一种兼顾全局与局部特性的机器人动态路径规划算法

A*算法的规划路径全局最优,但存在拐点多、拐角大的缺点,而人工势场法的规划路径虽然平滑,却无法满足全局最优的要求。为达到机器人快速到达、安全避障的目的,分别对A*算法和人工势场法进行改进,并将两者相结合,提出一种兼顾全局与局部特性的机器人动态路径规划算法。全局路径规划采用剔除冗余节点的A*算法,生成局部目标节点序列;局部路径规划采用改进的人工势场法,提出5方向障碍物探测法替代原有斥力模型,大大减小运算量;设置最小引力场,引导机器人脱离局部最小点;采用扇形区域探测法,有效规避小型动态障碍物。仿真实验结果表明:与传统A*算法与人工势场法相比,该方法不仅生成全局最优平滑路径,而且还能够在顾及机器人移动控制的前提下,及时规避动态障碍物。     

面向OSM数据的多模式路径规划方法

阐述基于OSM数据进行多模式路径规划的方法,首先从OSM数据中提取所需的道路信息,构建合适的道路网络模型和存储模型,然后对出行者习惯采用的不同代价标准进行分析,最后针对多种交通模式,采用最短路径算法,求解出两连通节点之间的最优路径。     

Dijkstra 最短路径算法的一种高效率实现

在已存在的一些最短路径算法测试总结的基础上,根据ＧＩＳ中网络计算的实际情况,从网络结构的拓扑表示以及Ｄｉｊｋｓｔｒａ 算法中快速搜索技术的实现入手,提出了一种Ｄｉｊｋｓｔｒａ 最短路径算法的高效率实现方法。     

顾及转向延误的时间依赖A*最短路径算法

现有的动态路径规划算法通常只考虑当前时刻交通信息,而忽略了路段行程时间依赖于进入该路段的时刻这一现实。而且,转向延误的存在使得传统的基于节点标号的最短路径算法不再有效。本文建立了基于路段的时间依赖网络模型,将转向延误时间引入到FIFO（先进先出）条件的定义中,并给出了满足FIFO条件的路段到达时间和转向延误时间计算式。以此模型为基础,并通过将时间因子引入到启发式评价函数中,发展了基于路段标号的时间依赖A*最短路径算法。实验表明,所提出的算法能预测并回避即将发生的交通拥堵,有效节省用户的出行时间。而其平均计算时间仅比传统算法增加了10%左右。此外,由于不再需要进行频繁的路径重优化,该算法能大幅提高路径规划的整体效率。     

Dijkstra最短路径算法的一种高效率实现

在已存在的一些最短路径算法测试总结的基础上,根据ＧＩＳ中网络计算的实际情况,从网络结构的拓扑表示以及Ｄｉｊｓｔｒａ算法中快速搜索技术的实现入手, 一种Ｄｉｊｋｓｔｒａ最短路径算不的高效率实现方法。     

线状障碍物的可视最短路径Voronoi图生成

对偶生成法和直接修正法是构造可视最短路径Voronoi图的两种基本途径。本文提出了分段剖分、整体剖分和统一剖分的修正思路;放宽了以往修正法中障碍物端点必为生长点的限制,并提出了能有效处理公共边的分簇算法和最适搜索算法,实现了在有限制的线状障碍物下对普通Voronoi图的有效修正,以生成可视最短路径Voronoi图。     

线状障碍物的可视最短路径Voronoi图生成

对偶生成法和直接修正法是构造可视最短路径Ｖｏｒｏｎｏｉ图的两种基本途径。本文提出了分段部分、整体部分和统一剖分的修理思路。放宽了以往修正法中障碍物端点必为生长点的限制,并提出了能有效处理公共连的分簇算法和最适搜索算法,实现了在有限制的线状障碍物下对普通Ｖｏｒｏｎｏｉ图的有效修正,以生成可视最短路径Ｖｏｒｏｎｏｉ图。     

GIS中网络最短路径的不确定性分析

提出G IS中线段长度的误差模型,推导出网络最短路径的不确定性模型,据此模型可以求出最短路径的可信度和在给定的置信水平下的最短路径长度的置信区间。     

激光点云在无人驾驶路径检测中的应用

针对室外自然环境下无人自主地面智能车辆的可通行区域检测问题,提出了一种基于激光点云的道路可通行区域检测算法,包括倾斜检测道路边界和水平探测前方障碍物。道路边界检测主要依据道路区域与非路区域之间存在的高度差异来筛选道路边界特征点,结合通行宽度阈值提取道路边界线;前方障碍物检测则依据扫描点间距离聚类成不同的点云簇,对点云簇进行识别和定位,从而确定障碍物位置和大小。结合道路边界和障碍物信息提取车辆的可通行区域,为无人车路径规划提供依据。试验表明,该算法能够较好地检测出无人车的可通行区域。     

面向BIM的室内拓扑-栅格分层路径规划方法

针对传统路径规划方法在复杂大场景环境下的搜索空间大、效率低、避障安全性差等问题,本文提出了一种基于BIM的室内拓扑-栅格分层路径规划方法。首先,建立复杂室内环境的BIM模型,提取模型中结构、障碍物和通道的语义、几何及其关联信息,通过栅格抽象映射得到基础导航地图,并结合层次图思想,开展地图空间分层,获取拓扑-栅格分层地图;然后,利用细化算法,生成拓扑层中各子区域之间的离线先验路网,联合Dijkstra算法选取其中的最优路径,并基于自主改进的A*算法,快速高效搜索栅格层的最优路径,通过组合拼接拓扑层与栅格层的局部最优路径,构建完整的全局最优路径;最后,将本文方法与标准A*算法及蚁群算法作对比,在保证计算效率的同时,不仅缩小了路径搜索空间,还确保了最优路径的安全性,综合验证了所提路径规划方法的优越性。     

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.     

