UAVs Research Articles

Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation

The application of computer vision in conjunction with GPS is essential for autonomous wind turbine inspection, particularly when the drone navigates through a wind farm to detect the turbine of interest. Although drones for such inspections use GPS, our study only focuses on the computer vision aspect of navigation that can be combined with GPS information for better navigation in a wind farm. Here, we employ an affordable, non-GPS-equipped drone within an indoor setting to serve educational needs, enhancing its accessibility. To address navigation without GPS, our solution leverages visual data captured by the drone’s front-facing and bottom-facing cameras. We utilize Hough transform, object detection, and QR codes to control drone positioning and calibration. This approach facilitates accurate navigation in a traveling salesman experiment, where the drone visits each wind turbine and returns to a designated launching point without relying on GPS. To perform experiments and investigate the performance of the proposed computer vision technique, the DJI Tello EDU drone and pedestal fans are used to represent commercial drones and wind turbines, respectively. Our detailed and timely experiments demonstrate the effectiveness of computer vision-based path planning in guiding the drone through a small-scale surrogate wind farm, ensuring energy-efficient paths, collision avoidance, and real-time adaptability. Although our efforts do not replicate the actual scenario of wind turbine inspection using drone technology, they provide valuable educational contributions for those willing to work in this area and educational institutions who are seeking to integrate projects like this into their courses, such as autonomous systems.

Multi-Scale Fusion Uncrewed Aerial Vehicle Detection Based on RT-DETR

With the rapid development of science and technology, uncrewed aerial vehicle (UAV) technology has shown a wide range of application prospects in various fields. The accuracy and real-time performance of UAV target detection play a vital role in ensuring safety and improving the work efficiency of UAVs. Aimed at the challenges faced by the current UAV detection field, this paper proposes the Gathering Cascaded Dilated DETR (GCD-DETR) model, which aims to improve the accuracy and efficiency of UAV target detection. The main innovations of this paper are as follows: (1) The Dilated Re-param Block is creatively applied to the dilatation-wise Residual module, which uses the large kernel convolution and the parallel small kernel convolution together and fuses the feature maps generated by multi-scale perception, greatly improving the feature extraction ability, thereby improving the accuracy of UAV detection. (2) The Gather-and-Distribute mechanism is introduced to effectively enhance the ability of multi-scale feature fusion so that the model can make full use of the feature information extracted from the backbone network and further improve the detection performance. (3) The Cascaded Group Attention mechanism is innovatively introduced, which not only saves the computational cost but also improves the diversity of attention by dividing the attention head in different ways, thus enhancing the ability of the model to process complex scenes. In order to verify the effectiveness of the proposed model, this paper conducts experiments on multiple UAV datasets of complex scenes. The experimental results show that the accuracy of the improved RT-DETR model proposed in this paper on the two UAV datasets reaches 0.956 and 0.978, respectively, which is 2% and 1.1% higher than that of the original RT-DETR model. At the same time, the FPS of the model is also improved by 10 frames per second, which achieves an effective balance between accuracy and speed.

Integrated optimization of scheduling for unmanned follow-me cars on airport surface

To promote the application of automated vehicles in large airports, in this study, we present an integrated optimization method for scheduling Unmanned follow-me cars. The scheduling process is divided into three phases: Dispatch, Guidance, and Recycle. For the Dispatch phase, we establish a vehicle assignment model, to allocate the vehicle resource equitably. For the Guidance phase, we offer an quantitative way, to measure the spacing between Unmanned follow-me car and aircraft. To optimize the efficiency of airport operation in the three phases and ensure safety, the collaborative planning model, and the conflict prediction model are established. An improved grey wolf optimization algorithm is adopted to enhance the convergence speed and generalization performance. A case study at Ezhou Huahu Airport in China demonstrates the effectiveness of the methods. The results show that the model of collaborative planning can make the balance of path selection, Unmanned follow-me car’s working time, and departure sequence. The convergence speed of the improved algorithm has been increased by 18.75%. The inequity index of vehicle assignment is only 0.015731, and the spatiotemporal distribution of conflicts is influenced by the airport’s surface layout.

