Flight Plan Research Articles (Page 1)

Context Advances in uncrewed aerial vehicle (UAV) technology have enabled the collection of previously infeasible data on wildlife species. One use of UAVs that has received considerable attention is density estimation of deer; however, the accuracy and precision of these estimates remain poorly understood. Aims Our goal was to assess the precision and accuracy of white-tailed deer density estimates gathered using UAVs equipped with thermal sensors during line transect sampling, and we evaluated the influence of transect spacing on the variability of these estimates. Methods In this study, we used a quadcopter UAV equipped with a thermal sensor to estimate white-tailed deer densities. We flew pre-programmed flight paths along transects spaced at 76 m and 152 m over the Auburn Captive Facility (ACF), a 174-ha high-fenced property in Alabama with a largely known deer density, and two additional high-fenced properties in Alabama with unknown deer densities during January–March 2023. Key results The deer density estimated for ACF by using camera trap survey data was 0.49 deer/ha (±0.06, 95% CI). Mean nightly UAV estimates were 0.53 deer/ha (±0.03, 95% CI) and 0.52 deer/ha (±0.07, 95% CI) for transects spaced 76 m and 152 m respectively, indicating high accuracy. Coefficients of variation (CV) for nightly density estimates of this site were 8.4% at 76 m and 18% at 152 m. We found similar results for the two additional properties. Transects spaced 76 m apart were found to be precise (2.4–4.6% CV) with as few as two flights, whereas transects spaced 152 m apart required additional samplings to reach a similar level of precision (5.6–11.3% CV). Conclusions Our study demonstrated that thermal drone technology can generate precise and accurate estimates of deer density during line transect surveys, given proper flight planning. Differences in coefficients of variation between transect spacings suggest that closer-spaced transects offer superior precision. Implications Our study has provided fundamental information with regards to transect spacing for thermal UAV deer abundance line transect surveys and demonstrated the efficacy of this technology for generating estimates of deer density for use in management and monitoring.

Abstract. High-quality 3D reconstruction of infrastructure using UAVs is essential for inspection, monitoring, and digital twin applications. Traditional flight planning methods rely on predefined paths and often struggle with complex geometries, leading to incomplete models and inefficiencies. This paper evaluates a state-of-the-art autonomous Next Best View (NBV) of MACARONS model (Mapping And Coverage Anticipation with RGB Online Self-Supervision), which enables online, self-supervised 3D reconstruction of large-scale scenes using only a monocular RGB sensor. The MACARONS NBV model autonomously adjusts UAV trajectories in real time based on predictions of unseen scene structure to improve reconstruction accuracy and surface detail recovery. Despite its advantages, a key limitation is its lack of consideration for camera coverage percentage from a photogrammetric perspective, which makes it challenging to consistently obtain an informative point cloud. The simulation results demonstrate that the autonomous NBV strategy significantly enhances both reconstruction quality and operational efficiency. To evaluate its effectiveness, we applied the MACARONS NBV model to two open-access 3D bridge models. The generated camera trajectories were imported into Blender, where we rendered high-resolution images using realistic camera intrinsics to overcome the limitations of the low-resolution depth predictions. From these images, we reconstructed point clouds and compared them to those produced by a traditional flight planning approach, as well as to the ground truth models. The comparison highlights the added value of autonomous view planning for accurate and efficient UAV-based 3D reconstruction. The two experiments showed a high coverage percentage of 88 % compared to the ground truth and 90% compared to traditional flight planning based on a 37.5% efficiency raise. This work highlights the potential and current limitations of prediction-based NBV in UAV photogrammetry and motivates further research into integrating coverage-aware planning.

