• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1478-1482
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• 2005

Since it is a very issue that figures out a current position of mobile robots, various methods have been proposed until nowadays. This paper proposes the sensor model of RFID(Radio Frequency Identification) and position estimation algorithm for mobile robots. We designed the sensor model of RFID in experimenting repeatedly. The sensor model of RFID in this case is that of stochastics according to sensing rate. Based on this stochastic sensor model, we designed the algorithm which estimates distance and direction of RFID tag. Therefore we made sure that RFID tag is used as landmark.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.85-91
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• 2000

Underwater sensor accuracy test which measures the detection range and bearing accuracies of sonar simulates sonar transmitting ping and underwater radiating noise of target vessels. In this test, because the position of sonar target is the reference position of test, the sonar target position should be precisely estimated. Hence, this paper suggests to apply USBL algorithm which adopts cross phase spectrum of received sensor signals, and presents its performance by range and bearing estimation simulations. As a result of simulations, suggested algorithm shows good accuracy for underwater sensor accuracy test near 5㏈ SNR.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.1
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• pp.30-36
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• 2001

This paper introduces a new indirect rotor position estimation algorithm for the SRM sensorless control, based on the magnetizing curves of aligned and unaligned rotor positions. Through the basic test method, the complete SRM magnetizing characterization is first constructed using a neural network training, and then used to estimate the rotor position. And also, the optimal phase is selected by the phase selector. In order to verify this approach, the proposed rotor position estimation algorithm using a neural network learning data is investigated. The experimental results show that the proposed control algorithm can be effectively applied to SRM sensorless control.

• International Journal of Ocean System Engineering
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• v.1 no.3
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• pp.165-170
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• 2011

In this paper, for the accurate estimation of the position and orientation of the UUV (unmanned underwater vehicle), an AHRS (Attitude Heading Reference System) was developed using the IMU (inertial measurement unit) sensor which provides information on acceleration and orientation in the object coordinate and the initial alignment algorithm and the E-KF (extended Kalman Filter). The initial position and orientation of the UUV are estimated using the initial alignment algorithm with 3-axis acceleration and geomagnetic information of the IMU sensor. The position and orientation of the UUV are estimated using the AHRS composed of 3-axis acceleration, velocity, and geomagnetic information and the E-KF. For the performance test of the orientation estimation of the AHRS, a testbed using IMU sensor(ADIS16405) and DSP28335 coded with an E-KF algorithm was developed and its performance was verified through tests.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.258-262
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• 2017

In this paper, we propose a new collision detection algorithm for human-robot collaboration. We use an IMU sensor located at the tip of the manipulator and the kinematic behavior of the manipulator to detect the unexpected collision between the robotic manipulator and environment. Unlike other method, the developed algorithm uses only the kinematic relationship between the manipulator joint and the end effector. Therefore, the collision estimation signal is not affected by the error of the dynamics model. The proposed collision detection algorithm detects the collision by comparing the estimated acceleration of the end effector derived from the position, velocity and acceleration trajectories of the robot joints with the actual acceleration measured by the sensor. In simulation, we compare the performance of our method with the conventional Residual Observer (ROB). Our method is less sensitive to the load variation because of the independency on the dynamic modeling of the manipulator.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.70-74
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• 2018

In order to ensure the flow of goods available and more flexible, reduce labor costs, many factories and industrial zones around the world are gradually moving to use automated solutions. One of them is to use Automated guided vehicles (AGV). Currently, there are a line tracing method as an AGV operating method, and a method of estimating the current position of the AGV and matching with a factory map and knowing the moving direction of the AGV. In this paper, we propose ceiling Light and guided line based moving direction estimation algorithm using multi-camera on the AGV in smart factory that can operate stable AGV by compensating the disadvantages of existing AGV operation method. The proposed algorithm is able to estimate its position and direction using a general - purpose camera instead of a sensor. Based on this, it can correct its movement error and estimate its own movement path.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.60-67
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• 2011

Visible light communication system transmits data by controlling light emission of LED and receives data through photo detecter, which is considered as one of strong candidates of next generation wireless communication systems. The transmission capacity of visible light communication system depends on light intensity emitted from LED, sensitivity of PD, distance between transmitter and receiver, angle of incidence at the receiver. In particular, the receiver's vertical and horizontal movement changes distance between transmitter and receiver and angle of incidence, which may degrades transmission capacity of system. In this paper, we propose an estimation algorithm of receiver's position and angle based on tracking of received light intensity for indoor visible light communication systems. The performance evaluation of proposed algorithm confirms that the estimation algorithm of receiver's position and angle is quite important for visible light communication system to improve its transmission capacity.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.408-414
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• 2009

A GPS/INS system is widely used in the UGV to estimate position during the mission. However, there are few restrictions when a GPS/INS system used alone. For example, GPS provides precise location information but easily interrupted by external factors like weather, environment, etc. INS provides continuous location data but positioning errors grew very rapidly with time. Therefore, it is necessary to integrating multi-sensors for more continuous and correct position estimation. In this paper, we propose location estimation algorithm of the UGV for GPS/INS integrated system that combines Fuzzy Inference and Unscented Particle Filter(UPF) to improve navigation. Fuzzy inference provides the simplest method integrating GPS/INS and UPF is non-linear estimation filter well suited to the correction of errors. The performance of the proposed algorithm was tested to compare with other algorithms. the results show that the proposed algorithm is more accuracy in position estimation and ensures continuous position tracking.

• 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.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.