• 로봇학회논문지
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• 제6권1호
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• pp.42-48
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• 2011

It is very important for a mobile robot to recognize and model its environments for navigation. However, the grid map constructed by sonar sensors cannot accurately represent the environment, especially the narrow environment, due to the angular uncertainty of sonar data. Therefore, we propose a map building scheme which combines sonar sensors and IR sensors. The maps built by sonar sensors and IR sensors are combined with different weights which are determined by the degree of translational and rotational motion of a robot. To increase the effectiveness of sensor fusion, we also propose optimal sensor arrangement through various experiments. The experimental results show that the proposed method can represent the environment such as narrow corridor and open door more accurately than conventional sonar sensor-based map building methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.423-423
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• 2000

This paper proposes an effective algorithm for following a wall by an autonomous mobile robot with sonar sensors and infrared sensors in an indoor environment. The proposed method uses deadreckoning to estimate the current position and orientation of a mobile robot. Sonar sensor data are used to estimate shape and position of wall using proposed algorithm. Infrared sensor data are used as assistant when sonar sensor data is uncertain. Simulation results using mobile robot show that the proposed algorithm is proper for the following wall.

• 로봇학회논문지
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• 제1권1호
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• pp.25-35
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• 2006

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home -like environment.

• 한국음향학회지
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• 제33권4호
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• pp.232-237
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• 2014

함정의 수동소나는 여러 개의 지향성과 무지향성 센서로 구성되어 있다. 함정 소나에 수신되는 음향 신호를 모의할 때, 일반적으로 임의의 소음원으로부터 소나에 장착된 모든 센서간의 음파 전달 모델링이 필요하다. 그러나 모든 센서에 대한 통합적인 계산은 시간이 많이 소모되며 소나 시뮬레이터의 성능을 저하시킨다. 본 연구에서는 음선 정보가 알려진 기준 센서가 존재한다고 할 때 그에 인접한 센서 위치에서의 소나 신호를 추정하는 근사적인 방법을 제안한다. 이 방법은 음선의 도달 시간에 대한 테일러 급수를 이용하여 개발되었으며 소나 개구면에 대한 Fraunhofer와 Fresnel 근사와 유사하다. 제안된 기법을 검증하기 위해 수동 소나에 대해 여러 수치실험이 수행되었다. 2차 항까지 테일러 근사를 적용한 근사법이 보다 우수한 결과를 보였다. 추가적으로 각각의 근사 해에 대한 오차 한계가 제시되었다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 춘계학술대회 논문집 기술교육전문연구회
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• pp.62-65
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• 2003

When a mobile robot moves around autonomously without man-made corrupted bye landmarks, it is essential to recognize the placement of surrounding objects especially for self localization, obstacle avoidance, and target classification and localization. To recognize the environment we use many Kinds of sensors, such as ultrasonic sensors, laser range finder, CCD camera, and so on. Among the sensors, ultra sonic sensors(sonar)are unexpensive and easy to use. In this paper, we analyze the sonar data and propose a method to recognize features of indoor environment. It is supposed that the environments are consisted of features of planes, edges, and corners, For the analysis, sonar data of plane, edge, and corner are accumulated for several given ranges. The data are filtered to eliminate some noise using the Kalman filter algorithm. Then, the data for each feature are compared each other to extract the character is ties of each feature. We demonstrate the applicability of the proposed method using the sonar data obtained form a sonar transducer rotating and scanning the range information around a indoor environment.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권10호
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• pp.703-713
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• 2004

This paper presents the new obstacle avoidance method that is composed of vision and sonar sensors, also a navigation algorithm is proposed. Sonar sensors provide poor information because the angular resolution of each sonar sensor is not exact. So they are not suitable to detect relative direction of obstacles. In addition, it is not easy to detect the obstacle by vision sensors because of an image disturbance. In This paper, the new obstacle direction measurement method that is composed of sonar sensors for exact distance information and vision sensors for abundance information. The modified splitting/merging algorithm is proposed, and it is robuster for an image disturbance than the edge detecting algorithm, and it is efficient for grouping of the obstacle. In order to verify our proposed algorithm, we compare the proposed algorithm with the edge detecting algorithm via experiments. The direction of obstacle and the relative distance are used for the inputs of the fuzzy controller. We design the angular velocity controllers for obstacle avoidance and for navigation to center in corridor, respectively. In order to verify stability and effectiveness of our proposed method, it is apply to a vision and sonar based mobile robot navigation system.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.593-599
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• 1999

A mobile robot in FMS environment should be able to nevigate itself. Therefore, path planning is necessary for the mobile robot to perform its tasks without being lost. Path planning using a network model gives oprimal paths to every pair of nodes but building this model demands accurate information of environments. In this paper, a method to build a network model using sonar sensors is presented. The main idea is to build a quad tree model by using sonar sensors and convert the model to a network model for path planning. The new method has been implemented on a mobile robot. Experimental results show that the mobile robot constructs an accurate network model using inaccurate sonar data.

• 한국군사과학기술학회지
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• 제21권4호
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• pp.481-488
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• 2018

This study suggest the improvement of the CFRP adhesive mixing ratio about acoustic window to enhance SONAR performance. The CFRP of submarine is produced by allocation an acoustic window zone by the beam width of SONAR sensors. During the sea trial, SONAR system's bearing and range accuracy data is not in tolerance due to debonding phenomenon on CFRP. The inappropriate mixing ratio of adhesives caused that peeling phenomenon occurred in the acoustic window part, which is the reason for the deterioration of SONAR sensors performance. The report includes explanation of test procedure of SONAR, root cause analysis, CFRP manufacturing procedure, laboratory tests results, and proof of the performance at the sea trial.

• 대한조선학회논문집
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• 제45권6호
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• pp.579-589
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• 2008

In the past few years, there are many studies and researches of the underwater vehicles which are carried out its mission using sonar sensors. MSCL(Marine System Control Lab.) at Inha University developed test-bed small ROV, ISRO. ISRO is an open-frame type and has 4 thrusters. ISRO can control 4 motions i.e surge, sway, yaw and heave with sonar sensors. ISRO is developed for inspection of ship hull, marine structure, plant of lake or river and so on. When ROV ISRO inspects something, it is necessary to control the position of ROV ISRO's for the movement and anti-collision with structures in the underwater. In this paper, we deal with the development of a small ROV and verification of the position control system via simulation and experiment using sonar sensors.

• Journal of Mechanical Science and Technology
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• 제16권3호
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• pp.302-309
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• 2002

This paper presents a technique for localization of a mobile robot using sonar sensors. Localization is the continual provision of knowledges of position that are deduced from its a priori position estimation. The environment of a robot is modeled by a two-dimensional grid map. We define a physically based sonar sensor model and employ an extended Kalman filter to estimate positions of the robot. Since the approach does not rely on an exact geometric model of an object, it is very simple and offers sufficient generality such that integration with concurrent mapping and localizing can be achieved without major modifications. The performance and simplicity of the approach are demonstrated with the results produced by sets of experiments using a mobile robot equipped with sonar sensors.