• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.317-321
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• 2023

After 'emphasizing the development of the precision reconnaissance drone' in 2021, North Korea unveiled two strategic drones in July 2023, just two years later. Despite the majority of experts offering negative assessments and stating that "the performance may not be good," North Korea can be seen as having not only enhanced its routine surveillance capabilities through strategic drones but also possessing limited long-range strike capabilities. In other words, although the performance of North Korea's strategic unmanned aerial vehicles (UAVs), namely the 'Satbyol-4' and '9' models, may not match that of U.S. drones, they appear to play a significant role in offsetting North Korea's considerable aerial and surveillance inferiority compared to the joint forces of South Korea and the United States. Based on these trends, North Korea seems to be concentrating on drone development to counterbalance its considerable aerial power and surveillance capabilities deficit compared to the joint forces of South Korea and the United States, especially as the global use of drones continues to increase.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1325-1332
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• 2023

Recently, much research is being conducted based on point cloud data for the growth of innovations such as construction automation in the transportation field and virtual national space. This data is often measured through remote control in terrain that is difficult for humans to access using devices such as UAVs and UGVs. Drones, one of the UAVs, are mainly used to acquire point cloud data, but photogrammetry using a vision camera, which takes a lot of time to create a point cloud map, is difficult to apply in construction sites where the terrain changes periodically and surveying is difficult. In this paper, we developed a point cloud mapping system by adopting non-repetitive scanning LiDAR and attempted to confirm improvements through field application. For accuracy analysis, a point cloud map was created through a 2 minute 40 second flight and about 30 seconds of software post-processing on a terrain measuring 144.5 × 138.8 m. As a result of comparing the actual measured distance for structures with an average of 4 m, an average error of 4.3 cm was recorded, confirming that the performance was within the error range applicable to the field.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.5
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• pp.409-421
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• 2023

In the air power, UAVs have played a large and diversified role in performing missions from simple to high-level complex ones. In particular, the suppression of enemy air defenses(SEAD) is very dangerous for a pilot so it is expected that the manned-unmanned teaming(MUM-T) system with tailless stealthy unmanned aerial vehicle(UAV) will greatly enhance effectiveness of the mission while ensuring the pilot safe. This paper describes simulation studies of remote airborne control(RAC) environment for performing the SEAD mission by MUM-T, by which the air force pilot remotely controls tailless UAVs individually or small UAVs in swarm. Through this simulation, air force pilot can derive the concept of MUM-T mission operation with various UAVs in the future, and it can be used to upgrade the MUM-T system by verifying the effectiveness of the mission.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.47-55
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• 2024

The paper presents a docking-type automatic landing system that works in tandem with Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The system utilizes a pyramid-shaped landing gear and pad for effective landing. In marine environments, a docking device guides the drone to land securely. To test the system, a ship's behavior was simulated using a 3-DoF motion platform, and the successful operation and utility of the docking-type automatic landing system were demonstrated.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.740-745
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• 2023

This study proposes the development of an antenna tracker system using a satellite system to stabilize the communication status of drones and extend the communication distance. The location information of the drone and the ground station was used to maximize communication gain in the general fixed antenna method between the ground station and the drone. We developed a tracker system that can automatically and continuously aim the ground station's antenna at the drone. It is expected that the use of antenna trackers will improve the stabilization of communication conditions and expand the communication distance, thereby leading to the advancement of the drone industry.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.331-337
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• 2024

Mission autonomy system should be embedded on UAV (unmanned aerial vehicle) for mosaic warfare where UAVs autonomously assign tasks to themselves. UxAS (unmanned x-systems autonomy service) proposed by Air force research laboratory is mission autonomy system for unmanned platforms. UxAS has extensible structure composed of numerous module services. We have developed mission autonomy system based on UxAS that performs mission allocation and path planning. In this paper, We present a method of analyzing and evaluating the mission autonomy software according to the performance evaluation index.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.427-436
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• 2010

This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.

• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.49-57
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• 2020

The KC-100 has completed civil type certification with the Ministry of Land, Infrastructure, and Transport, and is currently under development as an unmanned aerial vehicle as part of the Ministry of Land, Infrastructure, and Transport. The Certification Technology of small Unmanned Airplane system (CTsUA system), which is an unmanned KC-100, is being developed to enable the installation of heavy-duty mission equipment and long-time flight missions. This study investigated the process and results of analyzing various parameters such as aircraft weight, airspeed, flight altitude, required horsepower, and fuel consumption at each stage to construct a mission profile based on the operational concept of the CTsUA system. To maintain a maximum take-off weight of 3,600 lbs (1,633 kg), the analysis determined that the weight of the application equipment for the unmanned system should be kept below 80 lbs (36 kg).