技术领域Technical field
本发明属于路径规划技术领域，具体涉及一种四角格网高精度长距离越野路径规划方法。The invention belongs to the technical field of path planning, and specifically relates to a four-corner grid high-precision long-distance off-road path planning method.
背景技术Background technique
越野路径规划是在山地、丛林等越野环境下给定起终点，通过给定的起终点按照一定规则规划一条最优路径(时间最短、距离最短或者最合理的路径)，保障用户顺利达到目的地。Off-road path planning is to give a start and end point in off-road environments such as mountains and jungles, and plan an optimal path (the shortest time, the shortest distance or the most reasonable path) through the given start and end points according to certain rules to ensure that users can reach their destination smoothly. .
越野路径规划的研究工作在军事、消防、民用等多个领域有着广泛的应用。起初，越野路径规划主要用于军事战场分析，指挥车辆、人员的行动。如今越野路径规划在军事领域仍有重要的应用，主要是作战区域作战对象的路径规划以及军用机器人的行动等。而随着人工智能、无人驾驶等科学技术的发展，越野路径规划技术也迅速扩展到民用领域，如越野应急救援、旅友自驾形成规划等。由此可见，越野路径规划技术的研究工作有着重要的意义。Research work on cross-country path planning has been widely used in military, firefighting, civilian and other fields. Initially, cross-country path planning was mainly used for military battlefield analysis and commanding the movements of vehicles and personnel. Today, cross-country path planning still has important applications in the military field, mainly in the path planning of combat objects in combat areas and the actions of military robots. With the development of science and technology such as artificial intelligence and driverless driving, off-road path planning technology has rapidly expanded into civilian fields, such as off-road emergency rescue and self-driving planning for travelers. It can be seen that the research work on cross-country path planning technology is of great significance.
根据路径规划技术使用的数据类型不同，可以划分为基于矢量数据的路径规划和基于栅格数据的路径规划。基于矢量数据的路径规划主要用于城市交通路线的选择，主要通过已知路网拓扑结构来完成规划。According to the different types of data used in path planning technology, it can be divided into path planning based on vector data and path planning based on raster data. Path planning based on vector data is mainly used for the selection of urban traffic routes, and the planning is mainly completed through the known road network topology.
当前，在越野路径规划中，在没有路网的情况下，则多以栅格数据为基础进行路径规划。基于栅格数据的越野路径规划，是将整个越野环境量化分成若干个大小相同的栅格，之后再根据搜索算法进行规划。在越野路径规划研究中，若想有一个好的规划效果，则需要精度较高的栅格数据。而当使用高精度的栅格数据进行路径规划时，若起终点之间距离过长，会出现搜索算法计算时间过长甚至计算奔溃等问题。Currently, in cross-country route planning, when there is no road network, route planning is mostly based on raster data. Off-road path planning based on raster data quantifies the entire off-road environment into several grids of the same size, and then plans based on the search algorithm. In cross-country path planning research, if you want to have a good planning effect, you need high-precision raster data. When using high-precision raster data for path planning, if the distance between the start and end points is too long, problems such as the search algorithm taking too long to calculate or even crash may occur.
发明内容Contents of the invention
本发明提供了一种四角格网高精度长距离越野路径规划方法，用以解决现有技术中的方法运算效率慢、容易运算崩溃的问题。The invention provides a four-corner grid high-precision long-distance cross-country path planning method to solve the problems of slow calculation efficiency and easy calculation collapse of methods in the prior art.
