Search Results (7)

This paper presents methods for a UAV-based survey of the site “Kuklica” near Kratovo, North Macedonia. Kuklica is a rare natural complex with earth pyramids, and because of its exceptional scientific, educational, touristic, and cultural significance, it was proclaimed to be a Natural Monument in 2008. However, after the proclamation, the interest in visiting this site and the threats in terms of its potential degradation rapidly grew, increasing the need for a detailed survey of the site and monitoring. Given the site’s small size (0.5 km²), the freely available satellite images and digital elevation models are not suitable for comprehensive analysis and monitoring of the site, especially in terms of the individual forms within the site. Instead, new tools are increasingly being used for such tasks, including UAVs (unmanned aerial vehicles) and LiDAR (Light Detection and Ranging). Since professional LiDAR is very expensive and still not readily available, we used a low-cost UAV (DJI Mini 4 Pro) to carry out a detailed survey. First, the flight path, the altitude of the UAV, the camera angle, and the photo recording intervals were precisely planned and defined. Also, the ground markers (checkpoints) were carefully selected. Then, the photos taken by the drone were aligned and processed using Agisoft Metashape software (v. 2.1.4), producing a digital elevation model and orthophoto imagery with a very high (sub-decimeter) resolution. Following this procedure, more than 140 earth pyramids were delineated, ranging in height from 1 to 2 m and to 30 m at their highest. At this stage, a very accurate UAV-based 3D model of the most remarkable earth pyramids was developed (the accuracy was checked using the iPhone 14 Pro LiDAR module), and their morphometrical properties were calculated. Also, the site’s erosion rate and flash flood potential were calculated, showing high susceptibility to both. The final goal was to monitor the changes and to minimize the degradation of the unique landscape, thus better protecting the geosite and its value. Full article

The stratospheric airship, as a near-space vehicle, is increasingly utilized in scientific exploration and Earth observation due to its long endurance and regional observation capabilities. However, due to the complex characteristics of the stratospheric wind field environment, trajectory planning for stratospheric airships is a significant challenge. Unlike lower atmospheric levels, the stratosphere presents a wind field characterized by significant variability in wind speed and direction, which can drastically affect the stability of the airship’s trajectory. Recent advances in deep reinforcement learning (DRL) have presented promising avenues for trajectory planning. DRL algorithms have demonstrated the ability to learn complex control strategies autonomously by interacting with the environment. In particular, the proximal policy optimization (PPO) algorithm has shown effectiveness in continuous control tasks and is well suited to the non-linear, high-dimensional problem of trajectory planning in dynamic environments. This paper proposes a trajectory planning method for stratospheric airships based on the PPO algorithm. The primary contributions of this paper include establishing a continuous action space model for stratospheric airship motion; enabling more precise control and adjustments across a broader range of actions; integrating time-varying wind field data into the reinforcement learning environment; enhancing the policy network’s adaptability and generalization to various environmental conditions; and enabling the algorithm to automatically adjust and optimize flight paths in real time using wind speed information, reducing the need for human intervention. Experimental results show that, within its wind resistance capability, the airship can achieve long-duration regional station-keeping, with a maximum station-keeping time ratio (STR) of up to 0.997. Full article

One of the crucial branches of activity at the Łukasiewicz Research Network—Institute of Aviation is developing a suborbital rocket vehicle capable of launching small payloads beyond the Earth’s atmosphere, reaching over 100 km in altitude. Ensuring safety is a primary concern, particularly given the finite flight zone and impact area. Crucial to safety analysis is the wind profile, especially in the very first seconds of a flight, when rocket velocity is of the same order as the wind speed. Traditional near-ground wind data sources, ranging from wind towers to numerical models of the atmosphere, have limitations. Wind towers are costly and unfeasible at many test ranges used for launches, while numerical modeling may not reflect the specific ground profile near the launcher due to their large cell size (2 to +10 km). Meteorological balloons are not favorable for such measurements as they aim to provide the launch operator with a wind profile at high altitudes, and are launched only 1–2 times per flight attempt. Our study sought to prototype a wind measurement system designed to acquire near-ground wind profile data. It focuses on measuring wind direction and speed at near-ground altitudes with higher flight frequency, offering data on demand shortly before launch to help ensure safety. This atmosphere sounding system consists of an Unmanned Aerial Vehicle (UAV) equipped with an onboard ultrasonic wind sensor. Some reports in the literature have discussed the possibility of using UAV-borne anemometers, but the topic of measurement errors introduced by placing the anemometer onboard an UAV remains under studied. Limited research in this area underlines the need for experimental validation of design choices–for specific types of UAVs, anemometers, and mounting. This paper presents a literature review, a detailed overview of the prototyped system, and flight test results in both natural (outdoor) and controlled (indoor, no wind) conditions. Data from the UAV system’s anemometer was benchmarked against a stationary reference weather station, in order to examine the influence of the UAV’s rotor on the anemometer readings. Our findings show a wind speed Root Mean Square Error (RMSE) of 5 m/s and a directional RMSE of below 5.3° (both averaged for 1 min). The results were also compared with similar UAV-based wind measurements. The prototyped system was successfully used in a suborbital rocket launch campaign, thus demonstrating the feasibility of integrating UAVs with dedicated sensors for performing regular meteorological measurements in automatic mode. Full article

