基于四自由度西红柿采摘机器人视觉系统的研究
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摘要
西红柿在我国的种植范围几乎覆盖全国,种植面积居世界前列,其采摘是劳动密集型作业。人工采摘西红柿,成本高、时间长、且质量难以保证,很难适应规模化、工厂化种植发展的需要,因此西红柿自动化采摘是一个急需解决的问题。果实的识别、定位是机器人实现采摘动作的前提,而这些功能的实现又依赖于其视觉系统。利用视觉系统对自然场景下的果实进行识别、定位是实现机器人自动化采摘的重要一步,因此视觉系统的研究对于实现自动化采摘具有极大的意义。本文基于项目组研制开发的四自由度西红柿采摘机器人,研究开发了适宜于该机器人的视觉系统,其内容主要包括:
对现有的西红柿栽培模式、生长特性及西红柿成熟过程中色彩变化与果实成熟度的关系进行了研究。在此基础上,搭建了西红柿采摘机器人视觉系统的硬件和软件平台,进行了该系统软件结构的设计。
设计开发了适宜于四自由度西红柿采摘机器人视觉的图像实时采集系统。本文通过USB接口将摄像头与主机连接,进行图像实时采集,基于VFW技术利用VisualC++6.0编辑软件进行图像采集系统的软件开发。试验可得,该系统采集效果较好,并一定程度上提高了图像采集的灵活性,降低了成本。
首次根据采摘要求对不同成熟期的西红柿果实进行识别。通过对图像中采摘西红柿与背景色彩分量的分布统计分析,获取了有效分割采摘物与背景的颜色指标。提出彩色图像的等尺度灰度化法,将彩色图像的分割转化为灰度图像的分割,经试验可知灰度化后的图像较好地保留了彩色图像中色彩指标差异的信息,从而简化了图像分割及处理的难度,提高了处理速度。采用阈值分割方法进行图像分割,确定了最佳的分割算法与色彩指标的组合,提出采用门限Otsu法对原有Otsu法进行了改进。本文最终确定当采摘西红柿为成熟果时,选择基于OUT_I_2颜色指标的门限Otsu法进行图像分割,其门限值为T=115;当采摘对象为绿熟果时,选择基于OUT_H颜色指标的门限Otsu法进行图像分割,其门限值为T=50。
通过对采摘西红柿形状特征的研究分析结合本课题的实际需要,确定了采摘对象所需的形状特征量,并完成了相关特征量的求取。采用行程标记算法对分割后的图像中的目标进行标记,搜索方向旋转法进行目标轮廓跟踪。对分割后的图像依次进行形态学处理、果实目标提取、果实目标区域填充,逐步去除图像中的干扰,获取较为完整的西红柿轮廓。在采摘西红柿质心确定中,提出将识别后图像中的果实单个提取,再依据果实外形轮廓是否完整,分别采用中心矩法及基于几何学原理的方法进行质心提取。
针对课题任务要求及双目视觉系统的特点,结合实验室现有条件,实现了一种内外参数分离的平面模板标定方法。通过平面模板进行摄像头的标定,获取摄像头的内外参数,在此基础上对摄像头进行了畸变补偿的非线性优化,利用Levenberg-Marquardt迭代优化算法获取最终优化参数,并对摄像头进行了双目立体标定。
通过对四自由度西红柿采摘机器人的采摘空间的分析,提出并建立了视觉系统识别定位的视野范围、双目视觉系统中左右摄像头基线取值范围与机器人采摘空间的相互关系。提出了一种基于西红柿质心特征并辅以极线约束、唯一性约束及视差梯度范围约束的匹配方法来实现果实目标的匹配。利用平行双目立体视觉模型进行采摘西红柿质心的三维重建,获取质心空间坐标,并通过摄像头坐标系与机器人坐标系相互转换关系的研究,确定采摘西红柿质心在机器人坐标系中的空间坐标。
采用面向对象的程序设计方法,研制开发了适宜于四自由度西红柿采摘机器人的视觉系统。并在实验室的环境下对该视觉系统进行了测试,对识别、定位中误差产生的原因进行了相关分析。测试结果表明,成熟果的识别可以达到98%,绿熟果的识别可达89%,定位误差可控制在15mm以内,其识别及定位效果均能满足四自由度西红柿采摘机器人的工作要求。
Tomato planting area in China almost covered the whole country, and is forefront of the world. Because artificial picking tomato is high cost, long time, and difficult to guarantee the quality, it is very difficult to large-scale and industrialized cultivation. Using visual system to identify and locate the fruit in natural scenes, it is an important step in robot automation picking. Study on the visual system is great significance to reality the automated picking. Based on the four degrees of freedom tomato picking robot of the project team research and development, the vision system of the robot is researched and developed in paper.
Main contents including:
The existing tomato cultivation mode, growth properties, and the relationship between color change in the process of the tomato mature and the maturity of the fruit is studied. On this basis, hardware and software platforms of tomato harvesting robot vision system, and the flow chart of software system are established.
