Gyroscopes

A gyroscope is an inertial sensor based on the principle of conservation of angular momentum. It achieves attitude sensing by measuring the angular velocity of an object’s rotation. The evolution of technology from mechanical to MEMS (microelectromechanical systems) has led to its widespread application in aerospace, consumer electronics, industrial control, and other fields. Modern gyroscopes have achieved a perfect balance between nanometer-level precision and millimeter-level size.

 

1. What are the Core Types of Gyroscopes?

Mechanical Gyroscopes

Use the precession effect of high-speed rotating rotors (such as gyro rotors) to detect angular displacement. Traditional navigation systems still rely on these high-precision devices.

 

Fiber Optic Gyroscopes (FOG)

Based on the Sagnac effect, they calculate angular velocity by detecting the phase difference of light in a circular optical path and have strong resistance to electromagnetic interference.

 

MEMS Gyroscopes

Use a micromechanical resonant structure to detect mass displacement via the Coriolis force, offering the advantages of low power consumption and small size (e.g., the 6-axis IMU module in smartphones).

 

2. Key Performance Parameters of Gyroscopes

Range: ±15°/s to ±2000°/s (consumer to industrial grade)

Bias Stability: 0.1°/hr to 10°/hr (determines long-term accuracy)

Noise Density: 0.01°/√Hz (affects dynamic response)

Bandwidth: DC to 200Hz (determines signal acquisition capability)

 

3. Typical Application Scenarios of Gyroscopes

Autonomous Driving: Vehicle yaw rate monitoring and rollover control

UAVs: Flight attitude stabilization and heading maintenance

VR Devices: Head motion tracking (latency <20ms)

Oil Exploration: Downhole drill bit orientation

 

4. Technology Trends of Gyroscopes

Multi-Sensor Fusion: 9-Axis IMU with accelerometers and magnetometers

Quantum Gyroscope: Ultra-high precision using atomic interferometry (for military navigation)

AI-Driven Calibration: Compensating for errors such as temperature drift through machine learning