Abstract

In this study, an automatic navigation land levelling system based on the global navigation satellite system (GNSS) and an inertial navigation system (INS) was developed to improve the efficiency and accuracy of land levelling. The developed system platform was composed of an integrated positioning unit, steering and lifting controller, and vehicle control terminal. Automatic navigation land levelling vehicle terminal software was designed to realize data communication, processing and storage, and display. Through the cooperation of the platform and software, the system completed the automatic navigation terrain survey, automatic planning/design and automatic navigation land levelling functions. In addition, a GNSS/INS integrated positioning method based on an adaptive square root cubature Kalman filter (ASRCKF) was designed, which obtained the correction positioning points of the tractor and scraper and adaptively predicted the state and measurement noise of the SRCKF. An automatic steering and lifting control method was designed to realize synchronous automatic navigation land levelling control, which was combined with the positioning method. Field experiments were carried out to validate the path tracking effect of the dynamic filtering navigation and a comparison of the manual and automatic land levelling operation performance. The navigation experimental results showed that the average root mean square error (RMSE) of the tractor and the scraper were 2.94 cm and 3.15 cm in tracking a straight path and 8.61 cm and 9.83 cm in tracking a curved path, respectively. The average RMSE of the path tracking based on the ASRCKF positioning method was reduced by 75.2% and 67.4% for the tractor and the scraper, respectively. The land levelling experimental results showed that the elevation distribution around the datum elevation of the automatic navigation land levelling increased by 8.7% compared to the manual operation. The GNSS/INS-based automatic navigation land levelling system can notably reduce the lateral position deviations, improve the control accuracy and land levelling efficiency, and meet the control accuracy requirements of unmanned tractor-scraper land levelling operations.

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