为解决上述技术问题，本发明所包括的技术方案以及技术方案对应的有益效果如下：In order to solve the above technical problems, the technical solutions included in the present invention and the corresponding beneficial effects of the technical solutions are as follows:
本发明提供了一种四角格网高精度长距离越野路径规划方法，包括如下步骤：The invention provides a four-corner grid high-precision long-distance off-road path planning method, which includes the following steps:
1)对需要进行路径规划的越野环境区域进行栅格化处理；1) Rasterize the off-road environment areas that require path planning;
2)判断起点和终点之间的距离是否大于设定距离；2) Determine whether the distance between the starting point and the end point is greater than the set distance;
3)若起点和终点之间的距离大于设定距离，则对越野环境区域内的栅格像元进行合并操作；进行合并操作后，在起点和终点之间进行路径规划，得到较优路径；3) If the distance between the starting point and the end point is greater than the set distance, the grid pixels in the off-road environment area are merged; after the merge operation, path planning is performed between the starting point and the end point to obtain a better path;
4)在较优路径上提取若干个参样点，且若干个参样点、起点和终点将较优路径划分成若干段分段路径，分别以每段分段路径的两端点为起终点重新进行路径规划，得到若干段较优分段路径；4) Extract several parameter sample points on the optimal path, and divide the optimal path into several segmented paths using several parameter sample points, starting points and end points, and re-use the two end points of each segmented path as the starting and ending points. Carry out path planning and obtain several optimal segmented paths;
5)将各段较优分段路径连通以得到最终路径。5) Connect the better segmented paths of each section to obtain the final path.
上述技术方案的有益效果为：该方法在判断起点和终点之间的距离较远的情况下，首先将越野环境区域内的栅格像元进行合并操作，以降低整体计算的处理量、提高处理速度，合并操作后进行路径规划以规划出较优路径，然后对规划出的较优路径进行分段处理，以每段分段路径的两端点为起终点进行高精度的路径规划以规划出较优分段路径，使得每段分段路径的规划都在高精度栅格下完成，最后将各段较优分段路径连通可以形成最终路径，从而在满足越野路径规划精度要求的同时提高了运算效率。The beneficial effects of the above technical solution are: when this method determines that the distance between the starting point and the end point is far, the raster pixels in the off-road environment area are first merged to reduce the overall calculation processing volume and improve processing. Speed, path planning is performed after the merging operation to plan a better path, and then the planned better path is segmented, and the two end points of each segmented path are used as the starting and ending points for high-precision path planning to plan a better path. The optimal segmented path enables the planning of each segmented path to be completed under a high-precision grid. Finally, the final path can be formed by connecting the optimal segmented paths of each segment, thus meeting the accuracy requirements of cross-country path planning and improving the computing efficiency. efficiency.
进一步的，步骤3)中，进行合并操作后，在起点和终点之间采用Dijkstra算法进行路径规划。Further, in step 3), after the merging operation is performed, the Dijkstra algorithm is used for path planning between the starting point and the end point.
进一步的，为了使规划的路径更加平缓，步骤4)中，以每段分段路径的两端点为起终点，采用改进的A*算法重新进行路径规划；所述改进的A*算法的代价函数为F＝G+H；其中，G为起始节点到当前节点代偿值；H为复合值，由欧氏距离和高程差决定。Further, in order to make the planned path smoother, in step 4), the two end points of each segmented path are used as the starting and ending points, and the improved A* algorithm is used to re-plan the path; the cost function of the improved A* algorithm It is F=G+H; where G is the compensation value from the starting node to the current node; H is the composite value, determined by the Euclidean distance and elevation difference.
进一步的，若步骤2)的判断结果为起点和终点之间的距离大于设定距离，则直接在起点和终点之间进行路径规划。Further, if the judgment result in step 2) is that the distance between the starting point and the end point is greater than the set distance, then path planning is directly performed between the starting point and the end point.
进一步的，采用改进的A*算法直接在起点和终点之间进行路径规划；所述改进的A*算法的代价函数为F＝G+H；其中，G为起始节点到当前节点代偿值；H为复合值，由欧氏距离和高程差决定。Further, the improved A* algorithm is used to directly plan the path between the starting point and the end point; the cost function of the improved A* algorithm is F=G+H; where G is the compensation value from the starting node to the current node. ;H is a composite value, determined by the Euclidean distance and elevation difference.
进一步的，步骤2)中，采用如下方法判断起点和终点之间的距离是否大于设定距离：Further, in step 2), the following method is used to determine whether the distance between the starting point and the end point is greater than the set distance:
计算起终点行号差值绝对值和起终点列号差值绝对值的乘积；Calculate the product of the absolute value of the difference between the start and end line numbers and the absolute value of the difference between the start and end column numbers;
判断乘积是否大于设定值：若大于设定值，则起点和终点之间的距离大于设定距离；否则，起点和终点之间的距离小于或者等于设定距离。Determine whether the product is greater than the set value: if it is greater than the set value, the distance between the starting point and the end point is greater than the set distance; otherwise, the distance between the starting point and the end point is less than or equal to the set distance.
