• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.450-456
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• 2013

This paper suggests the target tracking method improved for the collaboration of the quad rotor type UAV (Unmanned Aerial Vehicle) and omnidirectional Unmanned Ground Vehicle. If UAV shakes or UGV moves rapidly, the existing method generates a phenomenon that the tracking object loses the tracking target. To solve the problems, we propose an algorithm that can track continually when they lose the target. The proposed algorithm stores the vector of the landmark. And if the target was lost, the control signal was inputted so that the landmark could move continuously to the direction running out. Prior to the experiment, Proportional and integral control were used in 4 motors in order to calibrate the Heading value of the omnidirectional mobile robot. The landmark of UGV was recognized as the camera adhered to UAV and the target was traced through the proportional-integral-derivative control. Finally, the performance of the target tracking controller and proposed algorithm was evaluated through the experiment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.267-274
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• 2009

Recently, UAVs (Unmanned Aerial Vehicles) are expected much as the Unmanned Systems for various missions. These missions are often based on the Vision System. Especially, missions such as surveillance and pursuit have a process which is carried on through the transmitted vision data from the UAV. In case of small UAVs, monocular vision is often used to consider weights and expenses. Research of missions performance using the monocular vision is continued but, actually, ground and target model have difference in distance from the UAV. So, 3D distance measurement is still incorrect. In this study, Mean-Shift Algorithm, Optical Flow and Subspace Method are posed to estimate the relative depth. Mean-Shift Algorithm is used for target tracking and determining Region of Interest (ROI). Optical Flow includes image motion information using pixel intensity. After that, Subspace Method computes the translation and rotation of image and estimates the relative depth. Finally, we present the results of this study using images obtained from the UAV experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.367-371
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• 2004

This paper describes development of automatic flight control system for an unmanned target drone which is operated by Korean army as for anti-air gun shooting training. Current target drone is operated by pilot control of on-board servo motor via remote control system. Automatic flight control system for the target drone greatly reduces work load of ground pilot and can increase application area of the drone. Most UAVs being operated now days use high-priced sensors as AHRS and IMU to measure the attitude, but those are costly. This paper introduces the development of low-cost automatic flight control system with low-cost sensors. The integrated automatic flight control system has been developed by integrating combining power module, switching module, monitoring module and RC receiver as an one module. The performance of automatic flight control system is verified by flight test.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.189-196
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• 2024

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.262-269
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• 2016

Flight of an autonomous unmanned aerial vehicle (UAV) generally consists of four steps; take-off, ascent, descent, and finally landing. Among them, autonomous landing is a challenging task due to high risks and reliability problem. In case the landing site where the UAV is supposed to land is moving or oscillating, the situation becomes more unpredictable and it is far more difficult than landing on a stationary site. For these reasons, the accurate and precise control is required for an autonomous landing system of a UAV on top of a moving vehicle which is rolling or oscillating while moving. In this paper, a vision-only based landing algorithm using dynamic gimbal control is proposed. The conventional camera systems which are applied to the previous studies are fixed as downward facing or forward facing. The main disadvantage of these system is a narrow field of view (FOV). By controlling the gimbal to track the target dynamically, this problem can be ameliorated. Furthermore, the system helps the UAV follow the target faster than using only a fixed camera. With the artificial tag on a landing pad, the relative position and orientation of the UAV are acquired, and those estimated poses are used for gimbal control and UAV control for safe and stable landing on a moving vehicle. The outdoor experimental results show that this vision-based algorithm performs fairly well and can be applied to real situations.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.7-13
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• 2015

Unmanned air vehicles (UAVs) are getting popular not only as a private usage for the aerial photograph but military usage for the surveillance, reconnaissance and supply missions. For an UAV to successfully achieve these kind of missions, geolocation (localization) must be implied to track an interested target or fly by reference. In this research, we adopted multi-sensor fusion (MSF) algorithm to increase the accuracy of the geolocation and verified the algorithm using two multicopter UAVs. One UAV is equipped with an optical camera, and another UAV is equipped with an optical camera and a laser range finder. Throughout the experiment, we have obtained measurements about a fixed ground target and estimated the target position by a series of coordinate transformations and sequential Kalman filter. The result showed that the MSF has better performance in estimating target location than the case of using single sensor. Moreover, the experimental result implied that multi-sensor geolocation algorithm is able to have further improvements in localization accuracy and feasibility of other complicated applications such as moving target tracking and multiple target tracking.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.314-324
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• 2018

This paper proposes a linear distributed target tracking filter for multiple unmanned aerial vehicles(UAVs) sharing their passive sensor measurements through communication channels. Different from the conventional nonlinear filtering schemes, the distributed passive target tracking problem is newly formulated within the framework of a linear robust state estimation theory incorporated with a linear uncertain measurement equation including the coordinate transform uncertainty. To effectively cope with the performance degradation due to the coordinate transform uncertainty, a linear consistent robust Kalman filter(CRKF) theory is devised and applied for designing a distributed passive target tracking filter. Through the simulations for typical UAV surveillance mission, the superior performance of the proposed method over the existing schemes of distributed passive target tracking are demonstrated.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.24-34
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• 2011

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky or uninhabitable for human. UA V s are currently employed in many military missions such as reconnaissance, surveillance, enemy radar jamming, decoying, suppression of enemy air defense (SEAD), fixed and moving target attack, and air-to-air combat. UAVs also are employed in a number of civilian applications such as monitoring ozone depletion, inclement weather, traffic congestion, and taking images of dangerous territory. For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is to suggest a mathematical programming model and a $A^*PS$-PGA (A-star with Post Smoothing-Parallel Genetic Algorithm) for an UAV's path planning to maximize survivability. A mathematical programming model is composed by using MRPP (Most Reliable Path Problem) and TSP (Traveling Salesman Problem). A path planning algorithm for UAV is applied by transforming MRPP into SPP (Shortest Path Problem).

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.2
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• pp.12-17
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• 2017

Manned aircraft structural design is based on structural safety factor of 1.5, and this safety factor is equivalent to a probability of failure of between 10-2 and 10-3. The target failure probability of FARs is between 10-6 and 10-9 per flight according to aircraft type. NATO released STANAG 4703 to established the airworthiness requirements for small UAV which is less than 150kg. STANAG 4703 requires the Target Level of Safety according to MTOW. The requirements of failure probability for small UAV is between 10-4 and 10-5. In this paper, requirements of airworthiness certification for small UAV were investigated and the relationship of safety factors to the probability of structural failure is analyzed to reduce measure of safety factor and structural weight of unmanned aircraft.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.2
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• pp.47-53
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• 2018

In this paper, we investigate a collision avoidance algorithm for unmanned aerial vehicles using potential field based on the relative velocity of obstacles. The potential field consists of the attraction force and the repulsive force that are generated for the target and the obstacles. And the field can be classified into the attractive potential field generated by the target and the repulsive potential field generated by the obstacle, respectively. In this study, we construct an attractive potential field as a function of the distance between the UAV and the target position. On the other hand, a repulsive potential field is created by a function of distance and the relative velocity of the obstacle with respect to the UAV. The proposed potential field based collision avoidance algorithm is evaluate through simulations.