Journal of the Korean Society for Precision Engineering

Position Control of a Linear Motor Motion Stage Using Augmented Kalman Filter: Keun-Ho Kim, Hyeong-Joon Ahn; J. Korean Soc. Precis. Eng. 2025;42(11):887-892.
Published online November 1, 2025; DOI: https://doi.org/10.7736/JKSPE.025.011

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The rapid growth of semiconductor and display manufacturing highlights the demand for fast, precise motion stages. Advanced systems such as lithography and bio-stages require accuracy at the μm and nm levels, but linear motor stages face challenges from disturbances, model uncertainties, and measurement noise. Disturbances and uncertainties cause deviations from models, while noise limits control gains and performance. Disturbance Observers (DOBs) enhance performance by compensating for these effects using input–output data and a nominal inverse model. However, widening the disturbance estimation bandwidth increases noise sensitivity. Conversely, the Kalman Filter (KF) estimates system states from noisy measurements, reducing noise in position feedback, but it does not treat disturbances as states, limiting compensation. To address this, we propose an Augmented Kalman Filter (AKF)–based position control for linear motor stages. The system was modeled and identified through frequency response analysis, and DOB and AKF were implemented with a PIV servo filter. Experimental validation showed reduced following error, jitter, and control effort, demonstrating the improved control performance of the AKF approach over conventional methods.

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CNN-based Human Recognition and Extended Kalman Filter-based Position Tracking Using 360° LiDAR: Kibum Jung, Sung Hwan Kweon, Martin Byung-Guk Jun, Young Hun Jeong, Seung-Han Yang; J. Korean Soc. Precis. Eng. 2022;39(8):575-582.
Published online August 1, 2022; DOI: https://doi.org/10.7736/JKSPE.022.025

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The collaboration of robots and humans sharing workspace, can increase productivity and reduce production costs. However, occupational accidents resulting in injuries can increase, by removing the physical safety around the robot, and allowing the human to enter the workspace of the robot. In preventing occupational accidents, studies on recognizing humans, by installing various sensors around the robot and responding to humans, have been proposed. Using the LiDAR (Light Detection and Ranging) sensor, a wider range can be measured simultaneously, which has advantages in that the LiDAR sensor is less impacted by the brightness of light, and so on. This paper proposes a simple and fast method to recognize humans, and estimate the path of humans using a single stationary 360° LiDAR sensor. The moving object is extracted from background using the occupied grid map method, from the data measured by the sensor. From the extracted data, a human recognition model is created using CNN machine learning method, and the hyper-parameters of the model are set, using a grid search method to increase accuracy. The path of recognized human is estimated and tracked by the extended Kalman filter.

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A Kalman Filter for Inverse Dynamics of IMU-Based Real-Time Joint Torque Estimation: Ji Seok Choi, Chang June Lee, Jung Keun Lee; J. Korean Soc. Precis. Eng. 2022;39(1):69-77.
Published online January 1, 2022; DOI: https://doi.org/10.7736/JKSPE.021.085

Abstract
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One of the problems in inverse dynamics calculation for the inertial measurement unit (IMU)-based joint force and torque estimation is the amplified signal noises of segment kinematic data mainly due to the differentiation procedure and segmental soft tissue artifacts. In order to deal with this problem, appropriate filtering methods are often recommended for signal enhancement. Conventionally, a low-pass filter (LPF) is widely used for the kinematic data. However, the zero-phase LPF requires post-processing, while the real-time LPF causes an unignorable time lag. For this reason, it is inappropriate to use the LPF for real-time joint torque estimation. This paper proposes a Kalman filter (KF) for inverse dynamics of IMUbased joint torque estimation in real time without any time lag, while utilizing the smoothing capability of the KF. Experimental results showed that the proposed KF outperformed a real-time LPF in the estimation accuracy of hip joint force and torque during jogging on the spot by 100 and 29%, respectively. Although the proposed KF requires the process of adjusting covariance according to the dynamic conditions, it can be expected to improve the estimation performance in the field where joint force and torque need to be estimated in real time.

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Wearable Inertial Sensors-based Joint Kinetics Estimation of Lower Extremity Using a Recurrent Neural Network
Ji Seok Choi, Chang June Lee, Jung Keun Lee
Journal of the Korean Society for Precision Engineering.2023; 40(8): 655. CrossRef

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Location Tracking of Boiler Tube and Pipe Inspection Scanner Using IMU: Ju-Hyeon Park, Jung-Seok Seo, Gye-Jo Jung; J. Korean Soc. Precis. Eng. 2021;38(11):833-840.
Published online November 1, 2021; DOI: https://doi.org/10.7736/JKSPE.021.084

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This paper proposes an IMU method for location tracking in power plants and indoor environments without GPS. IMU-based sensors use accelerometer, angular accelerometer, earth magnetometer, and altimeter. It is a method for recognizing the movement of pedestrians or moving objects. However, errors can be caused, as noise and bias increase due to long-term measurement. VIO-SLAM type sensor T265, which uses a combination of cameras and IMU, and can accurately track paths in invisible spaces, is used in this study. In addition, this type of sensor can be corrected in real time with a filter function inserted into the sensor and errors can be minimized. As a comparison experiment with the encoder, it is possible to evaluate the location of the scanner within a ±10 mm error from the actual distance in 1,500 × 700 (mm) space. The usefulness of this method is verified by measuring real specimens of boiler pipes and tubes, which are the major components of power plants.