进一步的，步骤4)中，提取若干个参样点的规则为：相邻参样点之间的栅格像元数量相等。Further, in step 4), the rule for extracting several reference sample points is: the number of grid pixels between adjacent reference sample points is equal.
附图说明Description of the drawings
图1是本发明的四角格网高精度长距离越野路径规划方法的流程图；Figure 1 is a flow chart of the four-corner grid high-precision long-distance cross-country path planning method of the present invention;
图2是本发明所使用的改进的A*算法的流程图；Figure 2 is a flow chart of the improved A* algorithm used in the present invention;
图3是本发明的对64*64个栅格像元合为1个像元后的示意图；Figure 3 is a schematic diagram of the present invention after combining 64*64 grid pixels into one pixel;
图4是某地区的示意图；Figure 4 is a schematic diagram of a certain area;
图5是本发明的提取的采样点以及利用采样点得到的分段路径的示意图；Figure 5 is a schematic diagram of the extracted sampling points and the segmented paths obtained using the sampling points according to the present invention;
图6-1是本发明的采用本发明方法对某地区进行路径规划时规划出的路径的90度俯视示意图(关闭地形前)；Figure 6-1 is a 90-degree top view of the path planned when using the method of the present invention to plan a path in a certain area (before closing the terrain);
图6-2是本发明的采用本发明方法对某地区进行路径规划时规划出的路径的90度俯视示意图(关闭地形后)；Figure 6-2 is a 90-degree top view of the path planned when using the method of the present invention to plan a path in a certain area (after turning off the terrain);
图6-3是采用本发明方法对某地区进行路径规划时规划出的路径的三维侧视示意图(关闭地形前)；Figure 6-3 is a schematic three-dimensional side view of the path planned when using the method of the present invention to plan a path in a certain area (before turning off the terrain);
图6-4是采用本发明方法对某地区进行路径规划时规划出的路径的三维侧视示意图(关闭地形后)。Figure 6-4 is a schematic three-dimensional side view of the path planned when using the method of the present invention to plan a path in a certain area (after turning off the terrain).
具体实施方式Detailed ways
本发明的一种四角格网高精度长距离越野路径规划方法实施例，其流程图如图1所示，具体过程如下：An embodiment of a four-corner grid high-precision long-distance off-road path planning method of the present invention, the flow chart of which is shown in Figure 1, and the specific process is as follows:
步骤一，对需要进行路径规划的越野环境进行栅格化处理，将起点和终点之间的越野环境区域的地理坐标转化为栅格行列号，计算起终点行列号相乘绝对值乘积n。Step 1: Rasterize the off-road environment that requires path planning, convert the geographical coordinates of the off-road environment area between the starting point and the end point into raster row numbers, and calculate the absolute value product n of the start and end point number multiplied by the row number.
步骤二，判断步骤一计算得到的n值是否大于设定值(例如300000)：Step 2: Determine whether the n value calculated in step 1 is greater than the set value (for example, 300000):
若n值大于设定值，说明起点和终点之间的距离较远，则执行步骤三；If the n value is greater than the set value, it means that the distance between the starting point and the end point is far, then perform step three;
若n值不大于设定值，说明起点和终点之间的距离不远，则在越野环境区域内，在起点和终点之间直接利用改进的A*算法进行路径规划。其中，改进的A*算法的流程如图2所示，该种改进的A*算法加入了高程差的影响因子，供路径优先选择平缓路。另外该算法与现有的启发式A*算法不同，没有过多存储中间数据，可以减小内存压力，提高运算效率。其整个过程如下：If the n value is not greater than the set value, it means that the distance between the starting point and the end point is not far. In the off-road environment area, the improved A* algorithm is directly used for path planning between the starting point and the end point. Among them, the process of the improved A* algorithm is shown in Figure 2. This improved A* algorithm adds the influence factor of elevation difference to give priority to gentle roads. In addition, this algorithm is different from the existing heuristic A* algorithm in that it does not store too much intermediate data, which can reduce memory pressure and improve computing efficiency. The whole process is as follows:
1)首先建立open表、closed表，将各个表进行初始化。其中open表包含了所有的待搜索像元，全部初始化为0；closed表中记录已经读取过的像元已经标记为无法通行的像元，将这两类像元标记为1。1) First create the open table and closed table, and initialize each table. The open table contains all pixels to be searched, all initialized to 0; the pixels recorded in the closed table that have been read have been marked as inaccessible pixels, and these two types of pixels are marked as 1.
