Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method
A problem of estimating the movement and orientation of a mobile robot is examined in this paper. The strapdown inertial navigation systems are often engaged to solve this common obstacle. The most important and critically sensitive component of such positioning approximation system is a gyroscope....
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MDPI AG
2020-08-01
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| author | Andrii V. Rudyk Andriy O. Semenov Natalia Kryvinska Olena O. Semenova Volodymyr P. Kvasnikov Andrii P. Safonyk |
| author_facet | Andrii V. Rudyk Andriy O. Semenov Natalia Kryvinska Olena O. Semenova Volodymyr P. Kvasnikov Andrii P. Safonyk |
| author_sort | Andrii V. Rudyk |
| collection | DOAJ |
| description | A problem of estimating the movement and orientation of a mobile robot is examined in this paper. The strapdown inertial navigation systems are often engaged to solve this common obstacle. The most important and critically sensitive component of such positioning approximation system is a gyroscope. Thus, we analyze here the random error components of the gyroscope, such as bias instability and random rate walk, as well as those that cause the presence of white and exponentially correlated (Markov) noise and perform an optimization of these parameters. The MEMS gyroscopes of InvenSense MPU-6050 type for each axis of the gyroscope with a sampling frequency of 70 Hz are investigated, as a result, Allan variance graphs and the values of bias instability coefficient and angle random walk for each axis are determined. It was found that in the output signals of the gyroscopes there is no Markov noise and random rate walk, and the X and Z axes are noisier than the Y axis. In the process of inertial measurement unit (IMU) calibration, the correction coefficients are calculated, which allow partial compensating the influence of destabilizing factors and determining the perpendicularity inaccuracy for sensitivity axes, and the conversion coefficients for each axis, which transform the sensor source codes into the measure unit and bias for each axis. The output signals of the calibrated gyroscope are noisy and offset from zero to all axes, so processing accelerometer and gyroscope data by the alpha-beta filter or Kalman filter is required to reduce noise influence. |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:47:06Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-a33e521f59eb432d9b08bf7c098763a82023-11-20T11:34:06ZengMDPI AGSensors1424-82202020-08-012017484110.3390/s20174841Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance MethodAndrii V. Rudyk0Andriy O. Semenov1Natalia Kryvinska2Olena O. Semenova3Volodymyr P. Kvasnikov4Andrii P. Safonyk5Department of Automation, Electrical Engineering and Computer-Integrated Technologies, National University of Water and Environmental Engineering, Soborna Street 11, 33000 Rivne, UkraineFaculty for Infocommunications, Radioelectronics and Nanosystems, Vinnytsia National Technical University, Khmelnytske shose 95, 21000 Vinnytsia, UkraineDepartment of Industrial Engineering, University of Applied Sciences - Technikum Wien, 1200 Vienna, AustriaFaculty for Infocommunications, Radioelectronics and Nanosystems, Vinnytsia National Technical University, Khmelnytske shose 95, 21000 Vinnytsia, UkraineDepartment of Computerized Electrotechnical Systems and Technologies, National Aviation University, Prospect Kosmonavta Komarova, 1, 02000 Kyiv, UkraineDepartment of Automation, Electrical Engineering and Computer-Integrated Technologies, National University of Water and Environmental Engineering, Soborna Street 11, 33000 Rivne, UkraineA problem of estimating the movement and orientation of a mobile robot is examined in this paper. The strapdown inertial navigation systems are often engaged to solve this common obstacle. The most important and critically sensitive component of such positioning approximation system is a gyroscope. Thus, we analyze here the random error components of the gyroscope, such as bias instability and random rate walk, as well as those that cause the presence of white and exponentially correlated (Markov) noise and perform an optimization of these parameters. The MEMS gyroscopes of InvenSense MPU-6050 type for each axis of the gyroscope with a sampling frequency of 70 Hz are investigated, as a result, Allan variance graphs and the values of bias instability coefficient and angle random walk for each axis are determined. It was found that in the output signals of the gyroscopes there is no Markov noise and random rate walk, and the X and Z axes are noisier than the Y axis. In the process of inertial measurement unit (IMU) calibration, the correction coefficients are calculated, which allow partial compensating the influence of destabilizing factors and determining the perpendicularity inaccuracy for sensitivity axes, and the conversion coefficients for each axis, which transform the sensor source codes into the measure unit and bias for each axis. The output signals of the calibrated gyroscope are noisy and offset from zero to all axes, so processing accelerometer and gyroscope data by the alpha-beta filter or Kalman filter is required to reduce noise influence.https://www.mdpi.com/1424-8220/20/17/4841mobile robotsstrapdown inertial navigation systems (SINS)gyroscopeerror equationAllan deviationbias instability |
| spellingShingle | Andrii V. Rudyk Andriy O. Semenov Natalia Kryvinska Olena O. Semenova Volodymyr P. Kvasnikov Andrii P. Safonyk Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method Sensors mobile robots strapdown inertial navigation systems (SINS) gyroscope error equation Allan deviation bias instability |
| title | Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method |
| title_full | Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method |
| title_fullStr | Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method |
| title_full_unstemmed | Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method |
| title_short | Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method |
| title_sort | strapdown inertial navigation systems for positioning mobile robots mems gyroscopes random errors analysis using allan variance method |
| topic | mobile robots strapdown inertial navigation systems (SINS) gyroscope error equation Allan deviation bias instability |
| url | https://www.mdpi.com/1424-8220/20/17/4841 |
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