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Indoor Localization of a Mobile Robot based on Unscented Kalman Filter Using Sonar Sensors: Soo Hee Seo, Jong Hwan Lim; J. Korean Soc. Precis. Eng. 2021;38(4):245-252.
Published online April 1, 2021; DOI: https://doi.org/10.7736/JKSPE.021.006

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This paper proposes a UKF-Based indoor localization method that evaluates the optimal position of a robot by fusing the position information from encoders and the distance information of the obstacle measured by ultrasonic sensors. UKF is a method of evaluating the robot’s position by transforming optimal sigma points extracted using the unscented transform and is advantageous for the localization of a nonlinear system. To solve the problem of the specular reflection effect of ultrasonic sensors, we propose a validation gate that evaluates the reliability of the ranges measured by sonar sensors, that can maximize the quality of the position evaluation. The experimental results showed that the method is stable and convergence of the position error regardless of the size of the initial position error and the length of the sampling time.

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Unscented Kalman Filter Based 3D Localization of Outdoor Mobile Robots: Woo Seok Lee, Min Ho Choi, Jong Hwan Lim; J. Korean Soc. Precis. Eng. 2020;37(5):331-338.
Published online May 1, 2020; DOI: https://doi.org/10.7736/JKSPE.019.066

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This paper proposes a practical method, for evaluating 3-D positioning of outdoor mobile robots using the Unscented Kalman Filter (UKF). The UKF method does not require the linearization process unlike conventional EKF localization, so it can minimize effects of errors caused by linearization of non-linear models for position estimation. Also, this method does not require Jacobian calculations difficult to calculate in the actual implementation. The 3-D position of the robot is predicted using an encoder and tilt sensor, and the optimal position is estimated by fusing these predicted positions with the GPS and digital compass information. Experimental results revealed the proposed method is stable for localization of the 3D position regardless of initial error size, and observation period.

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Research on Parameter Compensation Method and Control Strategy of Mobile Robot Dynamics Model Based on Digital Twin
Renjun Li, Xiaoyu Shang, Yang Wang, Chunbai Liu, Linsen Song, Yiwen Zhang, Lidong Gu, Xinming Zhang
Sensors.2024; 24(24): 8101. CrossRef
A Study on Improving the Sensitivity of High-Precision Real-Time Location Receive based on UWB Radar Communication for Precise Landing of a Drone Station
Sung-Ho Hong, Jae-Youl Lee, Dong Ho Shin, Jehun Hahm, Kap-Ho Seo, Jin-Ho Suh
Journal of the Korean Society for Precision Engineering.2022; 39(5): 323. CrossRef

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Extended Kalman Filter Based 3D Localization Method for Outdoor Mobile Robots: Woo Seok Lee, Min Ho Choi, Jong Hwan Lim; J. Korean Soc. Precis. Eng. 2019;36(9):851-858.
Published online September 1, 2019; DOI: https://doi.org/10.7736/KSPE.2019.36.9.851

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This paper proposes a 3D localization method for an outdoor mobile robot. This method assesses the 3D position including the altitude information, which is impossible in the existing 2D localization method. In this method, the 3D position of the robot is predicted using an encoder and an inclination sensor. The predicted position is fused with the position information obtained from the DGPS and the digital compass using extended kalman filter to evaluate the 3D position of the robot. The experimental results showed that the proposed method can effectively evaluate the 3D position of the robot in a sloping environment. Moreover, this method was found to be more effective than the conventional 2D localization method even in the evaluation of the plane position where altitude information is unnecessary.