2)创建一个vector容器m_openList，进行容纳路径上的点。2) Create a vector container m_openList to accommodate points on the path.
3)根据起始点和终点的地理坐标转化为行列号，根据行列号在数据中取出对应的像元的值。将规划起始坐标作为起始节点m_startPos，将规划终点坐标作为目标节点m_endPos。3) Convert the geographical coordinates of the starting point and the end point into row and column numbers, and extract the corresponding pixel values from the data based on the row and row numbers. Set the planning start coordinate as the starting node m_startPos, and set the planning end coordinate as the target node m_endPos.
4)创建一个移动指针，指向当前节点。4) Create a moving pointer pointing to the current node.
5)将起始点以行列号的形式存入到m_openList中，作为起始父节点，移动指针指向该节点，依次判断父节点周围的八个点的通行性以及F值。通行性通过子节点是否在closed表中进行判断，在可通行的前提下，F由两部分组成，G值和H值。F值的计算公式如下：5) Store the starting point in m_openList in the form of a row and column number as the starting parent node, move the pointer to point to the node, and determine the accessibility and F value of the eight points around the parent node in turn. Passability is judged by whether the child node is in the closed table. Under the premise of passability, F consists of two parts, the G value and the H value. The calculation formula of F value is as follows:
F＝G+HF=G+H
其中，G值是起始节点m_startPos到当前节点代偿值，若相邻的子节点是不存在权重值的情况，则分别用10或者14来等距离计算，若相邻的子节点是存在权重值的情况，则以权重值作为评判标准；H值是一个复合值，由欧式距离和高程差来决定，欧氏距离是当前点到终点的距离，主要起到一个指向作用，高程差是父点和当前点的高程的差值，主要用于选择相对平坦的地势，最终计算出当前点的F值。Among them, the G value is the compensation value from the starting node m_startPos to the current node. If the adjacent child node does not have a weight value, use 10 or 14 to calculate the equidistant distance. If the adjacent child node has a weight value, value, the weight value is used as the criterion; the H value is a composite value, determined by the Euclidean distance and the elevation difference. The Euclidean distance is the distance from the current point to the end point, which mainly plays a pointing role, and the elevation difference is the parent The difference in elevation between the point and the current point is mainly used to select relatively flat terrain, and ultimately calculate the F value of the current point.
6)分别对比八个点的F值选择其中的最小值，添加到m_openList中，作为道路的当前点，移动指针指向该点，并标记此点为1，表示在道路上。6) Compare the F values of the eight points and select the minimum value, add it to m_openList as the current point of the road, move the pointer to point to this point, and mark this point as 1, indicating that it is on the road.
7)以当前像元为父节点，查找周围八个方向节点情况，首先判断是否被标记，若被标记，判断在路径上的位置，如果没找到，则直接跳过，说明为不可通行点，如果找到位置。在路上说明路径有重叠，进行折中处理，所谓折中处理就是出现交叉路径的情况，去掉交叉的部分。如果该父节点可通行并且不在路上，则进行储存比较，并选择周围八个方向子节点中F值最小的节点作为路径下一个节点。7) With the current pixel as the parent node, search for the surrounding eight direction nodes. First, determine whether it is marked. If it is marked, determine its position on the path. If it is not found, skip it directly, indicating that it is an inaccessible point. If the location is found. On the road, explain that the paths overlap and perform compromise processing. The so-called compromise processing is to remove the intersection when there are cross paths. If the parent node is passable and not on the road, a storage comparison is performed, and the node with the smallest F value among the eight surrounding child nodes is selected as the next node on the path.
