UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.     

危险天气下基于多重Morphin算法的终端区三维实时改航方法

基于多重Morphin算法,建立了终端区 三维实时改航方法。该方法首先根据航空器当前飞行状态按不同的转弯角和爬升/下滑角生 成一组弧线,随后在每条弧线的末端按同样方式反复运行,形成若干条由弧线组成的路径, 最后对所有路径进行综合评估,找到当前时刻的改航路径。算例分析表明,该方法提供的改 航路径可以保证航空器运行的安全与高效,在危险天气出现时更加充分地利用终端区空域资 源,同时计算时间短、可行性高。     

多阶段高斯伪谱法在编队最优控制中的应用

针对编队协同攻击时间最优控制问题,首先通过分析编队协同攻击作战过程,将编队协同攻击任务划分为编队形成、编队保持和协同攻击三个阶段。在简化控制延迟与效率的基础上,建立三阶段通用的空间运动方程。然后结合各阶段的任务特点,综合考虑弹间防碰、期望队形参数、交班误差、导弹自身控制限制,及攻击目标精度等因素,建立各阶段的最优控制模型。基于高斯伪谱法,提出编队形成-保持-攻击一体化的时间最优控制算法。通过大量仿真,在合理选择勒让德-高斯（LG）节点的前提下,利用该方法可快速得到优化的控制指令,弹道及约束性能的仿真结果显示,编队协同攻击全过程可满足任务需求及相应约束限制,校验了算法的可行性。     

基于比例危险-比例优势模型的加速寿命试验设计

将基于信息的优化方法引入基于非参数模型的加速寿命试验优化设计中,针对恒定应力和步进应力两种应力加载方式,分别给出了基于比例危险-比例优势模型的试验优化设计方法.通过对数似然函数建立Fisher信息矩阵和方差-协方差矩阵,并采用基于信息的优化方法建立最优化问题.这种试验优化设计方法可以有效地提高模型参数评估精度,并且避免了传统的优化方法(即将一个与可靠性相关的函数的渐进方差在一个给定区间内的积分值作为优化目标)当目标函数中给定的积分区间变化时将得到不同优化结果的局限.最后给出了应用该方法进行加速寿命试验优化设计的仿真实例.     

快速城市化进程中新型农村社区规划探索

新型农村社区是在我国快速城市化进程中,适应新农村建设需要,探索形成的一种新的空间行政单元.文章分析了新型农村社区规划研究的背景,回顾了社区规划相关理论的发展演化,以新乡卫辉市孙杏村新型社区规划为例,总结了新型农村社区规划的核心内容和主要特点,认为农村社区规划有利于避免农村无序建房和重复投资等浪费现象,节约土地,共享基础设施,促进城市化进程.     

卫星测控站站点设置的基本原理分析与探讨

针对卫星发射和运行过程中,如何设置测控站以实现全程跟踪的问题进行了分析。对所有测控站与卫星运行轨道共面的情况进行研究,采用相邻站点测控区域相切的思想,获得了对卫星全程跟踪所需最少测控站数目的函数关系式。针对卫星高度为300、1000、2000km三种情况分别进行求解,结果需要设置的最少测控站数目依次为13、7、5。     

一种新的元胞自动机航迹规划算法

基于元胞自动机理论提出一种新的航迹规划的算法,该算法克服了传统元胞自动机航迹规划算法只能针对存在安全飞行区域情况下规划航迹的局限性,新方法能够在全空域受到地空威胁的条件下,选择威胁较小区域规划出航迹。     

一种凸多边形区域的无人机覆盖航迹规划算法

 覆盖航迹规划技术对于提高无人机的侦察能力和目标搜索能力具有重要的意义。首先从能量、路程、时间角度,理论上证明了转弯过程比直线平飞过程的效率低。其次给出了凸多边形跨度和宽度的定义,并把凸多边形区域的覆盖航迹规划问题转化为求凸多边形宽度的问题。最后证明了凸多边形区域的宽度只可能出现于“点边式”跨度之中,并给出了一种高效的“点边式”宽度算法。无人机只需要沿着宽度出现时的支撑平线方向飞行,即可以取得最少的转弯次数。通过仿真分析,证明了所提出的算法能够有效解决无人机在凸多边形区域中的覆盖航迹规划问题。     

考虑时间因素的敏捷自主航天器四维姿态运动规划方法研究

为解决敏捷自主航天器在轨灵巧、精确姿态运动问题,针对复杂空间环境与其自身运动特点,提出了基于改进的快速搜索随机树的四维姿态运动规划方法。仿真算例检验了该规划方法的有效性,结果表明它不仅满足敏捷自主航天器在一定约束条件下指定时间到达指定地点的四维运动要求,而且对四维姿态运动离线与在线规划两种模式也具备较强的适应性。