Near space has attracted major countries’ attention due to the fact that it is a new cognitive space of Earth and acts as an important national security space. Near-space solar-powered unmanned aerial vehicles (UAVs) are becoming a focus of research in the aviation field. However, it is difficult for solar-powered UAVs to climb such high heights and achieving optimal cruising levels is challenging. A balloon-borne-based aircraft that rises with the help of a balloon avoids difficult climbing processes and initiates a new research direction in the near-space aviation domain. Simultaneously, the special mode of taking off poses a great challenge for the pull-up control of balloon-borne aircraft, especially for large wingspan aircraft. In this paper, we propose an adaptive launch control for the pull-up process of large-scale balloon-borne-based aircraft. First, the flight control of the pull-up process for a large-scale balloon-borne-based aircraft is analyzed. Then, a flight dynamic model considering elastic deformation is established. Finally, an adaptive aircraft pull-up control algorithm is proposed. We evaluate it by performing simulation experiments and comparing it with the latest control algorithm utilized in physical experiments. The experiment’s results demonstrate the effectiveness of the proposed algorithm with respect to overcoming challenges in controlling pull-up processes and its superiority compared to the latest control algorithm. Full article

In this study, different in-situ and close-range sensing surveying techniques were compared based on the spatial differences of the resultant datasets. In this context, the DJI Phantom 3 Advanced and Trimble UX5 Unmanned Aerial Vehicle (UAV) platforms, Zoller + Fröhlich 5010C phase comparison for continuous wave-based Terrestrial Laser Scanning (TLS) system and Network Real Time Kinematic (NRTK) Global Navigation Satellite System (GNSS) receiver were used to obtain the horizontal and vertical information about the study area. All data were collected in a gently (mean slope angle 4%) inclined, flat vegetation-free, bare-earth valley bottom near Istanbul, Turkey (the size is approximately 0.7 ha). UAV data acquisitions were performed at 25-, 50-, 120-m (with DJI Phantom 3 Advanced) and 350-m (with Trimble UX5) flight altitudes (above ground level, AGL). The imagery was processed with the state-of-the-art SfM (Structure-from-Motion) photogrammetry software. The ortho-mosaics and digital elevation models were generated from UAV-based photogrammetric and TLS-based data. GNSS- and TLS-based data were used as reference to calculate the accuracy of the UAV-based geodata. The UAV-results were assessed in 1D (points), 2D (areas) and 3D (volumes) based on the horizontal (X- and Y-directions) and vertical (Z-direction) differences. Various error measures, including the RMSE (Root Mean Square Error), ME (Mean Error) or MAE (Mean Average Error), and simple descriptive statistics were used to calculate the residuals. The comparison of the results is simplified by applying a normalization procedure commonly used in multi-criteria-decision-making analysis or visualizing offset. According to the results, low-altitude (25 and 50 m AGL) flights feature higher accuracy in the horizontal dimension (e.g., mean errors of 0.085 and 0.064 m, respectively) but lower accuracy in the Z-dimension (e.g., false positive volumes of 2402 and 1160 m³, respectively) compared to the higher-altitude flights (i.e., 120 and 350 m AGL). The accuracy difference with regard to the observed terrain heights are particularly striking, depending on the compared error measure, up to a factor of 40 (i.e., false positive values for 120 vs. 50 m AGL). This error is attributed to the “doming-effect”—a broad-scale systematic deformation of the reconstructed terrain surface, which is commonly known in SfM photogrammetry and results from inaccuracies in modeling the radial distortion of the camera lens. Within the scope of the study, the “doming-effect” was modeled as a functional surface by using the spatial differences and the results were indicated that the “doming-effect” increases inversely proportional to the flight altitude. Full article

When flight vehicles (e.g., aerospace vehicles, Low Earth Orbit (LEO) satellites, near-space aircrafts, Unmanned Aerial Vehicles (UAVs) and drones) fly at high speed, their surfaces suffer the micro-pressure from high-altitude thin air. The long-term effect of this pressure causes the surface components of flight vehicle to deform or fall off, which can lead to a serious accident. To solve this problem, this paper proposes a sensitivity-compensated micro-pressure flexible sensor based on hyper-elastic plastic material and plate parallel capacitance. The sensor is able to measure a range of 0–6 kPa micro-pressure suffered by the flight vehicle’s surface with high sensitivity and flexible devices. In this paper, we propose the principle, structure design and fabrication of the sensitivity-compensated micro-pressure flexible sensor. We carried out experiments to obtain the static characteristic curve between micro-pressure and the output capacitance of the sensor devices, and investigated the relationship between sensitivity and geometric parameters. We also compared the performance of the flexible sensor before and after sensitivity compensation. The result shows that the sensor can measure a range of 0–2 kPa and 2–6 kPa with a sensitivity of 0.27 kPa⁻¹ and 0.021 kPa⁻¹, which are 80% and 141.38% higher than the sensor before compensation; a linearity of 1.39% and 2.88%, which are 51.7% and 13.1% higher than the sensor before compensation; and a hysteresis and repeatability of 4.95% and 2.38%, respectively. The sensor has potential applications in flight vehicles to measure the micro-pressure with high sensitivity and flexibility. Full article

Strapdown inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation is a fully autonomous and high precision method, which has been widely used to improve the hitting accuracy and quick reaction capability of near-Earth flight vehicles. The installation errors between SINS and star sensors have been one of the main factors that restrict the actual accuracy of SINS/CNS. In this paper, an integration algorithm based on the star vector observations is derived considering the star sensor installation error. Then, the star sensor installation error is accurately estimated based on Kalman Filtering (KF). Meanwhile, a local observability analysis is performed on the rank of observability matrix obtained via linearization observation equation, and the observable conditions are presented and validated. The number of star vectors should be greater than or equal to 2, and the times of posture adjustment also should be greater than or equal to 2. Simulations indicate that the star sensor installation error could be readily observable based on the maneuvering condition; moreover, the attitude errors of SINS are less than 7 arc-seconds. This analysis method and conclusion are useful in the ballistic trajectory design of near-Earth flight vehicles. Full article