• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1053-1059
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• 2013

Heading bias effects in PNS using IMUs attached to shoes are analyzed in this paper. The navigation algorithms of a single foot PNS where one IMU is attached to a foot and dual foot PNSs where two IMUs are attached to each foot are derived. Two navigation algorithms are proposed for the dual foot PNS: 1) the positions from the independent right and left foot PNSs are averaged to provide the final position, 2) the right and left foot PNSs are correlated and it provides positions of each foot. Furthermore, it is proven that two methods are equal. Using the derived navigation algorithms the effect of heading bias caused by a misalignment of the moving direction and IMU is analyzed. The analysis explains the position error of a single foot PNS is diverged while the heading bias is effectively compensated in dual foot PNSs because of the symmetry of heading biases. The experimental results confirm the analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.470-476
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• 2016

This paper proposes a practical algorithm for the reduction of measurement errors due to drift in a micro-electromechanical system (MEMS) gyros that are used for a mobile robot. Any drift in a MEMS gyro will cause an unbounded growth of errors in the estimation of heading, which makes it nearly useless in applications that require high accuracy over a long operating time. In proposed method, maneuvers of a cleaning robot are observed through encoders' measurement process and a decision to correct bias drift will be made if necessary. The method used in this paper is called the "heading estimation filter". To evaluate the accuracy of the proposed method, a comparison was made between the estimation of the heading of the cleaning robot and one from a motion capture system.

• The Journal of Korea Robotics Society
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• v.6 no.4
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• pp.334-343
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• 2011

This paper presents a new approach for mobile robot heading detection using MEMS Gyro north finding method in the indoor environment. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system's precision and effectiveness. In order to find out the heading angle, a single axis MEMS gyroscope to sense the angle between the robot heading direction and the north is used. To reach enough estimation accuracy and reduce detection time, the least square method (LSM) for the signal fitting and parameter estimation is applied. Through a turn-table, we setup a carouseling system to decrease the substantial bias effect on gyroscope's heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system's accuracy, usefulness and adaptability are shown.

• Journal of Hydrospace Technology
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• v.1 no.1
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• pp.75-88
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• 1995

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship's direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dimension in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.3
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• pp.368-377
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• 2009

In this paper we study the gyro bias calibration method of SDINS(Strap-Down Inertial Navigation System). Generally, SDINS's calibration is performed in 2-axis(or 3-axis) rate table with chamber for varying ambient temperature. We assumed that the majority of calibration-parameter except for gyro bias is knowned. During gyrobias calibration procedure, it can be induced some disturbances(accelerometer's short-term error induced rate table rotation and anti-vibration mount's rotation). In these cases, old gyro-bias calibration methods(using velocity error or attitude error) have an error, because these disturbances are not detectable at the same time. So that, we propose a new gyro-bias calibration method(heading error minimizing using equivalent linear transformation) that can detect anti-vibration mount's rotation. And we confirm efficiency of the new gyro-bias calibration method by simulation.

• Journal of Ocean Engineering and Technology
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• v.31 no.2
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• pp.177-182
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• 2017

This paper describes an unscented Kalman filter approach to estimate the bias in magnetic field measurements. A microelectromechanical systems attitude heading reference system (MEMS AHRS) was used to measure the magnetic field, together with the acceleration and angular rate. A magnetic field is usually used for yaw detection, while the acceleration serves to detect the roll and pitch. Magnetic field measurements are vulnerable to distortion due to hard-iron effect and soft-iron effect. The bias in the measurement accounts for the hard-iron effect, and this paper focuses on an approach to estimate this bias. The proposed method is compared with other methods through experiments that implement the navigation of an underwater robot using an AHRS and Doppler velocity log. The results verify that the compensation of the bias by the proposed method improves the navigation performance more than or comparable to the compensation by other methods.

• Journal of Ocean Engineering and Technology
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• v.31 no.1
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• pp.28-35
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• 2017

This paper introduces a GPS-aided dead reckoning algorithm that asymptotically estimates the heading bias error of a magnetic compass based on geodetic north, improves the position error accumulated by dead reckoning, and helps the estimated position of an AUV to represent a position in the NED coordinate system, by receiving GPS position information when surfaced. Based on the results of a simulation, the locational error was bounded with a modest distance, after estimating the AUV position and heading bias error of the magnetic compass when surfaced. In other words, it was verified that proposed algorithm improves the position error in the NED coordinate system.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.4
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• pp.8-16
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• 1993

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dim in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.265-274
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• 2019

The paper describes an application of unscented Kalman filter(UKF) for attitude estimation of an unmanned vehicle(UV), which is equipped with a low-cost attitude heading reference system (AHRS). The roll, pitch and yaw required at the correction stage of the UKF are calculated from the measurements of acceleration and geomagnetic field. The roll and pitch are attributed to the measurement of acceleration, while yaw is calculated from the geomagnetic field measurement. Since the measurement of geomagnetic field is vulnerable to distortion by hard-iron and soft-iron effects, the calculated yaw has more uncertainty than the calculated roll and pitch. To reduce the uncertainty of geomagnetic field measurement, the proposed method estimates bias in the geomagnetic field measurement and compensates for the bias for more accurate calculation of yaw. The proposed method is verified through navigation experiments of a UV in a test pool. The results show that the proposed method yields more accurate attitude estimation; thus, it results more accurate location estimation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.34-39
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• 1993

Presented in this paper is a complete error covariance analysis for strapdown inertial navigation system(SDINS). We have found that in SDINS the cross-coupling terms in gyrocompass alignment errors can significantly influence the SDINS error propagation. Initial heading error has a close correlation with the east component of gyro bias erro, while initial level tilt errors are closely related to accelerometer bias errors. In addition, pseudo-state variables are introduced in covariance analysis for SDINS utilizing the characteristics of gyrocompass alignment errors. This approach simplifies the covariance analysis because it makes the initial error covariance matrix to a diagonal form. Thus a real implementation becomes easier. The approach is conformed by comparing the results for a simplified case with the covariance analysis obtained from the conventional SDINS error model.