面向直升机低空突防的三维航线规划算法研究

直升机低空突防航线规划对保障直升机安全飞行,顺利完成任务至关重要。综合考虑地形、雷达探测、火力威胁的影响,将规划空间栅格化剖分,设计直升机低空突防航线;根据障碍物分布以及直升机自身性能的限制,对突防航线进行优化得到最佳突防航线。仿真实验验证了算法的有效性。     

一种利用改进A*算法的无人机航迹规划

提出了一种改进的A*算法解决大范围三维战场环境的无人机航迹规划问题。针对低空突防中无人机需满足生存率高、耗油量小等要求,算法综合考虑了航线高度、被探测概率、航线长度等权重因子,在该目标空间中搜索一条两个航路点之间的最优航线。同时为了满足UAV安全高度、升降率、转弯半径等性能约束,提出了一系列航线优化算法,得到最终的可飞航线。     

一种面向带状复杂区域倾斜摄影航线优化方法

针对带状复杂区域的倾斜摄影外业影像采集的数据冗余问题，该文提出了一种面向带状复杂区域的倾斜摄影航线优化方法。该方法基于前期勘测成果以及高清卫星影像数据，较好地确定了精确的测区范围并获取了测区的点、线、面等GIS空间数据；建立了以航线数目为目标函数的数学优化模型，并运用粒子群优化算法实现了对航线数目和飞行参数的快速求解；基于CAD、GIS以及大疆智图插件和开发接口进行航线规划和飞行设置，实现了数据的交换融合。该文以云南某道路工程项目为例进行试验。结果表明，引入较为成熟的最优化数学模型并运用数学方法建立起航线数目和航线规划参数之间的联系，对参数进行科学量化和约束，降低对工程经验的依赖。基于该模型建立的航线比常规航线规划软件所规划的航线节约了10%的飞行时间，影像照片数量上减少了9%,综合采集效率提升9.5%,所建立的倾斜摄影实景模型分辨率和精度均符合规范和项目要求。     

一种基于复杂环境信息的导航路径动态规划算法

针对现有路径规划算法受复杂环境信息影响较大的原因,该文通过分析环境信息,综合考虑行车的时效性、安全性要求,提出一种动态路径规划辅助决策方法。该方法通过对实时环境信息进行建模和量化处理,得到不同环境特征下的道路实际权重,为路径动态规划提供先决条件;然后利用基于一阶马尔可夫链的动态路径规划算法对路径进行规划,计算出若干可选路径可供驾驶员根据实际情况进行抉择;并通过与静态权值的路径分析算法相比较得到,它能够更好地满足复杂环境信息下最优路径的规划需求,算法效率受环境因素影响较小。     

不同地形环境下无人机航线规划及三维建模分析

针对不同地形环境下存在的飞行安全性低、数据冗余、构建三维模型质量和精度差等问题,本文以单镜头四旋翼小型无人机为试验设备,对平坦地形环境下的复杂单体建筑使用井字形交叉航线和环视航线;对复杂山地地形环境下的泥石流沟谷使用水平航线、井字形交叉航线及视频航线构建三维模型,并结合航线参数、三维模型质量和精度,探讨了不同地形环境下无人机航线规划方案对三维模型构建的影响。经评价分析可知:①使用环视航线能安全、快速地获取平坦地形下的复杂单体建筑影像,构建完整、质量好、精度高的三维模型;②使用井字形交叉航线能构建山地地形下泥石流沟谷的完整、质量好、精度高的三维模型,但飞行安全性、时效性较低;③使用视频航线,影像重叠度高,能构建完整、质量好的三维模型,模型精度并无明显差异。     

复杂地势下轻小型无人机LiDAR自主航线设计

针对现有的LiDAR航线设计软件绝大多数针对大中型无人机,主要依靠飞行人员根据实际经验敷设航线,少数采用传统摄影测量改变基线长度的方式实现复杂地势下LiDAR航线自动敷设,但高地势地区航线较密,飞行成本高,有局限性,目前能实现轻小型无人机载LiDAR航线自主敷设的软件很少等问题,参照轻小型机载LiDAR的特点,该文提出一种复杂地势下基于DEM改变航高的航线设计方法。以延庆某山区进行试验。结果表明:该方法适当放宽分区高差限制,综合考虑地形信息、飞机性能等要素,使得单条航带点云密度得到保证,提高航线规划效率,节约飞行时间。将考虑DEM与未考虑DEM两种方式对比,确保重叠度,满足工程点云密度,便于后期数据处理。     