Given the increasing importance of sustainability in the aviation industry, the integration of aircraft with new propulsion technologies that produce fewer, or no emissions is essential. The aim of this paper is to analyze the feasibility of the integration of aircraft with new propulsion technologies in current network planning of flights. We present a methodology to select suitable routes based on the performance of the two selected aircraft (Airbus zero emissions (ZEROe) turboprop hydrogen aircraft as well as a Wright Spirit electric aircraft). The network of a major German airline is used as a case study. Possible routes are selected, and new route network flight plans are generated to fulfill the passenger (PAX) demand. New propulsion technologies create additional complexities from an airline and airport perspective, which are listed and then categorized in 4 areas regarding the selected aircraft types. The results show that aircraft with new propulsion technologies can be integrated into existing route network Flight planning, but new complexities will arise and must be considered from an airline as well as airport perspective to guarantee smooth operations. This paper focuses as a proof of concept on strategical planning of different flights and not the planning of one specific single flight.

Purpose Experimental design and analysis techniques featuring a factorial design were applied to a popular multi-rotor flight simulation tool, eCalc, to validate its hover time flight prediction capability for a quadcopter style unmanned aerial vehicle (UAV). The general applicability of formal experiment design was demonstrated for unmanned aircraft system flight test. Design/methodology/approach A representative quadcopter was chosen to illustrate the efficacy of the statistically based flight test methodology. Five factors including battery capacity, battery cell count, propeller diameter, propeller pitch and video transmitter power were used in a 25-1 fractional factorial design. All factors were presumed to have an influence on the flight profile. Flight data from 16 separate flights were gathered, using a fully randomized test schedule. Similarly, identical multi-rotor configurations were evaluated in eCalc using a full factorial 25 design. Findings Regression models from each experiment demonstrated a strong model where all five factors and many interactions were significant. A total of 16 flight data test points and five confirmation points showed hover time predictions were within ± 15% accuracy as eCalc claims. Results validated eCalc as a reliable model for predicting multi-rotor hover times. The fractional factorial proved an effective tool for efficient flight test, an inherently low signal/noise environment. Originality/value Formal experiment design applied to flight test is rarely featured in the open literature. This study highlights the reliability of the method and eCalc as a prediction tool for quadcopter hover times, adding to its credibility for quadcopter style UAV flight planning and design.

Uncontrollable weed growth and weed-to-crop differentiation impacts directly crop yield and resource effectiveness. In precision agriculture, effective and sustainable weed management therefore remains a crucial aspect. While conventional aerial imaging techniques, often restricted to a single date acquisition and static spectral analysis, are not favorable for accurate differentiation of weeds and crops across different growth stages, leading to high false positives, inefficient spraying, and wastage of herbicides; therefore, the current research attempts propositional limitations to address an integrated approach to multi-stage precision weed management from spatiotemporal data fusion, context-aware deep segmentation, and adaptive treatment optimization. The entire pipeline starts with Adaptive Multispectral-Spatiotemporal Fusion (AMSTF), whereby spatial-spectral features from multispectral imagery are fused through temporal growth patterns using repeated drone flights to gain improved reliability for detection with less false positive instances. The probability maps generated are then refined by Context-Aware Multi-Scale Deep Weed Segmentation (CAMDWS), a dual-branch CNN that captures micro-scale leaf texture as well as macro-scale patch distribution for more precise weed boundaries. The outputs of segmentation are forwarded unto Autonomous Weed Treatment Path Optimization (AWTPO), which uses modified Dijkstra graph optimization to establish fuel, battery, and payload-efficient drone waypoints. The optimized flight plan feeds into Variable-Rate Micro-Droplet Weed Neutralization (VRMDWN), allowing species-adjusted targeting for specific droplet sizes and flow rates for herbicide application. Finally, Post-Treatment Weed Recurrence Prediction (PTWRP) uses reinforcement learning of images obtained after spray and historical patterns for recurrence risk, facilitating proactive micro-treatments. Experimental evaluations indicate an improvement in the range of 6-8% in detection accuracy, 18-22% gain in spraying efficiency, and a reduction in herbicide use of up to 32%. Such a holistic approach would make weed-crop discrimination, thereby minimizing chemical wastage while introducing a predictive long-term sustainable weed suppression strategy for yield protections.