Research on Group Behavior Modeling and Individual Interaction Modes with Informed Leaders

This study investigates coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence condition based on experiments involving real biological groups, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled-force Cucker–Smale model with informed leaders. Incorporating informed leaders into the leader-follower group model enables a more accurate representation of biological group behaviors. The paper then extracts the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. Employing time slicing and rolling time windows, the study methodically analyzes group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network’s superior performance. The Kolmogorov-Smirnov test demonstrates that the FLH network conforms to a power-law distribution, a prevalent law in nature. This result further illuminates the crucial role that power-law distribution plays in the evolutionary processes of biological communities. This study offers new perspectives on the evolution of biological groups, contributing to our understanding of the behaviors of both natural and artificial systems, such as animal migration and autonomous drone operations.

Statistical Evaluation of the Accuracy of Consumer Drone Photogrammetry at a Romanesque Church in Eastern Bavaria (Germany)

For several years, photogrammetric surveys are quite often applied for surveying cultural heritage. For a detailed and accurate result, it is indispensable that the calculated point cloud represents the original geometry of the building in high quality, with vertical and horizontal structures in the same manner. Whereas many published papers in the field of digital heritage documentation are dealing with creating such models or using photogrammetric data for damage detection, only a few papers are investigating the crucial fact of accuracy and how it can be influenced by the used camera resolution. We want to present a case study, in which we compare the photogrammetric point cloud of a normal consumer drone with a high-end device. Beside the actual survey results showing the details of a Romanesque church, a statistical calculation of deviation is shown. It becomes evident that there is only a difference within some millimetres between the corresponding point clouds in most parts of the monument. Hence, it can be stated that, for most applications in heritage protection, affordable consumer unmanned aerial vehicles (UAVs) are sufficient. Only if faint details have to be resolved will a better resolution of the mounted camera be required.

Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments.

The utilization of drone technology thrives in diverse domains, including aviation, military operations, and logistics. The pervasive adoption of this technology aims to enhance efficiency while mitigating hazards and expenditures. In complex contexts, the governing parameters of uncrewed aerial vehicles (UAV) require real-time adjustments for flight safety and efficacy. To improve the attitude estimation accuracy, this article introduces a ATT-Bi-LSTM framework for optimizing UAVs through adaptive parameter control, which integrates the state information gleaned from communication signals. The ATT-Bi-LSTM achieves data feature extraction by means of a two-layer Bidirectional Long Short-Term Memory (BI-LSTM) at its inception to enhance the feature. Subsequently, it harnesses the attention mechanism to amplify the LSTM network's output, thereby enabling the optimal control of UAV positioning. During the empirical phase, we employ optical system data for the comparative validation of the model. The outcomes underscore the commendable performance of the proposed framework in this study, particularly with regard to the three pivotal position indicators: yaw, pitch, and roll. In the comparison of indicators such as RMSR and MAE, the proposed model has the lowest error, which provides algorithm support and important reference for future UAV optimization control research.

The Loss of Soil Parent Material: Detecting and Measuring the Erosion of Saprolite