基于改进RRT算法的狭长空间无人车辆路径规划

针对狭长空间无人车辆路径规划系统，提出一种基于改进的快速搜索随机树(rapidly-exploring random trees,RRT)路径规划算法，以解决传统RRT算法随机性较大、路径缺乏安全性的问题.该算法通过加入自适应目标概率采样策略、动态步长策略对传统的RRT算法进行改进，同时考虑到实际情况中无人驾驶车辆的动力学约束，该算法加入车辆碰撞约束和路径转角约束，并针对转角约束会导致迭代次数激增的问题提出了一种限制区域内随机转向的策略，最终得到一条安全性较高的路径.采用计算机仿真对所提算法和现有算法的性能进行对比验证.所提算法在狭长空间相较于传统人工势场引导下的RRT算法迭代次数降低了33.09%，规划时间减少了6.44%，路径长度减少了0.06%，并且在简单环境和复杂障碍物环境下规划能力均有提升.所提算法规划效率更高、迭代次数更少.     

基于改进A*算法的无人航道测量船路径规划方法

无人航道测量船由于具有低成本、高效率、便捷等优点,在航道测量领域受到越来越多的关注。在避碰过程中,为保证无人航道测量船测量数据的有效性,新规划的避碰路线应尽可能地与原规划测量航线一致。针对传统A*算法所规划的路径在避开障碍物之后无法快速回到预设航线上的问题,提出了一种改进的A*算法。该算法主要是在原始代价函数的基础上,新增了一个与当前点到预设航线的垂直距离相关的代价值,且该代价值的取值与无人航道测量船所处的位置相关。首先在MATLAB仿真环境下对改进A*算法进行仿真实验,然后利用无人航道测量船实船平台开展航行验证实验并进行围栏分析。实验结果表明,相比于传统A*算法,在保证安全的前提下,改进A*算法能够使无人航道测量船在避开障碍物之后更快地回到预设航线。     

顾及复杂环境约束的无人机三维航迹快速规划

针对在地形、禁飞区、气象、危险物、通视约束等复杂环境下,无人机三维航迹规划存在效率差与精度低的问题,本文通过设计多层扩展A*算法,提出了一种顾及复杂环境约束的无人机三维航迹快速规划方法。该方法剖析典型复杂环境对无人机航迹的多种约束信息等,构建三维航迹规划环境模型;通过结合无人机自身性能约束,设计多层扩展A*算法进行三维分层扩展,搜索获取无人机最优参考航迹;采用线简化和线平滑的方式对参考航迹进行简化与优化;最后选择典型案例区域开展试验分析,证明本文方法能够快速准确地规划出复杂环境下的最优可行航线。     

多目标遗传算法在水下机器人路径规划中的应用

依据VCF电子海图,提出了一种对水下机器人进行路径规划的多目标遗传算法,采用可变长的实数坐标编码、适应度函数评价了影响航路优劣的多个因素,设计了选择、交叉、变异、修补、删除等遗传算子以及种群置换方法。在生成初始种群和设计遗传算子时引入领域知识,使所生成路径尽量不穿越障碍区域,有效地提高了大范围路径规划算法的收敛速度。试验表明,采用该MOGA算法进行路径规划可提高算法的收敛速度和全局寻优能力。     

A Hybrid Link‐Node Approach for Finding Shortest Paths in Road Networks with Turn Restrictions

Turn restrictions, such as ‘no left turn’ or ‘no U‐turn’, are commonly encountered in real road networks. These turn restrictions must be explicitly considered in the shortest path problem and ignoring them may lead to infeasible paths. In the present study, a hybrid link‐node Dijkstra's (HLND) algorithm is proposed to exactly solve the shortest path problem in road networks with turn restrictions. A new hybrid link–node labelling approach is devised by using a link–based labelling strategy at restricted nodes with turn restrictions, and a node‐based labelling strategy at unrestricted nodes without turn restrictions. Computational results for several real road networks show that the proposed HLND algorithm obtains the same optimal results as the link‐based Dijkstra's algorithm, while having a similar computational performance to the classical node‐based Dijkstra's algorithm.     

Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data

Optimal paths computed by conventional path-planning algorithms are usually not “optimal” since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.     