8)假设判断父节点的八个方向都不可行，则进行回退，重新在上一级父节点中找剩余的子点中的最小值点，如果回退到起始点则说明没找到路径，算法结束。8) Assuming that none of the eight directions of the parent node is feasible, go back and find the minimum point among the remaining child points in the upper-level parent node again. If you go back to the starting point, it means that the path has not been found. The algorithm ends.
9)重复步骤4)～7)，直至终节点添加到m_openList中，表明路径已经找到。9) Repeat steps 4) to 7) until the end node is added to m_openList, indicating that the path has been found.
需说明的是，改进的A*算法是H复合值中加入了高程差的因子，辅助规划的路径更平缓。算法的改进使得计算出的路径不是最优的效果，但是可以提高运算的效率，即使在庞大数据量下也不会计算崩溃。在进行测试时发现，传统的A*算法在对十公里的路径进行规划时间需要七八分钟，更远的(例如接近三十公里)情况下一个小时都可能出不来结果，改进后的A*只需不到十秒。因此使用该改进的A*算法在更高精度地图下的实际应用价值更高。It should be noted that the improved A* algorithm adds a factor of elevation difference to the H composite value, and the assisted planning path is smoother. The improvement of the algorithm makes the calculated path less than the optimal effect, but it can improve the efficiency of the operation and prevent the calculation from crashing even with a huge amount of data. During the test, it was found that the traditional A* algorithm takes seven or eight minutes to plan a ten-kilometer path, and may not be able to produce results in an hour if it is further away (for example, close to thirty kilometers). The improved A* algorithm *Takes less than ten seconds. Therefore, the practical application value of using this improved A* algorithm under higher-precision maps is higher.
步骤三，在n值大于设定值的情况下，对栅格像元进行合并操作。这里的合并操作，是对所有栅格像元均进行合并操作处理，即对栅格图的分辨率进行了改变。例如，合并后的栅格像元大小为64*64，如附图3所示，整个正方形为一个合并后的栅格像元，正方形中的小格子为初始的栅格像元。一般情况下合并为64*64的情况后，n值不会超过所设定的300000。但是，若出现了合并后的n值仍旧超过所设定的300000，则再一次进行合并操作，合并为128*128，以此类推。Step 3: When the n value is greater than the set value, merge the raster pixels. The merging operation here is to merge all raster pixels, that is, to change the resolution of the raster image. For example, the size of the merged raster pixel is 64*64. As shown in Figure 3, the entire square is a merged raster pixel, and the small grid in the square is the initial raster pixel. Under normal circumstances, after merging into 64*64, the n value will not exceed the set 300000. However, if the merged n value still exceeds the set 300000, the merge operation will be performed again to 128*128, and so on.
步骤四，进行合并操作后，在起点和终点之间利用Dijkstra算法进行路径规划，规划出一条较优路径。其中，Dijkstra算法的过程如下：Step 4: After merging, use the Dijkstra algorithm to plan a path between the starting point and the end point to plan a better path. Among them, the process of Dijkstra's algorithm is as follows:
1)创建节点与起始点的距离矩阵distanceFromStart，将起始点的值设置为0，其余元素设置为无穷大inf。1) Create the distance matrix distanceFromStart between the node and the starting point, set the value of the starting point to 0, and set the remaining elements to infinity inf.
2)创建节点的父节点矩阵parent，初始化为0，表示无父节点。2) Create the parent node matrix parent of the node, which is initialized to 0, indicating no parent node.
3)每次迭代更新时，找出distanceFromStart中未完成遍历节点中总评估成本最低的节点，设置为当前节点current_node；设置current_node.dist＝inf，(防止再次遍历)，并标记为已完成遍历；对curren_node的四个相邻节点中的未遍历节点进行判定，判断相邻节点N.g否大于current_node.g+edge.cost，如果大于，则N.g＝current_node.g+edge.cost，N.parent_node＝current_node。3) During each iteration update, find the node with the lowest total evaluation cost among the uncompleted traversal nodes in distanceFromStart, and set it as the current node current_node; set current_node.dist=inf, (to prevent another traversal), and mark it as completed traversal; Determine the untraversed nodes among the four adjacent nodes of curren_node and determine whether the adjacent node N.g is greater than current_node.g+edge.cost. If it is greater, then N.g=current_node.g+edge.cost, N.parent_node=current_node .
