• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.797-804
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• 2013

In this paper, a study of a new hovering six dof underwater robot with redundant horizontal thrusters, titled HAUV (hovering AUV), is presented. The results of study on the structure design, deployment of thrusters, and development of the developed control system of the AUV was presented. For the HAUV structure, a structure design and an analysis of the thrusting system was performed. For navigation, a sensor fusion board which can proceed various sensor signals to identify correct positions and speeds was developed and a total control system including EKF (Extended Kalman Filter) was designed. Rolling, pitching and depth control tests of the HAUV have been performed, and relatively small angle error and depth tracking error results were shown.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.10
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• pp.1551-1560
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• 1997

This study is devoted to the detection of the 3-dimensional point obstacles on the plane by using accumulated scan line images. The proposed accumulating only one scan line allow to process image at real time. And the change of motion of the feature in image is small because of the short time between image frames, so it does not take much time to track features. To obtain recursive optimal obstacles position and robot motion along to the motion of camera, Kalman filter algorithm is used. After using Kalman filter in case of the fixed environment, 3-dimensional obstacles point map is obtained. The position and motion of moving obstacles can also be obtained by pre-segmentation. Finally, to solve the stereo ambiguity problem from multiple matches, the camera motion is actively used to discard mis-matched features. To get relative distance of obstacles from camera, parallel stereo camera setup is used. In order to evaluate the proposed algorithm, experiments are carried out by a small test vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.81-81
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• 2000

We use camera to apply human vision system in measurement. To do that, we need to know about camera parameters. The camera parameters are consisted of internal parameters and external parameters. we can fix scale factor&focal length in internal parameters, we can acquire external parameters. And we want to use these parameters in automatically driven vehicle by using camera. When we observe an camera parameters in respect with that the external parameters are important parameters. We can acquire external parameter as fixing focal length&scale factor. To get lane coordinate in image, we propose a lane detection filter. After searching lanes, we can seek vanishing point. And then y-axis seek y-sxis rotation component(${\beta}$). By using these parameter, we can find x-axis translation component(Xo). Before we make stepping motor rotate to be y-axis rotation component(${\beta}$), '0', we estimate image coordinates of lane at (t+1). Using this point, we apply this system to Kalman filter. And then we calculate to new parameters whick make minimum error.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.326-333
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• 2001

This paper focuses on a method to connect mobile devices such as mobile robot. Automated Guided Vehicle (AGV) and Unmanned Container Transporter(UCT) to a fieldbus. In this paper, the IEEE 802.11 wireless LAN is used to extend a Profibus network for the mobile devices. In order to integrate these two networks, a gateway is developed using two threads and an internal buffer. Furthermore, a polling algorithm is applied at the gateway in order to satisfy real-time requirements on data communication, Finally, the performance measures such as data latency and throughput are experimentally evaluated on a wirelessly-extended Profibus network. The results shows the feasibility of the wireless extension of Profilbus for various mobile device.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.23-28
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• 1997

We discuss development of a simulator for automated manufacturing systems (AMSs) which have sophisticated automated material handling equipments and complicated work flows. The simulator is designed to satisfy the following requirements. A user should be able to easily configure or specify an AMS through a graphical user interface (GUI) and minimal data input. The user should be able to model diverse and complied control logic for automated material handling systems like automated guided vehicle (AGV) systems, robot workcell systems and conveyor systems as well as complicated job flow program. Real time animation is desired. Finally, the simulator should be easily maintained and extended. To satisfy the requirements, we use an object-oriented paradigm for modeling, designing, and programming of the simulator. We use an object-oriented modeling framework to design the modeling elements library, and take the process interaction approach for scheduling processes and events. To model a user-defined diverse control logic, we also develop a script language and its interpreter. We explain design and implementation strategies. We implement the simulator using Visual C++ 4.2 and Open GL on Windows NT and the Windows95. Some modeling examples will be demonstrated.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.440-445
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• 2010

This paper proposes a single accelerometer sensor control algorithm to mobile inverted pendulum, generally called 'Segway', and evaluates the performance of this system comparing to the conventional ones. The commercialized 'Prototype Segway-PT' is initially considered as a next-generation transport vehicle. However, this robot is operated by three gyroscopes and two accelerometers to control the posture and speed, and it requires the complex signal processing for fusing the two sets of data. As the result of this, the growth rate of market size of 'Segway' is slow because of its high price mainly. In this paper, the mobile inverted pendulum is operated by a single accelerometer to simplify the control system to lower the price. Low pass filter is one of the good sensors to reducing the variation of an accelerometer, but it has time delay. This time delay disturbs real-time mobile inverted pendulum control. Like this, other various algorithms are used for this system, but each one has its own weak point. So this paper proposes a new filtering method, median filter and EKF. Median filter is used to image processing to reject impulse elements like salt and pepper noise. As the major performance evaluation parameter for the accelerometer, the high-frequency to low frequency ratio from FFT (Fast Fourier Transform) is used. Effectiveness of the proposed algorithms has been verified through the real experiments and the results are demonstrated.

• The Journal of Korea Robotics Society
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• v.11 no.2
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• pp.51-59
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• 2016

In this paper we present (1) analysis of imaging sonar measurement for two-view relative pose estimation of an autonomous vehicle and (2) bundle adjustment and 3D reconstruction method using imaging sonar. Sonar has been a popular sensor for underwater application due to its robustness to water turbidity and visibility in water medium. While vision based motion estimation has been applied to many ground vehicles for motion estimation and 3D reconstruction, imaging sonar addresses challenges in relative sensor frame motion. We focus on the fact that the sonar measurement inherently poses ambiguity in its measurement. This paper illustrates the source of the ambiguity in sonar measurements and summarizes assumptions for sonar based robot navigation. For validation, we synthetically generated underwater seafloor with varying complexity to analyze the error in the motion estimation.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.965-973
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• 2011

Small unmanned ground vehicles(SUGVs) are typically operational on unstructured environments such as crashed building, mountain area, caves, and so on. On those terrains, driving control can suffer from the unexpected ground disturbances which occasionally lead turnover situation. In this paper, we have proposed an algorithm which sustains driving stability of a SUGV as preventing from turnover. The algorithm exploits potential field method in order to determine the stability of the robot. Then, the flipper and manipulator posture of the SUGV is optimized from local optimization algorithm known as gradient descent method. The proposed algorithm is verified using 3D dynamic simulation, and results showed that the proposed algorithm contributes to driving stability of SUGV.

• Journal of Ocean Engineering and Technology
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• v.31 no.2
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• pp.170-176
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• 2017

In this paper, we propose an underwater localization scheme through the fusion of an inertial navigation system (INS) and the received signal strength (RSS) of electromagnetic (EM) wave sensors to guarantee precise localization performance with high sampling rates. In this localization scheme, the INS predicts the pose of the unmanned underwater vehicle (UUV) by dead reckoning at every step, and the RF sensors corrects the UUV position functions using the Earth-fixed reference when the UUV is located in underwater wireless sensor networks (UWSN). The localization scheme and state modeling were conducted in the extended Kalman filter framework, and UUV localization experiments were conducted in a basin environment. The scheme achieved reliable localization accuracy during long-term navigation, demonstrating the feasibility of exploiting EM wave attenuation as Earth-fixed reference sensors.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.