• 한국정보통신학회논문지
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• 제19권11호
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• pp.2554-2561
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• 2015

본 연구에서는 이동 로봇의 군집제어기에 관한 연구로써 구조가 단순한 PID 제어기의 장점을 살리고, 추종 로봇의 동역학 특성에 강인한 성능을 내는 적응 PID 제어기를 제안하고자 한다. 모의실험을 통하여 제안된 적응 PID 제어기가 일반적인 PID 제어기에 비하여 군집 제어에서 추종 로봇의 추종 성능인 일정 거리 와 일정 각도를 잘 유지하며, 추종 로봇의 무게가 변화될 경우에도 잘 추종함을 알 수 있다. 이는 제안된 적응 PID 제어기가 이득을 변화시켜 최적의 성능을 나타냄을 알 수 있다. 이를 통해 제안된 적응 PID 제어기의 성능이 우수함을 검증할 수 있다.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1668-1672
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• 2005

This paper presents the wall following control of a small indoor airship (blimp). The purpose of the wall following control is that a blimp maintains its position and pose and flies along the wall. A blimp has great inertia and it is affected by temperature, atmospheric pressure, disturbance and air flow around blimp. In order to fly indoors, a volume of blimp should be small. The volume of a blimp becomes small then the buoyancy of a blimp should be smaller. Therefore, it is difficult to attach additional equipments on the blimp which are necessary to control blimp. For these reasons, it is difficult to control the pose and position of the blimp during the wall following. In our research, to cope with its defects, we developed new blimp. Generally, a blimp is controlled by using rudders and elevators, however our developed blimp has no rudders and elevators, and it has faster responses than general blimps. Our developed blimp is designed to smoothly follow the wall by using low-cost small ultra sonic sensors instead of high-cost sensors. Finally, the controller is designed to robustly control the pose and position of the blimp which could control in spite of arbitrary disturbance during the wall following, and the effectiveness of the controller is verified by experiment.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1249-1255
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• 2004

A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.415-415
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• 2000

The functions of a mobile robot such as obstacle knowledge and collision avoidance for in-door cleaning are necessary features, as has been much studied in the field of industrial automatic guided vehicle or general mobile robot. A mobile robot, in order to avoid collision with obstacles, has to gather data with environment knowledge sensors and recognize environment and the shape of obstacles from the data. In the study, a wall-following algorithm was suggested as a autonomous moving algorithm in which a mobile robot can recognize obstacles in indoor like environment and do cleaning work in effect. The system suggested in the study is for cleaning of nuclear material dusts generated in the process of nuclear fuel manufacturing and decontamination of devices in disorder which is performed in M6 radioactive ray shield hot-cell in IMEF(Irradiated Material Examination Facility) in the Korea Atomic Energy Research Institute.

• 로봇학회논문지
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• 제6권3호
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• pp.255-262
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• 2011

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

• 제어로봇시스템학회논문지
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• 제20권9호
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• pp.957-963
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• 2014

This paper proposes a predictive control for an efficient human following robot using Kinect sensor. Especially, this research is focused on detecting of foot-end-point and foot-vector instead of human body which can be occluded easily by the obstacles. Recognition of the foot-end-point by the Kinect sensor is reliable since the two feet images can be utilized, which increases the detection possibility of the human motion. Depth image features and a decision tree have been utilized to estimate the foot end-point precisely. A tracking point average algorithm is also adopted in this research to estimate the location of foot accurately. Using the continuous locations of foot, the human motion trajectory is estimated to guide the mobile robot along a smooth path to the human. It is verified through the experiments that detecting foot-end-point is more reliable and efficient than detecting the human body. Finally, the tracking performance of the mobile robot is demonstrated with a human motion along an 'L' shape course.

• 한국지능시스템학회논문지
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• 제13권3호
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• pp.287-292
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• 2003

로봇의 기능이 다양해지며 복잡해지고 있다. 주위의 환경을 감지하는 센서로는 거리정보 뿐만 아니라 영상 정보, 음성 정보까지 이용하고 있다. 본 논문에서는 다양한 입력정보를 가진 로봇을 제어하기 위한 알고리즘으로 2-layer fuzzy control을 제안한다. 장애물 회피의 경우에 다수의 거리 센서를 이용하는데 이것을 앞쪽, 왼쪽, 오른쪽으로 분류하여 3개의 sub-controller를 가지고 퍼지 추론을 한 다음, 2단계에서는 이 3개의 sub-controller의 출력으로 조합된 퍼지 추론을 하여 통합적인 제어를 한다. 본문에서는 2-layer fuzzy controller와 비슷한 구조를 갖는 hierarchical fuzzy controller와 비교를 하였으며 robot following에도 적용하여 각각에 대한 시뮬레이션과 실험을 통해 성능을 확인한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.49.1-49
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• 2002

$\textbullet$ Intelligent Space 1. Optimal Camera Arrangement 2. People Tracking 3. Physical Robot 4. Robot Control 5. People Following Robot $\textbullet$ Initial stage for making high-level human robot interaction. http://dfs.iis.u-tokyo.ac.jp/∼leejooho/ispace/.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.83.6-83
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• 2002

$\textbullet$ Disturbance observer $\textbullet$ Performance enhancement $\textbullet$ LFT formulation $\textbullet$ Structured singular value $\textbullet$ Maximum bandwidth of DOB $\textbullet$ DVD experiments $\textbullet$ Robust stable disturbance observer