• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.350-356
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• 2020

Workers have been replaced by mobile manipulators for factory automation in recent years. One of the typical tasks for automation is that a mobile manipulator moves to a target location and picks and places an object on the worktable. However, due to the pose estimation error of the mobile platform, the robot cannot reach the exact target position, which prevents the manipulator from being able to accurately pick and place the object on the worktable. In this study, we developed an automatic alignment system using a low-cost camera mounted on the end-effector of a collaborative robot. Camera calibration and pose estimation methods were also proposed for the automatic alignment system. This algorithm uses a markerboard composed of markers to calibrate the camera and then precisely estimate the camera pose. Experimental results demonstrate that the mobile manipulator can perform successful pick and place tasks on various conditions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.280-285
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• 2011

Recently, with the development of service robots and the concept of ubiquitous, the position estimation of mobile objects has received great interest. Some of the localization schemes are introduced, which provide the relative location of the moving objects subjected to accumulated errors. To implement a real time localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver gets the synchronization signal from the mobile robot and the ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. In some case, the mobile robot can acquire the ultrasonic signals from only one or two beacons, due to the obstacles located along the moving path. In this paper, a position estimation scheme using fewer than three sensors is developed. Also, the extended Kalman filter algorithm is applied for the improvement of position estimation accuracy of the mobile robot.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1779-1788
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• 2012

Recently, demands of application services in consideration of interior environment according to the stream of times, Ubiquitous. In case of interior location-based service, WLAN is now mostly used. But it is largely affected by environmental changes. To solve this problem, lots of studies on UWB are underway. The reason why studies on UWB are much made lies in that it is not much affected by environment changes owing to radio wave characteristics. So this study suggests the location correction algorithm which derives values with less influence of environment and high accuracy and corrects with more accurate location information using Ubisense system based on UWB technologies. The location correction algorithm suggested is one made after constructing environment database and use it to estimate more accurate location from the location measuring system in a high position.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.112-121
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• 2016

ADAS(Advanced Driver Assistance Systems) and street furniture information collecting car like as MMS(Mobile Mapping System), they require object location estimation method for recognizing spatial information of object in road images. But, the case of conventional methods, these methods require additional hardware module for gathering spatial information of object and have high computational complexity. In this paper, for a coordinate of road sign in single camera image, a position estimation scheme of object in road images is proposed using the relationship between the pixel and object size in real world. In this scheme, coordinate value and direction are used to get coordinate value of a road sign in images after estimating the equation related on pixel and real size of road sign. By experiments with test video set, it is confirmed that proposed method has high accuracy for mapping estimated object coordinate into commercial map. Therefore, proposed method can be used for MMS in commercial region.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.44-52
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• 2014

Leakage is an important factor to be considered for the management of underground water supply pipelines in a smart water grid system, especially if the pipelines are aged and buried under the pavement or various structures of a highly populated city. Because the exact detection of the location of such leaks in pipelines is essential for their efficient operation, a new methodology for leak location detection based on frequency band variation, windowing filters, and probability is proposed in this paper. Because the exact detection of the leak location depends on the precision of estimation of time delay between sensor signals due to leak noise, some window functions that offer weightings at significant frequencies are applied for calculating the improved cross-correlation function. Experimental results obtained by applying this methodology to an actual buried water supply pipeline, ~ 253.9 m long and made of cast iron, revealed that the approach of frequency band variation with those windows and probability offers better performance for leak location detection.

• Smart Structures and Systems
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• v.24 no.5
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.47-53
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• 2012

In cellular geo-location systems, positioning performance is influenced by hearability of receivers. Hearability can be enhanced by using long integration at the receiver. When unknown residual frequency remains in baseband signals, however, the coherent integration loss increases as the residual frequency becomes larger. Consequently, length of coherent integration is determined by the residual frequency. By precise estimation and compensation of the residual frequency, integration length can be enlarged. This paper presents a residual frequency estimator for WiBro geo-location and analyzes its performance in multipath environment. By computer simulation, an optimal receiver structure to enhance the hearability of WiBro geo-location is proposed.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.44 no.4
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• pp.423-430
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• 2011

The most satisfactory split-beam transducer for fish sizing maintains a wide bearing angle region for correct fish tracking without interference from side lobes and lower sensitivity to fish echoes outside of the main lobe region to correctly measure the angular location of free-swimming fishes in the sound beam. To evaluate the performance of an experimentally developed 50 kHz split-beam transducer, the angular location of a target was derived from the electrical phase difference between the resultant signals for the pair of transducer quadrants in the horizontal and vertical planes consisting of 32 transducer elements. The electrical phase difference was calculated by cross-spectral density analysis for the signals from the pair of receiving transducer quadrants, and the directivity correction factor for a developed split-beam transducer was estimated as the fourth-order polynomial of the off-axis beam angle for the angular location of the target. The experimental results demonstrate that the distance between the acoustic centers for the pair of receiving transducer quadrants can be controlled to less than one wavelength by optimization with amplitude-weighting transformers, and a smaller center spacing provides a range of greater angular location for tracking of a fish target. In particular, a side lobe level of -25.2 dB and an intercenter spacing of $0.96\lambda$($\lambda$= wavelength) obtained in this study suggest that the angular location of fish targets distributing within a range of approximately ${\pm}28^{\circ}$ without interference from side lobes can be measured.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.313-314
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• 2022

The intelligent maritime traffic information service provides a service to prevent collisions and stranding of ships based on the location information of ships periodically collected from ship equipment such as LTE-Maritime transceivers and AIS installed on ships. provided in real time. However, the above service may reduce the reliability of ship location information because GPS location information for measuring the ship's location may be cut off during transmission through LTE-Maritime or AIS networks, or phenomena such as location jumps and delays may occur. This study aims to estimate reliable position information to some extent even in an abnormal section through ship position prediction based on the existing received position information using the Kalman filter, which is an optimal estimation filter based on probability.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.2
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• pp.220-226
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• 2013

Recently, with the rapid development of factory automation and logistics system, a few workers were able to manage the broad workplace such as large vessels and warehouse. To estimate the exact location of these workers in the conventional wireless indoor localization systems, three or more fixed nodes are generally used to recognize the location of a mobile node consisting of a single node. However, these methods are inefficient in terms of node deployment because the broad workplace requires a lot of fixed nodes compared to workers(mobile nodes). Therefore, to efficiently deploy fixed nodes in these environments that need a few workers, this paper presents a novel estimation algorithm which can reduce the number of fixed nodes by efficiently recognizing the relative coordinates of two fixed nodes through a mobile node composed of three nodes. Also, to minimize the distance errors between mobile node and fixed node, rounding estimation(RE) technique is proposed. Experimental results show that the error rate of localization is improved, by using proposed RE technique, 90.9% compared to conventional trilateration in the free space. In addition, despite the number of fixed nodes can be reduced by up to 50% in the indoor free space, the proposed estimation algorithm recognizes precise location which has average error of 0.15m.