In a probe and drogue aerial refueling system, the bow wave of the receiver aircraft will produce a strong aerodynamic effect on the drogue once the receiver follows the drogue at a close distance. It is a major diffi...In a probe and drogue aerial refueling system, the bow wave of the receiver aircraft will produce a strong aerodynamic effect on the drogue once the receiver follows the drogue at a close distance. It is a major difficulty of docking control in the probe and drogue refueling. This paper analyses the bow wave effect and presents a simple method to model it. Firstly, the inviscid flow around the receiver is modeled based on the stream function defined by basic stream singularities. Secondly, a correction function is developed to eliminate the error caused by the absence of air vis- cosity. Then, the aerodynamic coefficients are used to calculate the induced aerodynamic force on the drogue. The obtained model is in an analytical form that can be easily applied to the controller design and the real-time simulations. In the verification part, computational fluid dynamics （CFD） simulation tests are conducted to validate the obtained flow fields and aerodynamic forces. Finally, the modeling method is applied to an F-16 receiver aircraft in a previously developed autonomous aerial refueling simulation system. The simulations results are analyzed and compared with the NASA flight-test data, which demonstrates the effectiveness of the proposed method.展开更多
针对自动驾驶车辆在同时定位与建图(simultaneously location and mapping,SLAM)过程中,定位累积误差不断增大,导致全局一致性地图无法构建问题,提出一种基于改进词袋模型的自驾车辆SLAM闭环检测算法。对传统词袋模型进行优化,通过Canop...针对自动驾驶车辆在同时定位与建图(simultaneously location and mapping,SLAM)过程中,定位累积误差不断增大,导致全局一致性地图无法构建问题,提出一种基于改进词袋模型的自驾车辆SLAM闭环检测算法。对传统词袋模型进行优化,通过Canopy K-means聚类算法,产生词汇树;当前图像与候选图像的相似度大于阈值,认为匹配成功;采用时序法和关键区域协方差矩阵法对匹配成功的图像进行双重验证。分别在KITTI公开数据集和自采数据集测试方法的有效性。准确率-召回率曲线表明,改进后的算法与改进前的算法相比,在准确率为80%的情况下,召回率可提高12%。展开更多
针对传统BOW(Bag of Words)模型用于场景图像分类时的不足,通过引入关联规则的MFI(Maximum Frequent Itemsets)和Topology模型对其进行改进。为了突出同类图像的视觉单词,提取同类图像的MFI后,对其中频繁出现的视觉单词进行加权处理,增...针对传统BOW(Bag of Words)模型用于场景图像分类时的不足,通过引入关联规则的MFI(Maximum Frequent Itemsets)和Topology模型对其进行改进。为了突出同类图像的视觉单词,提取同类图像的MFI后,对其中频繁出现的视觉单词进行加权处理,增强同类图像的共有特征。同时,为了提高视觉词典的生成效率,利用Topology模型对原始模型进行分工并行处理。通过COREL和Caltech-256图像库的实验,证明改进后的模型提高了对场景图像的分类性能,并验证了其Topology模型的有效性和可行性。展开更多
基金supported by the National Natural Science Foundation of China(Nos.61473012 and 51375462)
文摘In a probe and drogue aerial refueling system, the bow wave of the receiver aircraft will produce a strong aerodynamic effect on the drogue once the receiver follows the drogue at a close distance. It is a major difficulty of docking control in the probe and drogue refueling. This paper analyses the bow wave effect and presents a simple method to model it. Firstly, the inviscid flow around the receiver is modeled based on the stream function defined by basic stream singularities. Secondly, a correction function is developed to eliminate the error caused by the absence of air vis- cosity. Then, the aerodynamic coefficients are used to calculate the induced aerodynamic force on the drogue. The obtained model is in an analytical form that can be easily applied to the controller design and the real-time simulations. In the verification part, computational fluid dynamics （CFD） simulation tests are conducted to validate the obtained flow fields and aerodynamic forces. Finally, the modeling method is applied to an F-16 receiver aircraft in a previously developed autonomous aerial refueling simulation system. The simulations results are analyzed and compared with the NASA flight-test data, which demonstrates the effectiveness of the proposed method.
文摘针对自动驾驶车辆在同时定位与建图(simultaneously location and mapping,SLAM)过程中,定位累积误差不断增大,导致全局一致性地图无法构建问题,提出一种基于改进词袋模型的自驾车辆SLAM闭环检测算法。对传统词袋模型进行优化,通过Canopy K-means聚类算法,产生词汇树;当前图像与候选图像的相似度大于阈值,认为匹配成功;采用时序法和关键区域协方差矩阵法对匹配成功的图像进行双重验证。分别在KITTI公开数据集和自采数据集测试方法的有效性。准确率-召回率曲线表明,改进后的算法与改进前的算法相比,在准确率为80%的情况下,召回率可提高12%。
文摘针对传统BOW(Bag of Words)模型用于场景图像分类时的不足,通过引入关联规则的MFI(Maximum Frequent Itemsets)和Topology模型对其进行改进。为了突出同类图像的视觉单词,提取同类图像的MFI后,对其中频繁出现的视觉单词进行加权处理,增强同类图像的共有特征。同时,为了提高视觉词典的生成效率,利用Topology模型对原始模型进行分工并行处理。通过COREL和Caltech-256图像库的实验,证明改进后的模型提高了对场景图像的分类性能,并验证了其Topology模型的有效性和可行性。
