To avoid impacts and vibrations during the processes of acceleration and deceleration while possessing flexible working ways for cable-suspended parallel robots(CSPRs),point-to-point trajectory planning demands an und...To avoid impacts and vibrations during the processes of acceleration and deceleration while possessing flexible working ways for cable-suspended parallel robots(CSPRs),point-to-point trajectory planning demands an under-constrained cable-suspended parallel robot(UCPR)with variable angle and height cable mast as described in this paper.The end-effector of the UCPR with three cables can achieve three translational degrees of freedom(DOFs).The inverse kinematic and dynamic modeling of the UCPR considering the angle and height of cable mast are completed.The motion trajectory of the end-effector comprising six segments is given.The connection points of the trajectory segments(except for point P3 in the X direction)are devised to have zero instantaneous velocities,which ensure that the acceleration has continuity and the planned acceleration curve achieves smooth transition.The trajectory is respectively planned using three algebraic methods,including fifth degree polynomial,cycloid trajectory,and double-S velocity curve.The results indicate that the trajectory planned by fifth degree polynomial method is much closer to the given trajectory of the end-effector.Numerical simulation and experiments are accomplished for the given trajectory based on fifth degree polynomial planning.At the points where the velocity suddenly changes,the length and tension variation curves of the planned and unplanned three cables are compared and analyzed.The OptiTrack motion capture system is adopted to track the end-effector of the UCPR during the experiment.The effectiveness and feasibility of fifth degree polynomial planning are validated.展开更多
The seasonal variability of the significant wave height（SWH） in the South China Sea（SCS） is investigated using the most up-to-date gridded daily altimeter data for the period of September 2009 to August 2015. The ...The seasonal variability of the significant wave height（SWH） in the South China Sea（SCS） is investigated using the most up-to-date gridded daily altimeter data for the period of September 2009 to August 2015. The results indicate that the SWH shows a uniform seasonal variation in the whole SCS, with its maxima occurring in December/January and minima in May. Throughout the year, the SWH in the SCS is the largest around Luzon Strait（LS） and then gradually decreases southward across the basin. The surface wind speed has a similar seasonal variation, but with different spatial distributions in most months of the year. Further analysis indicates that the observed SWH variations are dominated by swell. The wind sea height, however, is much smaller. It is the the largest in two regions southwest of Taiwan Island and southeast of Vietnam Coast during the northeasterly monsoon, while the largest in the central/southern SCS during the southwesterly monsoon. The extreme wave condition also experiences a significant seasonal variation. In most regions of the northern and central SCS, the maxima of the 99 th percentile SWH that are larger than the SWH theoretically calculated with the wind speed for the fully developed seas mainly appear in August–November, closely related to strong tropical cyclone activities.Compared with previous studies, it is also implied that the wave climate in the Pacific Ocean plays an important role in the wave climate variations in the SCS.展开更多
【目的】以人工落叶松为例,探索基于无人机激光雷达(Unmanned aerial vehicle LiDAR,UAVLiDAR)点云的单木探测提取树高的误差对胸径反演的影响并校准,实现单木参数(胸径、树高)的准确度量,为大尺度高效便捷估测单木参数提供新的思路。...【目的】以人工落叶松为例,探索基于无人机激光雷达(Unmanned aerial vehicle LiDAR,UAVLiDAR)点云的单木探测提取树高的误差对胸径反演的影响并校准,实现单木参数(胸径、树高)的准确度量,为大尺度高效便捷估测单木参数提供新的思路。【方法】以东北林业大学帽儿山实验林场13块4个龄组(幼龄林、中龄林、近熟林和成熟林)的落叶松人工林样地UAV-LiDAR数据及野外调查数据为数据源,基于UAVLiDAR点云的单木探测提取的树高,分别以普通最小二乘法(Ordinary least squares,OLS)和3种误差变量回归(标准主轴(Standard major axis,SMA)、远程主轴(Ranged major axis,RMA)和极大似然估计(Maximum likelihood estimate,MLE))构建胸径-树高模型,研究探测误差对各龄组人工落叶松胸径反演的影响并校准。【结果】利用UAV-LiDAR点云的单木探测提取4个龄组树高的相对均方根误差(rRMSE),误差范围为3.41%~5.14%;在胸径-树高模型预测方面,3种误差变量回归均优于OLS,RMA预测效果最好,4个龄组反演单木胸径的rRMSE降低了2.21%~3.58%。【结论】当满足模型假设时,误差变量回归比OLS在预测响应变量方面表现更好,是估计无偏的模型系数的理想方法,本研究中RMA方法表现最好;本研究所构建的人工落叶松胸径反演模型具有较高的预估精度,各项误差均保持在合理范围内,可实现应用UAV-LiDAR高效便捷地估测大尺度森林单木参数的目的,可在实践中推广。展开更多
基金National Natural Science Foundation of China(Grant Nos.51925502,51575150).