Purpose As quadcopter unmanned aerial vehicles (UAVs) become increasingly prevalent in applications such as delivery services, environmental monitoring and aerial photography, optimizing their energy consumption remains a paramount challenge. This paper aims to address this critical aspect to enhance operational efficiency and extend mission durations by reviewing the latest trends and techniques in energy optimization. Design/methodology/approach This review systematically examines various strategies for reducing energy consumption, incorporating flight path optimization algorithms that account for wind conditions and terrain features, adaptive control systems capable of dynamically adjusting flight parameters in realtime and models and simulations for accurate energy consumption estimation. By analyzing recent advancements and comparing their effectiveness, the paper highlights both achievements and gaps in the field. Findings Key findings indicate significant progress in the development of sophisticated algorithms and control systems that contribute to energy savings. Integrating environmental factors, such as wind patterns and turbulence, into flight planning and control can lead to substantial improvements in energy efficiency. In addition, emerging trends, such as machine learning techniques for predictive modeling and real-time optimization, show great potential for further innovation. Originality/value The paper provides a comprehensive review of state-of-the-art techniques in energy optimization for quadcopter UAVs, highlighting the most recent advancements while identifying areas requiring further research. By placing emphasis on environmental integration and real-time adaptive strategies, it offers unique insights into future directions, underscoring how machine learning and advanced control methodologies can maximize energy efficiency in evolving UAV applications.

Abstract. This study investigated the effectiveness of photogrammetric surveying for the digital reproduction of historical documents unsuited to traditional scanning techniques due to their fragility or large dimensions. The experiment focused on a mid-18th-century territorial map, known as the “Calcato”, that in Italian means ‘trodden’, in the sense of a landscape that has been explored and described through walking it on foot. The objective was to obtain high-resolution, metrically accurate digital outputs by designing a specific acquisition protocol, including a tailored flight plan and the use of a metric camera. Image acquisition was conducted indoors through oblique photography from external positions. To address surface irregularities caused by the semi-rigid support, a high-precision digital surface model (DSM) was generated to enable accurate orthorectification. Geometric reliability was ensured by establishing a topographic control network, defining the coordinates of six ground control points with sub-millimetric precision. The resulting orthophoto validated both the methodological approach and its implementation, providing a reliable and detailed representation suitable for territorial analysis. The outcomes contribute to the objectives of the PRIN project and offer a replicable methodological reference for the digital reproduction of large-format historical documents, supporting the safeguarding of documentary heritage and the dissemination of its informational content.

Abstract. This study presents the design, fabrication, and performance validation of a precision 3-axis gimbal system for drone-mounted applications. Traditional fixed bracket systems have faced challenges in maintaining stable imaging angles and mitigating vibration, primarily due to sensor weight and structural limitations. These issues have resulted in diminished image alignment and reduced data reliability. To overcome these limitations, a precision 3-axis gimbal and external power supply system were developed, and their effectiveness in enhancing image quality was demonstrated using UgCS-based flight planning software. The system underwent two rounds of performance testing, which confirmed notable improvements in geometric calibration, power stability, imaging efficiency, and image alignment accuracy. The results of this study are expected to provide a strong foundation for precision monitoring and analysis using hyperspectral images in large-scale natural heritage applications.