顾及步行习惯的室内导航网络及其生成算法

室内导航网络是实现室内路径规划和导航的基础。针对现有室内导航网络存在拓扑连通结构不合理、生成路径几何形态不自然等问题,本文依据人类长期以来形成的“抄近路”习性及“避碰”安全需求,提出了一种符合人行走习惯的室内导航网络及其自动生成算法。该导航网络将室内可通行空间划分为普通房间和走廊,普通房间内网络映射为房间结点与门结点的直线连接;形状复杂的走廊空间则依据空间尺度和凹凸性被细分为狭窄走廊空间和开阔走廊空间,狭窄走廊空间以中轴线作为路线,开阔走廊空间则将门结点和通道口结点连接形成完全图;最后将房门结点连接到走廊路线,形成完整室内导航网络。试验结果表明:该模型及其生成算法能够依据室内空间尺度对复杂通行空间进行合理分区,生成与空间特征一致的导航网络结构,使得规划生成的最短路径形态更符合人们的行走习惯。     

基于道路网络分析的Voronoi面域图构建算法

提出一种基于网络分析的Voronoi面域图和加权Voronoi面域图构建算法。鉴于道路网络在城市中心地、公共设施引力传导与功能覆盖上的重要作用,采用网络最短路径距离分析和最短路径时间分析构建的Voronoi面域图可以模拟出中心功能的辐射影响范围空间划分的实际情形,进而为空间分析和空间优化提供有力支持。算法过程主要包括:设施邻近道路结点检索和分界结点计算;基于网络最短路径分析Dijkstra算法和分界结点计算的网络Voronoi划分;基于空间离散化、邻近道路分析的空间Voronoi划分及其矢量化处理算法。计算实验结果表明本文提出算法可靠和高效,能够模拟出具有预期精度和形态复杂的网络Voronoi面域图形。     

最简单路径寻找方法研究

许多认知研究表明,路径指令的复杂性和形式在人类导航中和路径的长度一样重要。在很多情形下,我们所需要的不是到达目的地的最短路径,而是一条最简单路径,也就是容易描述、理解、记忆或者执行路线的导航指令。大多数自动导航系统都依赖于计算最短路径问题的解,而不是找到最简单路径。本文在分析人们对道路转向的认知研究基础上,采用了适合道路转向的路段-链数据模型,并从权重函数选择、算法具体实现和算法分析三个方面具体论述了最简单路径算法的主要思想和具体实施。     

基于典型事例推理的路径规划方法研究(英文)

Case-based reasoning is an AI technique in which the previous solutions are stored for future use. People are used to guiding themselves according to those routes that are stored in their memories and have been used by them before. It is just based on people’s preference to familiar routes, which are gained through the study of the cognitive activities. We propose to apply the intelligent method based on the case reasoning to path planning. It is impossible for a case base to store all the solutions to all the shortest paths; therefore, part of them should be stored. However, which routes should be stored and which should not be? How do we adapt the cases that have already been stored and how do we acquire the shortest route based on them? All these issues need to be explained by integrating knowledge of the network on account of case-based reasoning techniques. This paper suggests the case-based reasoning in another point. This means finding some irreplaceable links on the basis of the complete analysis of the problems space, which are called the must_be_passed link between the source and destination. Merely compute the shortest path case from those best exit/entry nodes of the grids to the irreplaceable links, and then add them into the case base storing for future use. This method is based on case-based reasoning technique and completely considers the properties of the problem space. In addition to the use of knowledge of the natural grid in the route network, this method is more efficient than existing algorithms on computing efficiency.     

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.     

An optimum vehicular path algorithm for traffic network based on hierarchical spatial reasoning

Human beings' intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers' choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large-scale traffic networks.     

An optimum vehicular path algorithm for traffic network based on hierarchical spatial reasoning

Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms. It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic, so as to improve running efficiency and suitability of shortest path algorithm for traffic network. The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path. It is argued that the shortest path, no matter distance shortest or time shortest, is usually not the favorite of drivers in practice. Some factors difficult to expect or quantify influence the drivers’ choice greatly. It makes the drivers prefer choosing a less shortest, but more reliable or flexible path to travel on. The presented optimum path algorithm, in addition to the improvement of the running efficiency of shortest path algorithms up to several times, reduces the emergence of those factors, conforms to the intellection characteristic of human beings, and is more easily accepted by drivers. Moreover, it does not require the completeness of networks in the lowest hierachy and the applicability and fault tolerance of the algorithm have improved. The experiment result shows the advantages of the presented algorithm. The authors argued that the algorithm has great potential application for navigation systems of large-scale traffic networks.     

顾及地形起伏的步行最优路径分析算法

将行人的生理因素与GIS路径分析有机结合起来,根据生理学研究进展,建立了步行体能消耗计算模型,并提出了基于坡度转换的等效水平距离计算原则,从而将三维空间距离转换为等体能消耗平面距离,实现了顾及地形起伏的最优路径算法。实验结果表明,该算法具有兼顾坡度与距离关系的优势,提高了路径分析方法的有效性。     

GIS网络分析中最短路径的实现

本文提出了一种基于矢量角度的最短路径搜索算法,设计出一种类似于面向对象的数据存储结构来存储网络图中的节点及弧段对象,在最短路径的搜索上引入矢量夹角标量值作为搜索因子,充分利用了网络图中各点元素和线元素间的拓扑关系,提高了搜索的趋势性,同时还考虑了各弧段的长度值(或权值),较好的将网络图中对象的空间信息和属性信息相结合。