Through analysis and study on existing image acquisition methods, the image acquisition method of the visual system is determined, and image acquisition software system of the robot visual is designed. The paper selected camera directly connects with mainframe to real-time image acquisition. Based on VFW, image acquisition system software is developed with Visual C++ 6.0 editing software. Test results obtain the acquisition system better meet actual needs, and improve the image acquisition flexibility, reduce system costs.
This paper is the first to identify picking tomato of different maturity. Through the distribution statistical analysis of picking tomatoes and background color components, access to effective color indicators of segment picking tomato and background. Bring forward equal scale gray about color image, the color images segmentation will be turned into a gray image segmentation problem. Test result got to that the gray image can be better retain differences information of color images indicators, thus simplified the difficulty of segmentation and deal with image, and increased processing speed. The light and acquisition equipment brought about image noise is solved by using light compensation method and median filtering method. Threshold segmentation method used image segmentation in paper. The best combinations of the segmentation methods and color indicators are determined. The original Otsu method is improved. The image under different circumstances can be better segmented through the improve Otsu method.
Based on the color and shape feature, the tomato is identified and the center of the tomato is got. Determined the shape features of the picking object, and calculated the features. The trip marking algorithm is used to the goal marker, the search direction rotation algorithm is used to goal outline extraction. Through morphological processing, object extraction, and filling empty, gradually removed the image interference and accessed to better integrated tomato. In the process of the tomato center acquired, the paper will research to three mutual locations, and the single fruit in the image can be better extracted.
Base on the mission requirements and laboratory conditions, the paper adopted the plane template calibration method between the traditional calibration method and self-calibration method. Through self-made plane template, camera is calibrated, and the camera's internal and external parameters are accessed. To have finished the nonlinear optimization of the camera distortion compensates and to obtain optimal parameters with Levenberg-Marquardt iterative optimization algorithm.The binocular stereo camera calibration is finished.
Two core issues of the Binocular Stereo Vision are three-dimensional reconstruction and feature match. In the paper, working space of the four degrees of freedom tomato picking robot is analyzed, the vision scope of the visual identification and positioning system and the baseline value of the cameras in the binocular vision system are established. Based on the center characteristics of the tomato and epipolar constraint, the only constraint, parallax gradient constraint, goal matching is realized. In three-dimensional reconstruction, space coordinates of the picking tomatoes is achieved with the parallel binocular stereo model. Through studied conversion relationship between camera coordinates and robot coordinates, the tomato coordinate in the robot space coordinates is final achieved.
It has developed to the vision system of four degrees of freedom tomato picking robot. In the laboratory environment, tomato picking robot vision system is tested, and the error of the identification and location is analyzed. The testing results indicate that: The identification of the mature tomato can be up to 98%; the identification of green ripeing tomato can be up to 89 %; positioning error is in 15 mm. The effect of recognition and positioning can meet requirements of the four degrees of freedom tomato picking robot.
对现有的西红柿栽培模式、生长特性及西红柿成熟过程中色彩变化与果实成熟度的关系进行了研究。在此基础上,搭建了西红柿采摘机器人视觉系统的硬件和软件平台,进行了该系统软件结构的设计。
设计开发了适宜于四自由度西红柿采摘机器人视觉的图像实时采集系统。本文通过USB接口将摄像头与主机连接,进行图像实时采集,基于VFW技术利用VisualC++6.0编辑软件进行图像采集系统的软件开发。试验可得,该系统采集效果较好,并一定程度上提高了图像采集的灵活性,降低了成本。
首次根据采摘要求对不同成熟期的西红柿果实进行识别。通过对图像中采摘西红柿与背景色彩分量的分布统计分析,获取了有效分割采摘物与背景的颜色指标。提出彩色图像的等尺度灰度化法,将彩色图像的分割转化为灰度图像的分割,经试验可知灰度化后的图像较好地保留了彩色图像中色彩指标差异的信息,从而简化了图像分割及处理的难度,提高了处理速度。采用阈值分割方法进行图像分割,确定了最佳的分割算法与色彩指标的组合,提出采用门限Otsu法对原有Otsu法进行了改进。本文最终确定当采摘西红柿为成熟果时,选择基于OUT_I_2颜色指标的门限Otsu法进行图像分割,其门限值为T=115;当采摘对象为绿熟果时,选择基于OUT_H颜色指标的门限Otsu法进行图像分割,其门限值为T=50。
通过对采摘西红柿形状特征的研究分析结合本课题的实际需要,确定了采摘对象所需的形状特征量,并完成了相关特征量的求取。采用行程标记算法对分割后的图像中的目标进行标记,搜索方向旋转法进行目标轮廓跟踪。对分割后的图像依次进行形态学处理、果实目标提取、果实目标区域填充,逐步去除图像中的干扰,获取较为完整的西红柿轮廓。在采摘西红柿质心确定中,提出将识别后图像中的果实单个提取,再依据果实外形轮廓是否完整,分别采用中心矩法及基于几何学原理的方法进行质心提取。
针对课题任务要求及双目视觉系统的特点,结合实验室现有条件,实现了一种内外参数分离的平面模板标定方法。通过平面模板进行摄像头的标定,获取摄像头的内外参数,在此基础上对摄像头进行了畸变补偿的非线性优化,利用Levenberg-Marquardt迭代优化算法获取最终优化参数,并对摄像头进行了双目立体标定。
通过对四自由度西红柿采摘机器人的采摘空间的分析,提出并建立了视觉系统识别定位的视野范围、双目视觉系统中左右摄像头基线取值范围与机器人采摘空间的相互关系。提出了一种基于西红柿质心特征并辅以极线约束、唯一性约束及视差梯度范围约束的匹配方法来实现果实目标的匹配。利用平行双目立体视觉模型进行采摘西红柿质心的三维重建,获取质心空间坐标,并通过摄像头坐标系与机器人坐标系相互转换关系的研究,确定采摘西红柿质心在机器人坐标系中的空间坐标。
