• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.581-589
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• 2015

This paper describes a monocular vision-based formation flight technology using two fixed wing unmanned aerial vehicles. To measuring relative position and attitude of a leader aircraft, a monocular camera installed in the front of the follower aircraft captures an image of the leader, and position and attitude are measured from the image using the KLT feature point tracker and POSIT algorithm. To verify the feasibility of this vision processing algorithm, a field test was performed using two light sports aircraft, and our experimental results show that the proposed monocular vision-based measurement algorithm is feasible. Performance verification for the proposed formation flight technology was carried out using the X-Plane flight simulator. The formation flight simulation system consists of two PCs playing the role of leader and follower. When the leader flies by the command of user, the follower aircraft tracks the leader by designed guidance and a PI control law, and all the information about leader was measured using monocular vision. This simulation shows that guidance using relative attitude information tracks the leader aircraft better than not using attitude information. This simulation shows absolute average errors for the relative position as follows: X-axis: 2.88 m, Y-axis: 2.09 m, and Z-axis: 0.44 m.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.5
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• pp.445-452
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• 2017

The wing leading edge skin in this research is an essential structural factor for improving wings' aeromechanical functions, protecting the interior elements of the wings from external damage including birds, and navigating planes safely. The study compared and reviewed models manufactured for optimal light-weight wings of composite UAVs. It compared and investigated displacement forms of torsion loads through finite element analysis using MSC. Patran/Nastran. By confirming the improvement of light-weighting performance according to lamination type, thickness change and shape through torsion strength tests of each model, the research suggested the optimal light-weight wing leading edge skin for small composite UAVs.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.9
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• pp.833-841
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• 2016

The 200 W electrically powered unmanned aerial vehicle, which is studied in this research, uses solar cells, a fuel cell and batteries as the main power source simultaneously. The output of each power source performs power control for each power source by the active power control method so that an adequate capacity of the battery could be maintained while limiting the maximum output of the fuel cell. The output variation for each power source under the active power control method was identified through an integrated ground test. In addition, the effect of limiting the maximum output of the fuel cell on the output variation of the entire system was experimentally identified, and it was confirmed that the adequate maximum output value of the fuel cell for preventing the overdischarge of six series-connected, small size batteries for fuel cell systems is 150 W.

• Composites Research
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• v.32 no.1
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• pp.45-49
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• 2019

Fiber reinforced composites fabricated by the resin film infusion (RFI) process, which is one of the outof-autoclave process, have the advantage of significantly reducing the processing cost in large structures while having excellent mechanical properties and uniform impregnation of the resin. In this study, we applied RFI carbon fiber composites to unmanned aerial vehicle structures to improve structural safety and achieve weight reduction. The tensile test results showed that the strength was 46% higher than that of generic T300 grade plain weave carbon fiber composites. As a result of the layup design and finite element analysis of the composite wing structure using the above material properties, the wing tip deflection is decreased by 31%, the structural safety factor is increased by 28% and the weight of the entire structure can be reduced by more than 10% compared to the reference model using glass fiber composite material.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.4
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• pp.300-308
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• 2019

For drones to be used for industrial or agricultural applications, it is necessary to increase the payload and endurance. Currently, the payload and endurance are limited by the battery technology for electric powered drones. In addition, charging or replacing the batteries may not be a practical solution at the field that requires near continuous operation. In this paper, a procedure to optimize the power system of an electric hybrid drone that consists of an internal combustion engine, a generator, a battery, and electric motors is presented. The example drone for crop dusting is sized for easy transportation with a maximum takeoff weight of 200 kg. The two main rotors that are mechanically connected to the internal combustion engine provides most of the lift. The drone is controled by four electric motors that are driven by the generator. By analyzing the flow of the energy, a methodology to select the optimum propeller and motor among the commercially available models is described. Then, a procedure of finding the optimum operational condition along with the proper gear reduction ratios for the internal combustion engine based on the test data is presented.

• Journal of Aerospace System Engineering
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• v.15 no.1
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• pp.102-110
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• 2021

Recently, multi-copters equipped with various collision avoidance sensors have been introduced to improve flight stability. LiDAR is used to recognize a three-dimensional position. Multiple cameras and real-time SLAM technology are also used to calculate the relative position to obstacles. A three-dimensional depth sensor with a small process and camera is also used. In this study, a small collision-avoidance multi-copter system capable of in-door flight was developed as a platform for the development of collision avoidance software technology. The multi-copter system was equipped with LiDAR, 3D depth sensor, and small image processing board. Object recognition and collision avoidance functions based on the YOLO algorithm were verified through flight tests. This paper deals with recent trends in drone collision avoidance technology, system design/manufacturing process, and flight test results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.971-980
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• 2021

Recently popularized drone technologies have revealed that low-cost small unmanned aerial vehicles(UAVs) can be a significant threat to prevailing power by operating in group or in swarms. Researchers in many countries have tried to utilize integrated swarm unmanned aerial system(SUAS) in the battlefield. Agency for Defense Development also identified four core technologies in developing SUAS: swarm control, swarm network, swarm information, and swarm collaboration, and the authors started researches on swarm control and network technologies in order to be able to operate vehicle platforms as the first stage. This paper introduces design and integration of SUAS consisting of small fixed-wing UAVs, swarm control and network algorithms, a ground control system, and a launcher, with which swarm control and network technologies have been verified by flight tests. 19 fixed-wing UAVs succeeded in swarm flight in the final flight test for the first time as a domestic research.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.6
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• pp.25-33
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• 2022

In this study, a monitoring method using high-resolution images acquired by unmanned aerial vehicles and deep learning algorithms was proposed for the management of the Sinduri coastal sand dunes. Class classification was done using U-net, a semantic division method. The classification target classified 3 types of sand dune vegetation into 4 classes, and the model was trained and tested with a total of 320 training images and 48 test images. Ignored label was applied to improve the performance of the model, and then evaluated by applying two loss functions, CE Loss and BCE Loss. As a result of the evaluation, when CE Loss was applied, the value of mIoU for each class was the highest, but it can be judged that the performance of BCE Loss is better considering the time efficiency consumed in learning. It is meaningful as a pilot application of unmanned aerial vehicles and deep learning as a method to monitor and manage sand dune vegetation. The possibility of using the deep learning image analysis technology to monitor sand dune vegetation has been confirmed, and it is expected that the proposed method can be used not only in sand dune vegetation but also in various fields such as forests and grasslands.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.93-104
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• 2023

Recently, the technology of drones is developing remarkably. The role of military drones is so great that they can cause serious damage to the enemy's important strategic assets without any damage to our allies in all battlefield environments (land, sea, air). However, the battleship combat management system currently operated by the Korean Navy is vulnerable to defense because there is no customized defense system against drones. As drones continue to develop, they are bound to pose a major threat to navy in the future. This paper proposes a way for the warfare software of naval combat management system sets a combat mode suitable for anti-drone battle, evaluates the threat priority in order to preemptively respond to drone threats and eliminate drone threats through automatic allocation of self-ship-mounted weapons and sensors, and through a test of the improved warfare software in a simulated environment, it was proved that the time to respond to the drone was improved by 62%.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.