作　　者： ; ; ; ; ; ;

机构地区： 华南农业大学工程学院南方农业机械与装备关键技术省部共建教育部重点实验室

出　　处： 《农业工程学报》 2015年第2期14-21,共8页

摘　　要： 针对葡萄果梗颜色复杂多变、轮廓不规则等影响因素使得采摘机器人难以准确对采摘点进行定位的问题,该文提出一种基于改进聚类图像分割和点线最小距离约束的采摘点定位新方法。首先通过分析葡萄图像的颜色空间,提取最能突显夏黑葡萄的HSI色彩空间分量H,运用改进的人工蜂群优化模糊聚类方法对葡萄果图像进行分割;然后对分割图像进行形态学去噪处理,提取最大连通区域,计算该区域质心、轮廓极值点、外接矩形;再根据质心坐标与葡萄簇边缘信息确定采摘点的感兴趣区域,在区域内进行累计概率霍夫直线检测,求解所有检测得出的直线到质心之间的距离,最后选取点线距离最小的直线作为采摘点所在线,并取线段中点坐标作为采摘点。以从晴天顺光、晴天遮阴、阴天光照下采集的300幅夏黑葡萄进行分类试验,结果表明,该方法的采摘点定位准确率达88.33%,平均定位时间为0.3467 s,可满足采摘机器人对采摘点的定位需求,为葡萄采摘机器人提供了一种新的采摘点求解方法。 In wine brewing process, the most time-consuming and laborious is grape picking. In order to improve the target location accuracy and work efficiency of grape-picking robot, reduce the mechanical damage that was caused by improper positioning of grape picking point, in view of some influence factors such as the various colors of grape stem and the irregular contour of grape, which make picking robot hard to locate picking point accurately, a new method of picking point location based on the improved image segmentation with clustering and the constraint by minimum distance between dot and line was put forward in this paper. Because the picking time was often chosen in sunny or cloudy day, 300 images of summer black grape were collected using D5200 Nikon digital camera in sunny or cloudy days, which were taken as test materials; the shooting distance between camera and grape cluster was about 80 cm, and the sizes of these images were zoomed to 800＆#215;600 pixels. Firstly, the color space of gathered images was analyzed and the component H of HSI color space that can mostly highlight summer black grape was found. The H component of images was extracted and median filtering was performed on these images. Grape fruit image was segmented by using fuzzy clustering that was improved by artificial swarm. Solving the minimum value of fuzzy objective function of FCM clustering algorithm was transformed into solving the maximum value of artificial swarm fitness function by improving the fitness function of the artificial swarm optimization algorithm. Then, the segmented image was processed with morphological denoising, the maximum connected region was extracted, and the regional barycenter, extreme point and external rectangular were calculated. Secondly, the interest region of picking point was determined according to the barycentric coordinates and edge information of grape image. Taking 0.6 times of the length of the external rectangular as the length of region of interest （ROI）, and taking 0.5 times of the vertica

关 键 词： 机器人 图像分割 定位 葡萄 采摘点

领　　域： [自动化与计算机技术] [自动化与计算机技术]