IMPROVING THE ACCURACY OF UAV MODULE MOTION CONTROL USING INERTIAL SYSTEMS WITH HOMOGENEOUS SENSORS

The proposed method in the work focuses on improving the control and management of the movement of additional modules of an unmanned aerial vehicle (UAV) through the use of inertial measurement systems, which employ multiple sensors of the same type. The implementation of this method is based on the use of two MEMS accelerometers MPU6050. The influence of using two accelerometers combined with a Kalman filter (KF) on the accuracy of measurements in tracking the movement of the UAV without a fixed reference point has been investigated. It is shown that the use of additional sensors allows comparing and combining data from two independent sources, which contributes to an increase in measurement accuracy. The use of the KF in this system helps to reduce random fluctuations and noise. The integration of two sensors with the KF, which automatically adjusts the system's new data, ensures improved accuracy in tracking the movement of the object. This is critically important for systems that require maintaining a planned trajectory in cases of damage, absence, or loss of connection with external positioning systems.It has been demonstrated that UAVs often operate in challenging environments where traditional positioning systems, such as GPS, may face limitations due to poor satellite visibility or other interference. In such conditions, the use of inertial systems provides more reliable movement tracking, significantly enhancing the autonomy of the UAV. The work also describes the prospect of integrating the MEMS accelerometer into an inertial movement tracking system based on artificial intelligence (AI). It discusses the issues of optimizing the number of sensors when developing data processing algorithms using neural networks and machine learning methods.