面向飞行器可视导航的复杂空地环境动态3维表示模型

针对飞行器可视导航中复杂空地环境时空要素统一精准表示的难题,本文提出了面向飞行器可视导航的复杂空地环境动态3维表示的GIS数据模型和相应的时刻数据存储结构,并用UML图进行了描述。该模型综合考虑了复杂空地环境要素多维、动态、边界模糊等特点,刻画了多维动态时空数据在空间、时间、尺度、语义四个方面的特征及其相互关系。最后,以某机场的空地环境数据的组织、分析和可视化为例,验证了该模型的实用性。     

Assessing Raster GIS Approximation for Euclidean Shortest Path Routing

Identifying a route that avoids obstacles in continuous space is important for infrastructure alignment, robotic travel, and virtual object path planning, among others, because movement through space is not restricted to a predefined road or other network. Vector and raster GIS (geographic information system) solution approaches have been developed to find good/efficient routes. On the vector side, recent solution approaches exploit spatial knowledge and utilize GIS functionality, offering significant computational advantages in finding an optimal solution to this path routing problem. Raster‐based shortest path techniques are widely applied in route planning for wayfinding, corridor alignment, robotics and video gaming to derive an obstacle avoiding path, but represent an approximation approach for solving this problem. This research compares vector and raster approaches for identifying obstacle‐avoiding shortest paths/routes. Empirical assessment is carried out for a number of planning applications, highlighting representational issues, computational requirements and resulting path efficiency.     

On-the-fly GPS-based attitude determination using single- and double-differenced carrier phase measurements

Carrier phase measurements are primary observations for GPS attitude determination. Although the satellite-related errors can be virtually eliminated by forming single differences, the baseline-related errors such as line biases are still present in the single-differenced carrier phase measurements. It is, therefore, difficult to resolve the single-differenced integer ambiguities due to the line biases. By forming double differences, the line biases of the single-differenced carrier phase measurements can be effectively removed. However, the main disadvantages of this method lie in the fact that the double-differenced measurements are mathematically correlated and consequently the attitude obtained from the double differences is noisy. This paper presents a new algorithm through which both single and double differences are used simultaneously to resolve these problems in real-time. The solution of the integer ambiguities can be obtained by searching for the most likely grid point in the attitude domain that is independent of the correlation with the double differences. Next, the line biases and corresponding single difference integer ambiguities can be resolved on the fly by using the noisy attitude solution obtained from the previous double difference procedure. In addition, the relationship between the physical signal path difference and the line bias is formed. A new method is also applied to derive the attitude angles through finding the optimal solution of the attitude matrix element. The proposed new procedure is validated using ground and flight tests. Results have demonstrated that the new algorithm is effective and can satisfy the requirement of real-time applications.     

面向三维重建的无人机航拍路径规划技术研究综述北大核心CSCD

在对城市大场景的三维重建中,使用无人机在低空采集图像成为主流方案,通过优化无人机的航拍路径,能有效提高场景三维重建的精度和效率。为此,近年来致力于发展新的无人机航拍路径规划技术和方法,包括视点规划和飞行路径规划,取得了显著成果。本文通过概述无人机航拍路径规划的基本流程,详述基于二维模型、三维模型和未知环境下的航拍路径规划技术,对比不同路径规划方法的优缺点,探讨无人机航拍路径规划的发展趋势,为开展无人机航拍控制、大场景三维重建研究提供重要参考。     

A fast algorithm for finding K shortest paths using generalized spur path reuse technique

The problem of finding the K shortest paths (KSPs) between a pair of nodes in a road network is an important network optimization problem with broad applications. Yen's algorithm is a classical algorithm for exactly solving the KSP problem. However, it requires numerous shortest path searches, which can be computationally intensive for real large networks. This study proposes a fast algorithm by introducing a generalized spur path reuse technique. Using this technique, shortest paths calculated during the KSP finding process are stored. Accordingly, many shortest path searches can be avoided by reusing these stored paths. The results of computational experiments on several large‐scale road networks show that the introduced generalized spur path reuse technique can avoid more than 98% of shortest path searches in the KSP finding process. The proposed algorithm speeds up Yen's algorithm by up to 98.7 times in experimental networks.     

元胞自动机最短路径算法优化

概括了当前GIS中最短路径算法,分析了元胞自动机在最短路径分析算法中的原理及应用现状,并从两个方面对基于元胞自动机的最短路径算法进行优化即直线优化的元胞自动机最短路径算法。(1)将A*算法中的启发函数引入元胞自动机模型,提出了直线优化元胞自动机最短路径模型;(2)考虑道路网特征对最短路径算法的影响,得出具有道路网自适应性的最短路径分析模型。最后选取不同形态特征的shp道路网数据,验证了优化算法在实际应用中的适用性和高效性。     

无人机三维航迹规划改进方法

在地形起伏的复杂环境下进行遥感观测任务需要进行三维航迹规划以适应多变的自然环境,而且在规划航迹时需要考虑减少飞行时间和能量消耗以提高作业效率.本文提出了一种曲面趋平化算法(CSFA),首先采用准均匀B样条曲面拟合算法对CSFA算法处理后的目标区域DEM进行插值,构建区域的自然环境地形模型;然后为了减少时间和能量的消耗,...