基于机器视觉的林地割草机树干识别与避障技术研究  

Research on trunk recognition and obstacle avoidance technology of woodland mower based on machine vision

作　　者：吴慢玉 曹成茂[1,2] 李旭 陈传涛 刘建国 孙燕 WU Manyu;CAO Chengmao;LI Xu;CHEN Chuantao;LIU Jianguo;SU Yan(College of Engineering,Anhui Agricultural University,Hefei 230036,China;Anhui Intelligent Agricultural Machinery Equipment Laboratory,Hefei 230036,China)

机构地区：[1]安徽农业大学工学院,安徽合肥230036 [2]安徽省智能农机装备工程实验室,安徽合肥230036

出　　处：《江西农业大学学报》2025年第2期505-520,共16页Acta Agriculturae Universitatis Jiangxiensis

基　　金：国家自然科学基金面上项目(52075003、51475002)。

摘　　要：【目的】山核桃林树木高大、间距不一,割草机实现对树干精确识别定位和实时避障,是保证高效除草的前提。【方法】通过双目相机采集林地实时图像信息,基于YOLOv8n轻量化树干根部检测算法和三阶贝塞尔曲线规划避障路径方法,实现快速识别、精准定位、实时避障。首先,根据双目相机得出的RGB和深度图像利用YOLOV8n检测模型对树干根部进行识别与定位;其次,将在相机坐标系下定位到的树干根部位置,通过防碰撞和曲率约束,生成最优贝塞尔曲线避障路径;最后,将相机坐标系下的避障路径转化为经纬度坐标系下进行最后的自主避障工作。【结果】树干根部检测结果表明:树干根部的识别准确率89.9%,基本无漏检;树干根部定位精度试验结果表明:测得纵向距离和横向距离平均相对误差分别为1.07%和3.46%;避障仿真结果表明:避障过程中割草机与树木之间最近距离为6.31cm,避障路径跟踪最大横向误差为2.01cm,平均横向误差为0.85cm;割草机避障试验结果表明:割草机距离树木最近距离为15.26cm,最大横向误差和平均横向误差分别为6.23cm和2.71cm。避障结束后,能够精准回归到全局路径上。【结论】该方法应用在复杂林地割草机上,可以快速识别并精准定位树干根部位置,且在短时间内规划避障轨迹避开障碍树木作业,为林地割草机械实现无人化作业提供参考。[Objective]In view of the forest environment of pecan forest with tall trees and different spacing,the accurate identification and positioning of the trunk and real-time obstacle avoidance of the lawn mower are the prerequisites of ensuring efficient weeding.[Method]In this study,real-time image information of the forest floor was collected by binocular camera.Based on YOLOv8n lightweight trunk root detection algorithm and third-order Bezier curve obstacle avoidance path method,fast identification,accurate positioning and real-time obstacle avoidance are realized.Firstly,according to the RGB and depth images derived from the binocular camera,the YOLOV8n detection model was used to identify and locate the tree trunk roots;secondly,the position of the tree trunk roots located under the camera coordinate system wasused to generate the optimal Bezier curve obstacle avoidance path through the anti-collision and curvature constraints;finally,the obstacle avoidance path under the camera coordinate system is transformed into the latitude/longitude coordinate system to carry out the final autonomous obstacle avoidance work.[Result]The results of the trunk root detection show that the recognition accuracy of the trunk root is 89.9%,there is basically no missed detection;the results of the trunk root positioning accuracy test show that the average relative error rate of the measured longitudinal distance and lateral distance is 1.07%and 3.46%respectively;the results of the obstacle avoidance simulation show that the closest distance between the lawnmower and the tree in the process of obstacle avoidance is 6.31 cm,and the maximum transverse error of the tracking of the obstacle avoidance path is 2.01 cm,the average lateral error is 0.85 cm;the results of the mower obstacle avoidance test show that the closest distance between the mower and the tree is 15.26 cm,the maximum lateral error and the average lateral error are 6.23 cm and 2.71 cm,respectively.At the end of the obstacle avoidance process,it can accurately return to

关 键 词：割草机 避障 双目相机 YOLOv8n 实时检测 精准定位 三阶贝塞尔曲线 

分 类 号：S225[农业科学—农业机械化工程]