最小二乘法与SVM组合的林果行间自主导航方法

为了提高作业装备在果园与树林行间的自主导航性能,该研究提出一种基于最小二乘法与支持向量机(Support Vector Machine,SVM)融合的树行识别与导航方法。研究采用履带式小型喷雾机为作业平台,通过低成本的单线激光雷达获取果园或树林环境点云数据,融合姿态传感器进行数据校正,利用最小二乘法拟合识别树行,结合SVM算法,预测果园行间中心线,作为作业平台的参考导航线。在桃园、柑橘园、松树林3种不同的行间环境对导航算法进行了测试验证,并以松树林导航为例进行分析。试验结果表明:该导航算法最大横向偏差为107.7 mm,横向偏差绝对平均值不超过17.8 mm,结合作业平台的行驶轨迹,说明该导航算法能够保证作业平台沿树行行间中心线自主导航行驶,能够满足作业装备在果园与树林行间自主导航作业的需求。

Inter-row automatic navigation method by combining least square and SVM in forestry

Autonomous navigation has widely been served as agricultural working platforms in smart farming. A few kinds of sensors, such as GPS and camera, are commonly used as conventional. However, the automatic navigation cannot be extended suitable for orchard environment, due mainly to canopy closing and the variation of light intensity. In this study, an inter-row automatic navigation was developed for a track chassis using an inertial measurement unit (IMU) and light detection and ranging (LIDAR), thereby improving the capability of in-orchard navigation under agricultural working platforms. The track chassis was specifically developed for orchard conditions, including a chassis, a driving implement, a power implement, and a range extender, where the specific size was 1 575 mm×1 190 mm×1 355 mm. The detection and control systems were performed on a host and a slave computer. The host computer was in charge of data processing to obtain navigation paths and orders, while the slave one was to control motors using pulse-width modulation (PWM). An SC-AHRS-100D2 was selected as the IMU in sensors and units, while RPLIDAR S1 was utilized as the LIDAR scanner. The orientation and pose of the platform were acquired under the IMU. Meanwhile, the orchard condition was also scanned by the LIDAR. First, the orientation and pose from the IMU were exploited to modify the data from the LIDAR, so that the platform remained in correct moving directions. Quaternions were transformed into Euler angles during the data processing. The tree lines on both sides were then extracted using least square, where an average line between two lines was calculated. Next, mathematical models were established to combine with the support vector machine (SVM). An optimized classification line of environment between tree lines was computed as the navigation path of the track chassis platform, in order to ensure a maximum interval between the tree lines on both sides. Moreover, a proportional-incremental-differential (PID) controller was employed to control the platform motion using the path information, where the lateral bias was selected as the evaluation standard. A series of field tests were conducted in the Bajiajiaoye Park (Dongsheng Street, Haidian District, Beijing), an apple orchard in Pinggu District, Beijing, and a citrus orchard in Guangan County, Sichuan Province of China. The data captured in the Bajiajiaoye Park was taken as the research case with several real conditions, where the trees were selected as the test environment. LIDAR was installed in the front of the track chassis, while each condition was tested three times. The speed of the chassis was 0.5 m/s. The results showed that the maximum size of the absolute value of lateral errors was 17.8 mm, and the maximum number of lateral errors was 107.7 mm. High performance was achieved in the automatic navigation, while the track chassis followed the central line between the fruit trees, according to the statistical values of lateral errors and the trajectory of the track chassis. Furthermore, excellent adaptability was also obtained for various situations. This finding can offer a potential technical reference on the wayfinding for the autonomous navigation of ground sprayers in orchards and forestry.