柑橘采摘机器人成熟果实定位及障碍物检测研究
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摘要
随着农业生产的飞速发展和农业劳动力成本的迅速上升,农业劳动力的匮乏将成为许多发达国家和发展中国家共同面对的问题。目前机器人技术在农业上应用研究越来越成为热门课题,与工业机器人特定的工作环境不同,农业机器人主要在自然场景下工作,要面对更为复杂多变的情况,有更多的问题需要解决。
本研究作为柑橘采摘机器人研究的一部分,利用双目立体视觉技术,研究自然场景下的成熟柑橘的识别和定位方法、障碍物(树枝)的检测方法,为未来开发收获机器人采摘柑橘进行前期视觉系统方面的准备工作,这部分相关的研究也是水果收获机器人实用化的关键。
本研究的主要内容和方法如下:
1.成熟果实定位
果实定位主要通过识别和匹配等步骤完成。识别的目的是把成熟柑橘从背景中识别出来,为空间定位做准备。本研究利用对RGB颜色系统中的色差分量2R-G-B值进行迭代,自动寻找阈值的方法分割柑橘图像;将区域分割后的彩色图像转化为二值图像;经过形态学运算消除噪声;对图像进行区域标记,根据区域面积和区域最小外接矩形长宽比设定阈值,去除小块及非类圆形干扰区域;进行区域填充、轮廓提取,并采用优化圆形Hough变换拟合出成熟果实图像中的圆心坐标、半径等特征值;然后以单个柑橘为匹配对象,采用基于特征的立体匹配方法,加入柑橘的区域重心、外接矩形尺寸等特征值作为匹配约束来实现左右两幅图像中对应果实的唯一匹配,研究结果显示正确匹配率达80%以上。最后在对摄像机内外部参数进行了标定的情况下,求得柑橘的空间三维坐标,并利用激光测距仪进行了对比验证,当测量距离小于等于1.5m时,平均误差不超过1%。
2.障碍物检测
为保持算法的可执行性,障碍物检测的步骤和果实定位基本相同,采用对图像2R-G-B和2G-R-B色差分量值进行迭代,并结合图像灰度阈值法能快速有效的分割出图像树枝区域,通过图像二值化、形态学运算、区域标记、空洞填充提取出图像树枝区域;通过区域细线化提取树枝骨架,并进行骨架修剪、恢复遮挡骨架等处理;然后找出骨架中端点、分支点等特征点并记录它们的连接关系;最后通过对特征点的立体匹配恢复障碍物的三维信息。试验表明障碍物的正确识别率为67.3%,当障碍物实际距离大于1.5m时,识别误差增大。
通过研究,在成熟水果识别、匹配和定位方面取得了较大进展,提出了一种果实收获机器人的障碍物检测方法。本文的研究内容对我国开展农业收获机器人视觉识别技术领域的研究具有参考价值,为进一步的研究打下了基础,对提高我国农业的国际竞争力有重要的经济意义。
With the rapid development of agricultural production,the cost of agriculture labor force will become more and more costly.In recent years,the agricultural application of robot technique have already become popular issue,because of the shortage of the agriculture labor force both in developed countries and developing countries.Different from industrial robot,which works in particular environment,the agriculture robot mainly works in the natural environment,and the agriculture robot has to face more complicated and uncertain circumstance,thus there are more problems to be resolved.
As a part of research on citrus picking robots,this research used binocular stereo vision to researching on recognition and location mature citrus,obstacle(branches) detection under natural environment.The main contents and methods are as follows:
1.Mature fruit location
The main steps to complete that are image recognition and stereo matching.The purpose of image recognition is recognizing mature citrus region from image to make preparation for location.This research used iterate on 2R-G-B chromatism component in RGB color system to finding threshold automatically to segment original image. Then the segmented images were converted to two-value images and eliminated noise by morphological operation.Region labeling was done and eliminated region which was small or had great differences from circle by defining threshold which based on area and length to width ratio of the smallest circumscribed rectangles of each region. Then region filling and contour extraction operation were been done and used improved Circular Hough Transformation(CHT) to found out circle centre and radius of each region's approaching round.Then used feature-based match and added extra characteristics such as barycenter of orange region in the image,sizes of each region's approaching round to get correct match results of each image pair.Experimental results show that the matching accuracy can reach over 80%.Finally,after calibrated the camera interior and exterior parameters,the 3-d space coordinate of each orange was been obtained and used the laser range finder to verification and comparison. Results show that the average error ratio is below 1%when the measuring distance is no more than 1.5m.
