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This paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

This study investigates a new aircraft flight trajectory optimisation method, derived from the Non-dominated Sorting Genetic Algorithm II method used for multi-objective optimisations. The new method determines, in parallel, a set of optimal flight plan solutions for a flight. Each solution is optimal (requires minimum fuel) for a Required Time of Arrival constraint from a set of candidate time constraints selected for the final waypoint of the flight section under optimisation. The set of candidate time constraints is chosen so that their bounds are contiguous, i.e. they completely cover a selected time domain. The proposed flight trajectory optimisation method may be applied in future operational paradigms, such as Trajectory-Based Operations/free flight, where aircraft do not need to follow predetermined routes. The intended application of the proposed method is to support Decision Makers in the planning phase when there is a time constraint or a preferred crossing time at the final point of the flight section under optimisation. The Decision Makers can select, from the set of optimal flight plans, the one that best fits their criteria (minimum fuel burn or observes a selected time constraint). If the Air Traffic Management system rejects the flight plan, then they can choose the next best solution from the set without having to perform another optimisation. The method applies for optimisations performed on lateral and/or vertical flight plan components. Seven proposed method variants were evaluated, and ten test runs were performed for each variant. For five variants, the worst results yielded a fuel burn less than 90kg (0.14%) over the ‘global’ optimum. The worst variant yielded a maximum of 321kg (0.56%) over the ‘global’ optimum.

The vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. Meanwhile, existing global optimization strategies often face issues of inefficiency and overly broad adjustment scopes when dealing with large-scale plan conflicts. To address these challenges, this study proposes an innovative flight plan conflict management framework. First, by introducing a probabilistic model of flight time errors, a new conflict detection mechanism based on confidence intervals is constructed, significantly enhancing the ability to foresee non-obvious conflict risks. Furthermore, based on complex network theory, the framework accurately identifies a small number of “critical flight plans” that play a core role in the conflict network, revealing their key impact on chain reactions of conflicts. On this basis, a phased optimization strategy is adopted, prioritizing the adjustment of spatiotemporal parameters (departure time and speed) for these critical plans to systematically resolve most conflicts. Subsequently, only fine-tuning the speeds of non-critical plans is required to address remaining local conflicts, thereby minimizing interference with the overall operational order. Simulation results demonstrate that this framework not only significantly improves the comprehensiveness of conflict detection but also effectively reduces the total number of conflicts. Additionally, the proposed phased artificial lemming algorithm (ALA) outperforms traditional optimization algorithms in terms of solution quality. This work provides an important theoretical foundation and a practically valuable solution for developing robust and efficient UAM dynamic scheduling systems, holding promise to support the safe and orderly operation of large-scale urban air traffic in the future.

The use of Unmanned Aircraft has grown significantly in Thailand and worldwide, particularly for operations below 450 feet. However, unlike manned aviation, there remains a lack of integrated digital platforms to manage flight plans that align with regulatory and operational requirements specific to low altitude activity. This study employed both secondary research and expert interviews to gather technical and regulatory user requirements. The data were analyzed and validated using Structural Equation Modeling to identify key variables influencing safety operations. Based on these findings, a standardized low altitude flight plan format was developed and converted into a prototype web platform called GoFly. The system enables operators to register aircraft and pilot credentials and to submit flight plans digitally. This platform addresses the current fragmentation in Thailand’s flight planning process by centralizing operations and enhancing regulatory compliance. The study contributes to the foundational development of a digital Unmanned Aircraft Traffic Management system tailored for emerging airspace users in Thailand and demonstrates potential scalability to other international regulatory contexts.

High-end consumer quadcopter UAVs or so-called “prosumer devices”, have made inroads into the mapping industry over the past few years, arguably displacing more expensive purpose-built systems. In particular, the DJI Phantom series quadcopters, marketed primarily for videography, have shown considerable promise due to their relatively high-quality cameras. Camera pre-calibration has long been a part of the aerial photogrammetric workflow with calibration certificates being provided by operators for every project flown. Most UAV data, however, is processed today in Structure-from-Motion software where the calibration is generated “on-the-fly” from the same image-set being used for mapping. Often the scenes being mapped and their flight-plans are inappropriate for calibration as they do not have enough variation in altitude to produce a good focal-length solution, and do not have cross-strips to improve the estimation of the principal point. What we propose is a new type of flight-plan that can be run on highly textured scenes of varying height prior to mapping missions that will significantly improve the estimation of the interior orientation parameters and, as a consequence, improve the overall accuracy of projects undertaken with these sorts of UAV systems. We also note that embedded manufacturer camera profiles, which correct for distortion automatically, should be removed prior to all photogrammetric processing, something that is often overlooked as these profiles are not made visible to the end user in most image conversion software, particularly Adobe’s CameraRAW.