4)重复步骤3)，直至current_node为目标点为止。4) Repeat step 3) until current_node is the target point.
5)根据父节点矩阵parent回溯最优路径。5) Backtrack the optimal path according to the parent node matrix parent.
步骤五，在规划出的较优路径上，按照制定的规则提取几个参样点，且这些参样点、以及起点和终点将步骤四中规划出的较优路径划分成若干段分段路径。本实施例中，制定的规则为：从起点开始，每经过若干个(例如20个)栅格像元取一个参样点，直至到达终点。Step 5: On the planned optimal path, extract several parameter sample points according to the established rules, and these parameter sample points, as well as the starting point and end point, divide the optimal path planned in Step 4 into several segmented paths. . In this embodiment, the rules formulated are: starting from the starting point, taking a reference sample point after passing several (for example, 20) grid pixels until reaching the end point.
步骤六，分别以每一段分段路径的两端点为起终点，采用改进的A*算法重新进行路径规划，得到若干段较优分段路径。Step 6: Taking the two end points of each segmented path as the starting and ending points, use the improved A* algorithm to re-plan the path to obtain several segments of better segmented paths.
步骤七，将步骤六中得到的各段较优分段路径连通以得到最终路径，并作为起点和终点间规划出的最优路径。Step seven: Connect the better segmented paths obtained in step six to obtain the final path, which is used as the optimal path planned between the starting point and the end point.
至此，便可完成路径规划。该方法在判断起点和终点之间的距离较远的情况下，首先将越野环境区域内的栅格像元进行合并处理，以降低整体计算的处理量、提高处理速度，合并处理后进行路径规划以规划出较优路径，然后对规划出的较优路径进行分段处理，以每段分段路径的两端点为起终点重新进行高精度的路径规划以规划出较优分段路径，使得每段分段路径的规划都在高精度栅格下完成，最后将各段较优分段路径连通可以形成最终路径，从而在满足越野路径规划精度要求的同时提高了运算效率，解决了现有技术中的运算效率慢、容易运算崩溃的问题。At this point, path planning can be completed. When the distance between the starting point and the end point is judged to be far, this method first merges the raster pixels in the off-road environment area to reduce the overall calculation processing volume and improve the processing speed, and then performs path planning after merging. In order to plan a better path, and then perform segmentation processing on the planned better path, re-carry out high-precision path planning with the two end points of each segmented path as the starting and ending points to plan a better segmented path, so that each segmented path can be The planning of segmented paths is completed under a high-precision grid. Finally, the final path can be formed by connecting the better segmented paths of each segment, thereby improving the computing efficiency while meeting the accuracy requirements of cross-country path planning, and solving the existing technical problems. The calculation efficiency is slow and the calculation is easy to crash.
下面将本发明方法应用于具体的实例中以说明该方法的有效性。选择某地区(如图4所示)的影像数据、DEM数据、以及SHP数据(包括道路、居民地、植被、地质等)进行实施，首先将该规划区域的越野环境量化为栅格数据，然后利用本发明方法进行分段式路径规划。采用的部分参数如下：设定值为300000，提取参样点时，每隔20个栅格像元提取一个参样点(如图5所示)。其最终规划结果如图6-1～图6-4所示，该条路径规划的长度为32.717公里，规划计算时长为9秒。The method of the present invention is applied to specific examples below to illustrate the effectiveness of the method. Select the image data, DEM data, and SHP data (including roads, residential areas, vegetation, geology, etc.) of a certain area (as shown in Figure 4) for implementation. First, the off-road environment of the planned area is quantified into raster data, and then The method of the invention is used for segmented path planning. Some of the parameters used are as follows: the set value is 300000. When extracting reference sample points, extract a reference sample point every 20 grid pixels (as shown in Figure 5). The final planning results are shown in Figures 6-1 to 6-4. The planned length of the path is 32.717 kilometers, and the planning calculation time is 9 seconds.
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