Hilly areas necessitate a field road vehicle with high automation to reduce the amount of labor required to transport agricultural\nproducts and to increase productivity. In this paper, an adaptive integrated navigation method (combining global navigation\nsatellite system (GNSS) and inertial navigation system (INS)) and path tracking control strategy of field road vehicles are\nstudied in view of the problems of frequent GNSS outages and high automatic control precision requirement in hilly areas. An\nindirect Kalman filter (KF) is designed for the GNSS/INS information fusion. A modified method for calculating the KF\nadaptive factor is proposed to effectively suppress the divergence of the KF and a threshold judgement method to abandon the\nabnormal GNSS measurement is proposed to deal with GNSS interruptions. To achieve automated driving, a five-layer fuzzy\nneural network controller, which takes the lateral deviation, heading deviation, and path curvature as input and the steering\nangle as output, is proposed to control vehicle autonomous tracking of the navigation trajectory accurately. The proposed\nsystem was evaluated through simulation and experimental tests on a field road. The simulation results showed that the adjusted\nKF fusion algorithm can effectively reduce the deviation of a single GNSS measurement and improve the overall accuracy. The\ntest results showed the maximum deviation of the actual travel trajectory from the expected trajectory of the vehicle in the\nhorizontal direction was 12.2 cm and the average deviation was 5.3 cm. During GNSS outages due to obstacles, the maximum\ndeviation in the horizontal direction was 12.7 cm and the average deviation was 6.1 cm. The results show that the designed\nGNSS/INS integrated navigation system and trajectory tracking control strategy can control a vehicle automatically while\ndriving along a field road in a hilly area.
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