多约束条件下的无人机轨迹快速规划

多约束条件下无人机轨迹快速规划是无人机航迹规划的重要内容。通过构造无人机在定位误差约束下的数学模型,结合航迹点和转弯半径推导出空间圆弧轨迹方程,利用贪心算法对无人机的航迹进行快速规划,结合软件仿真得到飞行过程中的最优航迹方案,为应用仓储物流路径规划领域以及车辆无人驾驶目标路径规划提供了思路。     

普通骨板内固定治疗玩具犬桡尺骨骨折的回顾性分析

本试验旨在评估普通骨板内固定治疗玩具犬桡尺骨骨折的临床效果及并发症。回顾了中国农业大学动物医院使用普通骨板（圆洞骨板或兽医可剪裁骨板）开放式复位和内固定治疗桡尺骨骨折的玩具犬的病历记录，并对这些病例进行回访。入选病例满足以下条件：体重不超过7 kg；回访时间大于12个月；病例信息记录完整。结果显示：共纳入63只犬的64例桡尺骨骨折，49例（76.6%）术后无跛行，7例（10.9%）术后勉强可见到跛行，6例（9.3%）存在轻度跛行，2例（3.1%）存在中度跛行。严重并发症的发生率是6.3%，轻微并发症的发生率是27%。试验表明，普通骨板内固定能有效治疗玩具犬桡尺骨骨折，该方法临床效果良好，且严重并发症发生率低。     

Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds

Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (R_c), which were then input to the newly established allometric equation with tree H and R_c as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R²) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R² and RMSE of extracted R_c were 0.9006 and 0.28 m; the R² and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and R_c to accurate AGB estimation of urban forest trees.