采用面向对象的程序设计方法,研制开发了适宜于四自由度西红柿采摘机器人的视觉系统。并在实验室的环境下对该视觉系统进行了测试,对识别、定位中误差产生的原因进行了相关分析。测试结果表明,成熟果的识别可以达到98%,绿熟果的识别可达89%,定位误差可控制在15mm以内,其识别及定位效果均能满足四自由度西红柿采摘机器人的工作要求。
Tomato planting area in China almost covered the whole country, and is forefront of the world. Because artificial picking tomato is high cost, long time, and difficult to guarantee the quality, it is very difficult to large-scale and industrialized cultivation. Using visual system to identify and locate the fruit in natural scenes, it is an important step in robot automation picking. Study on the visual system is great significance to reality the automated picking. Based on the four degrees of freedom tomato picking robot of the project team research and development, the vision system of the robot is researched and developed in paper.
Main contents including:
The existing tomato cultivation mode, growth properties, and the relationship between color change in the process of the tomato mature and the maturity of the fruit is studied. On this basis, hardware and software platforms of tomato harvesting robot vision system, and the flow chart of software system are established.
Through analysis and study on existing image acquisition methods, the image acquisition method of the visual system is determined, and image acquisition software system of the robot visual is designed. The paper selected camera directly connects with mainframe to real-time image acquisition. Based on VFW, image acquisition system software is developed with Visual C++ 6.0 editing software. Test results obtain the acquisition system better meet actual needs, and improve the image acquisition flexibility, reduce system costs.
This paper is the first to identify picking tomato of different maturity. Through the distribution statistical analysis of picking tomatoes and background color components, access to effective color indicators of segment picking tomato and background. Bring forward equal scale gray about color image, the color images segmentation will be turned into a gray image segmentation problem. Test result got to that the gray image can be better retain differences information of color images indicators, thus simplified the difficulty of segmentation and deal with image, and increased processing speed. The light and acquisition equipment brought about image noise is solved by using light compensation method and median filtering method. Threshold segmentation method used image segmentation in paper. The best combinations of the segmentation methods and color indicators are determined. The original Otsu method is improved. The image under different circumstances can be better segmented through the improve Otsu method.
Based on the color and shape feature, the tomato is identified and the center of the tomato is got. Determined the shape features of the picking object, and calculated the features. The trip marking algorithm is used to the goal marker, the search direction rotation algorithm is used to goal outline extraction. Through morphological processing, object extraction, and filling empty, gradually removed the image interference and accessed to better integrated tomato. In the process of the tomato center acquired, the paper will research to three mutual locations, and the single fruit in the image can be better extracted.
Base on the mission requirements and laboratory conditions, the paper adopted the plane template calibration method between the traditional calibration method and self-calibration method. Through self-made plane template, camera is calibrated, and the camera's internal and external parameters are accessed. To have finished the nonlinear optimization of the camera distortion compensates and to obtain optimal parameters with Levenberg-Marquardt iterative optimization algorithm.The binocular stereo camera calibration is finished.
Two core issues of the Binocular Stereo Vision are three-dimensional reconstruction and feature match. In the paper, working space of the four degrees of freedom tomato picking robot is analyzed, the vision scope of the visual identification and positioning system and the baseline value of the cameras in the binocular vision system are established. Based on the center characteristics of the tomato and epipolar constraint, the only constraint, parallax gradient constraint, goal matching is realized. In three-dimensional reconstruction, space coordinates of the picking tomatoes is achieved with the parallel binocular stereo model. Through studied conversion relationship between camera coordinates and robot coordinates, the tomato coordinate in the robot space coordinates is final achieved.
It has developed to the vision system of four degrees of freedom tomato picking robot. In the laboratory environment, tomato picking robot vision system is tested, and the error of the identification and location is analyzed. The testing results indicate that: The identification of the mature tomato can be up to 98%; the identification of green ripeing tomato can be up to 89 %; positioning error is in 15 mm. The effect of recognition and positioning can meet requirements of the four degrees of freedom tomato picking robot.
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