棉田施药机器人视觉导航方法与田间试验  

Visual navigation method and field experiment of cotton spraying robots

作　　者：樊湘鹏 许燕[3,4] 周建平 FAN Xiangpeng;XU Yan;ZHOU Jianping(Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,China;Key Laboratory of Agricultural Big Data,Ministry of Agriculture and Rural Affairs,Beijing 100081,China;College of Mechanical Engineering,Xinjiang University,Urumqi 830047,China;Agriculture and Animal Husbandry Robot and Intelligent Equipment Engineering Research Center of Xinjiang Uygur Autonomous Region,Urumqi 830047,China)

机构地区：[1]中国农业科学院农业信息研究所,北京100081 [2]农业农村部农业大数据重点实验室,北京100081 [3]新疆大学机械工程学院,乌鲁木齐830047 [4]新疆维吾尔自治区农牧机器人及智能装备工程研究中心,乌鲁木齐830047

出　　处：《农业工程学报》2025年第6期52-61,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：中国博士后科学基金面上资助项目(2023M733814);北京市自然科学基金项目(6244056)。

摘　　要：视觉导航是田间作业机器人的主流导航方法,为解决因棉苗稀疏、缺苗和杂草等因素导致导航路径提取困难这一问题,该研究建立了基于改进RANSAC(random sample consensus)算法和最小二乘拟合的视觉导航方法,并开展田间视觉导航路径跟踪试验。首先获取棉田前视导航图像,利用改进超绿灰度特征变换提高作物行与背景的区分度,结合自适应阈值分割方法将作物行从背景中分割出来并进行形态学滤波去噪得到二值化图像;然后根据图像中作物行目标区域的分布特征,利用改进RANSAC算法去除离散点,并对最优特征点聚类,以保证最终提取的作物行中心线的准确性,最后采用最小二乘法拟合得到导航路径。试验结果表明,传统RANSAC方法的行识别率为96.5%,平均误差角为1.41°,单幅图像平均耗时0.087 s;改进RANSAC方法去除离群点后的行识别率为98.9%,平均误差角为0.53°,导航路径提取性能得到大幅提升。利用自主开发的施药机器人及视觉导航系统在棉花苗期开展田间路径跟踪试验,施药机器人在3种不同初始状态和3种不同运动速度下自主导航行驶,最大横向偏差不超过2.59 cm,且无轧苗现象发生,满足“一膜三垄六行”种植模式下的自主导航作业要求,研究结果可为其他农业机器人的自主导航方法开发提供参考。Visual navigation of robots has been widely used to extract information from their surroundings to determine subsequent activity in agricultural fields.However,the navigation accuracy has been limited to the very complex scene in the field,particularly under light variation and plant growth.It is also difficult to extract the crop path using sparse cotton seedlings,missing seedlings,and weeds in the cotton seedling stage scenario.In this study,a visual navigation method was established using improved RANSAC(random sample consensus).A series of experiments was also carried out on navigation path tracking in the cotton field.Firstly,the images were captured at multiple growth stages during cotton seedlings using agricultural robot with a camera.Then the crop rows and background were distinguished to fully separate by adaptive threshold segmentation.The binary images were also denoised using morphological filtering.According to the region of interest,the outlier points beyond the crop row were removed from the image by the improved RANSAC algorithm.The detection and clustering of feature points were carried out to ensure the accuracy of the final extracted center line of the crop row.Finally,the navigation path was obtained after the least square fitting.The experimental results show that the better fitting path was obtained after the removal of the outliers using improved RANSAC,which was in line with the actual position for the center line of the crop row.Specifically,the line recognition rate was 96.5%using the traditional RANSAC algorithm,the average error angle was 1.41°,and the average time of image processing was 0.087 s.By contrast,the line recognition rate increased to 98.4%after removing outliers by the improved RANSAC algorithm,and the average error angle was only 0.53°.The performance of center line extraction was significantly improved using a modified RANSAC algorithm,compared with the original.In addition,a comparison was also made between the improved and traditional Hough transform.The effectivene

关 键 词：视觉导航 机器人 棉田 行识别 改进RANSAC 路径拟合 跟踪试验 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置] TP391.4[自动化与计算机技术—控制科学与工程]