erosion monitoring is necessary for the calibration and validation of erosion models and to study erosion patterns in landscapes. Conventional methods for quantification of rill erosion are based on labour-intensive field measurements. In contrast, remote sensing techniques promise fast, non-invasive, systematic and larger-scale surveying. Thus, the main objective of this study was to develop and evaluate automated and transferable methodologies for mapping the spatial extent of erosion rills from a single acquisition of remote sensing data. Data collected by an uncrewed aerial vehicle was used to deliver a highly detailed digital elevation model (DEM) of the analysed area. Rills were classified by two methods with different settings. One approach was based on a series of decision rules applied on DEM-derived geomorphological terrain attributes. The second approach utilized the random forest machine learning algorithm. The methods were tested on three agricultural fields representing different erosion patterns and vegetation covers. Our study showed that the proposed methods can ensure recognition of rills with accuracies between 80 and 90% depending on rill characteristics. In some cases, however, the methods were sensitive to very small rill incisions and to similar geometry of rills to other features. Additionally, their performance was influenced by the vegetation structure and cover. Besides these challenges, the introduced approach was capable of mapping rills fully automatically at the field scale and can, therefore, support a fast and flexible assessment of erosion magnitudes.

As drones become more widespread throughout modern industry, the demand for drone automation increases. Drones are used for many applications, but their effectiveness relies heavily on their battery life. By designing, implementing, and evaluating an automatic drone landing and battery exchange system, drone missions can be more streamlined and efficient by eliminating the need for manual battery exchange. Previous projects within this topic rely on high-precision landing combined with a manipulator with low degrees of freedom for battery removal. This project offers a solution that allows less strict landing requirements to better fit drones of different sizes and shapes for a wide variety of applications. This autonomous drone landing and battery exchange system uses a robotic arm with 6 degrees of freedom for battery removal and on-board image processing to locate and land on a large, rotatable landing pad.

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<p>  </p> <p>In this work, a conceptual design of a new configuration for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) or drone, with three distinct flight modes (cruise, hover, and transition), is proposed. The drone is sized to meet mission objectives typical for a search and rescue operation and modelled in Surfaces, an aircraft modelling software, to validate its sizing and compute its stability/aerodynamic coefficients. A 3D model of the drone during the different flight modes is generated in Catia V5 and a nonlinear mathematical model of the drone in cruise mode (forward flight) is determined using Newton’s laws. A linearized model of the drone in forward flight is derived from the nonlinear model, about a trim altitude, and its flying qualities assessed. The inherent transient behavior of the drone is improved by implementing a stability augmentation system or feedback loop. Four autopilot systems are designed and tested on the linear drone model and finally implemented on the nonlinear model. The nonlinear model of the drone and autopilots are tested by simulating the flight of the drone through a set of lateral waypoints, representing victims in need of emergency assistance in a search and rescue mission. </p> <p><br></p>

京都大学 / 新制・課程博士 / 博士(農学) / 甲第23944号 / 農博第2493号 / 新制||農||1090(附属図書館) / 学位論文||R4||N5379(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 德地 直子, 教授 北山 兼弘, 教授 神﨑 護, 准教授 伊勢 武史 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM