Abstract
In order to improve the endurance of unmanned aerial vehicles (UAVs), a power supply controller with automatic switching between solar energy and battery was designed. The controller is ...mainly composed of an AT89C51 single chip microcomputer. By collecting ambient light intensity and comparing it with the preset light intensity of the system, the controller completes automatic switching of the power supply for solar unmanned aerial vehicles, thereby achieving continuous and stable power supply for solar unmanned aerial vehicles. In addition, the implementation method of the single-chip microcomputer program in the controller is also introduced. The control core is to use Schmidt de shake control to switch between solar energy and battery, and to test the various functions of the controller through the flight experiment of a small solar powered unmanned aerial vehicle. Tests have found that solar powered drones using this controller can navigate for a long time in weather with varying light intensity, and the device operates well, effectively improving the power stability of solar powered drones.
We propose a magnetometer system fitted on an unmanned aerial vehicle (UAV, or drone) and a data-processing method for detecting metal antipersonnel landmines (M16) in the demilitarized zone (DMZ) in ...Korea, which is an undeveloped natural environment. The performance of the laser altimeter was improved so that the drone could fly at a low and stable altitude, even in a natural environment with dust and bushes, and a magnetometer was installed on a pendulum to minimize the effects of magnetic noise and vibration from the drone. At a flight altitude of 1 m, the criterion for M16 is 5 nT. Simple low-pass filtering eliminates magnetic swing noise due to pendulum motion, and the moving average method eliminates changes related to the heading of the magnetometer. Magnetic exploration was conducted in an actual mine-removal area near the DMZ in Korea, with nine magnetic anomalies of more than 5 nT detected and a variety of metallic substances found within a 1-m radius of each detection site. The proposed UAV-based landmine detection system is expected to reduce risk to detection personnel and shorten the landmine-detection period by providing accurate scientific information about the detection area prior to military landmine-detection efforts.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In order to improve the reliability and performance of photovoltaic systems, a fault diagnosis method for photovoltaic modules based on infrared images and improved MobileNet‐V3 is proposed. Firstly, ...the defect images of open‐source photovoltaic modules and their existing problems are analysed; based on the existing problems, image enhancement and data enhancement are performed on the infrared defect images of photovoltaic modules, so that the infrared images meet the requirements of image availability and sample quantity. Finally, the basic MobileNet‐V3 network is improved to realize fault classification of photovoltaic modules. The experimental results show that, compared with the traditional CNN and the basic MobileNet V3, the proposed fault classification method not only has high accuracy and fast diagnosis speed, but also has a high recognition rate for various fault categories, which has good practicability and application value.
In order to improve the reliability and performance of photovoltaic systems, a fault diagnosis method for photovoltaic modules based on infrared images and improved MobileNet‐V3 is proposed. The fault classification method not only has high accuracy and fast diagnosis speed, but also has a high recognition rate for various fault categories, which has good practicability and application value.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be ...limited due to the ground sampling resolution or cloud cover. Straightforward and accurate surveillance methods are needed to quantify rates of grass invasion, offer appropriate vegetation tracking reports, and apply optimal control methods. This paper presents a pipeline process to detect and generate a pixel-wise segmentation of invasive grasses, using buffel grass (Cenchrus ciliaris) and spinifex (Triodia sp.) as examples. The process integrates unmanned aerial vehicles (UAVs) also commonly known as drones, high-resolution red, green, blue colour model (RGB) cameras, and a data processing approach based on machine learning algorithms. The methods are illustrated with data acquired in Cape Range National Park, Western Australia (WA), Australia, orthorectified in Agisoft Photoscan Pro, and processed in Python programming language, scikit-learn, and eXtreme Gradient Boosting (XGBoost) libraries. In total, 342,626 samples were extracted from the obtained data set and labelled into six classes. Segmentation results provided an individual detection rate of 97% for buffel grass and 96% for spinifex, with a global multiclass pixel-wise detection rate of 97%. Obtained results were robust against illumination changes, object rotation, occlusion, background cluttering, and floral density variation.
