• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.916-919
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• 1996

An real-time active beacon localization system for mobile robots is developed and implemented. This system permits the estimation of robot positions when detecting light sources by PSD(Position Sensitive Detector) sensor which are placed sparsely over the robot's work space as beacons(or landmarks). An LSE(Least Square Estimation) method is introduced to calibrate the internal parameters of a model for the beacon and robot position. The proposed system has two operational modes of position estimation. One is the initial position calculation by the detection of two or more light sources positions of which are known. The other is the continuous position compensation that calculates the position and heading of the robot using the IEKF(Iterated Extended Kalman Filter) applied to the beacon and dead-reckoning data. Practical experiments show that the estimated position obtained by this system is precise enough to be useful for the navigation of robots.

• 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.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.104-108
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• 2003

This paper explained algorithm for a initial pole position estimation of a permanent magnet linear synchronous motor(PMLSM). Generally this motor is considered initial pole position with a position sensor such as incremental encoder for the precise initial pole position estimation and high performance. But this is based on the principle that the initial pole position is accomplished by the PI controller using the maximum values of a position error generated by the new proposed two reference frames and also by using a rated force for input. the proposed algorithm does not utilize the general methods such as impedance ratio, EMF and using the magnetic saturation. In other words, this can be applied without respect to variety of the motor structure because of insensitivity to the motor parameters. In conclusion, simulation results are presented to confirm performance of initial pole position estimation method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.385-393
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• 2016

This paper presents the new position estimation method which contains the optical displacement sensor and the dead reckoning based position estimation algorithm for automation of hull cleaning robot. To evaluate feasibility of the proposed position estimation method on the hull cleaning robot, it was applied on the small scale robot model which has an identical drive method with the hull cleaning robot and then a set of the position estimation experiments were performed. The experimental results of the position estimation demonstrate that the estimated results with the optical displacement sensors is more accurate than used rotary encoder method. In addition, it continuously calculated the robot position quite close to the real robot driving path. In a follow-up study, the proposed position estimation method will be complemented and exploited on the actual hull cleaning robot by adding additional sensor modules that correct measurement errors.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.202-207
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• 2016

Estimation of vertical position is critical in applications of sports science and fall detection and also controls of unmanned aerial vehicles and motor boats. Due to low accuracy of GPS(global positioning system) in the vertical direction, the integration of IMU(inertial measurement unit) with the GPS is not suitable for the vertical position estimation. This paper investigates an IMU-barometer integration for estimation of vertical position (as well as vertical velocity). In particular, a new two-step Kalman/complementary filter is proposed for accurate and efficient estimation using 6-axis IMU and barometer signals. The two-step filter is composed of (i) a Kalman filter that estimates vertical acceleration via tilt orientation of the sensor using the IMU signals and (ii) a complementary filter that estimates vertical position using the barometer signal and the vertical acceleration from the first step. The estimation performance was evaluated against a reference optical motion capture system. In the experimental results, the averaged estimation error of the proposed method was 19.7 cm while that of the raw barometer signal was 43.4 cm.

• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.237-244
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• 2007

Estimation of target position is one of the main functions of surveillance UAVs, and is being used to various purposes but generally noisy target position is estimated due to the existence of random measurement errors. In this report, a method of diminishing target position estimation error by calculating target position using Kalman Filtered optimum values such as position, attitude of UAV and sight vector of optical instrument, is proposed.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.2
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• pp.139-146
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• 2018

This work puts forward a theoretical analysis on position estimation performance of interior permanent magnet synchronous motor (IPMSM) according to the injection frequency in carrier-based sensorless operation. The effects of spatial harmonics on inductance and voltage distortion due to the nonideal characteristics of IPMSM and inverter are examined as factors influencing the position estimation performance. Furthermore, the position estimation performance is analyzed by calculating the current at the switching instant in several operating conditions. In summary, the half switching frequency injection is more robust to the nonideal characteristics of IPMSM, especially with light load condition. The validity of the analysis is verified by the simulation and experimental results.

• Journal of Sensor Science and Technology
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• v.29 no.4
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• pp.237-242
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• 2020

This paper deals with relative position estimation using a Kalman filter (KF) based on inertial sensors that have been widely used in various biomechanics-related outdoor applications. In previous studies, the relative position is determined using relative orientation and predetermined segment-to-joint (S2J) vectors, which are assumed to be constant. However, because body segments are influenced by soft tissue artifacts (STAs), including the deformation and sliding of the skin over the underlying bone structures, they are not constant, resulting in significant errors during relative position estimation. In this study, relative position estimation was performed using a KF, where the S2J vectors were adopted as time-varying states. The joint constraint and the variations of the S2J vectors were used to develop a measurement model of the proposed KF. Accordingly, the covariance matrix corresponding to the variations of the S2J vectors continuously changed within the ranges of the STA-causing flexion angles. The experimental results of the knee flexion tests showed that the proposed KF decreased the estimation errors in the longitudinal and lateral directions by 8.86 and 17.89 mm, respectively, compared with a conventional approach based on the application of constant S2J vectors.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.160-164
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• 2007

MRAS observer, which is based on adaptive control theory, estimates speed and position by using optimal observer gains on the basis of Lyapunov stability theory. However, in case of MRAS theory, position estimation error is in existence because of non-linearity for inductance variation and limit cycles for position estimation. The adaptive sliding observer based on the variable structure control theory estimates the speed and position for zero of estimation error by using the sliding surface equal to the error between speed and position estimation. The binary observer estimates the rotor speed and rotor flux with alleviation of the high-frequency chattering, and retains the benefits achieved in the conventional sliding observer, such as robustness to parameter and disturbance variations. The speed and position sensorless control of SRM under the load and inductance variation is verified by the experimental results.