• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.164-170
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• 2013

Quadrotor UAV (Unmanned Aerial Vehicle) is a flying robotic platform which has drawn lots of attention in the recent years. The attraction comes from the fact that it is able to perform agile VTOL (Vertical Take-Off Landing) and hovering functions. In addition, the efficient modular structure composed of four electric rotors makes its design easier compared to other single-rotor type helicopters. In many cases, a quadrotor often utilizes vision systems in order to obtain altitude control and navigation solution in hostile environments where GPS receivers are not working or deniable. For carrying out their successful missions, it is essential for flight control systems to have fast and stable control responses of heading angle outputs. This paper presents a Fuzzy Logic based lateral PI controller to stabilize and control the quadrotor vehicle equipped with vision systems. The advantage of using the fuzzy based PI controller lies in the fact that it could acquire a desired output response of a heading angle even in presence of disturbances and uncertainties. The performance comparison of the newly proposed Fuzzy-PI controller and the conventional PI controller was carried out with various simulation results.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.493-498
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• 2020

The fourth industrial revolution is the first topic thrown by Klaus Schwab at the Davos World Economic Forum in January 2016, meaning the next industrial revolution led by the Internet of Things (IOT), artificial intelligence (AI), robot technology and life sciences. In addition, in our lives, humans, computers and machines are connected organically, and organic relationships are evolving and developing at a furious rate in all areas of life. Since the 1953 armistice agreement, South Korea has remained in a state of confrontation with North Korea, and there have been continued fighting by the North, including naval skirmishes in the West Sea, artillery attacks on Yeonpyeong Island, the sinking of the Cheonan warship, and unmanned aerial vehicles and ankle mines. To prepare for such a local initiative, our military is constantly preparing and will have to strengthen its combat capabilities by developing and introducing advanced military equipment. After all, the military drone industry linked to the Fourth Industrial Revolution following the development of new war should continue its research on military drones in line with accurate diagnosis and the rapid development of future science and technology and IT technologies.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.37-38
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• 2017

최근 방제 및 예찰과 같은 농작업에 단일 UAV(Unmanned Aerial Vehicle)시스템이 적용되고 있지만, 가반하중과 체공시간 등 기존시스템의 문제가 점차 대두되면서 작업 시간을 보다 단축시키고 작업 효율을 극대화 할 수 있는 농업용 멀티 UAV시스템의 필요성이 증대되고 있다. 본 논문에서는 작업자가 다수의 농업용 UAV를 효과적으로 제어할 수 있는 분산군집제어 알고리즘을 제안하며 알고리즘 검증 및 평가를 위한 시뮬레이터를 소개한다. 분산군집제어는 UAV 제어 계층, VP(Virtual Point) 제어 계층, 원격제어 계층으로 이루어진 3계층 제어구조를 가진다. UAV 제어 계층에서 각 UAV는 point mass로 모델링 되는 VP의 이상적인 경로를 추종하도록 제어한다. VP 제어 계층에서 각 VP는 입력 $p_i(t)=u^c_i+u^o_i+u^{co}_i+u^h_i$-(1)을 받아 제어되는데 여기서, $u^c_i{\in}{\mathbb{R}}^3$는 VP 사이의 충돌방지제어, $u^o_i{\in}{\mathbb{R}}^3$는 장애물과의 충돌방지제어, $u^{co}_i{\in}{\mathbb{R}}^3$는 UAV 상호간의 협조제어, $u^h_i{\in}{\mathbb{R}}^3$는 작업자로부터의 원격제어명령이다. (1)의 제어입력에서 충돌방지제어는 각 $u^i_c:=-{\sum\limits_{j{\in}{\eta}_i}}{\frac {{\partial}{\phi}_{ij}^c({\parallel}p_i-p_j{\parallel})^T}{{\partial}p_i}}$-(2), $u^o_c:=-{\sum\limits_{r{\in}O_i}}{\frac {{\partial}{\phi}_{ir}^o({\parallel}p_i-p^o_r{\parallel})^T}{{\partial}p_i}}$-(3)로 정의되면 ${\phi}^c_{ij}$와 ${\phi}^o_{ir}$는 포텐셜 함수를 나타낸다. 원격제어 계층에서 작업자는 햅틱 인터페이스를 통해 VP의 속도를 제어하게 된다. 이때 스케일변수 ${\lambda}$에 대하여 VP의 원격제어명령은 $u^t_i(t)={\lambda}q(t)$로 정의한다. UAV 시뮬레이터는 리눅스 환경에서 ROS(Robot Operating Systems)를 기반한 3차원 시뮬레이터인 Gazebo상에 구축하였으며, 마스터와 슬레이브 간의 제어 명령은 TCPROS를 통해 서로 주고받는다. UAV는 PX4 기반의 3DR Solo 모델을 사용하였으며 MAVROS를 통해 MAVLink 통신 프로토콜에 접속하여 UAV의 고도, 속도 및 가속도 등의 상태정보를 받을 수 있다. 현재 멀티 드론 시스템을 Gazebo 환경에 구축하였으며, 추후 시뮬레이터 상에 분산군집제어 알고리즘을 구현하여 검증 및 평가를 진행하고자 한다.