Huang Peikui, Zhang Zhigang, Luo Xiwen, Liu Zhaopeng, Wang Hui, Yue Binbin, Gao Weiwei. Development of external acceleration identification and attitude estimation system of field working vehicle[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(3): 9-15. DOI: 10.11975/j.issn.1002-6819.2019.03.002
    Citation: Huang Peikui, Zhang Zhigang, Luo Xiwen, Liu Zhaopeng, Wang Hui, Yue Binbin, Gao Weiwei. Development of external acceleration identification and attitude estimation system of field working vehicle[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(3): 9-15. DOI: 10.11975/j.issn.1002-6819.2019.03.002

    Development of external acceleration identification and attitude estimation system of field working vehicle

    • Abstract: The development of precision agriculture, intelligent agricultural machinery and equipment are an effective way to alleviate the current tense situation in world food security. The complex field environment and meticulous work effects require the agricultural machinery to have the ability to perceive the attitude of the agricultural machinery accurately in real time. For example, the precision navigation control and the leveling control of agricultural implements are all dependent on the accurate measurement of attitude. What's more, the attitude of agricultural implements is one of the key parameters of agricultural mechanics modeling and agricultural implements safety warning learning. However, the external acceleration of the vehicle, which is generally present under dynamic operation conditions, poses a challenge. In order to further improve the precision operation of agricultural machinery and equipment, the paper developed a minimal hardware system for external acceleration identification and attitude estimation used for field working vehicles, and verified by the experiments taken place on the Innova 2100 shaker and the ZP9500 high level sprayer in the field. Modern micro-electromechanical systems (MEMS) technologies provide the moderate-cost and miniaturized solutions for the development of attitude reference system. By using of highly-integrated inertial measurement units (IMUs) ADIS16445 provided by ADI company and micro ARM processor STM32F446 provided by ST company, the hardware platform was built. ADIS16445 ISensor(r) included tri-axial gyroscopes and tri-axial accelerometers, the raw sensors data was sampled by STM32F446RC processor through SPI interface. The attitude calculation was carried out based on the direction cosine matrix algorithm. Based on the gyroscope and accelerometer measurement model, an one order external acceleration measurement model was proposed, and a Kalman filter fusion algorithm with 4 state vectors was established. Since the bias of gyroscopes and accelerometers was stable after hardware preheated, the impact of bias of MEMS inertial sensors in the fusion algorithm was not considered. Innova 2100 shaker is a standard equipment of rotary motion, by setting different rotation speed, different centripetal accelerations can be achieved to verify the external acceleration identification. Innova 2100 shaker test results showed that the measure error was less than 0.214 m/s2 under the external acceleration lower than 10g. Field experiments were conducted on Innova 2100 shaker and the ZP9500 high level sprayer provided by LOVOL company with the assistance of attitude and position reference system (AHRS) MTi300 provided by Xsens company. The MTi300 AHRS provide high precision attitude and heading output with high stability and fast dynamic response with dynamic measurement accuracy of 0.3 °, which make it widely used in navigation implements, automobiles, agricultural implements and other fields. Experiment results from high level sprayer showed that compared to the MTi300 AHRS, the average measurement error of roll angle was 0.069 ° and the maximal measurement error was 0.23 ° respectively. The average measurement error of pitch angle was 0.078 ° and the maximal measurement error was 0.39 ° respectively. Test results verified that the proposed Kalman filter algorithm was accuracy and stable, which can improving the quality of agricultural implements operations and have more applicability.
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