Abstract. Although unmanned aerial vehicle (UAV) mapping and photogrammetry have become common and relatively accessible for surveying and mapping cultural heritage sites, conducting surveys to model sites in conflict/post conflict zones remains challenging. This is particularly true for sites in a country like Afghanistan, where limited accessibility, the presence of Unexploded ordnance (UXOs), portability of the equipment, cost efficiency, as well as absence of data connectivity and Ground Control Point establishment pose major challenges. In this paper, we discuss the adopted strategy and implemented methodology to create a 3D model from both inside and outside of a section of the fortified citadel of Shahr-e Zohak which is part of the UNESCO World Heritage Property of the Cultural Landscape and Archaeological Remains of the Bamiyan Valley in Afghanistan. In particular, we examine in this paper what acquisition strategy was set for this site by going through the reasoning for selecting specific equipment and drones, the flight parameters, the camera settings, as well as how we prepared the dataset at the flight planning stage to allow merging GPS referenced data from the external flights of the UAV with non-GPS referenced data from the flights inside the domes and built structures. Succinctly, we go through the modelling strategy and parameters that have generated optimal results using both Agisoft Metashape and Bentley Itwin Capture.Our results show that using Skydio’s X10 and S2 drones and setting a low Ground to surface distance (between 1 and 5 meters) and high overlap (75%-95%) allowed us to achieve 3D models with an average accuracy of 1 millimetre per pixel for a 120m long and 30m wide section of the fortified citadel of Shahr-i Zohak. These results also show that it is indeed possible to use UAV based photogrammetry to generate 3D models that can be used for damage assessments which is particularly useful in areas where it is difficult or impossible to bring international experts or institutions to conduct this work on site. Finally, this research highlights the capabilities as well as limitations of this method and provide practical guidelines for future works in comparably challenging environments.

The utilisation of UAV imagery for the creation of digital maps is a compelling subject within the domains of photogrammetry and remote sensing. This work introduces a hierarchical method for automating the process of building, extracting, and outlining using images captured by drones. The flight plan should be initially planned to provide about 60-70\% overlap to guarantee thorough coverage and precise image matching. The altitude of the drone should be adjusted based on the intended resolution to achieve a balance between capturing fine details and covering a larger region. Next, the technique of photogrammetric image matching was utilised to generate orthophotos and the Digital Surface Model (DSM). Moreover, the Digital Terrain Model (DTM) was extracted from the DSM to differentiate non-ground objects, including buildings. Subsequently, building segments were identified by applying a threshold to the difference between the Digital Surface Model (DSM) and the Digital Terrain Model (DTM), enabling accurate extraction of building segments. Finally, building polygons were generated involving two stages: coarse and refined, considering the least squares adjustment process to guarantee accuracy and detail. The proposed method was applied to drone images captured on the campus of Al-Muthanna University in the southwest of Iraq. The qualitative and quantitative investigation indicated that the building polygons obtained were highly promising, with approximately one-meter geometric accuracy. Nevertheless, accurately differentiating between buildings and other human-made structures (such as tents) and resolving issues related to mismatching error still pose significant difficulties, highlighting the need for additional investigation and development.

Closures of the Russian and Ukrainian airspace following the Russian invasion of Ukraine in February 2022 have influenced international air transport. Flights have to be re-routed leading to increases in mission distance, flight time, fuel consumption and CO2 emissions. However, the climate impact of aviation is also significantly determined by non-CO2 effects which do not only depend on emission quantities but also emission location and time. Therefore, this paper aims to quantify the climate impact from Russian and Ukrainian airspace closures in context of the Ukrainian crisis. The analysis is built on open-source flight track data as provided by The OpenSky Network applied in the Integrated Trajectory Calculation Module. Climate impact evaluation is performed in a climatological approach using climate chemistry response model AirClim. The analysis confirms an increase in fuel consumption and CO2 effects for a mission-specific comparison of pre invasion and post invasion air traffic scenarios. By contrast, the climate impact from non-CO2 species decreases disproportionately leading to a slight reduction of the total climate impact. This is caused by changes in emission latitude and altitude. On a larger temporal scale, a comparison of annual pre and post invasion scenarios is also influenced by changes in flight plans and fleet composition. While airspace closures have significantly influenced aviation in terms of fuel consumption, flight time and operating cost leading to economic disadvantages, an environmental disadvantage regarding the climate impact of aviation cannot be confirmed.

