• Journal of IKEEE
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• v.22 no.2
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• pp.440-445
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• 2018

The work presented in this paper deals with a navigation problem for a multiple mobile robots in unknown dynamic environments. The environments are completely unknown to the robots; thus, proximity sensors installed on the robots' bodies must be used to detect information about the surroundings. In order to guide the robots along collision-free paths to reach their goal positions, a navigation method based on a combination of primary strategies has been developed. Most of these strategies are achieved by means of fuzzy logic controllers, and are uniformly applied in every robot. In order to improve the performance of the proposed fuzzy logic, the genetic algorithms were used to evolve the membership functions and rules set of the fuzzy controller. The simulation experiments verified that the proposed method effectively addresses the navigation problem.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.149-158
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• 2024

This paper presents a robust autonomous navigation and reconnaissance system for tracked robots, designed to handle complex multi-floor indoor environments with stairs. We introduce a localization algorithm that adjusts scan matching parameters to robustly estimate positions and create maps in environments with scarce features, such as narrow rooms and staircases. Our system also features a path planning algorithm that calculates distance costs from surrounding obstacles, integrated with a specialized PID controller tuned to the robot's differential kinematics for collision-free navigation in confined spaces. The perception module leverages multi-image fusion and camera-LiDAR fusion to accurately detect and map the 3D positions of objects around the robot in real time. Through practical tests in real settings, we have verified that our system performs reliably. Based on this reliability, we expect that our research team's autonomous reconnaissance system will be practically utilized in actual disaster situations and environments that are difficult for humans to access, thereby making a significant contribution.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.3
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• pp.221-226
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• 2012

This research presents an analysis of algorithm for ship track-keeping along a given trajectory. The maneuver of a free running model ship guiding through a simple path are presented. In order to solve the above problem, a desired trajectory is usually determined by GPS points in a pre-fixed place then these points are set in a pre-programmed navigation so that the ship would be automatically tracked. Proportional-Derivative(PD) control which is useful for fast response controllers was used in this program as a course keeping system. A high accuracy GPS receiver was installed on the model ship that could provide positions frequently, the system will compare and give out the remaining distance and heading to the target way-point. The results of ship auto track-keeping experiment will be explained in order to illustrate the adjustment in controlling parameters. These results can be utilized as a preliminary step to carry out the experiment of ship collision avoidance system and automatic berthing in the future.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.641-646
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• 2010

This paper presents algorithms for real-time navigation of a humanoid robot with a stereo vision but no other sensors. Using the algorithms, a robot can recognize its 3D environment by retrieving SIFT features from images, estimate its position through the Kalman filter, and plan its path to reach a destination avoiding obstacles. Our approach focuses on estimating the robot’s central walking path trajectory rather than its actual walking motion by using an approximate model. This strategy makes it possible to apply mobile robot localization approaches to humanoid robot localization. Simple collision free path planning and motion control enable the autonomous robot navigation. Experimental results demonstrate the feasibility of our approach.

• Journal of Ocean Engineering and Technology
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• v.23 no.5
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• pp.61-70
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• 2009

An AUV (Autonomous Underwater Vehicle) is an unmanned underwater vessel to investigate sea environments and deep sea resource. To be completely autonomous, AUV must have the ability to home and dock to the launcher. In this paper, we consider a class of homing trajectory planning problem for an AUV with kinematic and tactical constraints in horizontal plane. Since the AUV under consideration has underactuated characteristics, trajectory for this kind of AUV must be designed considering the underactuated characteristics. Otherwise, the AUV cannot follow the trajectory. Proposed homing trajectory panning method that called VGM (Virtual Goal Method) based on visibility graph takes the underactated characteristics into consideration. And it guarantees shortest collision free trajectory. For tracking control, we propose a PD controller by simple guidance law. Finally, we validate the trajectory planning algorithm and tracking controller by numerical simulation and ocean engineering basin experiment in KORDI.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.679-687
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• 2019

This paper presents the autonomous swimming technology developed for an Autonomous Underwater Vehicle (AUV) operating in the underwater jacket structure environment. To prevent the position divergence of the inertial navigation system constructed for the primary navigation solution for the vehicle, we've developed kinds of marker-recognition based underwater localization methods using both of optical and acoustic cameras. However, these two methods all require the artificial markers to be located near to the cameras mounted on the vehicle. Therefore, in the case of the vehicle far away from the structure where the markers are usually mounted on, we may need alternative position-aiding solution to guarantee the navigation accuracy. For this purpose, we develop a sonar image processing based underwater localization method using a Forward Looking Sonar (FLS) mounted in front of the vehicle. The primary purpose of this FLS is to detect the obstacles in front of the vehicle. According to the detected obstacle(s), we apply an Occupancy Grid Map (OGM) based path planning algorithm to derive an obstacle collision-free reference path. Experimental studies are carried out in the water tank and also in the Pohang Yeongilman port sea environment to demonstrate the effectiveness of the proposed autonomous swimming technology.

• 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 Institute of Electronics Engineers of Korea SC
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• v.43 no.6 s.312
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• pp.1-8
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• 2006

Many researches on path planning and obstacle avoidance for the fundamentals of mobile robot have been done recently. Informations from various sensors can be used to find obstacles and plan feasible path. In spite of many solutions of finding optimal path, each can be applied in only a constrained condition. This means that it is difficult to find university good algorithm. An optimal path with a complicated computation generates a time delay which cannot avoid moving obstacles. In this paper, we propose an algorithm of path planning and obstacle avoidance for mobile robot. We call the proposed method Random Access Sequence(RAS) method. In the proposed method, a small region is set first and numbers are assigned to its neighbors, then the path is selected using these numbers and cumulative numbers. It has an advantage of fast planning time and completeness of path if one exists. This means that new path selection may be possible within short time and that helps a robot to avoid obstacle in dynamic environments. Using the information of the start and destination position, the RAS can be performed for collision-free navigation by reforming feasible paths repeatedly in dynamic environments.

• 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 Internet Computing and Services
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• v.15 no.3
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• pp.63-69
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• 2014

For the safety messages in IEEE 802.11p vehicles network environment(WAVE), strict periodic beacon broadcasting requires status advertisement to assist the driver for safety. WAVE standards apply multiple radios and multiple channels to provide open public road safety services and improve the comfort and efficiency of driving. Although WAVE standards have been proposed multi-channel multi-radio, the standards neither consider the WAVE multi-radio environment nor its effect on the beacon broadcasting. Most of beacon broadcasting is designed to be delivered on only one physical device and one control channel by the WAVE standard. also conflict-free channel assignment of the fewest channels to a given set of radio nodes without causing collision is NP-hard, even with the knowledge of the network topology and all nodes have the same transmission radio. Based on the latest standard IEEE 802.11p and IEEE 1609.4, this paper proposes an interference aware-based channel assignment algorithm with Nash bargaining solution that minimizes interference and increases throughput with wireless mesh network, which is deigned for multi-radio multi-cahnnel structure of WAVE. The proposed algorithm is validated against numerical simulation results and results show that our proposed algorithm is improvements on 8 channels with 3 radios compared to Tabu and random channel allocation algorithm.