形态图计算中的投影空间维数和投影空间模型

确定投影空间维数和建立投影空间模型是计算机视觉领域中形态图计算时一个十分重要的基本问题，本文根据态图计算时特征视图拓扑结构等价的特点，指出轴测投影下的投影空间是二维空间，透视投影空间为三维空间，并分别了投影空间模型的建立方法，从而使复杂物体形科计算是简单可行。     

Banach空间中微分包含解的存在性

本文在无穷维Ｂａｎａｃｈ空产是中讨论微分包含解的存在性，先给出了几个普通微分包含的比较定理，讨论了近似解与解的关系，然后得到了Ｂａｎａｃｈ空间中微分包含解的存在性定理。     

模拟月壤研制的初步设想

模拟月壤是月球样品的地球化学复制品，作者总结世界上已有的5种模拟月壤JSC-1，MLS．1，MLS-2，MKS-1和FJS-1的研制过程、方法与基本理化性质，认为系列化模拟月壤研制对中国首次月球探测有重要意义，在此基础上，作者提出系列化模拟月壤研制的基本思路．     

空间积分声光相关器——脉冲信号检测

论述了空间积分声光相关器对脉冲调制连续波信号的瞬时响应，分析了从实时光电检测器中检测出的波形，该波形是脉冲宽度，加权函数参量和与脉冲位置相关的空间衍射光分布的函数。进一步提出了脉冲序列相关时的光分布关系并通过实验加以论证。     

首颗自主地月转移微卫星“龙江二号”

本文对世界首颗独立完成地月转移、近月制动、环月飞行的微卫星“龙江二号”进行了介绍。首先阐述了龙江二号的任务目标和总体方案；然后重点分析了轨道设计与控制、复杂电磁干扰的标定与抑制、宽视场三维基线干涉测量等关键技术难点；接着回顾了任务的实施过程；最后详细介绍了低频射电探测仪、沙特光学相机和VHF/UHF通信模块与学生微型CMOS相机等有效载荷，并展示了上述载荷获取的初步成果。     

对空间碎片近距随遇悬停的控制方法及悬停燃耗分析

对空间碎片检视或抓捕操控中的悬停控制及燃耗问题进行了研究。通过C W方程，建立任务星近距随遇定点悬停控制模型，通过设计状态反馈控制器分析定点悬停的可控性以及推控要求，结果表明近距悬停需要与悬停位置相关的两个正交方向上的常值连续推力控制量，以及用于抵抗扰动的三轴向上的反馈变推力控制量。分析了近距随遇悬停的推控分系统配置，建立了长时近距随遇悬停的燃耗及燃耗速率的数学模型，最后分析了既满足安全距离需求、又满足悬停方位需求的最小悬停燃耗模型。     

MIDACO software performance on interplanetary trajectory benchmarks

A numerical study of the MIDACO optimization software on the well known GTOP benchmark set, published by the European Space Agency (ESA), is presented. The GTOP database provides trajectory models of real-world interplanetary space missions such as Cassini, Messenger or Rosetta. The trajectory models are formulated as constrained nonlinear optimization problems and are known to be difficult to solve.     

卷簧式浅层月壤取样器设计及动力学分析

面向浅层月壤取样,文章设计了新颖的卷簧式可伸缩小型月壤取样器,它包括柔性取样臂和振动式取样头。对取样器进行了动力学有限元分析,研究了取样臂为不同长度时取样头的振动模态及频率响应,得出取样臂一阶横振频率与取样臂长度呈负幂函数关系;对取样器测控系统进行了振动试验,通过扫频方式测量取样臂为不同长度时的自振频率,给出了取样臂长...     

航天器安全防护保障探讨

把对航天器安全的威胁分为硬和软两大类，分析了对航天器产生威胁的类别，以及为保证航天器安全而需要采取的措施。介绍了研究的内容，需要建设的空间信息对抗系统和空间信息探测网。     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.