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In honey bees, drones disperse to specific locations in order to mate before returning to their natal nests. However, in apiaries of Apis mellifera, drones have been witnessed returning to non-natal ...nests; this behavior may also sometimes occur at natural nests. This behavior could be due to the unnaturally high density of nests in apiaries. In this study, we genotyped colonies of the Apis cerana honey bee collected in its invasive range of Far North Queensland, Australia, to determine whether there is evidence of drone drift between colonies. We found no such evidence of drone drift. Some drones that do not match the resident queen's genotype are instead likely due to queen turnover or worker reproduction. The modes through which individuals disperse prior to reproduction has important consequences for gene flow in populations. In honey bees (Apis sp.), drones (males) reproduce within a short flight range of their natal nest, leaving and returning each afternoon within a narrow mating window. Drones are assumed to return to their natal nests as they depend on workers to feed them. However, in apiaries, drones are reported to regularly make navigation errors and return to a non-natal nest, where they are accepted and fed by unrelated workers. If such a "drone drift" occurred in wild populations, it could facilitate some further degree of dispersal for males, particularly if drones drift into host nests some distance away from their natal nest. Here, we investigated whether drone drift occurs in an invasive population of the Asian honey bee (Apis cerana). Based on the genotypes of 1462 drones from 19 colonies, we found only a single drone that could be considered a candidate drifter (~0.07%). In three other colonies, drones whose genotypes differed from the inferred queen were best explained by recent queen turnover or worker-laying. We concluded that drone drift in this population is low at best, and A. cerana drones either rarely make navigation errors in wild populations or are not accepted into foreign nests when they do so. We therefore confirm that drone dispersal distance is limited to the distance of daily drone flights from natal nests, a key assumption of both colony density estimates based on sampling of drone congregation areas and population genetic models of gene flow in honey bees.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
With the development of UAV technology, the task allocation problem of multiple UAVs is remarkable, but most of these existing heuristic methods are easy to fall into the problem of local ...optimization. In view of this limitation, deep transfer reinforcement learning is applied to the task allocation problem of multiple unmanned aerial vehicles, which provides a new idea about solving this kind of problem. The deep migration reinforcement learning algorithm based on QMIX is designed. The algorithm first compares the target task with the source task in the strategy base to find the task with the highest similarity, and then migrates the network parameters obtained from the source task after training, stored in the strategy base, so as to accelerate the convergence of the QMIX algorithm. Simulation results show that the proposed algorithm is significantly better than the traditional heuristic method of allocation in terms of efficiency and has the same running time.
Unmanned aerial vehicles (UAV, drones) used as delivery vehicles have received increasing attention due to their mobility and accessibility to remote areas. The purpose of this study is to evaluate ...the environmental impacts of drone versus motorcycle delivery and to compare the expected environmental improvements due to drone delivery in urban and rural areas. In addition, the potential environmental contributions of electric motorcycles were assessed to determine the effects of introducing this new type of vehicle. Changes in the national electricity generation plan were also examined. The results showed that global warming potential (GWP) per 1 km delivery by drone was one-sixth that of motorcycle delivery, and the particulates produced by drone delivery were half that of motorcycle delivery. The actual environmental impact reduction in consideration of the delivery distance was 13 times higher in a rural area than in an urban area. Increasing the use of environmentally friendly electricity systems, such as solar and wind power, would further enhance the environmental effects of a drone delivery system.
Unmanned aerial vehicles (UAVs), also known as drones, have already been widely implemented in wireless networks for promoting network performance and enabling new services. To efficiently explore ...the diversity introduced by the mobility of UAV, many efforts have been made in the design of the UAV trajectory under various wireless scenarios. However, the continuity of a UAV trajectory in both time and topology forces researchers to approximate the UAV trajectory by a discrete model, which always results in a sub-optimal solution. To tackle the difficulty and obtain the optimal trajectory, in this work we introduce an artificial potential field (APF) to reformulate the objective in trajectory design, with which the UAV trajectory problem can be completely equivalent to a mechanical problem. In such mechanical problem, the UAV trajectory is represented by an extremely soft and thin rope with variable density carrying UAV speed information, and the original objective of optimizing the system performance is transformed to minimizing the overall artificial potential energy on the rope. As a result, the rope in the optimal solution stays in a state of equilibrium and the UAV trajectory can be equivalently optimized by designing the shape of a rope under the APF via mechanical principles. We provide a case study to describe in detail the problem equivalence, i.e., taking a single-user network as an example in which the throughput between UAV and the user is considered as the objective performance. In particular, the optimal trajectory of a UAV is constructed based on mechanical principles, while the global optimality is also rigorously proved and further confirmed via simulations. Moreover, we also highlight that the novel strategy of constructing equivalent mechanical problem has the possibilities to be extended to various UAV trajectory problems under different scenarios with different performance optimization objectives.
In this paper, we present a novel multiclass-target classification method for mmWave frequency modulated continuous wave (FMCW) radar operating in the frequency range of 77 - 81 GHz, based on custom ...range-angle heatmaps and machine learning tools. The elevation field of view (FoV) is increased by orienting the Radar antennas in elevation. In this orientation, the radar focuses the beam in elevation to improve the elevation FoV. The azimuth FoV is improved by mechanically rotating the Radar horizontally, which has antenna elements oriented in the elevation direction. The data from the Radar measurements obtained by mechanical rotation of the Radar in Azimuth are used to generate a range-angle heatmap. The measurements are taken in a variety of real-world scenarios with various objects such as humans, a car, and an unmanned aerial vehicle (UAV), also known as a drone. The proposed technique achieves accuracy of 97.6 % and 99.6 % for classifying the UAV and humans, respectively, and accuracy of 98.1 % for classifying the car from the range-angle FoV heatmap. Such a Radar classification technique will be extremely useful for a wide range of applications in cost-effective and dependable autonomous systems, including ground station traffic monitoring and surveillance, as well as control systems for both on-ground and aerial vehicles.
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