• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권9호
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• pp.281-290
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• 2020

시각-언어 이동 문제는 시각 이해와 언어 이해 능력을 함께 요구하는 복합 지능 문제이다. 본 논문에서는 시각-언어 이동 에이전트를 위한 새로운 학습 모델을 제안한다. 이 모델은 데모 데이터에 기초한 모방 학습과 행동 보상에 기초한 강화 학습을 함께 결합한 복합 학습을 채택하고 있다. 따라서 이 모델은 데모 데이터에 편향될 수 있는 모방 학습의 문제와 상대적으로 낮은 데이터 효율성을 갖는 강화 학습의 문제를 상호 보완적으로 해소할 수 있다. 또한, 제안 모델에서는 기존의 목표 기반 보상 함수들의 문제점을 해결하기 위해 설계된 새로운 경로 기반 보상 함수를 이용한다. 본 논문에서는 Matterport3D 시뮬레이션 환경과 R2R 벤치마크 데이터 집합을 이용한 다양한 실험들을 통해, 제안 모델의 높은 성능을 입증하였다.

• 로봇학회논문지
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• 제6권2호
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• pp.182-188
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• 2011

This paper describes a method of road tracking by using a vision and laser with extracting road boundary (road lane and curb) for navigation of intelligent transport robot in structured road environments. Road boundary information plays a major role in developing such intelligent robot. For global navigation, we use a global positioning system achieved by means of a global planner and local navigation accomplished with recognizing road lane and curb which is road boundary on the road and estimating the location of lane and curb from the current robot with EKF(Extended Kalman Filter) algorithm in the road assumed that it has prior information. The complete system has been tested on the electronic vehicles which is equipped with cameras, lasers, GPS. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision system by our approach for detecting the curb of road and lane boundary detection.

• 로봇학회논문지
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• 제8권1호
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• pp.51-57
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• 2013

Recently, the number of jellyfish has been rapidly grown because of the global warming, the increase of marine structures, pollution, and etc. The increased jellyfish is a threat to the marine ecosystem and induces a huge damage to fishery industries, seaside power plants, and beach industries. To overcome this problem, a manual jellyfish dissecting device and pump system for jellyfish removal have been developed by researchers. However, the systems need too many human operators and their benefit to cost is not so good. Thus, in this paper, the design, implementation, and experiments of autonomous jellyfish removal robot system, named JEROS, have been presented. The JEROS consists of an unmanned surface vehicle (USV), a device for jellyfish removal, an electrical control system, an autonomous navigation system, and a vision-based jellyfish detection system. The USV was designed as a twin hull-type ship, and a jellyfish removal device consists of a net for gathering jellyfish and a blades-equipped propeller for dissecting jellyfish. The autonomous navigation system starts by generating an efficient path for jellyfish removal when the location of jellyfish is received from a remote server or recognized by a vision system. The location of JEROS is estimated by IMU (Inertial Measurement Unit) and GPS, and jellyfish is eliminated while tracking the path. The performance of the vision-based jellyfish recognition, navigation, and jellyfish removal was demonstrated through field tests in the Masan and Jindong harbors in the southern coast of Korea.

• Journal of Mechanical Science and Technology
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• 제17권10호
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• pp.1411-1422
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• 2003

The detection of free spaces between obstacles in a scene is a prerequisite for navigation of a mobile robot. Especially for stereo vision-based navigation, the problem of correspondence between two images is well known to be of crucial importance. This paper describes multi-range approach of area-based stereo matching for grid mapping and visual navigation in uncertain environment. Camera calibration parameters are optimized by evolutionary algorithm for successful stereo matching. To obtain reliable disparity information from both images, stereo images are to be decomposed into three pairs of images with different resolution based on measurement of disparities. The advantage of multi-range approach is that we can get more reliable disparity in each defined range because disparities from high resolution image are used for farther object a while disparities from low resolution images are used for close objects. The reliable disparity map is combined through post-processing for rejecting incorrect disparity information from each disparity map. The real distance from a disparity image is converted into an occupancy grid representation of a mobile robot. We have investigated the possibility of multi-range approach for the detection of obstacles and visual mapping through various experiments.

• 한국통신학회논문지
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• 제40권11호
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• pp.2252-2260
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• 2015

본 논문에서는 상용 차량에 많이 쓰이는 GNSS, 비전 센서, 레이더 센서를 이용한 협력 항법시스템을 제안하였다. 기존의 무선신호(예:UWB) 기반의 협력 항법시스템 (Hybrid-Sum Product Algorithm over Wireless Network, H-SPAWN)을 바탕으로 하여 무선신호 방식 대신 비전 센서와 레이더 센서를 모델링하여 알고리즘을 구성하였다. 모의실험을 통해, 사용하는 센서에 따른 성능을 비교분석하였으며, 특히 레이더 센서를 사용할 시 다른 두 센서(비전, UWB) 대비 최대 50%의 오차저감 효과를 보임을 확인하였다. 따라서 상용 차량에 쓰이는 센서 기반의 협력 항법시스템은 기존의 협력항법 시스템의 정확도를 향상시키면서 적용비용을 줄일 수 있는 기술이 될 수 있을 것이다.

• 항공우주
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• 통권95호
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• pp.24-27
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• 2007

• Journal of Positioning, Navigation, and Timing
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• 제3권1호
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• pp.1-10
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• 2014

Recently, monitoring of two-body ground vehicles carrying extremely hazardous materials has been considered as one of the most important national issues. This issue induces large cost in terms of national economy and social benefit. To monitor and counteract accidents promptly, an efficient methodology is required. For accident monitoring, GPS can be utilized in most cases. However, it is widely known that GPS cannot provide sufficient continuity in urban cannons and tunnels. To complement the weakness of GPS, this paper proposes an accident monitoring method based on a monocular vision sensor. The proposed method estimates angular acceleration from a sequence of image frames captured by a monocular vision sensor. The possibility of using angular acceleration is investigated to determine the occurrence of accidents such as jackknifing and rollover. By an experiment based on actual measurements, the feasibility of the proposed method is evaluated.

• Journal of information and communication convergence engineering
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• 제11권1호
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• pp.24-29
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• 2013

In this paper, we propose a fuzzy inference model for a navigation algorithm for a mobile robot that intelligently searches goal location in unknown dynamic environments. Our model uses sensor fusion based on situational commands using an ultrasonic sensor. Instead of using the "physical sensor fusion" method, which generates the trajectory of a robot based upon the environment model and sensory data, a "command fusion" method is used to govern the robot motions. The navigation strategy is based on a combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance based on a hierarchical behavior-based control architecture. To identify the environments, a command fusion technique is introduced where the sensory data of the ultrasonic sensors and a vision sensor are fused into the identification process. The result of experiment has shown that highlights interesting aspects of the goal seeking, obstacle avoiding, decision making process that arise from navigation interaction.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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• pp.321-332
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• 1999

It is important to detect objects movement for obstacle recognition and path searching of UCT(unmanned container transporters) with vision sensor. This paper shows the method to draw out objects and to trace the trajectory of the moving object using a CCD camera and it describes the method to recognize the shape of objects by neural network. We can transform pixel points to objects position of the real space using the proposed viewport. This proposed technique is used by the single vision system based on floor map.