Soil parent material is a fundamental natural resource for the generation of new soils. Through weathering processes, soil parent materials provide many of the basic building blocks for soils and have a significant bearing on the physico-chemical makeup of the soil profile. Parent materials are critical for governing the stock, quality, and functionality of the soil they form. Most research on soil parent materials to date has aimed to establish and measure the processes by which soil is generated from them. Comparatively little work has been performed to assess the rates at which soil parent materials erode if they are exposed at the land surface. This is despite the threat that the erosion of soil parent materials poses to the process of soil formation and the loss of the essential ecosystem services those soils would have provided. A salient but unanswered question is whether the erosion of soil parent materials, when exposed at the land surface, outpaces the rates at which soils form from them. This study represents one of the first to detect and measure the loss of soil parent material. We applied Uncrewed Aerial Vehicle Structure-From-Motion (UAV-SfM) photogrammetry to detect, map, and quantify the erosion rates of an exposed saprolitic (i.e., weathered bedrock) surface on an agricultural hillslope in Brazil. We then utilized a global inventory of soil formation to compare these erosion rates with the rates at which soils form in equivalent lithologies and climatic contexts. We found that the measured saprolite erosion rates were between 14 and 3766 times faster than those of soil formation in similar climatic and lithological conditions. While these findings demonstrate that saprolite erosion can inhibit soil formation, our observations of above-ground vegetation on the exposed saprolitic surface suggests that weathered bedrock has the potential to sustain some biomass production even in the absence of traditional soils. This opens up a new avenue of enquiry within soil science: to what extent can saprolite and, by extension, soil parent materials deliver soil ecosystem services?

UAV Detect and Avoid from UTM-Dependent Surveillance

A hierarchical unmanned aircraft system (UAS) traffic management (UTM) system has deployed 45 ground transceiver stations (GTS) for UAS services in Taiwan. This UTM system covers most areas for UAV-dependent surveillance using ADS-B Like technology. UTM Controller can monitor all UAV flights under transparent surveillance in low airspace. Controller-initiated UAV “detect and avoid” (DAA) mechanism assists UAV separation to ensure flight safety on UTM for small multirotor UAVs. From similar concept to traffic alert and collision avoidance system (TCAS) for the manned aircraft system, the UTM software executes DAA functions to generate approach alerts to UTM Controller. Conflict is detected by heading arrow extrapolation from multiple approaching UAVs by their time to conflict (TTC) on icons. Traffic advisory (TA) and resolution advisory (RA) are pronounced on UTM console to controllers. The less priority UAV pilot will receive the controller-pilot communication (CPC) to perform avoidance resolution. In UTM, the surveillance data period is broadcasting at 5~8 seconds on LoRa (long-range wide-area network) chip. Referring to TA=48 seconds and RA=24 seconds, the signal delay in ADS-B Like system to UTM server is about 0.5 seconds and CPC response is measured about 3~5 seconds. From real flight tests, the RA is enough for the less priority pilot to maneuver UAV for avoidance. From real flight tests, the proposed DAA mechanism based on UTM-dependent surveillance is feasible to resolve multiple approaches. The developing UTM system using ADS-B Like technology is also examined of high availability with redundant reliability and performance stability for flight safety.

Method of Forming the Requirements to Information Support Characteristics of Control Systems of Unmanned Aircraft Transport Systems

Method of Forming the Requirements to Information Support Characteristics of Control Systems of Unmanned Aircraft Transport Systems

Generalizing minimum safe operating altitudes for fixed‐wing UAVs in real‐time

AbstractThis paper discusses a method of determining the minimum safe altitude of an uncrewed aerial vehicle (UAV) at any point within a designated airspace by conducting a glide reachability analysis. Recently, fixed‐wing UAVs are more regularly deployed near population centers and in extreme environments, requiring increasingly robust emergency systems and planning. The long‐ranges and adverse terrain associated with monitoring the Volcán de Fuego in Guatemala by a team from the University of Bristol (UoB) increases the likelihood that motor failure would result in the aircraft being unable to Return To Home (RTH) and impossible to retrieve. A method for delineating a boundary representing the minimum safe altitude required for the aircraft to safely glide to the airfield in the event of a motor failure was developed within MATLAB, defined by the UAV's minimum glide angle in wind. This model was subsequently compared with flight data from UoB missions around Fuego to better improve its accuracy and analyze the limitations of the missions.

