• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.127-131
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• 2003

This paper describes an initial pole position estimation method of a magnetic pole sensorless permanent magnet synchronous motor(PMSM) with an incremental encoder, The accurate initial pole position is estimated by using an efficient numerical method of Secant Method, which finds either of two zero torque/force positions and then the correct d-axis. It can be simply applicable to both rotary and linear PMSM because it only requires the tuned current controller and the relative position information. The experimental results show the validity of the proposed method with respect to highly accurate pole position estimation under the moderate moving distance and convergence time.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.2
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• pp.124-129
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• 2005

This paper presents an advanced method for an initial pole position estimation of a Permanent Magnet Linear Synchronous Motor(PMLSM) that has an accurate incremental encoder for servo applications but does not have Hall sensors as a magnetic pole sensor. By appropriately using the secant method as a numerical method the proposed algorithm finds either of two zero force positions and then the correct d-axis by applying a q-axis test current. It only requires the tuned current controller and the relative position information md so it can be simply applicable to a rotary PMSM. The experimental results show the validity of the proposed method, which has an excellent performance with respect to an accurate pole position estimation under the minimal moving distance(average of about 85㎛) during the estimation process.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.892-896
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• 2005

This paper presents method of depriving the relationship between static passive RFID tag and mobile robot In the field of tag-range. We use probabilistic sensor model of RFID reader by experiments. And we proposed estimation techniques by using direction of identification and relative-distance from the sensor model. Corresponding to distribution of identification, we can correct estimated tag position in relative coordinate. Simulation and Experimental Results show that the proposed method can provide good performance and thus be used fer mobile-robot localization.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.527-532
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• 2013

Depth estimation is an essential factor for robot vision, 3D scene modeling, and motion control. The depth estimation method is based on focusing values calculated in a series of images by a single camera at different distance between lens and object. In this paper, we proposed a relative depth estimation method using focus measure. The proposed method is implemented by focus value calculated for each image obtained at different lens position and then depth is finally estimated by considering relative distance of two patterns. We performed various experiments on the effective focus measures for depth estimation by using various patterns and their usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.786-791
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• 1992

When a mobile robot moves from one place to another, position error occurs due to the limit of accuracy of robot and the effect of environmental noise. In this paper. an accurate method of estimating the position and orientation of a mobile robot using the camera calibration is proposed. Kalman filter is used as the estimation algorithm. The uncertainty in the position of camera with repect to robot base frame is considered well as the position error of the robot. Besides developing the mathematical model for mobile robot calibration system, the effect of relative position between camera and calibration points is analyzed and the method to select the most accurate calibration points is also presented.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.91-94
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• 2001

In this paper, we proposed localization method of mobile robot using color landmark mounted on ceiling. This work is composed 2 parts : landmark recognition part which finds the position of multiple landmarks in image and identifies them and absolute position estimation part which estimates the location and orientation of mobile robot in indoor environment. In landmark recognition part, mobile robot detects artificial color landmarks using simple histogram intersection method in rg color space which is insensitive to the change of illumination. Then absolute position estimation part calculates relative position of the mobile robot to the detected landmarks. For the verification of proposed algorithm, ceiling-orientated camera was installed on a mobile robot and performance of localization was examined by designed artificial color landmarks. As the result of test, mobile robot could achieve the reliable landmark detection and accurately estimate the position of mobile robot in indoor environment.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.187-194
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• 2015

Accurate relative location estimation is a key requirement in indoor localization systems based on wireless sensor networks (WSNs). However, although these systems have applied not only various optimization algorithms but also fusion with sensors to achieve high accuracy in position determination, they are difficult to provide accurate relative azimuth and locations to users because of cumulative errors in inertial sensors with time and the influence of external magnetic fields. This paper based on ultra-wideband positioning system, which is relatively suitable for indoor localization compared to other wireless communications, presents an indoor localization system for estimating relative azimuth and location of location-unaware nodes, referred to as target nodes without applying any algorithms with complex variable and constraints to achieve high accuracy. In the proposed method, the target nodes comprising three mobile nodes estimate the relative distance and azimuth from two reference nodes that can be installed by users. In addition, in the process of estimating the relative localization information acquired from the reference nodes, positioning errors are minimized through a novel modified rounding estimation technique in which Kalman filter is applied without any time consumption algorithms. Experimental results show the feasibility and validity of the proposed system.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.623-629
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• 2012

In this paper, we analysis the performance of position estimation when adopting Dynamic TDMA(DTDMA) in JTIDS RelNav. JTIDS RelNav is widely understood as a new navigation solution possibly capable to support or take cover of the role of conventional systems including INS or GPS no matter if they are working well or not. By maximizing the efficiency of radio resource management in MAC(Medium Access Control) layer we enhance the system performance without any replacement of hardware.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.3
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• pp.219-229
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• 2018

This paper proposes Kalman Filter-based relative navigation algorithms for proximity tasks such as rendezvous/docking/cluster-operation of spacecraft using PSD Sensors and Infrared Beacon Modules. Numerical simulations are performed for comparative analysis of the performance of each relative-navigation technique. Based on the operation principle and optical modeling of the PSD Sensor and the Infrared Beacon Module used in the relative navigation algorithm, a measurement model for the Kalman filter is constructed. The Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF) are used as probabilistic relative navigation based on measurement fusion to utilize kinematics and dynamics information on translational and rotation motions of satellites. Relative position and relative attitude estimation performance of two filters is compared. Especially, through the simulation of various scenarios, performance changes are also investigated depending on the number of PSD Sensors and IR Beacons in target and chaser satellites.

• ETRI Journal
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• v.45 no.5
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• pp.758-767
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• 2023

In this study, we introduce a loosely coupled relative position estimation method that utilizes a decentralized ultrawideband (UWB), Global Navigation Support System and inertial navigation system for flight controllers (FCs). Key obstacles to multidrone collaboration include relative position errors and the absence of communication devices. To address this, we provide an extended Kalman filter-based algorithm and module that correct distance errors by fusing UWB data acquired through random communications. Via simulations, we confirm the feasibility of the algorithm and verify its distance error correction performance according to the amount of communications. Real-world tests confirm the algorithm's effectiveness on FCs and the potential for multidrone collaboration in real environments. This method can be used to correct relative multidrone positions during collaborative transportation and simultaneous localization and mapping applications.