2.Obstacle detection
To ensure executable of algorithms,the steps of obstacle detection are the same as that of mature fruit location.Used iterate on 2R-G-B and 2G-R-B chromatism component,combined with gray threshold method to segment image quickly and effectively.Got the branch regions by image binaryzation,morphological processing, region labeling and filling.Extracted skeleton of obstacle by thinning and did some processes so as to pruning the skeleton and recovering the occluded skeleton.Then obtained the feature points such as endpoints and branch points of the skeleton, recorded their connecting relationship.Finally the 3D information of obstacle was restored by stereo matching on feature points.Experimental results show that the identification accuracy of obstacle can reach 67.3%,the identification error ratio was increased when the actual distance of obstacle is more than 1.5m.
Through the research,some achievements have been made.Such as mature fruit recognition,match and location.This research also provides a method for fruit harvest robot to detect obstacle.The research results of this research have reference value for the study on visual recognition in the field of harvest robot in our country.They also provide a basis for further study and have important economic significance to enhance international competitive power of our country's agricultural.
本研究作为柑橘采摘机器人研究的一部分,利用双目立体视觉技术,研究自然场景下的成熟柑橘的识别和定位方法、障碍物(树枝)的检测方法,为未来开发收获机器人采摘柑橘进行前期视觉系统方面的准备工作,这部分相关的研究也是水果收获机器人实用化的关键。
本研究的主要内容和方法如下:
1.成熟果实定位
果实定位主要通过识别和匹配等步骤完成。识别的目的是把成熟柑橘从背景中识别出来,为空间定位做准备。本研究利用对RGB颜色系统中的色差分量2R-G-B值进行迭代,自动寻找阈值的方法分割柑橘图像;将区域分割后的彩色图像转化为二值图像;经过形态学运算消除噪声;对图像进行区域标记,根据区域面积和区域最小外接矩形长宽比设定阈值,去除小块及非类圆形干扰区域;进行区域填充、轮廓提取,并采用优化圆形Hough变换拟合出成熟果实图像中的圆心坐标、半径等特征值;然后以单个柑橘为匹配对象,采用基于特征的立体匹配方法,加入柑橘的区域重心、外接矩形尺寸等特征值作为匹配约束来实现左右两幅图像中对应果实的唯一匹配,研究结果显示正确匹配率达80%以上。最后在对摄像机内外部参数进行了标定的情况下,求得柑橘的空间三维坐标,并利用激光测距仪进行了对比验证,当测量距离小于等于1.5m时,平均误差不超过1%。
2.障碍物检测
为保持算法的可执行性,障碍物检测的步骤和果实定位基本相同,采用对图像2R-G-B和2G-R-B色差分量值进行迭代,并结合图像灰度阈值法能快速有效的分割出图像树枝区域,通过图像二值化、形态学运算、区域标记、空洞填充提取出图像树枝区域;通过区域细线化提取树枝骨架,并进行骨架修剪、恢复遮挡骨架等处理;然后找出骨架中端点、分支点等特征点并记录它们的连接关系;最后通过对特征点的立体匹配恢复障碍物的三维信息。试验表明障碍物的正确识别率为67.3%,当障碍物实际距离大于1.5m时,识别误差增大。
通过研究,在成熟水果识别、匹配和定位方面取得了较大进展,提出了一种果实收获机器人的障碍物检测方法。本文的研究内容对我国开展农业收获机器人视觉识别技术领域的研究具有参考价值,为进一步的研究打下了基础,对提高我国农业的国际竞争力有重要的经济意义。
With the rapid development of agricultural production,the cost of agriculture labor force will become more and more costly.In recent years,the agricultural application of robot technique have already become popular issue,because of the shortage of the agriculture labor force both in developed countries and developing countries.Different from industrial robot,which works in particular environment,the agriculture robot mainly works in the natural environment,and the agriculture robot has to face more complicated and uncertain circumstance,thus there are more problems to be resolved.