AI Enabled MED Drone for Healthcare Application

The project proposes the development of an AI-enabled MedDrone for medication delivery, introducing an innovative and efficient solution to enhance healthcare logistics. The MedDrone, equipped with artificial intelligence capabilities, aims to revolutionize the process of medication delivery by leveraging autonomous aerial technology. The system integrates advanced algorithms to optimize route planning, ensuring timely and accurate delivery of medications to designated locations. The AI component enables the drone to adapt to real-time variables such as weather conditions and traffic, enhancing the reliability and responsiveness of the delivery process. This project addresses the challenges of traditional medication distribution, offering a futuristic and intelligent approach to healthcare logistics through the deployment of AI-enabled autonomous drones for efficient and timely medication delivery. The proposed system integrates AI algorithms for real-time route optimization, obstacle avoidance, and decision-making, enabling the drone to autonomously navigate complex environments and deliver essential medical supplies such as first aid kits, defibrillators, and medications to the point of need. Through the utilization of machine learning techniques, the drone continuously learns from its interactions with the environment, enhancing its adaptability and performance over time. Additionally, the system incorporates advanced communication technologies to enable seamless coordination with emergency responders and healthcare professionals, ensuring timely delivery and proper utilization of resources. The efficacy of the proposed AI-based medical drone system is demonstrated through simulations and real-world trials, highlighting its potential to revolutionize emergency response efforts and improve healthcare accessibility, particularly in challenging or resource-constrained environments.

Predictive Modeling of Environmental Impact on Drone Datalink Communication System

In this study, we introduce an innovative model for evaluating the impact of environmental factors on drone-to-ground control station datalink communications. Our approach integrates both deterministic and stochastic processes to account for small-scale and large-scale fading effects, encompassing propagation attenuation, the Rician fading model, and Gaussian noise to accurately reflect real-world conditions. The model is implemented on signals transmitted using spread spectrum modulation. Through a comparative analysis of the model’s predictions against actual signals received in three distinct environments, the model’s efficacy in diverse scenarios is affirmed. Error metrics obtained from Monte Carlo simulations are employed to validate the theoretical results against experimental data. The proposed approach is pivotal for predicting the transmission range and understanding the electromagnetic susceptibility of the datalink, offering a substantial contribution to the optimization of remote drone control.

Aerial spraying for downy mildew control in grapevines using a remotely piloted aircraft

Downy mildew is a major problem for grape growers, as this disease is difficult to control. Synthetic fungicides are used to treat downy mildew with handheld backpack and tractor sprayer applications, with high chemical exposure by operators. As important tools for maximising yield, application technologies must be studied to optimise control efficiency. The objective of this study was to evaluate the efficiency of fungicide spray application using Remotely Piloted Aircraft (RPA) for the control of downy mildew in grapevine, with different spray volumes. The study was divided into two experiments using 4 vine lines, 10 blocks and 5 treatments with different mixture volumes: Experiment 1 with RPA application of 5 mixture volumes - 0, 22, 44, 66, and 88 L ha−1; Experiment 2 with RPA application of 3 mixture volumes - 44, 66, and 88 L ha−1 and a backpack application of one mixture volume - 800 L ha−1. Coverage percentage, droplet density and volume median diameter (VMD) were evaluated. Downy mildew severity on grapevine leaves was assessed using visual analysis and a diagrammatic scale. The application of 44 L ha−1 provided the greatest coverage and droplet density in the upper and middle strata; however, the backpack application had a better droplet distribution than the RPA application. Treatments of 44 L ha−1 with RPA and backpack application (800 L ha−1) provided the best disease control. In the trellis system, RPA application must be improved because of the low coverage in the lower parts of the plant, and further studies with different spray nozzles and application heights are needed.

