• The Journal of Korea Robotics Society
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• v.6 no.1
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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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• 2000.10a
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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.

• The Journal of Korea Robotics Society
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• v.1 no.1
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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.

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.232-237
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• 2014

A passive sonar of warship is composed of several directional or omni-directional sensors. In order to model the acoustic signal received into a warship sonar, the wave propagation modeling is usually required from arbitrary noise source to all sensors equipped to the sonar. However, the full calculation for all sensors is time-consuming and the performance of sonar simulator deteriorates. In this study, we suggest an asymptotic method to estimate the sonar signal arrived to sensors adjacent to the reference sensor, where it is assumed that all information of eigenrays is known. This method is developed using Taylor series for the time delay of eigenray and similar to Fraunhofer and Fresnel approximation for sonar aperture. To validate the proposed method, some numerical experiments are performed for the passive sonar. The approximation when the second-order term is kept is vastly superior. In addition, the error criterion for each approximation is provided with a practical example.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05b
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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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.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.4
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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.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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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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• v.16 no.3
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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.