• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.297-305
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• 2021

Monocular depth estimation helps the robot to understand the surrounding environments in 3D. Especially, deep-learning-based monocular depth estimation has been widely researched, because it may overcome the scale ambiguity problem, which is a main issue in classical methods. Those learning based methods can be mainly divided into three parts: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning trains the network from dense ground-truth depth information, unsupervised one trains it from images sequences and semi-supervised one trains it from stereo images and sparse ground-truth depth. We describe the basics of each method, and then explain the recent research efforts to enhance the depth estimation performance.

• ETRI Journal
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• v.43 no.4
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• pp.580-593
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• 2021

When operating in confined spaces or near obstacles, collision-free path planning is an essential requirement for autonomous exploration in unknown environments. This study presents an autonomous exploration technique using a carefully designed collision-free local planner. Using LiDAR range measurements, a local end-point selection method is designed, and the path is generated from the current position to the selected end-point. The generated path showed the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The results consistently demonstrated the safety and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flights in environment with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial robot systems. In addition, our drone performed an autonomous mission in the tunnel circuit competition (Phase 1) of the DARPA Subterranean Challenge.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.389-395
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• 2022

In order to ensure reliability the high-level automated driving such as Advanced Driver Assistance System (ADAS) and universal robot taxi provided by autonomous driving systems, the operation with high integrity must be generated within the defined Operation Design Domain (ODD). For this, the position and posture accuracy requirements of autonomous driving systems based on the safety driving requirements for autonomous vehicles and domestic road geometry standard are necessarily demanded. This paper presents localization requirements for safe road driving of autonomous ground vehicles based on the requirements of the positioning system installed on autonomous vehicle systems, the domestic road geometry standard and the dimensions of the vehicle to be designed. Based on this, 4 Protection Levels (PLs) such as longitudinal, lateral, vertical PLs, and attitude PL are calculated. The calculated results reveal that the PLs are more strict to urban roads than highways. The defined requirements can be used as a basis for guaranteeing the minimum reliability of the designed autonomous driving system on roads.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.349-350
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• 2022

Recently, AI (Artificial Intelligence) has been applied to various technologies such as automatic driving, robot and smart communication. Currently, AI system is developed by software-based method using tensor flow, and GPU (Graphic Processing Unit) is employed for processing unit. In this work, we developed an FPGA-based (Field Programmable Gate Array) AI system , and report on image recognition system to realize the AI system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1824-1832
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• 2011

In this paper, a smart home service robot McBot II is newly developed in much more practical and intelligent system than McBot I which we had developed a few years ago. Thus far, vacuum-cleaners have lightened the burden of household chores but the operational labor that vacuum-cleaners entail has been very severe. Recently, a cleaning robot was commercialized to solve but it also was not successful because it still had the problem of mess-cleanup, which pertained to the clean-up of large trash and the arrangement of newspapers, clothes, etc. Hence, we develop a new home mess-cleanup robot McBot II to completely overcome this problem on real environments. The mechanical design and the basic control of McBot II, which performs mess-cleanup function etc. in house, is actually focused in this paper. McBot II is mechanically modeled in the same method that the human works in door by using the waist and the hands. The big-ranged vertical lift and the shoulder joints to be able to forward move are mechanically designed for the operating function as the human's waist when the robot works. The mobility of McBot II is designed in the holonomic mobile robot for the collision avoidance of obstacle and the high speed navigation on the small area in door. Finally, good performance of McBot II, which has been optimally desinged, is confirmed through the experimental results for the control of the robotic body, mobility, arms and hands in this paper.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.6
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• pp.623-628
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• 2012

Conventional path planning methods have focused on the generation of an optimal shortest path to the goal. However, this optimal path cannot guarantee safe navigation, because it can often lead to a narrow area. Therefore, we propose a Coulomb's law-based safe path planning method that uses an information grid map. The information grid map includes four types of information: occupied, empty, guide, and dangerous areas. A safe path can be generated away from the dangerous area and close to the guide area by repulsive and attractive forces, respectively. Experiments and simulations show that the proposed method can generate paths inside the safe region and is useful for safe navigation.

• Journal of Advanced Navigation Technology
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• v.17 no.4
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• pp.386-395
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• 2013

This paper proposes a root-assisted MUSIC algorithm which uses a combination of the MUSIC and the root-MUSIC algorithm. This algorithm consists of two steps. Firstly, a coarse DOA is computed by the root-MUSIC algorithm. Secondly, a precise DOA estimation is carried out by the MUSIC algorithm in the reduced searching range. This paper analyzes the accuracy and the resolution performance of the proposed DOA estimation method using a software simulation platform.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.91-99
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• 2013

An autonomous unmanned underwater vehicle is a type of marine self-propelled robot that executes some specific mission and returns to base on completion of the task. In order to successfully execute the requested operations, the vehicle must be guided by an effective navigation algorithm that enables it to avoid obstacles and follow the best path. Architectures and principles for intelligent dynamic systems are being developed, not only in the underwater arena but also in related areas where the work does not fully justify the name. The problem of increasing the capacity of systems management is highly relevant based on the development of new methods for dynamic analysis, pattern recognition, artificial intelligence, and adaptation. Among the large variety of navigation methods that presently exist, the dynamic window approach is worth noting. It was originally presented by Fox et al. and has been implemented in indoor office robots. In this paper, the dynamic window approach is applied to the marine world by developing and extending it to manipulate vehicles in 3D marine environments. This algorithm is provided to enable efficient avoidance of obstacles and attainment of targets. Experiments conducted using the algorithm in MATLAB indicate that it is an effective obstacle avoidance approach for marine vehicles.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1029-1040
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• 2011

In this paper we propose an intelligent navigation algorithm for real world problem which can build a map without localization. Proposed algorithm operates online and furthermore does not require many memories for applying real world problem. After applying proposed algorithm to toy and huge data set, it does not require to calculate a whole eigenspace and need less memory compared to existing algorithm. Thus we can obtain that proposed algorithm is suitable for real world mobile navigation algorithm.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1892-1893
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• 2011

본 논문에서는 Centroidal Voronoi Tessellation을 이용하여 군집로봇의 협조탐색을 위한 공간분할기법을 제안한다. 탐색공간은 Centroidal Voronoi Tessellation을 이용하여 분할한다. 전역 경로 계획 및 군집 로봇 간의 충돌 회피는 포텐셜 필드를 이용한다. 탐색공간에 밀도 함수를 사용하여 공간분할의 유동성을 부여한다. 마지막으로, 군집로봇의 협조탐색의 가능성을 시뮬레이션을 통하여 확인한다.