Description: C#中实现最短路，该图算法描述的是这样的场景：图由节点和带有方向的边构成，每条边都有相应的权值，路径规划（最短路径）算法就是要找出从节点A到节点B的累积权值最小的路径。-achieve the most short-circuit, the algorithm described in the plan that is the scene : map with nodes and from the direction of the edges, and each side has a corresponding weights, Path Planning (Shortest Path) algorithm is to identify from the node A to node B, the cumulative value of the minimum right path. Platform: |
Size: 3072 |
Author:1 |
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Description: 取各障碍物顶点连线的中点为路径点，相互连接各路径点，将机器人移动的起点和终点限制在各路径点上，利用Dijkstra算法来求网络图的最短路径，找到从起点P1到终点Pn的最短路径，由于上述算法使用了连接线中点的条件，不是整个规划空间的最优路径，然后利用遗传算法对找到的最短路径各个路径点Pi (i=1,2,…n)调整，让各路径点在相应障碍物端点连线上滑动，利用Pi= Pi1+ti×(Pi2-Pi1)（ti∈[0,1] i=1,2,…n）即可确定相应的Pi，即为新的路径点，连接此路径点为最优路径。-Take the midpoint of each obstacle vertex point for the path, the path of each connection point, move the robot start and end points of each path on the limited use Dijkstra algorithm to find the shortest path network diagram to find the starting point P1 from the Pn the shortest path to the end, because the algorithm uses the midpoint of the conditions of cable, not the entire optimal path planning of space, and then use genetic algorithm to find the shortest path of each path points Pi (i = 1,2, ... n ) adjusted to the path of obstacles in the corresponding endpoint connection point slide, using Pi = Pi1+ ti × (Pi2-Pi1) (ti ∈ [0,1] i = 1,2, ... n) to determine the appropriate of Pi, is the new path point, the connection point for the optimal path for this path. Platform: |
Size: 2048 |
Author:biaoshi |
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Description: 利用Dijkstra算法来求网络图的最短路径，找到从起点P1到终点Pn的最短路径，由于上述算法使用了连接线中点的条件，不是整个规划空间的最优路径，然后利用遗传算法对找到的最短路径各个路径点Pi (i=1,2,…n)调整，让各路径点在相应障碍物端点连线上滑动-Use Dijkstra algorithm to find the shortest path network diagram to find the starting point P1 to the end of Pn from the shortest path algorithm is used as the midpoint of the condition of cable, not the entire optimal path planning of space, and then use genetic algorithm to find shortest path of each path points Pi (i = 1,2, ... n) adjusted to the path of obstacles in the corresponding endpoint connection point slide Platform: |
Size: 9216 |
Author:jack |
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Description: Many interesting route planning problems can be solved by computing shortest paths
in a suitably modeled, weighted graph representing a transportation network. Such networks
are naturally road networks or timetable networks of public transportation. For
large networks, the classical Dijkstra algorithm to compute shortest paths is too slow.
And therefore have faster algorithms been developed in recent years. These new algorithms
have in common that they use precomputation and store auxiliary data to speedup
subsequent shortest-path queries. Platform: |
Size: 28672 |
Author:manolin |
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Description: Many interesting route planning problems can be solved by computing shortest paths
in a suitably modeled, weighted graph representing a transportation network. Such networks
are naturally road networks or timetable networks of public transportation. For
large networks, the classical Dijkstra algorithm to compute shortest paths is too slow.
And therefore have faster algorithms been developed in recent years. These new algorithms
have in common that they use precomputation and store auxiliary data to speedup
subsequent shortest-path queries. Platform: |
Size: 11264 |
Author:manolin |
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Description: 這是有關於路徑規劃的演算法，也是路徑規劃裡面較為基本的一個演算法，可以計算出路徑最短的路線，但是當地圖的深度越來越大時，會使得這個演算法耗費較多的電腦運算資源，因為會把地圖每一點都進行運算進而產生出最短的路徑。-This is on the path planning algorithm, but also more basic path planning inside an algorithm that can calculate the shortest path route, but increasing the depth of the map, it will make this algorithm takes more computer computing resources, because each point will map for computing the shortest path and produce. Platform: |
Size: 183296 |
Author:victor shih |
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Search - shortest dijkstra path planning