Turbulence and icing are dangerous situations frequently encountered in flight. It is useful to detect them to enable pilots to adapt flight, to estimate aircraft structural fatigue or to design robust flight control laws. Unfortunately, there is a lack of available data covering such phenomena. To address this, we propose a database of 22 simulated flights over a total duration of 52 hours. Flight plans are based on actual flights. A control system is set up to ensure that the simulated aircraft follows the flight plans. Turbulence is added using a Von Karman spectra and icing is added using measures made on real flight icing. All data (aircraft state variables, commands, aircraft properties, control variables, simulator data) is made available. The code for running simulations is also made available to allow the generation of new data.

This study addresses the limitations of traditional numerical weather prediction models in wind forecasting for aviation operations by introducing a deep learning approach based on a spatiotemporal fusion model that enhances the temporal resolution and accuracy of wind forecasts. Specifically, the model integrates Global Forecast System (GFS) with European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) data, employing a 1-dimensional convolutional layer for spatial data fusion and a bidirectional long short-term memory network for spatiotemporal pattern recognition. The presented approach considerably improves upon the numeric model, increasing temporal resolution from 3-hour to 1-hour intervals and reducing mean absolute error by over 50% for wind speed and direction forecasts. The proposed model achieves 82.85% accuracy in wind direction predictions within a 20° angle, compared to 64.46% for the GFS model forecasts. Case studies demonstrate the proposed model’s superior performance in capturing wind variability, particularly in complex topographical settings like Madeira International Airport. These improvements have relevant implications for aviation safety, flight planning, and fuel consumption optimization. The geographic independence of the proposed approach suggests potential applicability across diverse regions.

This paper presents an in-depth study of innovative approaches to improving the methodology for flight planning of unmanned aerial vehicles (UAVs), particularly in the context of enhancing route planning efficiency under changing operational factors. For the first time, a comprehensive assessment of the effectiveness of choosing an optimal route among various developed options is conducted, sig- nificantly reducing the time and resources required to complete a mission. One of the main objectives of this research is the integration of advanced optimization algorithms into existing UAV platforms, enabling real-time adaptation to environmental changes. The paper introduces new approaches for assessing and selecting optimal routes, taking into account complex topographical conditions, dynamic factors such as changing weather, and stringent time and resource constraints. The proposed algorithms employ advanced optimization methods, such as genetic algorithms, artificial neural network-based problem-solving techniques, and adaptive fore- casting strategies. All these tools are integrated with powerful Geographic Information Systems (GIS), significantly improving route accuracy and allowing for dynamic adjustments in real time. A key aspect of the study is the minimization of energy consumption and flight duration, which is critical for improving efficiency and reducing operational costs for UAVs. The paper justifies why the proposed approaches are more effective than existing methodologies and how they provide significant advantages in various real-world scenarios, such as search operations, environmental monitoring, and cargo delivery. Comparative analysis shows that the new methods not only achieve better route efficiency but also enhance the ability to adapt to unforeseen changes in environmental conditions. The results of the research may have a significant impact on the future development of UAV software, expanding the possibilities for creating next-generation flight control systems capable of ensuring more reliable and efficient operations in diverse applications. In the future, real-world testing of the developed models is planned, which will allow for the verification of the effectiveness of the proposed methods in practice and assess their applicability under different operational conditions.

Unmanned aerial vehicles (UAVs) or unmanned aircraft systems (UAS) are other names for drones, which are aircraft that do not have a pilot on board. A human operator can control it remotely, or it can fly itself using pre-programmed flight plans that use sensors and computers on board. Applications for drones are many and include everything from photography and recreational flying to military surveillance and package delivery. Numerous industries use drones because they can carry out tasks that are hazardous, challenging, or impossible for people to do directly. In order to detect obstacles, drones frequently use cutting-edge technology such as GPS, sensors, cameras, and occasionally LiDAR. A brief explanation of earlier drone technology works is provided in this paper.