Aerodynamic response of a red-tailed hawk to discrete transverse gusts

A limiting factor in the design of smaller size uncrewed aerial vehicles is their inability to navigate through gust-laden environments. As a result, engineers have turned towards bio-inspired engineering approaches for gust mitigation techniques. In this study, the aerodynamics of a red-tailed hawk’s response to variable-magnitude discrete transverse gusts was investigated. The hawk was flown in an indoor flight arena instrumented by multiple high-speed cameras to quantify the 3D motion of the bird as it navigated through the gust. The hawk maintained its flapping motion across the gust in all runs; however, it encountered the gust at different points in the flapping pattern depending on the run and gust magnitude. The hawk responded with a downwards pitching motion of the wing, decreasing the wing pitch angle to between −20∘ and −5∘, and remained in this configuration until gust exit. The wing pitch data was then applied to a lower-order aerodynamic model that estimated lift coefficients across the wing. In gusts slower than the forward flight velocity (low gust ratio), the lift coefficient increases at a low-rate, to a maximum of around 2–2.5. In gusts faster than the forward flight velocity (high gust ratio), the lift coefficient initially increased rapidly, before increasing at a low-rate to a value around 4–5. In both regimes, the hawk’s observed height change due to gust interaction was similar (and small), despite larger estimated lift coefficients over the high gust regime. This suggests another mitigation factor apart from the wing response is present. One potential factor is the tail pitching response observed here, which prior work has shown serves to mitigate pitch disturbances from gusts.

SHM Implementation on a RPV Airplane Model Based on Machine Learning for Impact Detection

AbstractIn this study, an on-working structural health monitoring system for impact detection on remote piloted vehicle (RPV) airplane is proposed. The approach is based on the propagation of Lamb waves in metallic structures on which Pb[ZrxTi1−x]O3 (PZT) sensors are bonded for receiving vibrational signals due to impact events. The proposed method can be used to detect impacts in aerospace structures, i.e. skin fuselage and/or wing panels. After the detection, machine learning (ML) algorithms (polynomial regression and neural networks) are applied for processing the acquired ultrasounds waves in order to characterise the impacts, in terms of time of flight (ToF) and relative location. Several test cases are studied: the ML models are tested both without external noise (in laboratory) and introducing external RC engine vibration (on-working conditions). Furthermore, this work presents the implementation of a mini-equipment for acquisition and data processing based on Raspberry Pi. A good agreement between laboratory and in-flight results is achieved, in terms of distance between the actual and calculated impact location.

Survey of a snow petrel nesting site in a remote high mountain region to inform designation of an Antarctic Specially Protected Area

Despite the sensitivity of ecosystems in extreme conditions, only 0.029% of the Antarctic continent is currently granted special protection as Antarctic Specially Protected Area (ASPA). Additionally, protected areas are unevenly distributed and unrepresentative of the biodiversity of Antarctica. The Antarctic Treaty Consultative Parties are thus striving to establish a more coherent network of protected areas. In this context, the Otto-von-Gruber-Gebirge was identified as an area highly worthy of protection due to its unique ecosystem including one of the largest snow petrel breeding sites, and relative pristine nature. However, in the process of designation, an update of knowledge, particularly of the population status of snow petrel in the area, was recognized to be needed. This study was aimed at estimating a population census of snow petrel breeding pairs in the Lake Untersee catchment, an important subarea of the potentially protected area. Investigations were severely limited by the remoteness and harsh conditions of the area, enabling only a short fieldwork period with limited resources. Thus, a combination of remote sensing and traditional methods was applied. We conducted a ground survey of a smaller reference area, including locating nest sites and assessing nest site characteristics. Snow petrels usually nest in the cavities of large boulders and we thus classified habitat suitability based on boulder presence. To do so, the study area was remotely surveyed by unpiloted aerial vehicles to gain orthophotomosaics in sufficient resolution to distinguish on-ground conditions for breeding. We then calculated nest site densities for the reference area. We also studied nest site data such as nest cavity depth, orientation, and attendance to gain basic knowledge of the characteristics of the breeding site. Finally, we measured a sample of four live snow petrels to determine which morph was present at the site. We were able to obtain nest site data and to extrapolate it, which produced population numbers of 11,765 breeding pairs for the whole study area. This underpins the size of the breeding site and thereby its importance for the species.

