Attitude estimation method based on extended Kalman filter algorithm with 22 dimensional state vector for low-cost agricultural UAV

更新时间:2023-05-28

【摘要】To overcome the shortcomings of traditional artificial spraying pesticides and make more efficient prevention of diseases and pests, a coaxial sixteen-rotor unmanned aerial vehicle(UAV) with pesticide spraying system is designed. The coaxial sixteen-rotor UAV’s basic structure and attitude estimation method are explained. The whole system weights 25 kg, cruising speed can reach 15 m/s, and the flight time is more than 20 min. When the UAV takes large load, the traditional extended Kalman filter(EKF) attitude estimation method can not meet the work requirements under the condition of strong vibration, the attitude measure accuracy is poor and the attitude angle divergence is easily caused. Hence an attitude estimation method based on EKF algorithm with 22 dimensional state vector is proposed which can solve these problems. The UAV system consists of STM32 F429 as controller, integrating following measure sensors: accelerometer and gyroscope MPU6000, magnetometer LSM303 D, GPS NEO-M8 N and barometer. The attitude unit quaternion, velocity, position, earth magnetic field, biases error of gyroscope, accelerometer and magnetometer are introduced as the inertial navigation systems(INS) state vector, while magnetometer, global positioning system(GPS) and barometer are introduced as observation vector, thus making the estimate of the navigation information more accurate. The control strategy of coaxial sixteen-rotor UAV is based on the control method of combining active disturbance rejection control(ADRC) and proportion integral derivative(PID) control. Actual flight data are used to verify the algorithm, and the static experiment shows that the precision of roll angle and pitch angle of the algorithm are ±0.1 °, the precision of yaw angle is ±0.2 °. The attitude angle output of MTi sensor is used as reference. The dynamic experiment shows that the accuracy of attitude estimated by EKF algorithm is quite similar to that of MTi’s output, moreover, the algorithm has good real-time performance which meets the need of high maneuverability of agricultural UAV.

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