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田光兆,顾宝兴,Irshad Ali Mari,周俊,王海青.基于三目视觉的自主导航拖拉机行驶轨迹预测方法及试验[J].农业工程学报,2018,34(19):40-45.DOI:10.11975/j.issn.1002-6819.2018.19.005
基于三目视觉的自主导航拖拉机行驶轨迹预测方法及试验
投稿时间:2018-06-13  修订日期:2018-08-27
中文关键词:  拖拉机  自主导航  机器视觉  轨迹预测  灰色模型
基金项目:中央高校基本业务费资助项目(KYGX201701);国家自然科学基金资助项目(31401291);江苏省自然科学基金资助项目(BK20140729)
作者单位
田光兆 1.南京农业大学工学院南京 210031 
顾宝兴 1.南京农业大学工学院南京 210031 
Irshad Ali Mari 2. 巴基斯坦信德农业大学凯尔布尔工程技术学院凯尔布尔 66020 
周俊 1.南京农业大学工学院南京 210031 
王海青 1.南京农业大学工学院南京 210031 
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中文摘要:为了实现自主导航拖拉机离开卫星定位系统时能够持续可靠工作,该文提出了基于三目视觉的拖拉机行驶轨迹预测方法。该方法将三目相机分解为长短基线2套双目视觉系统分时独立工作。通过检测相邻时刻农业环境中同一特征点的坐标变化反推拖拉机在水平方向上的运动矢量,并通过灰色模型预测未来时刻的运动矢量变化,最终建立不同速度下的前进方向误差模型。试验结果表明:拖拉机行驶速度为0.2 m/s时,46.5 s后前进方向误差超过0.1 m,对应行驶距离为9.3 m。行驶速度上升到0.5 m/s时,该时间和行驶距离分别降低到17.2 s和8.6 m。当行驶速度上升到0.8 m/s时,该时间和距离分别快速降低至8.5 s和6.8 m。行驶速度越高,前进方向误差增速越高。该方法可用于短时预测拖拉机的行驶轨迹,为自主导航控制提供依据。
Tian Guangzhao,Gu Baoxing,Irshad Ali Mari,Zhou Jun,Wang Haiqing.Traveling trajectory prediction method and experiment of autonomous navigation tractor based on trinocular vision[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(19):40-45.DOI:10.11975/j.issn.1002-6819.2018.19.005
Traveling trajectory prediction method and experiment of autonomous navigation tractor based on trinocular vision
Author NameAffiliation
Tian Guangzhao 1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
 
Gu Baoxing 1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
 
Irshad Ali Mari 2. Khairpur College of Engineering and Technology Sindh Agriculture University, Khairpur 66020, Pakistan 
Zhou Jun 1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
 
Wang Haiqing 1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
 
Key words:tractor  automatic guidance  machine vision  trajectory prediction  gray model
Abstract: In order to make the autonomous navigation tractors work steadily and continuously without the satellite positioning system, a traveling trajectory prediction system and method based on trinocular vision were designed in this paper. The system was composed of a trinocular vision camera, an IEEE 1394 acquisition card and an embedded industrial personal computer (IPC). The right and left sub cameras constituted a binocular vision system with a long base line. The right and middle sub cameras constituted another binocular vision system with a narrow base line. To obtain more precise measurement results, the two binocular vision systems worked independently and in time-sharing. Then the motion vectors of tractor, which were in presentation of horizontal direction data, were calculated by the feature point coordinate changing in the working environment of the tractor. Finally, the error models which were in the direction of heading were established at different velocities, and the motion vectors of tractor were predicted by the models based on grey method. The contrast experiments were completed with a modified tractor of Dongfanghong SG250 at the speed of 0.2, 0.5 and 0.8m/s. During the experiments, the IPC was used to collect RTK-GPS data and predict movement tracks. The RTK-GPS used in the experiments was a kind of high-precision measuring device, and the measuring precision can reach 1-2 cm. Therefore, the location data of RTK-GPS were supposed as the standard which was used to compare with the data from trinocular vision system. The experimental results showed that the method mentioned above could accurately predict the trajectory of the tractor on the plane with an inevitable error which was mainly caused by the visual measurement error of the forward direction (z direction). When the tractor travelled at the speed of 0.2 m/s, the time and the distance that the error in forward direction exceeded 0.1 m equaled 46.5 s and 9.3 m, respectively. When the speed increased to 0.5 m/s, the time and the distance decreased to 17.2 s and 8.6 m, respectively. When the driving speed increased to 0.8 m/s, the time and distance quickly decreased to 8.5 s and 6.8 m, respectively. It showed that the higher the tractor traveling speed, the faster the error in forward direction increased. After that, the relationship between errors in forward direction and traveling time was acquired and analyzed by the way of nonlinear data fitting. In addition, the experimental results showed that the trend of lateral error (x direction) which was perpendicular to forward direction was not regular. When the speed was 0.2 m/s, the average error was 0.002?5 m with a standard deviation (STD) of 0.003?9. When the speed increased to 0.5 m/s and 0.8 m/s, the average error in lateral direction was 0.008?2 m with an STD of 0.012?4 and 0.003?6 m with an STD of 0.006?4. The result showed that the lateral error was very small and almost invariable. Therefore, the errors of trinocular vision were mainly caused by the errors of the forward direction. The root causes of the error were the natural light and time-delay during the image processing. According to the experimental data and results, the system and method proposed in this paper could be used to measure and predict the traveling trajectory of a tractor in the dry agricultural environment with the sudden loss of the satellite signal in a short period of time. The measured and predicted data could provide temporary help for the operations of autonomous tractors.
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