• The Journal of Korea Robotics Society
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• v.12 no.4
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• pp.402-410
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• 2017

Acoustic based localization is essential to operate autonomous robotic systems in underwater environment where the use of sensorial data is limited. This paper proposes a localization method using artificial underwater acoustic sources. The proposed method acquires directional angles of acoustic sources using time difference of arrivals of two hydrophones. For this purpose, a probabilistic approach is used for accurate estimation of the time delay. Then, Gaussian sum filter based SLAM technique is used to localize both acoustic sources and underwater vehicle. It is performed by using bearing of acoustic sources as measurement and inertial sensors as prediction model. The proposed method can handle directional ambiguity of time difference based source localization by generating Gaussian models corresponding to possible locations of both front and back sides. Through these processes, the proposed method can provide reliable localization method for underwater vehicles without any prior information of source locations. The performance of the proposed method is verified by experimental results conducted in a real sea environment.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.318-320
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• 2006

Inaccurate localization exposes a robot to many dangerous conditions. It could make a robot be moved to wrong direction or damaged by collision with surrounding obstacles. There are numerous approaches to self-localization, and there are different modalities as well (vision, laser range finders, ultrasonic sonars). Since sensor information is generally uncertain and contains noise, there are many researches to reduce the noise. But, the correctness is limited because most researches are based on statistical approach. The goal of our research is to measure more exact robot location by matching between built VRML 3D model and real vision image. To determine the position of mobile robot, landmark-localitzation technique has been applied. Landmarks are any detectable structure in the physical environment. Some use vertical lines, others use specially designed markers, In this paper, specially designed markers are used as landmarks. Given known focal length and a single image of three landmarks it is possible to compute the angular separation between the lines of sight of the landmarks. The image-processing and neural network pattern matching techniques are employed to recognize landmarks placed in a robot working environment. After self-localization, the 2D scene of the vision is overlaid with the VRML scene.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.2
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• pp.72-78
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• 2011

Localization information of an object can be a very useful element for many application areas. Presently, GPS is typically used for it, but many researches on the wireless localization technology are going on recently actively using mobile communication network, wireless sensor network, and ad hoc network in order to overcome the limitations of the GPS such as indoor, cost, power consumption, and etc. Therefore, this article surveys the most representative wireless localization techniques, which can be deployed in the wireless networks, and their principles and performances are analyzed based on the researched papers. In a conclusion, selection of a localization technique should consider the key design elements to a given application from the design elements such as localization environment, accuracy, time to fix, computation amount, implementation ease, and etc.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.59-66
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• 2006

In this study, we have proposed a motion equation to control synchro drive mobile robot, a path plan to compute and track the best path to given destination and a technique utilizing uniform distribution and cluster management based Monte Carlo localization to have track current position of moving robot. In the localization test which was repeated 73 times resulted as following. The average process time of original Monte Carlo localization was 12.8ms. The proposed cluster management Monte Carlo localization resulted 9.3ms. Also the proposed method resulted correctly in the cases where original method failed.

• Corrosion Science and Technology
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• v.7 no.6
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• pp.319-323
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• 2008

The polarization resistance of mild steel in 0.5M hydrochloric acid has been evaluated by using impedance (Z) and linear polarization (LPR) techniques and compared to the noise resistance obtained from electrochemical noise data. The degree of localization of this general corrosion has also been discussed by evaluating localization index and power spectral density. Polarization resistance obtained by LPR technique ($28\Omega$) was higher than that obtained by impedance technique ($15\Omega$). Noise resistance ($11\Omega$) was much lower than polarization resistance measured by both of above techniques. Higher polarization resistance obtained by LPR technique is generally caused by passivation effect in the presence of scales or deposits which can introduce an increased resistance as can low conductivity electrolytes. The reason why noise resistance is lower than polarization resistance is the effect of background noise detected by using three platinum electrodes cell in 0.5M hydrochloric acid. Slope($-\beta$) of power spectral density (PSD) obtained from analysis of noise data ($-\beta$ = 3.3) was much higher than 2 which indicates mild steel corroded uniformly. Localization index (LI) calculated from statistical analysis (LI=0.08) is much lower than 1 which indicates that mild steel did not corroded locally. However, LI value is still higher than $1x10^{-3}$ and this indicates that mild steel corroded locally in microscopic point of view.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.529-535
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• 2023

Noise originating from incipient propeller cavitation is assumed to come from a limited number of sources emitting a broadband signal. Conventional methods for cavitation localization have limitations because they cannot distinguish adjacent sound sources effectively due to low accuracy and resolution. On the other hand, sparse Bayesian learning technique demonstrates high-resolution restoration performance for sparse signals and offers greater resolution compared to conventional cavitation localization methods. In this paper, an incipient propeller cavitation localization method using sparse Bayesian learning is proposed and shown to be superior to the conventional method in terms of accuracy and resolution through experimental data from a model ship.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.323-324
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• 2008

In this paper, we present performance comparison of two methods to localize multiple robots. One is extended Kalman filter and the other is constraint propagation technique. Extended Kalman filter is conventional probabilistic method which gives the sub-optimal estimation rather than guarantee any boundary for true position of robot. In case of constraint propagation, it can give a boundary containing true robot position value. Especially, we deal with cooperative localization problem in outdoor environment for multiple robots equipped with GPS, gyro meter, wheel encoder. In simulation results, we present strength and weakness for localization methods based on extend Kalman filter and constraint propagation technique.

• Structural Engineering and Mechanics
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• v.8 no.2
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• pp.181-191
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• 1999

This paper presents an investigation of the mode localization and frequency loci veering phenomena in an aircraft with disordered external stores. Two theoretical analyses are carried out to study the occurring mechanism of the two phenomena: condensation technique in the subspace spanned by modes of interest and geometric mapping theory in the complex plane. Two simple criteria for predicting the occurrence of the mode localization and frequency loci veering are put forward. The prediction of the phenomena by our theoretically proposed criteria is in good agreement with that obtained through numerical calculations of characteristic solutions of the disordered system.

• 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.