Mobile robot autonomous navigation using MEMS gyro north finding method in Global Urban System

Yuan, Wei; Lee, Min Cheol

doi:10.1109/icma.2011.5985637

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…According to the outputs of the gyro and the accelerometer, we can solve the significant coefficients iteratively. The above formulas (12), (15), and (16) can be written in a matrix form.…”

Section: (22)mentioning

confidence: 99%

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Optimal Weighted Fusion Based on Recursive Least Squares for Dynamic North-Finding of MIMU on a Tilting Base

Xing

Chen

Zhang³

et al. 2019

IEEE Access

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Accurate estimation of orientation is very important in the field of inertial navigation. With the development of the technology of micro electro mechanical systems (MEMS), suppressing the influence of a gyro drift by means of continuous rotation (''carouseling'') has become a research hotspot. However, most of the current studies are still experiments conducted on the horizontal plane but carrying out north-seeking research on non-horizontal planes is also much valuable because it is common in practical scenarios, such as in the mining and drilling environment. In this paper, a feasible dynamic north-finder method for a micro inertial measurement unit (MIMU) based on the MEMS technology on a tilting plane is proposed. The fast Fourier transform (FFT) algorithm is used to analyze the appropriate rotation rate, and the tilting angle and heading angle are calculated in real time by an optimal weighted fusion based on recursive least squares (OWFBRLS) algorithm. Higher-precision orientation results can be achieved through the introduction of the optimal weighted fusion (OWF) theory. This paper demonstrates the experimental method and data processing in detail. The experimental results indicate that the proposed algorithm can provide accurate inclinations and headings, and the standard deviation (SD) of heading angle can reach approximately 0.6 • in 3 minutes, which is superior to the methods in the literature and still has high precision in the case of large tilting angles. These profound results prove the feasibility and effectiveness of the proposed method. INDEX TERMS MIMU, optimal weighted fusion, recursive least squares, fast Fourier transform, dynamic north-finding, tilting base.

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“…According to the outputs of the gyro and the accelerometer, we can solve the significant coefficients iteratively. The above formulas (12), (15), and (16) can be written in a matrix form.…”

Section: (22)mentioning

confidence: 99%

“…However, the method took long and the calculation of the EKF filter was complicated. Wei studied the pointing of a robot using a MEMS gyro and achieved an effective target pointing performance without the GPS [12].…”

Section: Introductionmentioning

confidence: 99%

Optimal Weighted Fusion Based on Recursive Least Squares for Dynamic North-Finding of MIMU on a Tilting Base

Xing

Chen

Zhang³

et al. 2019

IEEE Access

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show abstract

“…Iozan et al designed a north finding system using a MEMS gyroscope with a single-axis rotary table to compensate the gyroscope's bias [ 15 ]. Wei et al introduced a MEMS gyroscope north finding system for mobile robot with a single-axis turntable [ 16 ]. It could detect the heading angle while the robot is stationary.…”

Section: Introductionmentioning

confidence: 99%

Attitude Heading Reference System Using MEMS Inertial Sensors with Dual-Axis Rotation

Song

et al. 2014

Sensors

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This paper proposes a low cost and small size attitude and heading reference system based on MEMS inertial sensors. A dual-axis rotation structure with a proper rotary scheme according to the design principles is applied in the system to compensate for the attitude and heading drift caused by the large gyroscope biases. An optimization algorithm is applied to compensate for the installation angle error between the body frame and the rotation table's frame. Simulations and experiments are carried out to evaluate the performance of the AHRS. The results show that the proper rotation could significantly reduce the attitude and heading drifts. Moreover, the new AHRS is not affected by magnetic interference. After the rotation, the attitude and heading are almost just oscillating in a range. The attitude error is about 3° and the heading error is less than 3° which are at least 5 times better than the non-rotation condition.

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“…The classical localization systems for mobile robots use dead-reckoning sensors such as gyroscopes and encoders. 1,2 Some other systems use combination of ultrasonic sensors, cameras, and the Global Positioning System (GPS). 3,4 These systems are subject to accumulation errors that can result in inaccurate performance such as in the dead-reckoning sensors.…”

Section: Introductionmentioning

confidence: 99%

Auto-localization system for indoor mobile robot using RFID fusion

Abdelgawad

2014

Robotica

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SUMMARYAutonomous mobile robots need accurate localization techniques to perform assigned task. Radio Frequency Identification Technology (RFID) has become one of the main means to construct a real-time localization system. Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. This paper proposes an auto-localization system for indoor mobile robot using passive RFID. The proposed system reads any three different RFID tags which have a known location. The current location can be estimated using the Time Difference of Arrival (TDOA) scheme. In order to improve the system accuracy, the proposed system fuses the TDOA scheme for the three tags. A Kalman filter is used to minimize the estimated error and predict the next location. The simulation results validate the proposed framework.

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Mobile robot autonomous navigation using MEMS gyro north finding method in Global Urban System

Cited by 10 publications

References 6 publications

Optimal Weighted Fusion Based on Recursive Least Squares for Dynamic North-Finding of MIMU on a Tilting Base

Optimal Weighted Fusion Based on Recursive Least Squares for Dynamic North-Finding of MIMU on a Tilting Base

Attitude Heading Reference System Using MEMS Inertial Sensors with Dual-Axis Rotation

Auto-localization system for indoor mobile robot using RFID fusion

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