Autonomous Navigation and Path Tracking Control on Field Roads in Hilly Areas

• Published in: Journal of sensors 2019, Vol.2019 (2019), p.1-15
• Main Authors: Xu, Junjie, Liu, Dexiong, Sun, Hongwei, Li, Yunwu, Wang, Mingfeng
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptive systems; Agricultural production; Agriculture; Algorithms; Artificial neural networks; Automatic control; Automation; Autonomous navigation; Computer simulation; Control algorithms; Control systems; Data integration; Deviation; Divergence; Driver behavior; Driving; Farm machinery; Fuzzy control; Fuzzy logic; Global navigation satellite system; Horizontal orientation; Inertial navigation; Kalman filters; Methods; Multisensor fusion; Navigation systems; Neural networks; Optimization; Outages; Satellite tracking; Signal processing; Steering; Trajectory control; Vehicles
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2019/6738594

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