Evaluating and locating a suitable bedrock drilling site near zhongshan station with airborne and ground-based observations

Understanding the interaction between the Antarctic biogeochemical cycle and global environmental change requires a comprehensive investigation of the ice sheet and underlying bedrock. An effective approach to investigating subglacial characteristics is through bedrock drilling, making the identification of a suitable drilling site a crucial prerequisite. In this study, data from airborne ice-penetrating radar and an Uncrewed Aerial Vehicle (UAV) remote sensing were used in combination to obtain real-time and high-resolution information about the surface, ice thickness, and subglacial conditions in a local area near Zhongshan Station, and further to identify a drilling site that is both safe and optimal for the successful acquisition of bedrock samples beneath the East Antarctic Ice Sheet. By analyzing the airborne ice-penetrating radar data from the 35th Chinese National Antarctic Research Expedition (CHINARE) (2018–2019), a potential drilling site was selected based on ice thickness (≤200 m) and a dry basal condition with no channels or basal crevasses, to avoid drilling fluid leakage. UAV data confirmed stable surface topography, ice flow, and the absence of surface crevasses. The optimal site was identified as located close to the western flank of the upstream part of the Dålk Glacier. In 2019, CHINARE successfully retrieved a valuable bedrock core sample from the borehole at this location. This study provides a feasible and effective methodology for locating a bedrock drilling site by using data from airborne ice-penetrating radar and UAV remote sensing.

Discontinuous Surface Ruptures and Slip Distributions in the Epicentral Region of the 2021 Mw7.4 Maduo Earthquake, China

Discontinuous Surface Ruptures and Slip Distributions in the Epicentral Region of the 2021 Mw7.4 Maduo Earthquake, China

Joint extension speed dictates bio-inspired morphing trajectories for optimal longitudinal flight dynamics.

Avian wing morphing allows dynamic, active control of complex flight manoeuvres. Previous linear time-invariant (LTI) models have quantified the effect of varying fixed wing configurations but the time-dependent effects of morphing between different configurations is not well understood. To fill this gap, I implemented a linear parameter-varying (LPV) model for morphing wing gull flight. This approach models the wing joint angles as scheduled parameters and accounts for nonlinear kinematic and gravitational effects while interpolating between LTI models at discrete trim points. With the resulting model, I investigated the longitudinal response associated with various joint extension trajectories. By optimizing the extension trajectory for four independent objectives (speed and pitch angle overshoot, speed rise time and pitch angle settling time), I found that the extension trajectory inherent to the gull wing does not guarantee an optimal response but may provide a sufficient response with a simpler mechanical implementation. Furthermore, the results indicated that gulls likely require extension speed feedback. This morphing LPV model provides insights into underlying control mechanisms, which may allow for avian-like flight in future highly manoeuvrable uncrewed aerial vehicles.

Assessment of Pre‐ and Post‐Fire Fuel Availability for Wildfire Management Based on L‐Band Polarimetric SAR

AbstractMany communities coexist with wildfires that lead to loss of lives, property, and ecosystem services. Remote sensing tools can aid disaster response and post‐event assessment, offering fire agencies opportunities for additional surveillance with radar, an all‐weather instrument that can image day or night. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas are imaged with NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar L‐band instrument. We test whether polarimetric radar can detect fire scars, burn severity, and different fuel types through its sensitivity to different scattering mechanisms. Polarimetric SAR products are moved into geographic information system‐friendly formats, and in lieu of available field measurements are analyzed alongside agency data showing fire perimeters, burn progression outlines, and soil burn severity. We find that the HV polarization returns and the primary scattering mechanism, quantified through the Cloude‐Pottier decomposition, are the most sensitive parameters. Higher HV values pre‐fire correspond well to areas of moderate and high soil burn severity, and the pattern of fire progression follows higher HV to some extent. Using an HV difference threshold of 1.5 dB, the Bobcat burn scar is identified at 0.70 accuracy when compared with the published fire perimeter. Alpha 1 Angle can also demonstrate sensitivity to soil burn severity pre‐ and post‐fire, showing vegetation types with increased surface scattering post‐fire, which can be used to map burn scars and track recovery by vegetation type.