The integrated navigation system of multi rotor unmanned aerial vehicles needs to integrate data from multiple information sources to provide accurate and reliable positioning and navigation capabilities. The multi-source information fusion algorithm is the key to achieving this goal. These algorithms typically include the following aspects: 1** Inertial Navigation System (INS): It uses sensors such as accelerometers and gyroscopes to measure the acceleration and angular velocity of unmanned aerial vehicles, thereby deriving position, velocity, and heading information. However, INS may experience drift and require correction through other sensors. 2. Global Positioning System (GPS): GPS is a technology that determines the position by receiving signals from satellites. It provides high-precision location information, but in certain environments, such as urban high-rise areas or valley areas, the signal may be obstructed or interfered with. 3. * * Visual Sensor * *: Drones can obtain images of ground features through cameras or other visual sensors, and use visual odometry technology to infer position and posture. This method is very effective for short-term positioning, but may be affected by changes in lighting, weather, and ground features during long-term flights. 4. * * Ground measurement system * *: The ground measurement system can provide reference position information through base stations or other sensors on the ground, such as differential GPS or wireless positioning systems. Multi source information fusion algorithm usually uses Kalman filter or extended Kalman filter and other technologies to optimize and integrate information from different sensors to improve the accuracy and reliability of positioning and navigation. These algorithms can effectively handle sensor errors, drift, and uncertainty, and provide stable navigation solutions suitable for various environments and task requirements.
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1 окт 2024