<i>TiI</i>: A Novel Framework for Predicting Pre-Tactical Multiple Airports Traffic Flows by Integrating Flight Plans and Weather Data

Detailed geomorphological mapping employs imagery acquired by Remotely Piloted Aircraft (RPA) to investigate gullies, generating morphometric data. This study aimed to develop a detailed geomorphological map, defining geomorphological compartments and recording discrete landform features both within and around the gully, using traditional and adapted symbology. The study area corresponds to the Mombuca Gully in the Brazilian Cerrado, at Monte Carmelo (Minas Gerais, Brazil). The methodology was divided into pre-field, field, and post-field stages, encompassing flight planning; image acquisition with the DJI (Da-Jiang Innovations) Mavic Pro RPA in three scenarios; image treatment, processing, and photo-interpretation to generate morphometric and geomorphological maps. The study identified five geomorphological compartments and documented denudational features, aggradational features, anthropogenic landforms, technogenic deposits, alterations of original channel courses, and the installation of impoundments. Integrated analysis of these data enabled an understanding of natural erosive dynamics and anthropogenic influences. The results support methodological discussions on geomorphological mapping of gullies using high-resolution imagery.

Drone power management poses ongoing challenges that significantly impact operational effectiveness across various applications. This research examines path planning optimization, particularly focusing on distance minimization to enhance efficiency and performance. When drones must visit static ground stations, analyzing the constituent elements of flight paths reveals that segments connecting the launch pad to initial and final stations emerge as a distinct area for further path optimization. Given scenarios where launch pad relocation remains feasible, this study proposes several alternative methodologies for adjusting launch positions to minimize total flight distances across multiple drone operations. The investigation employed extensive experimentation involving diverse configurations with varying station counts and available drone units. Results demonstrate that repositioning the launch pad to serve as an optimal center point for all drone routes yields substantial improvements in total distance minimization, ranging from 4% to 22% across different operational scenarios. The geometric median approach consistently outperformed alternative positioning strategies, achieving these improvements while maintaining computational efficiency. These findings contribute to sustainable drone operations by reducing energy consumption through optimized flight planning. The methodology proves particularly valuable for applications requiring flexible launch point positioning, offering practical solutions for enhancing operational efficiency in environmental monitoring, precision agriculture, and infrastructure inspection tasks where energy conservation directly impacts mission success and operational viability.

Airspace monitoring is a crucial aspect of aviation safety and efficiency, focusing on the continuous observation and management of aircraft within controlled airspace to prevent collisions, optimizing flight paths, and ensuring smooth operations. Traditional airspace monitoring systems, which rely on radar and automatic dependent surveillance-broadcast technology, face challenges in managing the increasing volume of air traffic, signal interference, and the integration of diverse data sources such as weather information and flight plans. In response to these challenges, this research explores the development of a neural network-based system aimed at enhancing airspace monitoring around airports and improving overall aviation safety and efficiency.

The development of unmanned aerial vehicles (UAVs) has positively influenced various remote sensing techniques, making them more accessible to different types of users. Among these, photogrammetry and light detection and ranging (LiDAR) stand out for their versatility and possibilities in terrain modeling. This study evaluates the advantages of each one in various fields of knowledge and industry, comparing their possibilities in terms of positional accuracy, completeness, and efficiency in terrain modeling. It is evident that the use of these techniques in different areas generates an opportunity to implement algorithms or processes in mapping and cartography. Regarding their use, the advantage of the LiDAR sensor is identified in inhospitable and inaccessible areas covered by vegetation and with problems in the geodetic network. On the other hand, the versatility of photogrammetry is shown in small areas with exposed soil. The advantage of point cloud fusion or the combination of techniques in the construction industry and in archaeological and architectural surveys is also noted. Finally, emphasis is placed on variables to consider, such as georeferencing techniques, the ground control point (GCP) network, algorithms and software, and flight plan reviews, in order to improve their accuracy.