• Title/Summary/Keyword: Unmanned Quad-rotor

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Study on the Improved Target Tracking for the Collaborative Control of the UAV-UGV (UAV-UGV의 협업제어를 위한 향상된 Target Tracking에 관한 연구)

  • Choi, Jae-Young;Kim, Sung-Gaun
    • 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.

Visualization and Computational Analysis for Flow around Rotating Blades (회전하는 블레이드 주위의 유동가시화 및 전산유동해석)

  • Ki, Hyun;Choi, Jong-Wook;Kim, Sung-Cho
    • Journal of the Korean Society of Visualization
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    • v.8 no.1
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    • pp.39-45
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    • 2010
  • The optimal design is needed for the blade geometry of the quad-rotor blades which is mainly used for Unmanned Aerial Vehicle. To do this, it is important to analyze the wakes under the blades. In the present study, the flow around the rotating blades was analyzed using PIV(Particle Image Velocimetry) and CFD(Computational Fluid Dynamics). The maximum axial velocity was measured at about 60% position toward the radial direction of the blade. The positions of vorticities in the test section obtained by PIV and CFD were turned out to be almost alike. The values in the difference of pressure coefficients at the upper and the lower blades were increased depending on the radial direction. Then, the values were decreased at the blade tip. The data of the flow analysis in the present study are expected to be served as the design of blades and ducts for the thrust improvement in the future.

Tracking of Walking Human Based on Position Uncertainty of Dynamic Vision Sensor of Quadcopter UAV (UAV기반 동적영상센서의 위치불확실성을 통한 보행자 추정)

  • Lee, Junghyun;Jin, Taeseok
    • Journal of Institute of Control, Robotics and Systems
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    • v.22 no.1
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    • pp.24-30
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    • 2016
  • The accuracy of small and low-cost CCD cameras is insufficient to provide data for precisely tracking unmanned aerial vehicles (UAVs). This study shows how a quad rotor UAV can hover on a human targeted tracking object by using data from a CCD camera rather than imprecise GPS data. To realize this, quadcopter UAVs need to recognize their position and posture in known environments as well as unknown environments. Moreover, it is necessary for their localization to occur naturally. It is desirable for UAVs to estimate their position by solving uncertainty for quadcopter UAV hovering, as this is one of the most important problems. In this paper, we describe a method for determining the altitude of a quadcopter UAV using image information of a moving object like a walking human. This method combines the observed position from GPS sensors and the estimated position from images captured by a fixed camera to localize a UAV. Using the a priori known path of a quadcopter UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated quadcopter UAV's altitude. Since the equations are based on the geometric constraint equation, measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the quadcopter UAV. The Kalman filter scheme is applied for this method. Its performance is verified by a computer simulation and experiments.