文摘To avoid impacts and vibrations during the processes of acceleration and deceleration while possessing flexible working ways for cable-suspended parallel robots(CSPRs),point-to-point trajectory planning demands an under-constrained cable-suspended parallel robot(UCPR)with variable angle and height cable mast as described in this paper.The end-effector of the UCPR with three cables can achieve three translational degrees of freedom(DOFs).The inverse kinematic and dynamic modeling of the UCPR considering the angle and height of cable mast are completed.The motion trajectory of the end-effector comprising six segments is given.The connection points of the trajectory segments(except for point P3 in the X direction)are devised to have zero instantaneous velocities,which ensure that the acceleration has continuity and the planned acceleration curve achieves smooth transition.The trajectory is respectively planned using three algebraic methods,including fifth degree polynomial,cycloid trajectory,and double-S velocity curve.The results indicate that the trajectory planned by fifth degree polynomial method is much closer to the given trajectory of the end-effector.Numerical simulation and experiments are accomplished for the given trajectory based on fifth degree polynomial planning.At the points where the velocity suddenly changes,the length and tension variation curves of the planned and unplanned three cables are compared and analyzed.The OptiTrack motion capture system is adopted to track the end-effector of the UCPR during the experiment.The effectiveness and feasibility of fifth degree polynomial planning are validated.
基金The Shandong Provincial Natural Science Foundation under contract Nos ZR2015DQ006 and ZR2014DQ005the National Natural Science Foundation of China under contract Nos 41506008 and 41476002the China Postdoctoral Science Foundation under contract No.2015M570609
文摘The seasonal variability of the significant wave height（SWH） in the South China Sea（SCS） is investigated using the most up-to-date gridded daily altimeter data for the period of September 2009 to August 2015. The results indicate that the SWH shows a uniform seasonal variation in the whole SCS, with its maxima occurring in December/January and minima in May. Throughout the year, the SWH in the SCS is the largest around Luzon Strait（LS） and then gradually decreases southward across the basin. The surface wind speed has a similar seasonal variation, but with different spatial distributions in most months of the year. Further analysis indicates that the observed SWH variations are dominated by swell. The wind sea height, however, is much smaller. It is the the largest in two regions southwest of Taiwan Island and southeast of Vietnam Coast during the northeasterly monsoon, while the largest in the central/southern SCS during the southwesterly monsoon. The extreme wave condition also experiences a significant seasonal variation. In most regions of the northern and central SCS, the maxima of the 99 th percentile SWH that are larger than the SWH theoretically calculated with the wind speed for the fully developed seas mainly appear in August–November, closely related to strong tropical cyclone activities.Compared with previous studies, it is also implied that the wave climate in the Pacific Ocean plays an important role in the wave climate variations in the SCS.
文摘【目的】以人工落叶松为例,探索基于无人机激光雷达(Unmanned aerial vehicle LiDAR,UAVLiDAR)点云的单木探测提取树高的误差对胸径反演的影响并校准,实现单木参数(胸径、树高)的准确度量,为大尺度高效便捷估测单木参数提供新的思路。【方法】以东北林业大学帽儿山实验林场13块4个龄组(幼龄林、中龄林、近熟林和成熟林)的落叶松人工林样地UAV-LiDAR数据及野外调查数据为数据源,基于UAVLiDAR点云的单木探测提取的树高,分别以普通最小二乘法(Ordinary least squares,OLS)和3种误差变量回归(标准主轴(Standard major axis,SMA)、远程主轴(Ranged major axis,RMA)和极大似然估计(Maximum likelihood estimate,MLE))构建胸径-树高模型,研究探测误差对各龄组人工落叶松胸径反演的影响并校准。【结果】利用UAV-LiDAR点云的单木探测提取4个龄组树高的相对均方根误差(rRMSE),误差范围为3.41%~5.14%;在胸径-树高模型预测方面,3种误差变量回归均优于OLS,RMA预测效果最好,4个龄组反演单木胸径的rRMSE降低了2.21%~3.58%。【结论】当满足模型假设时,误差变量回归比OLS在预测响应变量方面表现更好,是估计无偏的模型系数的理想方法,本研究中RMA方法表现最好;本研究所构建的人工落叶松胸径反演模型具有较高的预估精度,各项误差均保持在合理范围内,可实现应用UAV-LiDAR高效便捷地估测大尺度森林单木参数的目的,可在实践中推广。
