无人机航迹规划常用算法

无人机航迹规划是实现无人机自主飞行的关键技术保障,航迹规划算法则是航迹规划的核心。首先介绍无人机航迹规划的相关理论,其次分析归纳了当前常用的航迹规划算法,最后阐述了航迹规划所面临的挑战及其发展方向。     

论校园文化建设的内容与规划——基于华山中学校园文化建设活动的系列思考之二

开展校园文化建设必须以马克思主义文化观作为指导思想,在此基础上,要全面了解校园文化建设的内容,进行科学规划,稳步实施,才能收到成效。本文结合华山中学的具体实践,阐述了对校园文化建设工作的认识,并且提出以校园文化建设为目标的教育形式才可以称得上是全面教育,它比侧重传授知识的教育模式更加接近教育的本质规律,更加反映人性发展的需要,也更加符合人类社会发展的方向。     

光电载荷安装误差标校的飞行航路

无人机搭载光电载荷进行安装误差标校时,飞行航路的选择与规划在很大程度上影响着标校算法的收敛性与误差识别结果。为增强标校飞行架次的有效性,提高安装误差可被识别的程度,改善标校算法的收敛性及标校精度,对标校的原理和标校算法进行了理论分析,并利用考察差异函数偏微分的方法找到了标校航路和安装误差标校之间的相互联系,然后结合标校实施过程中所遇到的实际问题提出标校航路规划应遵循的原则,给出了标校航路规划的参考范例。     

一种面向节点定位的移动信标动态路径规划方法

针对基于移动信标的传感器节点定位问题,提出一种基于在线决策的移动信标动态路径规划方法.针对以往算法大都只适用于节点均匀分布的局限,该方法用移动信标不断获取两跳范围内的未定位节点数目,并向最大覆盖未定位节点方向移动,不需要网络先验信息,即可实现路径的优化.仿真结果表明:与传统方法相比,该方法无需网络的先验信息,在移动路径长度上具有明显优越性,减少了信标的能量消耗,更适用于户外部署的大规模传感器网络.     

战场环境下无人车辆战术机动路径规划方法

针对不同类型威胁体存在的战场环境中无人车辆战术机动路径规划问题,提出了一种基于威胁代价地图的粒子群优化（Particle Swarm Optimization,PSO）方法。借助极坐标系中关键点的极角进行路径描述,并使用分段3次Hermite插值方法形成光滑路径,将路径规划问题转化为关键点极角的参数优化问题。针对基本PSO（BPSO）算法存在的早熟收敛和后期迭代效率低的缺陷,借鉴以群集方式生活的物种按照不同任务对种群进行分工的机制,提出了一种基于多任务子群协同的改进粒子群优化（Particle Swarm Optimization based on the Multi—tasking Subpopu—lation Cooperation,PSO-MSC）算法。借助该算法的快速收敛和全局寻优特性实现了最优路径规划。实验结果表明：该算法可以快速有效地实现战场环境下无人车辆的战术机动路径规划,且规划路径安全、平滑。     

浅议陆军合成旅装备一体化联合检验

近年来,装备试验深入推进。作为装备试验领域的最高形式,装备一体化联合检验已经成为陆军合成旅装备试验的新趋势和新常态。本文从装备一体化联合检验的概念入手,简要阐述了以作战效能、作战适用性、体系适用性和在役适用性为主的“一能三性”,从装备体系效能的联合检验、装备体系运用的具体检验和陆军合成旅的综合检验三个方面深入理解和认识装备一体化联合检验,提出了立足联合作战、立足贴近实战、紧盯短板弱项、聚焦摸清底数等四个需要重点关注的问题,以为陆军合成旅装备一体化联合检验的深入开展提供理论参考和借鉴。     

改进Dijkstra算法在大型城市轨道交通网计价系统中的应用

随着城市地铁建设的迅猛发展,国内众多城市地铁线路均已实现网络化运营。考虑到地铁票价制定的公益性,目前国内大部分城市均以最短路径或最少车站的方式计算线网两车站间的票价。以传统的Dijkstra算法为基准,对传统的Dijkstra算法进行改进,分别采用传统Dijkstra算法和改进Dijkstra算法对长沙地铁1～5号线线网最短距离行走路线进行测算。结果表明,改进后的Dijkstra算法不仅有效地提高了算法的时效性,克服了传统算法时间冗长的缺陷,在一定程度上消除了线路之间累积的误差且提高了线网的可延展性。     

Balancing and optimizing a portfolio of R&D projects

A mathematical formulation of an optimization model designed to select projects for inclusion in an R&D portfolio, subject to a wide variety of constraints (e.g., capital, headcount, strategic intent, etc.), is presented. The model is similar to others that have previously appeared in the literature and is in the form of a mixed integer programming (MIP) problem known as the multidimensional knapsack problem. Exact solution of such problems is generally difficult, but can be accomplished in reasonable time using specialized algorithms. The main contribution of this paper is an examination of two important issues related to formulation of project selection models such as the one presented here. If partial funding and implementation of projects is allowed, the resulting formulation is a linear programming (LP) problem which can be solved quite easily. Several plausible assumptions about how partial funding impacts project value are presented. In general, our examples suggest that the problem might best be formulated as a nonlinear programming (NLP) problem, but that there is a need for further research to determine an appropriate expression for the value of a partially funded project. In light of that gap in the current body of knowledge and for practical reasons, the LP relaxation of this model is preferred. The LP relaxation can be implemented in a spreadsheet (even for relatively large problems) and gives reasonable results when applied to a test problem based on GM's R&D project selection process. There has been much discussion in the literature on the topic of assigning a quantitative measure of value to each project. Although many alternatives are suggested, no one way is universally accepted as the preferred way. There does seem to be general agreement that all of the proposed methods are subject to considerable uncertainty. A systematic way to examine the sensitivity of project selection decisions to variations in the measure of value is developed. It is shown that the solution for the illustrative problem is reasonably robust to rather large variations in the measure of value. We cannot, however, conclude that this would be the case in general. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 18–40, 2001     

The selection allocation problem

The Selection Allocation Problem (SAP) is a single period decision problem which involves selecting profit‐maximizing (or cost‐minimizing) activities from various distinct groups, and determining the volume of those activities. The activities in each group are selected subject to the availability of that group's resource, which is provided by either pooling or blending raw inputs from several potential sources. Imbedded in the decision process is the additional task of determining how much raw input is to be allocated to each group to form the resource for that group. Instances of this problem can be found in many different areas, such as in tool selection for flexible manufacturing systems, facility location, and funding for social services. Our goal in this paper is to identify and exploit special structures in the (SAP) and use those structures to develop an efficient solution procedure. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 707–725, 1999     

基于Dijkstra算法的水平航迹规划

战术飞行任务的航迹规划是依靠地形信息和敌情信息,在某些约束条件下,找出作战飞机从起飞点到目标突防生存概率最大、航程最短的飞行轨迹的过程。采用简易方法建立了威胁模型,用Dijkstra算法实现了作战飞机整体最优水平航迹解算,并给出了具体算例,验证了算法的有效性和实用性。