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Strapdown Inertial Navigation Systems for Positioning Mobile Robots-MEMS Gyroscopes Random Errors Analysis Using

Andrii V Rudyk1, Andriy O Semenov2, Natalia Kryvinska3,4

  • 1Department of Automation, Electrical Engineering and Computer-Integrated Technologies, National University of Water and Environmental Engineering, Soborna Street 11, 33000 Rivne, Ukraine.

Sensors (Basel, Switzerland)
|September 2, 2020
PubMed
Summary
This summary is machine-generated.

This study analyzes MEMS gyroscope errors for mobile robot navigation. Gyroscope calibration reduces noise, but post-calibration filtering is still needed for accurate positioning.

Keywords:
Allan deviationangle random walkbias instabilityerror equationgyroscopemobile robotsstrapdown inertial navigation systems (SINS)

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Area of Science:

  • Robotics
  • Navigation Systems
  • Sensor Technology

Background:

  • Estimating mobile robot movement and orientation is crucial for navigation.
  • Strapdown inertial navigation systems (INS) commonly employ gyroscopes for this task.
  • Gyroscopes are sensitive components susceptible to various error sources.

Discussion:

  • Analysis of MEMS gyroscope (InvenSense MPU-6050) random error components, including bias instability and random rate walk.
  • Investigation of white and exponentially correlated (Markov) noise effects on gyroscope performance.
  • Determination of Allan variance graphs, bias instability coefficients, and angle random walk for each axis at a 70 Hz sampling frequency.

Key Insights:

  • Markov noise and random rate walk were not detected in the investigated gyroscopes.
  • The X and Z axes exhibited higher noise levels compared to the Y axis.
  • Calibration identified correction coefficients to mitigate destabilizing factors and axis misalignment.

Outlook:

  • Post-calibration gyroscope output signals remain noisy and offset, necessitating further noise reduction.
  • Alpha-beta or Kalman filters are recommended for processing accelerometer and gyroscope data to minimize noise influence.
  • Optimizing gyroscope parameters and implementing advanced filtering techniques are essential for enhanced robotic navigation accuracy.