Accurate data mapping and visualization are of crucial importance for the detection and monitoring of slope morphodynamics, including erosion processes and studying small erosional landforms (rills and gullies). The purpose of the current research is to examine how the flight geometry of unmanned aerial systems (UASs) could affect the accuracy of photogrammetric processing products, concerning small erosion landforms that are a result of slope wash and temporary small streams formed by rain. In October 2021, three UAS flights with a different geometry were carried out in a hilly to a low-mountain area with an average altitude of about 650 m where erosion processes are observed. UAS imagery processing was carried out using structure-from-motion (SfM) photogrammetry. High-resolution products such as photogrammetric-based point clouds, digital surface models (DSMs) and orthophotos were generated. The obtained data were compared and evaluated by the root mean square error (RMSE), length measurement, cloud-to-cloud comparison, and 3D spatial GIS analysis of DSMs. The results show small differences between the considered photogrammetric products generated by nadir-viewing and oblique-viewing (45°—single strip and 60°—cross strips) geometry. The complex analysis of the obtained photogrammetric products gives an advantage to the 60°—cross strips imagery, in studying erosional terrains with slow slope morphodynamics.

Historic aircraft trajectory data are valuable resources for various research in the field of air traffic management. With the widespread use of Automatic Dependent Surveillance-Broadcast (ADS-B), these data are relatively easier to obtain. However, there are instances, where the original flight plans that do not contain controller interventions are necessary, such as developing scenarios for Human-in-The-Loop (HiTL) simulations. Even though the air traffic control system keeps records of the flight plan data, they are more difficult to obtain and often are not in the correct format to be applied to simulations. In this study, an algorithm is developed, which can extract the flight plan from recorded ADS-B trajectory and the Aeronautical Information Publication (AIP) that contains all the route and procedure information. The algorithm was validated against HiTL simulation results, where both the resulting trajectories with controller interventions and the original flight plans are available and showed a 98 percent success rate. The algorithms are applied to find the flight plans of about one million flights in the year 2019 that contain trajectory points inside the Incheon Flight Information Region (FIR). This flight plan extraction algorithm will be useful not only for the fast-time or real-time simulations of the air traffic but also for aviation safety-related research areas, where the controller-pilot interactions are important.

Three-dimensional (3D) building model is gaining more scientific attention in recent times due to its application in various fields such as vehicle autonomous navigation, urban planning, heritage building documentation, gaming visualisation and tourism. The quality of the Level of Detail (LoD) of building models relies on the high-resolution data sets obtained for the building. As an alternative to laser scanners, Unmanned Aerial Vehicles (UAV) are efficient in collecting good quality images and generate reliable LoD3 of buildings (i.e. to model both roof and facades of a building) at comparatively lower cost and time. However, the complete collection of images on building facades is usually performed by manual flights along the different façade to assure a homogenous image coverage with the same resolution on each element: no offline autonomous procedure to define the main façade planes and acquire complete image sets independent of UAV platform have been developed yet. This paper proposes a novel methodology to generate the flight plan in correspondence of building facades. The Digital Surface Model (DSM) obtained from an initial nadir flight is used as an input to identify the target building and plan the image acquisition around it. The optimised flight plan ensures complete coverage of the building with a minimum number of images. The coordinates as well as the attitude of each planned image can be finally loaded on a UAV to perform the flight. In order to validate the proposed methodology, some tests performed on synthetic buildings of growing complexity and very different shapes are presented.

The paper presents a method for constructing a set of vertical flight path segments, that would compose an aircraft's vertical flight envelope, by using an aircraft performance model. This method is intended to be used for aircraft flight plan prediction and optimisation algorithms. The goal is to reduce the volume of recurring segment performance computations currently required for flight plan prediction or optimisation. The method presented in this paper applies to a free-flight scenario. The flight-path segments composing the vertical flight envelope belong to one of the unrestricted climb, constant-speed level flight, step-climb and continuous descent segments, performed at the consigned climb, cruise and descent speed schedules and at the consigned air temperature values. The method employs an aircraft model using linear interpolation tables. Nine test scenarios were utilised to assess the performances of the resulting flight envelopes as a function of the number of cruise altitudes and descent flight paths. The set of evaluated performance parameters includes the range of total flight times and still-air flight distances, and the vertical profiles describing the minimum and maximum flight times, and still-air flight distances. The advantages of the proposed method are multiple. First, it eliminates the need for repetitive aircraft performance computations of identical vertical flight plan segments, and provides the means for quick retrieval of the corresponding performance data for use in the construction of a full flight plan. Second, the vertical flight path look-up structure and the vertical flight-path graph describe a set of vertical flight paths that consider an aircraft's and flight plan's configuration parameters and cover its maximum flight envelope. Third, the look-up structure and the graph provide the means for rapid and clear identification of the available options for constructing a flight-plan segment, as well as for detecting the points associated with changes in the flight phases, including climb, cruise, step-climb and descent.