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Aviation is seeking for ways to reduce its climate impact caused by CO2 emissions and non-CO2 effects. Operational measures which change overall flight altitude have the potential to reduce climate impact of individual effects, comprising CO2 but in particular non-CO2 effects. We study the impact of changes of flight altitude, specifically aircraft flying 2000 feet higher and lower, with a set of global models comprising chemistry-transport, chemistry-climate and general circulation models integrating distinct aviation emission inventories representing such alternative flight altitudes, estimating changes in climate impact of aviation by quantifying radiative forcing and induced temperature change. We find in our sensitivity study that flying lower leads to a reduction of radiative forcing of non-CO2 effects together with slightly increased CO2 emissions and impacts, when cruise speed is not modified. Flying higher increases radiative forcing of non-CO2 effects by about 10%, together with a slight decrease of CO2 emissions and impacts. Overall, flying lower decreases aviation-induced temperature change by about 20%, as a decrease of non-CO2 impacts by about 30% dominates over slightly increasing CO2 impacts assuming a sustained emissions scenario. Those estimates are connected with a large but unquantified uncertainty. To improve the understanding of mechanisms controlling the aviation climate impact, we study the geographical distributions of aviation-induced modifications in the atmosphere, together with changes in global radiative forcing and suggest further efforts in order to reduce long standing uncertainties.

In this paper, a new methodology is proposed to enhance the conformal mapping applications in the process of optimum trajectory planning in Terrain Following (TF) and Terrain Avoidance (TA) Flights. The new approach uses the conformal mapping concept as a flattener tool to transform the constrained trajectory-planning problem with flight altitude restrictions due to the presence of obstacles into a regenerated problem with no obstacle and minimal height constraints. In this regard, the Schwarz-Christoffel theorem was utilized to incorporate the height constraints into the aircraft dynamic equations of motion. The regenerated optimal control problem was then solved by a numerical method, namely the direct Legendre-Gauss-Radau pseudospectral algorithm. A composite performance index of flight time, terrain masking, and aerodynamic control effort was optimized. Furthermore, to obtain realistic trajectories, the aircraft maximum climb and descent rates were imposed as inequality constraints in the solution algorithm. Several case studies for two-dimensional flight scenarios show the applicability of this approach in TF/TA trajectory planning. Extensive simulations confirm the efficiency of the proposed approach and verify the feasibility of solutions, satisfying all of the constraints underlying the problem.

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized.

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. However, systematic elevation error remains a problem with this technique. We aimed to analyze the reasons for this systematic error and propose strategies for its elimination. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100 m with a vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with a vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° and 15°). Each of these flights was performed twice to produce a full double grid. Models were reconstructed from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights at the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS RTK data and the deviation in the determined internal orientation parameters (focal length). In addition, we have shown that a combination of two flights with different image acquisition axes can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.

Although the morphology of oblique detonation waves (ODWs) has been widely studied, it remains impossible to predict the wave systems in the initiation region, which is a critical component in promoting engine applications. Such wave systems are usually viewed as secondary ODWs or compression waves (CWs), introducing some structural ambiguities and contradictions with recent observations. In this study, ODWs are simulated numerically in a stoichiometric hydrogen–air mixture and their morphological features are analysed. To cover a wide range of flight conditions physically, the control parameters are the flight altitude $H_{0}$ and Mach number $M_{1}$ of an ODW-based engine. Numerical results reveal the morphological variations with respect to $H_{0}$ and $M_{1}$, within which two special wave systems arise. One wave system indicates that the CW might induce an abrupt transition, and the other indicates that the classical secondary ODW might evolve into a normal detonation wave, another illustration of the well-known ‘detonation-behind-shock’ wave configurations. To clarify the mechanism of wave system variation, a geometric analysis of two characteristic heights demonstrates that the wave system could be predicted from the viewpoint of CW convergence. Moreover, analysis of the induction zone Mach number, compared with the corresponding Chapman–Jouguet Mach number, provides a criterion for the normal detonation wave formation. These semi-theoretical approaches collectively enhance our understanding of the wave system physically.

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.

The precision of the altitude measurement system installed in some type airplane does not fulfill the need of the airborne equipment, this paper propose a method based on the federated filter which utilizes the data of the air data computer system (ADS), inertial navigation system (INS) and the GPS to improve the precision and stability of the altitude output. The results of simulation show that the algorithm takes full advantage of all of the sub-system, which reduces the error of the ADS and improves the altitude output precision. Meanwhile the method may separate some faulted sub-system (e.g. GPS) and reform the system, which renders the system better fault tolerant and real-time performance.

The flexibility of UAV‐lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX − 1LR over a 300 × 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimetres or less, which shows great promise for future comparison studies. We discuss how these results apply in practice, explaining the potential trade‐offs of flying at higher altitudes and with alternate patterns. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV‐lidar sampling designs in savannas. A cross‐section of a lidar point cloud for a single tree (left) from a savanna in the Satara region of Kruger National Park, South Africa (visualised with CloudCompare 2.11). Smoothed vertical profiles of fractional canopy cover per 10 cm height bin are plotted for the same tree. These profiles were derived from a series of airborne lidar data collected from 4 different flight altitudes (60 m, 100 m, 180 m, and 300 m above ground) with an unoccupied aerial vehicle (UAV). As flight altitude increases (left‐right), the canopy cover profiles change shape and shift downward, demonstrating that UAV fight and sensor parameters can have a significant impact on lidar measurements of vegetation structure.