深空探测通信技术发展趋势及思考

阐述了深空探测通信的重要地位和意义，就深空探测通信中的主要问题和特点开展论述，分析了深空探测通信的国内外研究现状。提出了利用地球静止轨道卫星编队的方法构建我国自主的天基连续测控和通信系统的构想，并以月球为中继、利用地月系统内的拉格朗日点开展深空探测。建议当前开展深空探测通信中星际互联网络体系结构、高效信道编码技术和空间协议研究的构想。     

深空通信天线组阵关键技术及其发展趋势

天线组阵是未来深空通信中的重要发展方向之一。本文介绍了天线组阵的发展历程,重点讨论了组阵中的关键技术,包括阵址选择、阵元口径和数量设计、阵构型设计以及信号处理的方法,分析了上述技术对组阵性能的影响。最后,探讨了天线组阵的发展趋势。
     

深空通信中LDPC码的一种译码算法

LDPC码由于具有逼近仙农极限的性能和硬件可实现的编译码复杂度,在深空通信中具有重要的应用前景。文章在简要介绍CCSDS（空间通信系统咨询委员会）中LDPC码编码规范的基础之上,采用多维EXIT图对其译码门限和收敛速度进行了研究,最后重点研究了译码算法,通过改进归一化最小和算法提出了一种自适应归一化最小和算法,该算法保留了归一化最小和算法的所有优点,改善了不同信噪比条件下的译码性能,并解决了归一化因子的选择难题,适合在资源受限的深空通信中应用。
     

一种基于信息融合的深空巡航段自主导航算法

针对自主光学导航在深空巡航段应用中的问题,提出了一种基于信息融合的自主导航算法。由太阳敏感器测量获得太阳相对探测器的视线矢量,由分光计测量获得探测器相对太阳的径向速度,构建自主导航系统的两种信息观测方程;由基于状态估计误差协方差阵奇异值动态确定信息分配因子,用信息融合技术和扩展卡尔曼滤波算法实时估计探测器的位置与速度。对深度撞击任务的实际飞行数据的数值仿真结果表明:自主导航算法的轨道确定精度满足深空巡航段要求。     

A spanwise loss model for axial compressor stator based on machine learning

In the early stage of aircraft engine design, the through-flow method is an important tool for designers. The accuracy of the through-flow method depends heavily on the accuracy of the loss model. However, most existing models cannot(or cannot well) provide the spanwise loss distribution. To construct an effective spanwise loss model, both turbomachinery knowledge and machine learning skills were used in this paper. A large number of numerical simulations were carried out to build a database con...     

Energy-optimal trajectory planning for solar-powered aircraft using soft actor-critic

High-Altitude Long-Endurance(HALE) solar-powered Unmanned Aircraft Vehicles(UAVs) can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from researchers. Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day. In this work,a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning(RL) framework is...     

基于深度学习的空间站舱内服务机器人视觉跟踪

为提升舱内跟随服务机器人的任务辅助能力,解决机器人对航天员的视觉跟踪问题,提出了一种基于深度学习和概率模型的人体视觉跟踪算法。利用深度卷积神经网络实现了对穿着多样、姿态任意人体的稳定检测。结合人体检测结果,设计了运动预测概率模型,实现了对指定人员准确、连续的跟踪。算法对包含大多数航天员活动的多个数据集进行了验证。实验结果表明:提出的跟踪算法实现了对穿着多样、姿态任意人体的稳定跟踪,并有效避免了由于穿着相似、遮挡可能造成的误跟踪问题。该算法为空间站舱内跟随服务机器人对航天员的视觉跟踪提供了有效的解决方法。算法基于融合的RGB-D图像,工程上易于构建和实现,也可拓展到其他跟随服务机器人视觉跟踪任务中。     

基于DDPG-PID的机器人动态目标跟踪与避障控制研究

针对机器人在动态复杂环境下的操作问题,研究机械臂跟踪运动目标及避障的运动控制方法。采用传统PID控制方法与DDPG强化学习算法相结合的方式,利用PID控制使得机械臂的工作平面快速接近目标物并与之重合,再使用DDPG算法让机械臂在平面内自主学习追踪目标物投影同时避开障碍物投影,最终在三维空间中实现跟踪与避障。该方法将机械臂作为一个智能体,智能体通过感知目标物和障碍物的距离偏差来自主学习避障跟踪策略。将本方法用于多自由度机械臂路径规划与避障任务中,对比单纯使用DDPG算法将机械臂作为智能体在空间中进行决策控制,仿真结果显示本文所提出的方法收敛效果和控制性能更好,机械臂能在训练后实现目标物的稳定跟踪和障碍物的躲避。     

元强化学习综述

元强化学习是指自动从一组相关任务中学习强化学习所需归纳偏置的相关理论和方法,对于提高强化学习算法在困难场景下的样本效率和泛化能力具有重要用途。本文提出一种新的元强化学习框架,指出设计和分析一个元强化学习算法需要同时考虑学习经验（相关任务）、归纳偏置及学习目标3个独立因素及这3个因素之间的依赖关系。在此基础上对该领域的研究现状进行了分析和总结,特别对近年来元强化学习若干文献进行了分析和归类,并详细阐述了几种代表性算法的原理及各自特点。本文还对元强化学习常用的实验环境和性能评价方法进行了介绍,对该领域的不足和未来的发展方向进行了讨论和分析。     

Mining Spatial Associations with Limited Sensory Information

Abstract

Human navigation in an unknown environment requires an understanding of the spatial relationships of the terrain. For example, a soldier who is on a reconnaissance mission in a new city needs to “know” the spatial layout of the surroundings with high confidence. Oftentimes, this understanding must be acquired within a very short amount of time and with limited sensory inputs. The soldier would benefit from a digital avatar that draws inferences about the spatial layout of the city based on an initial set of observations and guides the soldier either in further exploring the environment or in making decisions based on these inferences. In this paper, we present and evaluate an inductive approach to learning spatial associations using sensory data that is available from the simulation environment of a computer game, Unreal Tournament. We study two kinds of spatial relationships between nodes on a level of a game map: nodes that are placed near each other to satisfy some spatial requirement and nodes that are placed near each other to satisfy the design preferences of a level architect. We show that we can infer both kinds of relationships using an association rule mining algorithm. Furthermore, we show how to use an ontology to distinguish between these relationships in order to discover different types of spatial arrangements on a specific map. We discuss how the inferred associations can be used to control an avatar that makes recommendations for navigating unexplored areas on a map. We conclude with some thoughts on the applicability of our methods to scenarios in the real world, beyond the simulation environment of a game, and on how the learned associations can be represented and queried by a simple question-answer type system.