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Research on Parameter Compensation Method and Control Strategy of Mobile Robot Dynamics Model Based on Digital Twin
Renjun Li, Xiaoyu Shang, Yang Wang, Chunbai Liu, Linsen Song, Yiwen Zhang, Lidong Gu, Xinming Zhang
Sensors.2024; 24(24): 8101. CrossRef
Unscented Kalman Filter Based 3D Localization of Outdoor Mobile Robots
Woo Seok Lee, Min Ho Choi, Jong Hwan Lim
Journal of the Korean Society for Precision Engineering.2020; 37(5): 331. CrossRef

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Kinematic Constraint-Projected Kalman Filter to Minimize Yaw Estimation Errors Induced by Magnetic Distortions: Tae Hyeong Jeon, Jung Keun Lee; J. Korean Soc. Precis. Eng. 2019;36(7):659-665.
Published online July 1, 2019; DOI: https://doi.org/10.7736/KSPE.2019.36.7.659

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With regard to 3D orientation estimation based on IMMU (Inertial Magnetic Measurement Unit) signals, the yaw estimation accuracy may be significantly degraded as a result of magnetic distortions. Consequently, several yaw estimation Kalman filters (KFs) possessing distortion compensation mechanisms have been proposed. However, majority of the conventional methods fail to effectively curb inaccuracies due to distortion when magnetic fields are extremely distorted. In this paper, we propose a new KF projecting a kinematic constraint to minimize yaw estimation errors induced by magnetic distortions. After the measurement update using magnetometer signals, the proposed method additionally corrects the yaw estimation through projection of a kinematic constraint on a conventional unconstrained KF. Experimental results show that the proposed KF outperformed the conventional KF by approximately 52-67%.

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Unscented Kalman Filter based Outdoor Localization of a Mobile Robot: Woo Seok Lee, Jong Hwan Lim; J. Korean Soc. Precis. Eng. 2019;36(2):183-190.
Published online February 1, 2019; DOI: https://doi.org/10.7736/KSPE.2019.36.2.183

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This paper proposes a practical method, for evaluating positioning of outdoor mobile robots using Unscented Kalman Filter (UKF). Since the UKF method does not require the linearization process unlike EKF localization, it can minimize effects of errors caused by linearization of non-linear models for position estimation. This method enables relatively high performance position estimation, using only non-inertial sensors such as low-precision GPS and a digital compass. Effectiveness of the UKF localization method was verified through actual experiments and performance of position estimation was compared with that of the existing EKF method. Experimental results revealed the proposed method has better performance than the EKF method, and it is stable regardless of initial error size, and observation period.

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Localization-based waiter robot for dynamic environment using Internet of Things
Muhammad Waqas Qaisar, Muhammad Mudassir Shakeel, Krzysztof Kędzia, José Mendes Machado, Ahmed Zubair Jan
International Journal of Information Technology.2025; 17(6): 3675. CrossRef
Research on Parameter Compensation Method and Control Strategy of Mobile Robot Dynamics Model Based on Digital Twin
Renjun Li, Xiaoyu Shang, Yang Wang, Chunbai Liu, Linsen Song, Yiwen Zhang, Lidong Gu, Xinming Zhang
Sensors.2024; 24(24): 8101. CrossRef
Indoor Localization of a Mobile Robot based on Unscented Kalman Filter Using Sonar Sensors
Soo Hee Seo, Jong Hwan Lim
Journal of the Korean Society for Precision Engineering.2021; 38(4): 245. CrossRef
Unscented Kalman Filter Based 3D Localization of Outdoor Mobile Robots
Woo Seok Lee, Min Ho Choi, Jong Hwan Lim
Journal of the Korean Society for Precision Engineering.2020; 37(5): 331. CrossRef

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The Comparison of Attitude Estimation Method Based on Kalman Filter with Considering External Acceleration and Bias Effect: Taeho Jang, Yuongshik Kim, Taesoo Jang; J. Korean Soc. Precis. Eng. 2018;35(8):745-752.
Published online August 1, 2018; DOI: https://doi.org/10.7736/KSPE.2018.35.8.745

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In this study we investigated Kalman filter-based attitude estimation algorithms, considering external acceleration and bias effects in several different scenarios. Towards these goals, gyro biases were first estimated, or calibrated, in all three applied algorithms. Whereas external acceleration effects were not considered in the first algorithm, external acceleration effects were compensated for in the second and third algorithms, using the Kalman filter’s residual and acceleration model. Low, intermediate, and high external acceleration scenarios were then implemented in our test-bed. Three different rotational frequencies (0.3, 3, and 6 ㎐) for roll and pitch rotations were applied. Performance of each estimation algorithm was analyzed using slopes, y-intercepts, and standard deviations obtained from the linear regression. Our results confirm that attitude estimation errors are linearly proportional to the magnitude of the applied external acceleration. Perhaps most importantly, our results show the second algorithm may be used to provide relatively uniform and accurate estimation performance for low- and high-frequency motions.

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Inertial Sensor-Based Relative Position Estimation between Upper Body Segments Considering Non-Rigidity of Human Bodies
Chang June Lee, Jung Keun Lee
Journal of the Korean Society for Precision Engineering.2021; 38(3): 215. CrossRef
Drift Reduction in IMU-based Joint Angle Estimation for Dynamic Motion-Involved Sports Applications
Jung Keun Lee, Chang June Lee
Journal of the Korean Society for Precision Engineering.2020; 37(7): 539. CrossRef