Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable.The acoustic signal is a marvelous choice fo...Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable.The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability.However,the traditional Acoustic Positioning Systems(APS)are expensive and incapable of positioning with limited acoustic observations.Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed.The first one is the single acoustic-beacon Range-only Matching Aided Navigation(RMAN)method,which is inspired by matching navigation without reference maps and presented for the first time.The second is the improved single acoustic-beacon Virtual Long Baseline(VLBL)method,which considers the impact of indicated relative position increments on virtual beacon reconstruction.Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available,named mAB-RMAN and mAB-VLBL.The comprehensive simulations and field investigations were conducted.The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline,specifically,the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90%and 98%when using single and double acoustic-beacons,respectively.These proposed techniques will provide new perspectives for underwater positioning,navigation,and timing.展开更多
The authors proposed a moving long baseline algorithm based on the extended Kalman filter （EKF） for cooperative navigation and localization of multi-unmanned underwater vehicles （UUVs）. Research on cooperative nav...The authors proposed a moving long baseline algorithm based on the extended Kalman filter （EKF） for cooperative navigation and localization of multi-unmanned underwater vehicles （UUVs）. Research on cooperative navigation and localization for multi-UUVs is important to solve navigation problems that restrict long and deep excursions. The authors investigated improvements in navigation accuracy. In the moving long base line （MLBL） structure, the master UUV is equipped with a high precision navigation system as a node of the moving long baseline, and the slave UUV is equipped with a low precision navigation system. They are both equipped with acoustic devices to measure relative location. Using traditional triangulation methods to calculate the position of the slave UUV may cause a faulty solution. An EKF was designed to solve this, combining the proprioceptive and exteroceptive sensors. Research results proved that the navigational accuracy is improved significantly with the MLBL method based on EKF.展开更多
Detection navigation baseline is primary for the automation of maize weeder in seedling.In the navigation technology based on machine vision,maize seeding or weed near the camera is photographed as a discrete area,whi...Detection navigation baseline is primary for the automation of maize weeder in seedling.In the navigation technology based on machine vision,maize seeding or weed near the camera is photographed as a discrete area,while a plant far away from the camera is photographed as a strip area along with other plants in the same row.The two problems cannot be solved by one method.However,in this paper,an algorithm of detection navigation baseline in the row-following operation of maize weeder based on axis extraction was proposed to solve the both problems.Firstly,plants are distinguished from the background based on color feature,and the binary image is acquired.Secondly,plants are described as a set of connected components with numbers after connected components labeling and noise clearing.Thirdly,the axes of all connected components are extracted according to the calculation method of rotary inertia in physics.Next,the abnormal connected components with axes are deleted because the angles between the axes and X-axis are above angle threshold.Then,the judgment model is built based on angle tolerance and distance tolerance,the connected components in a same row based on this model through two-step traversal are merged,and a new axis is re-extracted as the axis of the plant row.Finally,the navigation baselines are detected based on the axes of the plant row.The experimental results show that the accuracy of this algorithm is more than 93%,and the computing time is less than 1.6 s,which can meet the accuracy and real-time performance requirements of maize weeder.展开更多
为了实时、准确地提取作物行基准线,提出了一种将边缘检测和扫描滤波(Boundary detection and scan-filter,BDSF)相结合的基准线提取方法。首先对RGB颜色空间采用G-R颜色特征因子进行图像灰度化,再采用最大类间方差法(OSTU)对灰度图像...为了实时、准确地提取作物行基准线,提出了一种将边缘检测和扫描滤波(Boundary detection and scan-filter,BDSF)相结合的基准线提取方法。首先对RGB颜色空间采用G-R颜色特征因子进行图像灰度化,再采用最大类间方差法(OSTU)对灰度图像进行分割,得到二值化图像,获取较好的作物信息。然后分别对图像的底端和顶端部分进行垂直投影,获取作物行的位置,形成一个包含作物行直线的条形框;在这个条形框内,再用等面积的小条形框对图像进行扫描并统计有效点的个数。最后根据扫描的结果来提取导航线。试验结果表明,对比Hough算法和最小二乘法(Least square method,LSM),BDSF算法处理一幅分辨率为640像素×480像素的图像,平均耗时为67 ms,与LSM算法耗时相当,精度接近Hough算法;并且在杂草和株数稀缺情况下具有良好的适应性,能够快速准确地提取作物行基准线。展开更多
基金funding was provided by Natural Science Foundation of China(Grant numbers 42004067,62373367,42176195)。
文摘Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable.The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability.However,the traditional Acoustic Positioning Systems(APS)are expensive and incapable of positioning with limited acoustic observations.Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed.The first one is the single acoustic-beacon Range-only Matching Aided Navigation(RMAN)method,which is inspired by matching navigation without reference maps and presented for the first time.The second is the improved single acoustic-beacon Virtual Long Baseline(VLBL)method,which considers the impact of indicated relative position increments on virtual beacon reconstruction.Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available,named mAB-RMAN and mAB-VLBL.The comprehensive simulations and field investigations were conducted.The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline,specifically,the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90%and 98%when using single and double acoustic-beacons,respectively.These proposed techniques will provide new perspectives for underwater positioning,navigation,and timing.
基金Supported by the National Natural Science Foundation of China under Grant No.60875071the High Technology Research and Development Program of China under Grant No.2007AA0676the Program for New Century Excellent Talents in University under Grant No.NCET-06-0877
文摘The authors proposed a moving long baseline algorithm based on the extended Kalman filter （EKF） for cooperative navigation and localization of multi-unmanned underwater vehicles （UUVs）. Research on cooperative navigation and localization for multi-UUVs is important to solve navigation problems that restrict long and deep excursions. The authors investigated improvements in navigation accuracy. In the moving long base line （MLBL） structure, the master UUV is equipped with a high precision navigation system as a node of the moving long baseline, and the slave UUV is equipped with a low precision navigation system. They are both equipped with acoustic devices to measure relative location. Using traditional triangulation methods to calculate the position of the slave UUV may cause a faulty solution. An EKF was designed to solve this, combining the proprioceptive and exteroceptive sensors. Research results proved that the navigational accuracy is improved significantly with the MLBL method based on EKF.
基金This work was supported by National Key Research and Development Program of China(2017YED0701500)Shanxi Provincial Program for Youth Science and Technology(201801D221289)Shanxi Agriculture University Youth Science and Technology Innovation Project(J141802199).
文摘Detection navigation baseline is primary for the automation of maize weeder in seedling.In the navigation technology based on machine vision,maize seeding or weed near the camera is photographed as a discrete area,while a plant far away from the camera is photographed as a strip area along with other plants in the same row.The two problems cannot be solved by one method.However,in this paper,an algorithm of detection navigation baseline in the row-following operation of maize weeder based on axis extraction was proposed to solve the both problems.Firstly,plants are distinguished from the background based on color feature,and the binary image is acquired.Secondly,plants are described as a set of connected components with numbers after connected components labeling and noise clearing.Thirdly,the axes of all connected components are extracted according to the calculation method of rotary inertia in physics.Next,the abnormal connected components with axes are deleted because the angles between the axes and X-axis are above angle threshold.Then,the judgment model is built based on angle tolerance and distance tolerance,the connected components in a same row based on this model through two-step traversal are merged,and a new axis is re-extracted as the axis of the plant row.Finally,the navigation baselines are detected based on the axes of the plant row.The experimental results show that the accuracy of this algorithm is more than 93%,and the computing time is less than 1.6 s,which can meet the accuracy and real-time performance requirements of maize weeder.
