• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.253-264
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• 1999

Most autonomous mobile robots view only things in front of them, and as a result, they may collide with objects moving from the side or behind. To overcome this problem. an Active Omni-directional Range Sensor System has been built that can obtain an omni-directional range data through the use of a laser conic plane and a conic mirror. Also, mobile robot has to know its current location and heading angle by itself as accurately as possible to successfully navigate in real environments. To achieve this capability, we propose a self-localization algorithm of a mobile robot using an active omni-directional range sensor in an unknown environment. The proposed algorithm estimates the current position and head angle of a mobile robot by a registration of the range data obtained at two positions, current and previous. To show the effectiveness of the proposed algorithm, a series of simulations was conducted and the results show that the proposed algorithm is very efficient, and can be utilized for self-localization of a mobile robot in an unknown environment.

• 한국해양공학회지
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• 제30권3호
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• pp.221-226
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• 2016

An attitude controller for a 6-DOF hovering autonomous underwater vehicle (HAUV) is implemented. We add a vertical thruster, an underwater camera, a wireless communication device, and a DVL to the HAUV that was developed a year ago. The HAUV is composed of 5 thrusters, 2 servo-motors, and 4 apparatus parts. Two rotating thrusters control the surge, heave, and roll of the vehicle. The vertical thruster controls the pitch, and two horizontal thrusters control the sway and yaw of the vehicle. The HAUV’s movement in each direction is controlled by 6 PID controllers. Each PID controller controls the propulsive force and angle of a thruster. In a horizontal and vertical movement experiment, we showed the feasibility of the proposed controller by maintaining a given depth and heading angle of the HAUV.

• 한국정밀공학회지
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• 제30권2호
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• pp.177-184
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• 2013

This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

• 한국해양공학회지
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• 제19권2호
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• pp.82-89
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• 2005

This study deals with the development of the extended Kalman filter(EKF) algorithm for the localization of underwater mining vehicles. Both simulation and experimental studies in a test bed are carried out. For the experiments, a scale dawn tracked vehicle is run in a soil bin containing cohesive soil of bentonite-water mixture. To develop the EKF algorithm, we use a kinematic model including the inner/outer track slips and the slip angle for the vehicle. The measurements include the inner and outer wheel speeds from encoders, the heading angle from a compass sensor and a fiber optic rate gyro, and x and y coordinate position values from a vision system. The vision sensor replaces the LBL(Long Base Line) sonar system used in the real underwater positioning situations. Artificial noise signals mimicking the real LBL noise signal are added to the vision sensor information. To know the mean slip values of the tracks in both straight and cornering maneuver, several trial running experiments are executed before applying the EKF algorithm. Experimental results show the effectiveness of the EKF algorithm in rejecting the sensor measurements noise. Also, the simulation and experimental results show close correlations.

• 대한조선학회논문집
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• 제34권3호
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• pp.19-26
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• 1997

본 논문에서는 신경망을 이용한 선박자동조타장치의 개발에 관한 연구결과를 소개한다. 앞의 논문에서 소개된 Back-Propagation 알고리즘을 이용하여 선박의 자동운항을 위한 자동제어방법을 개발하였으며 그 결과 기준모델추구신경망제어기와 순간최적제어기를 설계하였다. 기준모델추구신경망제어기는 선수각과 선수각속도가 주어진 기준모델을 추구하도록 타각을 제어하도록 하였으며, 순간최적제어기는 현 상태에서 다음상태로의 천이를 최적화하도록 하였다. 신경망에 근거한 이들 제어기법을 간단한 선박조종수치모델에 적용한 결과 그 효용성을 확인할 수 있었다.

• 자동차안전학회지
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• 제13권4호
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• 한국환경농학회지
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• 제42권4호
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• pp.311-323
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• 2023

Miscanthus sinensis Andersson (Poaceae) cultivars exhibit excellent visual appeal as ornamental grasses and adapt well to diverse environmental conditions. This study focused on assessing the growth response and landscape durability of seven popular Miscanthus cultivars ('Gold Breeze', 'Strictus', 'Morning Light', 'Variegatus', 'Gracillimus', 'Kleine Fontäne', 'Common') under drought, rainfall and low temperature condition. The test cultivars were transplanted and cultivated on research plots in 2013, with data collected from June 2017 to February 2018. Plant materials were categorized into three types based on the amount of the water lost; group I ('Kleine Fontäne', 'Variegatus', 'Strictus'), experiencing the most significant water loss; group II ('Common', 'Gracillimus'); and group III ('Gold Breeze', 'Morning Light') where the least water loss occurred. The drought resistance index (DRI) remained low as water shortage conditions persisted. The lodged angle underwent more pronounced changes in reproductive growth stage than in vegetative growth stage, notably decreasing after heading. Discoloration patterns were classified into two types: group I ('Common', 'Gold Breeze', 'Kleine Fontäne', 'Strictus') and group II ('Gracillimus', 'Morning Light', 'Variegatus') based on the periods of peak duration.

• Journal of Ship and Ocean Technology
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• 제11권2호
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• pp.1-9
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• 2007

This paper describes the design and development of a hovering AUV constructed at Cheju National University and analyses the dynamic performance of the vehicle using simulation programs. The main purpose of this AUV is to carry out fundamental tests in its station keeping, attitude control, and desired position tracking. Its configuration is similar to the general ROV appearance for underwater works and its dimensions are 0.75m*0.5m*0.5m. It has 4 thrusters of 450 watts for longitudinal/lateral/vertical propulsion and is equipped with a pressure sensor for measuring water depth and a magnetic compass for measuring heading angle. The navigation of the vehicle is controlled by an on-board Pentium III-class computer, which runs with the help of the Windows XP operating system. These give us an appropriate environment for developing various algorithms needed for developing and advancing Hovering AUV.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.241-244
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• 1997

A dead reckoning navigation system is developed for autonomous mobile robot localization. The navigation system was implemented by novel sensor fusion using a Kalman filter. A differential encoder and the gyroscope error models are developed for the filter. An indirect Kalman filter scheme is adopted to reduce the computational burden and to enhance the navigation system reliability. The filter mutually compensates the encoder errors and the gyroscope errors. The experimental results show that the proposed mobile . robot navigation algorithm provides the reliable position and heading angle of the mobile robot without any help of the external positioning systems.

• Journal of Advanced Marine Engineering and Technology
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• 제29권1호
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• pp.124-131
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• 2005

This paper suggests LOS(Line-Of-Sight) guidance system of a surface vessel in straight-line navigation under ocean currents An LOS vector from the vessel to a point on the path between two way-points is decided and a heading angle is calculated to converge to follow the desired path based on the LOS vector This guidance system is called LOS guidance system. The suggested LOS guidance law has parameters to be properly chosen according to navigational environment. Parameters of LOS guidance system are optimized to reduce propulsive energy and/or position error between desired Position and present position of a ship using genetic algorithm which is a strong optimization algorithm with adaptational random search The effectiveness of the suggested LOS guidance system is assured through computer simulations.