Estimation Method and Experiment of Wheel Angle of Paddy Field Agricultural Machinery Based on Dual Observation Fusion Kalman Filter
Abstract
In order to solve the problem that the sudden change of the speed of automatic driving agricultural machinery in paddy field leads to inaccurate angle estimation, a steering wheel angle estimation method of agricultural machinery in paddy field was proposed based on dual observation fusion Kalman filter, and a steering wheel angle estimation model of agricultural machinery in paddy field was established. Firstly, the improved two-wheeled agricultural machinery sideslip model was used to obtain the front wheel steering angle of paddy agricultural machinery based on kinematics model. Secondly, the collected GPS speed and inertial navigation speed were compensated by weighted observation fusion method. Finally, a method for estimating the front wheel steering angle of paddy agricultural machinery based on dual observation fusion Kalman filter was proposed, which took the front wheel steering angle based on kinematics model and the front wheel steering angle based on steering motor coding as dual observation values, so as to estimate the front wheel steering angle of paddy agricultural machinery. In order to verify the proposed method, speed correction, front wheel steering angle estimation test and linear tracking test were carried out in paddy field on the platform of rice direct seeding machine. The results of speed correction test showed that the unevenness of paddy field hard bottom layer was the direct reason for the poor fitting accuracy of front wheel angle. The proposed method stabilized the speed of direct seeding machine in a certain range, and solved the problem of poor fitting accuracy of front wheel angle caused by the fluctuation of paddy field hard bottom layer. The front wheel steering angle estimation experiment showed that the average tracking error of the virtual wheel angle relative to the angle change of the angle sensor was 0.12°, the maximum deviation was 1.67°and the standard deviation was 0.4°. The method can accurately measure the steering angle of the front wheel of agricultural machinery, and finally control the direct seeding machine to track the target angle stably, which met the accuracy requirements of estimation of front wheel angle of agricultural machinery in paddy field. The results of linear tracking test showed that the average error was 3.14 cm and the standard deviation of position deviation was 2.11 cm in paddy field environment. The method proposed was suitable for unmanned paddy field, which improved the accuracy of corner estimation and the quality of agricultural machinery navigation.
Keywords: paddy field, agricultural machinery, autopilot, Kalman filter
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