• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.667-672
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• 2011

This paper presents to study the sensor fusion of positioning sensors using UKF(unscented Kalman filter) for positioning accuracy improvement of AGV(automatic guided vehicle). The major guidance systems for AGV are wired guidance and magnetic guidance system. Because they have high accuracy and fast response time, they are used in most of the FMS(flexible manufacturing system). However, they had weaknesses that are high maintenance cost and difficult of existing path modification. they are being changed to the laser navigation in recent years because of those problems. The laser navigation is global positioning sensor using reflecters on the wall, and it have high accuracy and easy to modify the path. However, its response time is slow and it is influenced easily by disturbance. In this paper, we propose the sensor fusion method of the laser navigation and local sensors using UKF. The proposed method is improvement method of accuracy through error analysis of sensors. For experiments, we used the axle-driven forklift AGV and compared the positioning results of the proposed method with positioning results of the laser navigation. In experimental result, we verified that the proposed method can improve positioning accuracy about 16%.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.54-59
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• 2010

This paper present to study the guidance system as localization technique using sensor fusion and line tracking technique using virtual line for AGV(autonomous guided vehicle). An existing AGV could drive on decided line only. And representative guidance systems of such guidance system are magnet-gyro guidance and wired guidance. However, those have had the high cost of installation and maintenance, and the difficulty of system change according to variation of working environment. To solve such problems, we make the localization system which is fused with a laser navigation and gyro, encoder. The system is robust against noise, and flexible according to working environment through sensor fusion. For line tracking of laser navigation without wire guidance, we set the virtual line in program, and design the driving controller based on difference of angle and distance between AGV's position and decided virtual line. To experiment, we use the AGV which is made by ourselves, and experiment the line tracking repeatedly on same experimental environment. In result, maximum distance error between decided virtual line and AGV's position was less than 49.93mm, and we verified that the proposed system is efficient for line tracking of actual AGV.

• Aerospace Engineering and Technology
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• v.13 no.2
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• pp.66-72
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• 2014

This paper introduces an asynchronous multiple radar fusion algorithm for space object tracking. To estimate orbital motion of space object, a multiple radar scenario which jointly measures single object with different sampling time indices is described. STK/ODTK is utilized to determine realization of orbital motion and joint coverage of multiple radars. Then, asynchronous fusion algorithm is adapted to enhance the estimation performance of orbital motion during which multiple radars measure the same time instances. Monte-Carlo simulation results demonstrate that the proposed asynchronous multi-sensor fusion scheme better than single linearized Kalman filter in an aspect of root mean square error.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.54-61
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• 2012

In this paper, the pose estimation method for the satellite GTB (Ground Test Bed) using vision/MEMS IMU (Inertial Measurement Unit) integrated system is presented. The GTB for verifying a satellite system on the ground is similar to the mobile robot having thrusters and a reaction wheel as actuators and floating on the floor by compressed air. The EKF (Extended Kalman Filter) is also used for fusion of MEMS IMU and vision system that consists of a single camera and infrared LEDs that is ceiling landmarks. The fusion filter generally utilizes the position of feature points from the image as measurement. However, this method can cause position error due to the bias of MEMS IMU when the camera image is not obtained if the bias is not properly estimated through the filter. Therefore, it is proposed that the fusion method which uses the position of feature points and the velocity of the camera determined from optical flow of feature points. It is verified by experiments that the performance of the proposed method is robust to the bias of IMU compared to the method that uses only the position of feature points.

• 전자공학회논문지 IE
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• v.43 no.3
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• pp.13-21
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• 2006

Ryu et al. proposed a multiple target angle tracking algorithm using the angular measurement obtained from the signal subspace estimated by the output of sensor array. Ryu's algorithm has good features that it has no data association problem and simple structure. But its performance is seriously degraded in the low signal-to-noise ratio, and it uses the angular measurement obtained from the signal subspace of sampling time, even though the signal subspace is continuously updated by the output of sensor array. For improving the tracking performance of Ryu's algorithm, a measurement fusion method is derived based on ML(Maximum Likelihood) in this paper, and it admits us to use the angular measurements obtained form the adjacent signal subspaces as well as the signal subspace of sampling time. The new target angle tracking algorithm is proposed using the derived measurement fusion method. The proposed algorithm has a better tracking performance than that of Ryu's algorithm and it sustains the good features of Ryu's algorithm.

• Journal of the Korea Computer Graphics Society
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• v.6 no.3
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• pp.9-15
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• 2000

We Propose a sensor fusion technique for motion capture system. In our system, two kinds of sensors are used for mutual assistance. Four magnetic sensors(markers) are attached on the upper arms and the back of the hands for assisting twelve optical sensors which are attached on the arms of a performer. The optical sensor information is not always complete because the optical markers can be hidden due to obstacles. In this case, magnetic sensor information is used to link discontinuous optical sensor information. We use a system identification techniques for modeling the relation between the sensors' signals. Dynamic systems are constructed from input-output data. We determine the best model from the set of candidate models using the canonical system identification techniques. Our approach is using a simple signal processing technique currently. In the future work, we will propose a new method using other signal processing techniques such as Wiener or Kalman filter.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.344-348
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• 1992

This paper presents a new methodology for on-line tool breakage detection by sensor fusion concept of an acoustic-emission (AE) sensor. A built-in piezoelectric force sensor was used to measure cutting force instead of a tool dynamometer to preserve the machine tool dynamics. he sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. When a tool is broken, the explicit changes of signals' pattern take place. A burst-type AE signal increases abruptly. Followingly, a cutting force drops significantly. Therefore a burst of AE signal is used as a triggering signal to inspect the following cutting force. Significant drop of cutting force is utilized to detect tool breakage. The algorithm was implemented in a DSP board for in-process tool breakage detection. The proposed monitoring system was capable of a good applicable tool breakage detection.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.174-177
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• 1995

Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.1016-1021
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• 2005

A piezoresistive pressure sensor using a silicone rubber membrane has been fabricated on the selectively diffused (100)-oriented n/n+/n silicon substrates by a unique silicon micromachining technique using porous silicon ething. The width, length and thickness of the beam were 120${\mu}m$, 600${\mu}m$ and 7${\mu}m$, respectively and the thickness of the silicone rubber membrane was 40${\mu}m$. By the fusion of silicon beam and silicone rubber membrane, the mechanical strength of the pressure sensor could be highly improved due to smaller shear stress. The effectiveness of the sensor was confirmed through an experiment and FEM simulation in which the pressure sensor was characterized.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.180-187
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• 2006

This paper presents a new sensor system, CALOS, for motion estimation and 3D reconstruction. The 2D laser sensor provides accurate depth information of a plane, not the whole 3D structure. On the contrary, the CCD cameras provide the projected image of whole 3D scene, not the depth of the scene. To overcome the limitations, we combine these two types of sensors, the laser sensor and the CCD cameras. We develop a motion estimation scheme appropriate for this sensor system. In the proposed scheme, the motion between two frames is estimated by using three points among the scan data and their corresponding image points, and refined by non-linear optimization. We validate the accuracy of the proposed method by 3D reconstruction using real images. The results show that the proposed system can be a practical solution for motion estimation as well as for 3D reconstruction.