• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.174-179
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• 2003

Through the simultaneous localization and map building (SLAM) technique, a robot can create maps about its unknown environment while it continuously localizes its position. Grid maps and feature maps have been widely used for SLAM together with application of probability methods and POMDP (partially observed Markov decision process). But this approach based on grid maps suffers from enormous computational burden. Topological maps, however, have drawn more attention these days because they are compact, provide natural interfaces, and are easily applicable to path planning in comparison with grid maps. Some topological SLAM techniques like GVG (generalized Voronoi diagram) were introduced, but it enables the robot to decide only whether the current position is part of GVG branch or not in the GVG algorithm. In this paper, therefore, to overcome these problems, we present a method for updating a global topological map from the local topological maps. These local topological maps are created through a labeled Voronoi diagram algorithm from the local grid map built based on the sensor information at the current robot position. And the nodes of a local topological map can be utilized as the features of the environment because it is robust in light of visibility problem. The geometric information of the feature is applied to the extended Kalman filter and the SLAM in the indoor environment is accomplished. A series of simulations have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can be applied relatively well.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1664-1673
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• 2016

Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative $LiCoO_2/LiFePO_4/LiNiMnCoO_2$ cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the $LiFePO_4$ cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

• Ocean and Polar Research
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• v.33 no.1
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• pp.69-76
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• 2011

A test miner named MineRo was constructed for the purpose of shallow water test of mining performance. In June of 2009, the performance test was conducted in depth of 100 m, 5 km away from Hupo-port (Korean East Sea), to assess if the developed system is able to collect and lift manganese nodules from seafloor. In August of 2010, in-situ test of automatic path tracking control of MineRo was performed in depth of 120 m at the same site. For path tracking control, a localization algorithm determining MineRo's position on seabed is prerequisite. This study proposes an improved underwater navigation algorithm through estimation of MineRo's kinematic parameters. In general, the kinematic parameters such as track slips and slip angle are indirectly calculated using the position data from USBL (Ultra-Short Base Line) system and heading data from gyro sensors. However, the obtained data values are likely to be different from the real values, primarily due to the random noise of position data. The aim of this study is to enhance the reliability of the algorithm by measuring kinematic parameters, track slips and slip angle.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.5
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• pp.405-412
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• 2015

This paper represents a filter design to estimate the airspeed of a spin-stabilized, trajectory-correctible artillery ammunition. Due to the limited power and space in operational point of view, the airspeed sensor is not installed, and thus the airspeed need to be estimated using limited sensor measurements. The only IMU measurements(three-axis specific forces and angular rates) are used in this application. The extended Kalman filter algorithm is applied since a linear filter can not cover the its wide operational range in airspeed and altitude. In the implementation of the EKF, the state and measurement equations are transformed into the no-roll frame for simple form of Jacobian matrix. The simulation study is conducted to evaluate the performance of the filter under various environment conditions of sensor noise and wind turbulence. In addition, the effect of the choice in filter design parameters, i.e. process error covariance matrices is analyzed on the performance of the estimation of airspeed and angular rates.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.12
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• pp.1076-1083
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• 2017

This paper discusses the error estimation in radar measurement data obtained while tracking a launch vehicle. It is known that typical radar measurement data consist of the true positional or orientation information on the vehicle being tracked, random noise and a deterministic bias due to radio refraction. Unlike previous research works, this paper proposes a tracking-error (mainly bias) estimation method solely based on the single radar measurement with no aid of other measurement such as GPS. The proposed method has been verified with real measurement data obtained while tracking the KSLV-I launch vehicle.

• Journal of Advanced Navigation Technology
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• v.3 no.2
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• pp.97-107
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• 1999

Passive sensors such as Laser Range Finder(LRF) and Forward Looking Infrared(FLIR) camera frequently used for tracking aircraft landing produce the measurements of elevation angle contaminated by large noise due to the exhaust plume disturbance. This results in poor tracking performance if the extended Kalman filter is used for estimation of the range and elevation which are corrupted by the non-Gaussian noise such as plume disturbance. In this paper, an adaptive estimation filter and the extended Kalman filter is combined to produce a combination-type filter. In this approach the adaptive filter is used for the plume-type disturbance noise and the extended Kalman filter is utilized for the measurement of Gaussian type. The proposed combination filter is effective for the trajectory estimation of landing aircraft under the influence of unknown bias and numerical simulations illustrate the performance of the proposed filter.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.167-179
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• 2017

In this paper, a simplified Cubature Kalman Filter (SCKF) is proposed to reduce the computation load of CKF, which is then used as a filter for transfer alignment of shipboard INS. CKF is an approximate Bayesian filter that can be applied to non-linear systems. When an initial estimation error is large, convergence characteristic of the CKF is more stable than that of the Extended Kalman Filter (EKF), and the reliability of the filter operation is more ensured than that of the Unscented Kalman Filter (UKF). However, when a system degree is large, the computation amount of CKF is also increased significantly, becoming a burden on real-time implementation in embedded systems. A simplified CKF is proposed to address this problem. This filter is applied to shipboard inertial navigation system (INS) transfer alignment. In the filter design for transfer alignment, measurement type and measurement update rate should be determined first, and if an application target is a ship, lever-arm problem, flexure of the hull, and asynchronous time problem between Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS) should be taken into consideration. In this paper, a transfer alignment filter based on SCKF is designed by considering these problems, and its performance is validated based on simulations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.3
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• pp.21-29
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• 2011

This paper presents a method that estimates distance and velocity of an object with reliability regardless of maneuver status of the target in stereo vision system. A stereo vision system can calculate a distance with disparity from left and right images. However, the distance estimation error may occur due to quantization error of image pixel. A sub-pixel interpolation method minimizes the quantization error and estimates accurate disparity with real value. Extended Kalman filter (EKF) was used to minimize the error covariance and estimate the object's velocity. However, divergence problem occurs due to model uncertainty when a target maneuvers highly, which makes the estimation error increase. In this paper, we propose a virtual model extended Kalman filter (VMEKF) method that minimizes the processing time and provides reliable estimation ability regardless of maneuver status. Computer simulations and experimental results in real road environments demonstrate that the proposed method gives a reliable estimation performance and reduces processing time under various maneuver status while comparing other estimation filters.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.272-275
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• 2014

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.