As a part of research on citrus picking robots,this research used binocular stereo vision to researching on recognition and location mature citrus,obstacle(branches) detection under natural environment.The main contents and methods are as follows:
1.Mature fruit location
The main steps to complete that are image recognition and stereo matching.The purpose of image recognition is recognizing mature citrus region from image to make preparation for location.This research used iterate on 2R-G-B chromatism component in RGB color system to finding threshold automatically to segment original image. Then the segmented images were converted to two-value images and eliminated noise by morphological operation.Region labeling was done and eliminated region which was small or had great differences from circle by defining threshold which based on area and length to width ratio of the smallest circumscribed rectangles of each region. Then region filling and contour extraction operation were been done and used improved Circular Hough Transformation(CHT) to found out circle centre and radius of each region's approaching round.Then used feature-based match and added extra characteristics such as barycenter of orange region in the image,sizes of each region's approaching round to get correct match results of each image pair.Experimental results show that the matching accuracy can reach over 80%.Finally,after calibrated the camera interior and exterior parameters,the 3-d space coordinate of each orange was been obtained and used the laser range finder to verification and comparison. Results show that the average error ratio is below 1%when the measuring distance is no more than 1.5m.
2.Obstacle detection
To ensure executable of algorithms,the steps of obstacle detection are the same as that of mature fruit location.Used iterate on 2R-G-B and 2G-R-B chromatism component,combined with gray threshold method to segment image quickly and effectively.Got the branch regions by image binaryzation,morphological processing, region labeling and filling.Extracted skeleton of obstacle by thinning and did some processes so as to pruning the skeleton and recovering the occluded skeleton.Then obtained the feature points such as endpoints and branch points of the skeleton, recorded their connecting relationship.Finally the 3D information of obstacle was restored by stereo matching on feature points.Experimental results show that the identification accuracy of obstacle can reach 67.3%,the identification error ratio was increased when the actual distance of obstacle is more than 1.5m.
Through the research,some achievements have been made.Such as mature fruit recognition,match and location.This research also provides a method for fruit harvest robot to detect obstacle.The research results of this research have reference value for the study on visual recognition in the field of harvest robot in our country.They also provide a basis for further study and have important economic significance to enhance international competitive power of our country's agricultural.
引文
[1]http://www.china-citrus.cn/ 中国柑橘网.
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[2]姜丽萍,陈树人.果实采摘机器人的研究综述.农业装备技术.2006,32(1):8-10.
[3]Shigehiko Hayashi,Katsunobu Ganno,Yukitsugu Ishii,et al.Robotic harvesting system for eggplants[J].JARQ,2002,36(3):163-168.
[4]MurakamiN,Inoue K,Otsuka K.Selective harvesting robot of cabbage[J].JSAM,1995,2:24-31.
[5]Harrell R C,Adsit P D,Munilla R D,etal.Robotic picking of citrus[J].Robotica,1990,8(4).269- 278.
[6]Jimenez A R,Ceres R,Pons J L.A survey of computer vision methods for locating fruit on trees[J].Transactions of the ASAE,2000,43(6):1911- 1920.
[7]Parrish,E.,and A.K.Goksel,1977.Pictorial pattern recognition applied to fruit harvesting.Transactions of the ASAE 20(5):822-827;
[8]D'Esnon,A G,Rabatel G,Pellenc R.A self-propelled robot to pick apples[J].ASAE paper No.87-1037,1987.
[9]Rabatel,G.A vision system for magali,the fruit picking robot[C].Agricultural Engineering,Pads,London,U.K.Paper 88293,AGENG88,Int,Conf.Ministry of Agriculture,Fisheries and Food 1988.
[10]A.Dale Whittaker,G.E.Miles,1987.Fruit location in a partially occluded image.Transactions of the ASAE 30(3):591-596;
[11]Illingworth,J.,and J,Kittlen.A survey of the hough transform[J].Computer Vision,Graph.& Image Proc.,1988,44:87-116
[12]Slaughter,D.,and R.C.Harrel.Color vision in robotic fruit harvesting.Transactions of the ASAE 1987,30(4):1144-1148.
[13]Harrell,R.C.,D.C.Slaughter etc.A fruit-tracking system for robotic harvesting.Machine Vision & Appli.1989,2:69-80.
[14]Harrell,R.C.,P.D.Adsit,R.D.Mumlla,and D.C.Slaughten.Robotic picking of citrus.Robotica 1990,8:269-278.
[15]Kassay,L.,1992.Hungarian robotic apple harvester.ASAE Paper No.92-7042,1-14.St.Joseph,Mich.:ASAE.
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