Dong-in Han

Autonomous flight test using angle of UAV’s velocity vector

This paper describes the result of the guidance using the heading angle from the magnetic heading vector and velocity vector when autonomous flight is performed by the point navigation guidance. The guidance logic makes difference between the line of sight angle and UAVpsilas heading angle as an error using PD controller. In fixed-wing, the accuracy of convergence is influenced by perturbation like the crosswind, and the flight path to the next waypoint affected also by the states of approach. Therefore, the robust guidance is needed at autonomous flight. When flight using magnetic heading vector, the result is shown that the vehicle does not follow a expected path even though heading vector of UAV is pointing at waypoint. Moreover, the vehicle does not pass the waypoint but the UAV has circular flight path sometimes. When flight using velocity vector, from the result of flight, the flight performed with smaller radius at waypoint, even if side slip was occurred. Considering the UAVpsilas usage and ability, the flight path is significantly influenced by perturbation in windy area or closed to each waypoint. It causes a lot of losses in mission performance or operation of UAV, so autonomous flight needed using guidance performed by velocity vector based on flight path in UAV.

Vision Based Estimation of 3-D Position of Target for Target Following Guidance/Control of UAV

This paper describes methods to estimate 3-D position of target with respect to reference frame through monocular image from unmanned aerial vehicle (UAV). 3-D position of target is used as information for surveillance, recognition and attack. In this paper. 3-D position of target is estimated to make guidance and control law, which can follow target, user interested. It is necessary that position of target is measured in image to solve 3-D position of target. In this paper, kalman filter is used to track and output position of target in image. Estimation of target`s 3-D position is possible using result of image tracking and information of UAV and camera. To estimate this, two algorithms are used. One is methode from arithmetic derivation of dynamics between UAV, carmer, and target. The other is LPV (Linear Parametric Varying). These methods have been run on simulation, and compared in this paper.

Two-stage trajectory planning for stable image acquisition of a fixed wing uav

We propose a two-stage trajectory planning (TSTP) method using monocular vision for keeping continuous surveillance or reconnaissance of a target, and for relaying communication between a ground vehicle and an unmanned aerial vehicle (UAV). The cost function for optimal trajectory includes the variation of the altitude and speed of the UAV to prevent sudden maneuvering. The position of the target in the image is also included in a cost function for continuous observation. A virtual image can be calculated from the relationship between the coordinates of the UAV and those of the camera. The proposed cost function can be affected to a greater degree than in generic optimization problems because it has the position of the target in the image. Therefore, TSTP is introduced to mitigate the problem induced by the image included in the cost function. TSTP consists of two stages of optimization. The stability of the UAV, and the relative distance between the target and the UAV on the horizontal plane, are optimized in the first stage. The results are used as initial guesses for the second stage to improve optimality. The second stage optimizes the stability of the UAV and the position of the target in the image. Proportional-integral-derivative (PID) controllers are employed to follow the optimized trajectory in both the lateral and longitudinal directions. Our simulation results show stable optimized trajectories, with a stable image and target tracking performance.

Pose Estimation of Leader Aircraft for Vision-based Formation Flight

This paper describes a vision-based only attitude estimation technique for the leader in the formation flight. The feature points in image obtained from the X-PLANE simulator are extracted by the SURF(Speed Up Robust Features) algorithm. We use POSIT(Pose from Orthography and Scaling with Iteration) algorithm to estimate attitude. Finally we verify that attitude estimation using vision only can yield small estimated error of .

Surge Control of Turbofan Engine Compressor with the Variable Inlet Guide Vane

Surge phenomenon can be occurred in a compressor when compressor performance of turbofan engine for an aircraft is changed considerably in a short time on the cases like take-off phase and changing of RPM from idle to maximum, because performance of aircraft engine is changed suddenly. This study is aimed to avoid surge in a compressor. Dynamic simulation in a compressor is modeled by simulink in specific condition. Fuel flow is control input, rpm and air mass flow are expressed in terms of transfer function. Surge margin is obtained by using compressor performance map from NPSS. VIGV(Variable Inlet Guide Vane) is controlled by PD controller with difference between surge margin and reference. Finally this paper verifies IGV can prevent surge phenomenon in a compressor.

Monocular Vision-Based Guidance and Control for a Formation Flight

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.

Development of Car Type Classification Algorithm on the UAV platform using NCC

This paper describes the algorithm recognizing car type from the image received from UAV and the recognition results between three types of car images. Using the NCC(Normalized Cross-Correlation) algorithm, geometric information is matched from template images. Template images are obtained from UAV and satellite map and indoor experiment is performed using satellite map. After verification of the possibility, experiment for verification of same car type recognition is performed using small UAV. In the experiment, same type cars are matched with 0.6 point similarity and truck with similar color distribution is not matched with template image of a sedan.