• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.3
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• pp.262-268
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• 2020

The IMO (International Maritime Organization) has mandated the restriction of SOx emissions to 0.5 % for all international sailing vessels since January 2020. And, a number of countries have designated emission control areas for stricter environmental regulations. Three representative methods have been suggested to cope with these regulations; using low-sulphur oil, installing a scrubber, or using LNG (Liquefied Natural Gas) as fuel. In this paper, economic analysis was performed by comparing the method of installing a scrubber with the method of using low-sulphur oil without installing additional equipment. We suggested plausible layouts and compared the pros and cons of dif erent scrubber types for retrofitting. We selected an international sailing ship as the target vessel and estimated payback time and benefits based on navigation route, fuel consumption, and installation and operation costs. Two case of oil prices were analyzed considering the uncertainty of fuel oil price fluctuation. We found that the expected payback time of investment varies from 1 year to 3.5 years depending on the operation ratio of emission control areas and the fuel oil price change.

• Journal of Astronomy and Space Sciences
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• v.15 no.2
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• pp.437-447
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• 1998

Orbit error analysis was performed for the GPS navigation solutions and ground station tracking data of the KOMPSAT (Korea Multi-Purpose SATellite), which will be launched in 1999 for cartography of Korean peninsula as main mission. A least square method was used for the orbit determination and prediction error simulation including tracking data noises and dynamic modeling errors. It was found that a short-term periodic orbit determination error was caused by the tracking data noise and dominant orbit prediction error was caused by solar flux uncertainty.

• The Journal of Korea Robotics Society
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• v.6 no.1
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• pp.42-48
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• 2011

It is very important for a mobile robot to recognize and model its environments for navigation. However, the grid map constructed by sonar sensors cannot accurately represent the environment, especially the narrow environment, due to the angular uncertainty of sonar data. Therefore, we propose a map building scheme which combines sonar sensors and IR sensors. The maps built by sonar sensors and IR sensors are combined with different weights which are determined by the degree of translational and rotational motion of a robot. To increase the effectiveness of sensor fusion, we also propose optimal sensor arrangement through various experiments. The experimental results show that the proposed method can represent the environment such as narrow corridor and open door more accurately than conventional sonar sensor-based map building methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.39-44
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• 1998

Determining the absolute location of a mobile robot is essential in the navigation of a mobile robot. In this paper, a method to determine the position of a mobile robot through the visual image of a landrnark using neural networks is proposed. In determining the position of a mobile robot on the world coordinate, there is a position error because of uncertainty in pixels, incorrect camera calibration and lens distortion. To reduce the errors, a method using a BPNN(Back Propagation Neural Network) is proposed. The experimental results are presented to illustrate the superiority of the proposed method when comparing with the conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.68.1-68
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• 2002

This paper considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. The Paradigm for neurocomputing that has its roots in rough set theory, and works well in cases where there is uncertainty about the values of measurements used to make decisions. In the case of the line-crawling robot (LCR) described in this paper, rough neurocomputing is used to classify sometimes noisy signals from sensors. The LCR is a robot designed to crawl along high-voltage transmission lines where noisy sensor signals are common because of the electromagnetic field surrounding conductors. In rough neurocomputing, training a network of neurons...

• Journal of the Korean Society of Industry Convergence
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• v.18 no.1
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• pp.30-36
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• 2015

This paper presents the theoretical development of a complete navigation problem of a nonholonomic mobile robot by using ultrasonic sensors. To solve this problem, a new method to computer a fuzzy perception of the environment is presented, dealing with the uncertainties and imprecision from the sensory system and taking into account nonholonomic constranits of the robot. Fuzzy perception, fuzzy controller are applied, both in the design of each reactive behavior and solving the problem of behavior combination, to implement a fuzzy behavior-based control architecture. The performance of the proposed obstacle avoidance robot controller in order to determine the exact dynamic system modeling system that uncertainty is difficult for nomadic controlled robot direction angle by ultrasonic sensors throughout controlled performance tests. In additionally, this study is an in different ways than the self-driving simulator in the development of ultrasonci sensors and unmanned remote control techniques used by the self-driving robot controlled driving through an unmanned remote controlled unmanned realize the performance of factory antomation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.219-222
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• 1996

During the navigation of mobile robot, one of the essential task is to determination the absolute location of mobile robot. In this paper, we proposed a method to determine the position of the camera from a landmark through the visual image of a quadrangle typed landmark using neural network. In determining the position of the camera on the world coordinate, there is difference between real value and calculated value because of uncertainty in pixels, incorrect camera calibration and lens distortion etc. This paper describes the solution of the above problem using BPN(Back Propagation Network). The experimental results show the superiority of the proposed method in comparison to conventional method in the performance of determining camera position.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.165-170
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• 1997

During the navigation of mobile robot, one of the essential task if to determine the absolute position of mobile robot. In this paper, a method to determine the position of the camera using a vanishing point and neural networks without landmark if proposed. In determining the position of the camera on the world coordinate, there are differences between the real value and the calculated value because of uncertainty in pixels, incorrect camera calibration and lens distortion etc. This paper describes the solution of the above problem using BPNN(Back Propagation Neural Network) and experimental results show the capability to adapt for a mobile robot.

• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.292-300
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• 2011

Global positioning system (GPS) is widely used to measure the position of a vehicle. However, the accuracy of the GPS can be severely affected by surrounding environmental conditions. To deal with this problem, the GPS and odometry data can be combined using an extended Kalman filter. For stable navigation of an outdoor mobile robot using the GPS, this paper proposes two methods to evaluate the reliability of the GPS data. The first method is to calculate the standard deviation of the GPS data and reflect it to deal with the uncertainty of the GPS data. The second method is to match the GPS data to the traversability map which can be obtained by classifying outdoor terrain data. By matching of the GPS data with the traversability map, we can determine whether to use the GPS data or not. The experimental results show that the proposed methods can enhance the performance of the GPS-based outdoor localization.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.121-128
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• 2018

Obstacle avoidance is one of the most important parts of autonomous mobile robot. In this study, we proposed safe and efficient local path planning of robot for obstacle avoidance. The proposed method detects and tracks obstacles using the 3D depth information of an RGB-D sensor for path prediction. Based on the tracked information of obstacles, the paths of the obstacles are predicted with probability circle-based spatial search (PCSS) method and Gaussian modeling is performed to reduce uncertainty and to create the cost function of caution. The possibility of collision with the robot is considered through the predicted path of the obstacles, and a local path is generated. This enables safe and efficient navigation of the robot. The results in various experiments show that the proposed method enables robots to navigate safely and effectively.