• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.11
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• pp.730-742
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• 2014

In the case of On-demand routing protocol in cognitive radio ad-hoc networks, broadcasting of control packets may occur common control channel overload and packet collisions during the routing procedure. This situation is to increase the overhead of path finding and also limited to find the accurate and reliable path. Since reliable channel and path finding is restricted, path life time is shorten and path reliability is reduced. In this paper, we propose a new routing algorithm that reduces control channel overhead and increases path life time by considering the probability of appearance of primary user and channel status of neighbor nodes. Each node performs periodic local sensing to detect primary user signal and to derive primary user activity patterns. The probability of primary appearance on the current channel and the channel status can be obtained based on the periodic sensing. In addition, each node identifies the quality of the channel by message exchange through a common channel with neighbor nodes, then determines Link_Levels with neighbor nodes. In the proposed method, the Link Level condition reduces the number of control messages that are generated during the route discovery process. The proposed method can improve path life time by choosing a path through Path_Reliability in which stability and quality are weighted depending on the location. Through simulation, we show that our proposed algorithm reduces packet collisions and increases path life time in comparison with the traditional algorithm.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.224-233
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• 2000

An important feature of virtual reality is the facility for the user to move around a virtual environment in a natural and easily controlled manner, Navigation. Navigation involves changing the perspective of the user in the virtual environment (VE). Natural locomotion methods are able to contribute to a sense of presence and reality. This paper focuses on the navigation method in the virtual environment, one of the major interfaces for the interactivity between human and virtual environments in virtual reality circumstances and worlds. It proposes a new navigation method: Intelligent Cruise-Control Navigation (ICCN), which provides a natural and user-centered navigation method in virtual environment and can improve the reality and the presence. Intelligent Cruise-Control Navigation is composed of three major phases: Constant Velocity Navigation, Collision Detection and Avoidance, and Path Adjustment. The ICCN can reduce the user's fatigue and improve the user's presence and reality in the virtual environment. Through the experimental study it has been determined that the ICCN will be a natural, straightforward, and useful interface in VE.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.36-41
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• 2011

Configuration space(C-space) including configuration obstacle(C-obstacle) is one of the most important concepts in mobile robot path planning. Using C-space and C-obstacles, the robot with different shapes and moving mechanisms can be considered as a point in the C-space. And, as a result, the collision-free path for the robot can be easily achieved. To make C-space including C-obstacle, many researchers used circular approximation method for the efficient caluculation time. This method can help us to save our time by approximating the shape of a robot as the minimum sized circle which can cover all the area of robot. But, by using the circle larger than the robot, more space are considered as the part of robot and, as a result, some obstacles which are very near each other may be considered as a combined one obstacle. To solve this problem, multi-level configuration space is proposed by this paper. This multi-level method also use the circular approximation method as the initial step. But, after finding the initial path, it will check how many obstacles are combined. And then, for each combined obstacle, more accurate C-space generation will be continued. To check the efficiency of the proposed algorithm, time for c-space generation are compared with the well-known accurate C-space generation method using various types of robot shape.

• Journal of the Korea Society for Simulation
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• v.16 no.1
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• pp.31-37
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• 2007

The importance of NPC's role in computer games is increasing. An NPC must perform its tasks by perceiving obstacles and other characters and by moving through them. It has been proposed to plan a natural-looking path against fixed obstacles by using a generalized visibility graph. In this paper we develop the execution module for an NPC to move efficiently along the path planned on the generalized visibility graph. The planned path consists of line segments and arc segments, so we define steering behaviors such as linear behaviors, circular behaviors, and an arriving behavior for NPC's movements to be realistic and utilize them during execution. The execution module also includes the collision detection capability to be able to detect dynamic obstacles and uses a decision tree to react differently according to the detected obstacles. The execution module is tested through the simulation based on the example scenario in which an NPC interferes the other moving NPC.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.91-98
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• 2010

Conventional path-finding has focused on finding short collision-free paths. However, as computer games become more sophisticated, it is required to take tactical information like ambush points or lines of enemy sight into account. One way to make this information have an effect on path-finding is to represent a heuristic function of a search algorithm as a weighted sum of tactics. In this paper we consider the problem of learning heuristic to optimize path-finding based on given tactical information. What is meant by learning is to produce a good weight vector for a heuristic function. Training examples for learning are given by a game level-designer and will be compared with search results in every search level to update weights. This paper proposes a learning algorithm integrated with search for tactical path-finding. The perceptron-like method for updating weights is described and a simulation tool for implementing these is presented. A level-designer can mark desired paths according to characters' properties in the heuristic learning tool and then it uses them as training examples to learn weights and shows traces of paths changing along with weight learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5558-5565
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• 2012

Roadside barriers are facility for preventing cars from out of path, and 7 classes of barrier are prescribed in criteria of road grades and speed limit, etc. However, overload and overspeed are increased according to improvement of vehicle performance, and falling over frequently occur in vehicle accident related in barriers. Therefore, enhancement of the existing design criteria of roadside barriers is demanded. In this research, vehicle crash simulation was carried out, and the condition for fracture of roadside barriers and vehicle overturn was evaluated in order to verify the defence performance of the barriers, which are SB5 steel barrier and SB6 concrete barrier adapted mainly to highway.

• Advances in robotics research
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• v.2 no.2
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• pp.161-182
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• 2018

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.

• Journal of Auto-vehicle Safety Association
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• v.9 no.2
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• pp.6-12
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• 2017

An autonomous driving system based on a precise digital map is developed. The system is implemented to the Hyundai's Tucsan fuel cell car, which has a camera, smart cruise control (SCC) and Blind spot detection (BSD) radars, 4-Layer LiDARs, and a standard GPS module. The precise digital map has various information such as lanes, speed bumps, crosswalks and land marks, etc. They can be distinguished as lane-level. The system fuses sensed data around the vehicle for localization and estimates the vehicle's location in the precise map. Objects around the vehicle are detected by the sensor fusion system. Collision threat assessment is performed by detecting dangerous vehicles on the precise map. When an obstacle is on the driving path, the system estimates time to collision and slow down the speed. The vehicle has driven autonomously in the Hyundai-Kia Namyang Research Center.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.498-504
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• 2005

A fuzzy logic for collision avoidance of underwater robots is proposed in this paper. The VFF(Virtual Force Field) method, which is widely used in the field of mobile robots, is modified for application to the autonomous navigation of underwater robots. This Modified Virtual Force Field(MVFF) method using the fuzzy logic can be used in either track keeping or obstacle avoidance. Fuzzy logics are devised to handle various situations which can be faced during autonomous navigation of underwater robots. The obstacle avoidance algorithm has the ability to handle multiple static obstacles. Results of simulation show that the proposed method can be efficiently applied to obstacle avoidance of the underwater robots.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2456-2458
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• 2003

Recently, an automatic crane control system is required with high speed and rapid transportation. Therefore, when container is transferred from the initial coordinate to the finial coordinate, the container paths should be built in terms of the least time and without sway. Therefore, we calculated the anti-collision path for avoiding collision in its movement to the finial coordinate in this paper. And we constructed the neural network predictive two degree of freedom PID (NNPPID) controller to control the precise navigation. The proposed Predictive control system is composed of the neural network predictor, two degree of freedom PID(TDOFPID) controller, neural network self-tuner which yields parameters of TDOFPID. We analyzed crane system through simulation, and proved excellency of control performance over the conventional controllers.