• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.433-440
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• 2001

In this paper, we propose an automatic navigation system of ship using multivariable fuzzy control system in dynamic sea environment. The proposed multivariable fuzzy control system consists of two subsystems with three inputs and two outputs. The effectiveness of the proposed multivariable fuzzy control system is shown by simulation results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.124-130
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• 2005

Obstacle avoidance of underwater robots based on a modified virtual force field algorithm is proposed in this paper. The VFF(Virtual Force Field) algorithm, which is widely used in the field of mobile robots, is modified for application to the obstacle avoidance of underwater robots. This Modified Virtual Force Field(MVFF) algorithm using the fuzzy lgoc can be used in moving obstacles avoidance. A fuzzy algorithm is devised to handle various situations which can be faced during autonomous navigation of underwater robots. The proposed obstacle avoidance algorithm has ability to handle multiple moving obstacles. Results of simulation show that the proposed algorithm can be efficiently applied to obstacle avoidance of the underwater robots.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.577-582
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• 2013

In this paper, the state-of-the-art IVEF Service based on e-Navigation System was represented. The unification of the data exchange format among maritime-related systems is one of vital user-needs of e-Navigation, advantageous in bringing maritime safety and security. This paper propose the method to exchange marine information in IVEF, as recommended by the IALA, between VTS centers and Korea's GICOMS as well as the government-related agencies. To achieve this, a system data flow was designed which it acts as client and server. It enables the sending and receiving of Radar and CCTV images in accordance with the IVEF recommendation document of IALA.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.428-428
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• 2000

A robust intelligent algorithm for AGV navigation control is presented here based on both magnetic and gyro sensors to track a reference trajectory. Since the proposed system uses an intermittent array of short magnetic tape strips, it lends itself to a very easy installation and maintenance compared to other types of positioning references such as electric wire, magnets, RF and laser beacons. The neural network is to predict the lateral deviation of the AGV in the intervals where no magnetic tape references are available. Further, the use of intelligent control ensures a robust and flexible control performance. Computer simulation of AGV control demonstrates its adequate tracking performances even where the sensor information is not available. Real experiments using Samsung AGV are also on the way for real verification

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.314-320
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• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.19-25
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• 2005

This paper presents a lateral control system for the autonomous navigation vehicle that was developed and tested by Robotics Centre of Ecole des Mines do Paris in France. A robust lane detection algorithm was developed for detecting different types of lane marker in the images taken by a CCD camera mounted on the vehicle. $^{RT}Maps$ that is a software framework far developing vision and data fusion applications, especially in a car was used for implementing lane detection and lateral control. The lateral control has been tested on the urban road in Paris and the demonstration has been shown to the public during IEEE Intelligent Vehicle Symposium 2002. Over 100 people experienced the automatic lateral control. The demo vehicle could run at a speed of 130km1h in the straight road and 50km/h in high curvature road stably.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.176-181
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• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.155-158
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• 2024

Multipath, a major error source in urban GNSS positioning (global navigation satellite system), pose a challenge due to its site-dependent nature, varying with the user's signal reception environment. In our previous study, we introduced a technique generating GNSS multipath map in urban canyon. However, due to uncertainty in initial GNSS positions, applying multipath maps required generating multiple candidate positions. In this study, we present an efficient method for applying multipath maps by projecting the multipath correction in position domain. This approach effectively applies multipath maps, addressing the challenges posed by urban user position uncertainties.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.10a
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• pp.176-183
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• 2006

A novel intelligent anti-collision decision-making supporting system is addressed in this paper. To obtain precise anti-collision information capability, an innovative neurofuzzy network is proposed and applied. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to train the parameters of the Fuzzy Inference System (FIS). The learning process is based on a hybrid learning algorithm and off-line training data. The training data are obtained by trial manoeuvre. This neurofuzzy network can be considered to be a self-learning system with the ability to learn new information adaptively without forgetting old knowledge. This supporting system can decrease ship operators' burden to deal with bridge data and help them to make a precise anti-collision decision.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.785-798
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• 2004

This paper introduces the development of a range sensor based integrated navigation system for a multi-functional indoor service robot, called PSR (Public Service Robot System). The proposed navigation system includes hardware integration for sensors and actuators, the development of crucial navigation algorithms like mapping, localization, and path planning, and planning scheme such as error/fault handling. Major advantages of the proposed system are as follows: 1) A range sensor based generalized navigation system. 2) No need for the modification of environments. 3) Intelligent navigation-related components. 4) Framework supporting the selection of multiple behaviors and error/fault handling schemes. Experimental results are presented in order to show the feasibility of the proposed navigation system. The result of this research has been successfully applied to our three service robots in a variety of task domains including a delivery, a patrol, a guide, and a floor cleaning task.