• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.155-162
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• 2009

In walking guide robot, a guide vehicle detects an obstacle distribution in the walking space using range sensors, and generates a 3D grid map to map the obstacle information and the tactile display. And the obstacle information is transferred to a blind pedestrian using tactile feedback. Based on the obstacle information a user plans a walking route and controls the guide vehicle. The algorithm for 3D detection of an obstacle distribution and the method of mapping the generated obstacle map and the tactile display device are proposed in this paper. The experiment for the 3D detection of an obstacle distribution using ultrasonic sensors is performed and estimated. The experimental system consisted of ultrasonic sensors and control system. In the experiment, the detection of fixed obstacles on the ground, the moving obstacle, and the detection of down-step are performed. The performance for the 3D detection of an obstacle distribution and space mapping is verified through the experiment.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.29-34
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• 2019

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.92-94
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• 2005

Unmanned ground vehicles(UGVs) are expected to play a key role in the future army. These UGVs would be used for weapons platforms. logistics carriers, reconnaissance, surveillance, and target acquisition in the rough terrain. Most of path planning methodologies for UGVs offer an optimal or sub-optimal shortest-path in a 20 space. However, those methodologies do not consider increment and reduction effects of relative distance when a UGV climbs up or goes down in the slope of rough terrain. In this paper, we propose a novel path planning methodology using the modified distance transform algorithm. Our proposed path planning methodology employs two kinds of map. One is binary obstacle map. The other is the DEM. With these two maps, the modified distance transform algorithm in which distance between cells is increased or decreased by weighting function of slope is suggested. The proposed methodology is verified by various simulations on the randomly generated DEM and obstacle map.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.796-799
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• 1999

In this paper, we propose an inference method for understanding intention of obstacle for collision avoidance using the grid-type map. In order to represent the environment using ultrasonic sensors, the grid-type map is first constructed. Then we detect the obstacle and infer the intention for collision avoidance using the CLA(Centroid of Largest Area) point of the grid-type map. To verify the proposed method, some experiments are performed.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.659-660
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• 2008

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.783-789
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• 2007

In this paper, firstly, we propose a generating method of the 3-D obstacle map using ultrasonic sensors. Secondly, we try to find the necessary stimulation conditions of compact tactile display device for effective transfer of obstacle information. The final goal of this research is the development of a walking guide system for the blind to walk safely. The walking guide system consists of a guide vehicle for the obstacle detection and a tactile display device for the transfer of the obstacle information. The guide vehicle, located in front of the walking blind, detects the obstacle using ultrasonic sensors. The processed information makes an obstacle map and transmits safe path and emergency situation to the blind by the tactile display. The tactile display device, located in the handle which is connected with the guide vehicle by cane, offers the processed obstacle information such as position, size, moving, shape of obstacle and safe path, etc. The concept of a walking guide system with tactile display is introduced, and experiments of 3-D obstacle detection and tactile perception are carried out and analyzed.

• Journal of Ocean Engineering and Technology
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• v.38 no.4
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• pp.187-198
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• 2024

Autonomous underwater vehicles (AUVs) have a limited bandwidth for real-time communication, limiting rapid responses to unexpected obstacles. This study addressed how AUVs can navigate to a target without a pre-existing obstacle map by generating one in real-time and avoiding obstacles. This paper proposes using forward-looking sonar with an occupancy grid map (OGM) for real-time obstacle mapping and a potential field algorithm for avoiding obstacles. The OGM segments the map into grids, updating the obstacle probability of each cell for precise, quick mapping. The potential field algorithm attracts the AUV towards the target and uses repulsive forces from obstacles for path planning, enhancing computational efficiency in a dynamic environment. Experiments were conducted in coastal waters with obstacles to verify the real-time obstacle mapping and avoidance algorithm. Despite the high noise in sonar data, the experimental results confirmed effective obstacle mapping and avoidance. The OGM-based potential field algorithm was computationally efficient, suitable for single-board computers, and demonstrated proper parameter adjustments through two distinct scenarios. The experiments also identified some of challenges, such as dynamic changes in detection rates, propulsion bubbles, and changes in repulsive forces caused by sudden obstacles. An enhanced algorithm to address these issues is currently under development.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.148-156
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• 2011

This paper presents an implementation of autonomous navigation of a mobile robot indoors. It explains methods for map building, localization, obstacle avoidance and path planning. Geometric map is used for localization and path planning. The localization method calculates sensor data based on the map for comparison with the real sensor data. Monte Carlo Localization(MCL) method is adopted for estimation of the robot position. For obstacle avoidance, an artificial potential field generates repulsive and attractive force to the robot. Dijkstra algorithm plans the shortest distance path from a start position to a goal point. The methods integrate into autonomous navigation method and implemented for indoor navigation. The experiments show that the proposed method works well for safe autonomous navigation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.552-556
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• 2016

Obstacle detection is a key feature in the safe driving control of electric wheelchairs. The suggested obstacle detection algorithm was designed to provide obstacle avoidance direction and detect the existence of cliffs. By means of this information, the wheelchair can determine where to steer and whether to stop or go. A 3D depth camera (Microsoft KINECT) is used to scan the 3D point data of the scene, extract information on obstacles, and produce a steering direction for obstacle avoidance. To be specific, ground detection is applied to extract the obstacle candidates from the scanned data and the candidates are projected onto a 2D map. The 2D map provides discretized information of the extracted obstacles to decide on the avoidance direction (left or right) of the wheelchair. As an additional function, cliff detection is developed. By defining the "cliffband," the ratio of the predefined band area and the detected area within the band area, the cliff detection algorithm can decide if a cliff is in front of the wheelchair. Vehicle tests were carried out by applying the algorithm to the electric wheelchair. Additionally, detailed functions of obstacle detection, such as providing avoidance direction and detecting the existence of cliffs, were demonstrated.