首页|基于YOLO v8-Seg的地栽草莓采摘机器人垄面视觉导航控制方法

基于YOLO v8-Seg的地栽草莓采摘机器人垄面视觉导航控制方法

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农业机械无人化作业离不开自主导航技术.随着传感器发展和计算机视觉技术的完善,农业机器人在温室大棚自主视觉导航作业逐渐成为可能.本文针对地栽草莓采摘机器人开展垄面视觉导航控制方法研究,分析地栽草莓种植农艺,基于YOLO v8实例分割算法获取草莓垄面特征,采用Canny边缘检测算法对垄面边缘信息进行提取.提出两条斜率分别为1和-1的直线遍历垄面边缘,通过统计截距信息,获取垄面上下各2个端点.进而得到垄面上下各2个端点的中心点坐标,连线垄面上下中心点成直线,即可获得垄面对应导航线.采集温室大棚环境下的地栽草莓垄面图像数据,经测试导航路径提取精度为96%,算法耗时30 ms.将算法部署至采用四轮阿克曼转向底盘的草莓采摘机器人,结合预瞄点跟踪算法,在仿真草莓垄上进行导航试验.经测试导航路径提取精度为94%,算法耗时30 ms.当行驶速度为0.2 m/s时,横向偏距最大为32.69 mm,均值为22.12 mm,均方根误差(RMSE)为5.37 mm,满足地栽草莓采摘机器人垄面自主导航控制.该控制方法配合采摘机器人自主采摘功能,可实现草莓采摘机器人无人自主作业.
Ridge Visual Navigation Control Method for Ground-planted Strawberry Picking Robots Based on YOLO v8-Seg Algorithm
The unmanned operation of agricultural machinery is inseparable from autonomous navigation technology.With the development of sensors and the improvement of computer vision technology,the autonomous visual navigation operation of agricultural robots in greenhouses has gradually become possible.Research on the visual navigation control method for ridge-surface operation of strawberry picking robots planted in the field was conducted.It analyzed the agricultural techniques of field-grown strawberries and acquired the features of strawberry ridges based on the YOLO v8 instance segmentation algorithm.The Canny edge detection algorithm was employed to extract the edge information of the ridge surface.Two straight lines with slopes of 1 and-1 were used to traverse the ridge surface,and the intercept information was statistically obtained to acquire the upper and lower endpoints of the ridge surface.The center point coordinates of the upper and lower endpoints on the ridge surface were then obtained.By connecting the upper and lower center points of the ridge surface into a straight line,the corresponding navigation line of the ridge can be obtained.An image dataset of the ridge surface of field-grown strawberries in the greenhouse environment was collected.After testing,the extraction accuracy of the navigation path was 96%,and the algorithm took 30 ms.The algorithm was deployed to the strawberry picking robot with a four-wheel Ackerman steering chassis.Combined with the preview point tracking algorithm,a navigation test was carried out on the simulated strawberry ridge.After testing,the extraction accuracy of the navigation path was 94%,and the algorithm took 30 ms.When the driving speed was 0.2 m/s,the maximum lateral offset was 32.69 mm,the average value was 22.12 mm,and the root mean square error(RMSE)was 5.37 mm,meeting the requirements for autonomous navigation control of the strawberry picking robot on the ridge surface.This control method,in conjunction with the autonomous picking function of the picking robot,can enable the unmanned autonomous operation of the strawberry picking robot.

ground-planted strawberriespicking robotinstance segmentationridge visual navigationpreview point trackingYOLO v8-Seg

应仇凯、程泓超、马锃宏、杜小强

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浙江理工大学机械工程学院,杭州 310018

浙江省种植装备技术重点实验室,杭州 310018

全省农业智能感知与机器人重点实验室,杭州 310018

地栽草莓 采摘机器人 实例分割 垄面视觉导航 预瞄点跟踪 YOLO v8-Seg

2024

农业机械学报
中国农业机械学会 中国农业机械化科学研究院

农业机械学报

CSTPCD北大核心
影响因子:1.904
ISSN:1000-1298
年,卷(期):2024.55(z1)