农业机械(农机)在多个地块作业,费用和效率有时需按地块统计,现有的农机监控系统仅能记录农机定位信息和作业状态信息,难以实现地块的自动精准划分。本文通过研究轨迹点属性特征,分析作业地块数量不确定性和轨迹点分布规律,采用基于密...农业机械(农机)在多个地块作业,费用和效率有时需按地块统计,现有的农机监控系统仅能记录农机定位信息和作业状态信息,难以实现地块的自动精准划分。本文通过研究轨迹点属性特征,分析作业地块数量不确定性和轨迹点分布规律,采用基于密度聚类方法(Density-based spatial clustering of applications with noise, DBSCAN)和分类器集成算法(BP_Adaboost)结合的方法划分地块。根据DBSCAN算法对农机轨迹点多数有效、识别错误集中的特点,结合BP_Adaboost算法挖掘多维度信息关联、容错能力强、分类效果好等优势,先利用DBSCAN得到初步的轨迹点状态类别,再利用BP_Adaboost算法建立训练模型对农机轨迹点状态精准识别,根据时间序列和类别标记划分地块。本文方法既解决了只依靠阈值和经纬度信息聚类不准确的问题,也减少了大量样本标记工作。利用该方法轨迹点状态识别准确率达96.75%,地块划分准确率为97.74%。展开更多
A tomato harvesting robot was developed in this study,which consisted of a four-wheel independent steering system,a 5-DOF harvesting system,a navigation system,and a binocular stereo vision system.The four-wheel indep...A tomato harvesting robot was developed in this study,which consisted of a four-wheel independent steering system,a 5-DOF harvesting system,a navigation system,and a binocular stereo vision system.The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry.The proportional-integral-derivative(PID)algorithm was used in the laser navigation control system.The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes,and obstacle avoidance strategies were proposed based on the C-space method.The maximum average absolute error between the set angle and the actual angle was about 0.14°,and the maximum standard deviation was about 0.04°.The laser navigation system was able to rapidly and accurately track the path,with the deviation being less than 8 cm.The load bearing capacity of the mechanical arm was about 1.5 kg.The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%.When the distance was less than 600 mm,the positioning error was less than 10 mm.The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato,with a success rate of about 86%.This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse.展开更多
文摘农业机械(农机)在多个地块作业,费用和效率有时需按地块统计,现有的农机监控系统仅能记录农机定位信息和作业状态信息,难以实现地块的自动精准划分。本文通过研究轨迹点属性特征,分析作业地块数量不确定性和轨迹点分布规律,采用基于密度聚类方法(Density-based spatial clustering of applications with noise, DBSCAN)和分类器集成算法(BP_Adaboost)结合的方法划分地块。根据DBSCAN算法对农机轨迹点多数有效、识别错误集中的特点,结合BP_Adaboost算法挖掘多维度信息关联、容错能力强、分类效果好等优势,先利用DBSCAN得到初步的轨迹点状态类别,再利用BP_Adaboost算法建立训练模型对农机轨迹点状态精准识别,根据时间序列和类别标记划分地块。本文方法既解决了只依靠阈值和经纬度信息聚类不准确的问题,也减少了大量样本标记工作。利用该方法轨迹点状态识别准确率达96.75%,地块划分准确率为97.74%。
基金supported by the National 863 planning project of China-digital design and intelligent control technology of agricultural facilities equipment(2013AA102406)the Beijing municipal science and technology project(Z161100004916118).
文摘A tomato harvesting robot was developed in this study,which consisted of a four-wheel independent steering system,a 5-DOF harvesting system,a navigation system,and a binocular stereo vision system.The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry.The proportional-integral-derivative(PID)algorithm was used in the laser navigation control system.The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes,and obstacle avoidance strategies were proposed based on the C-space method.The maximum average absolute error between the set angle and the actual angle was about 0.14°,and the maximum standard deviation was about 0.04°.The laser navigation system was able to rapidly and accurately track the path,with the deviation being less than 8 cm.The load bearing capacity of the mechanical arm was about 1.5 kg.The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%.When the distance was less than 600 mm,the positioning error was less than 10 mm.The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato,with a success rate of about 86%.This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse.
