CN113804209B - High-precision long-distance off-road path planning method for quadrangle grid - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Abstract
The invention belongs to the technical field of path planning, and particularly relates to a high-precision long-distance off-road path planning method for a quadrangle grid. The method comprises the steps of firstly carrying out rasterization treatment on the cross-country environment, and then calculating the product n of the absolute value of the phase difference of the start and end line signals. Judging whether the n value is larger than a set value, if so, merging pixels to reduce the processing amount of overall calculation, improve the processing speed and perform path planning to obtain a better path; extracting a plurality of reference points on the superior path, dividing the superior path into a plurality of sections of segmented paths by the plurality of reference points, starting points and ending points, and re-planning the paths by taking two end points of each section of segmented path as starting and ending points to obtain a plurality of sections of superior segmented paths, so that the planning of each section of segmented path is completed under a high-precision grid; and finally, the better segmented paths of all the segments are communicated to obtain a final path, so that the calculation efficiency is improved while the requirement of the off-road path planning precision is met.
Description
Technical Field
The invention belongs to the technical field of path planning, and particularly relates to a high-precision long-distance off-road path planning method for a quadrangle grid.
Background
The off-road path planning is to plan an optimal path (shortest time, shortest distance or most reasonable path) according to a certain rule by a given starting and ending point in off-road environments such as mountain areas, jungles and the like, so that a user can be ensured to smoothly reach a destination.
The research work of off-road path planning has wide application in a plurality of fields such as military, fire protection, civil use and the like. Initially, off-road path planning was mainly used for military battlefield analysis, directing the operation of vehicles, personnel. The off-road path planning is still important in the military field nowadays, and mainly comprises path planning of combat objects in combat areas, operation of military robots and the like. Along with the development of scientific technologies such as artificial intelligence, unmanned, and the like, the off-road path planning technology is rapidly expanded to the civil field, such as off-road emergency rescue, travel friend self-driving formation planning, and the like. It follows that the research work of the off-road path planning technology is of great significance.
The data types used according to the path planning technique are different and can be classified into vector data-based path planning and raster data-based path planning. The path planning based on vector data is mainly used for selecting urban traffic routes and is mainly completed through known road network topological structures.
Currently, in off-road path planning, path planning is performed based on raster data in the absence of a road network. The off-road path planning based on raster data is to quantitatively divide the whole off-road environment into a plurality of grids with the same size, and then plan according to a search algorithm. In off-road path planning research, higher accuracy raster data is required if a good planning effect is desired. When the high-precision raster data is used for path planning, if the distance between the starting point and the ending point is too long, the problems of too long calculation time of the search algorithm, calculation running and the like can occur.
Disclosure of Invention
The invention provides a high-precision long-distance off-road path planning method for a quadrangle grid, which is used for solving the problems of low operation efficiency and easy operation collapse of the method in the prior art.
In order to solve the technical problems, the technical scheme and the corresponding beneficial effects of the technical scheme are as follows:
the invention provides a high-precision long-distance off-road path planning method for a four-corner grid, which comprises the following steps of:
1) Performing rasterization treatment on an off-road environment area needing path planning;
2) Judging whether the distance between the starting point and the end point is larger than a set distance;
3) If the distance between the starting point and the end point is larger than the set distance, merging the grid pixels in the off-road environment area; after the merging operation, path planning is carried out between a starting point and a terminal point, and a better path is obtained;
4) Extracting a plurality of reference points on the superior path, dividing the superior path into a plurality of sections of segmented paths by the plurality of reference points, starting points and end points, and re-planning the paths by taking two end points of each section of segmented path as starting and ending points to obtain a plurality of sections of superior segmented paths;
5) The preferred segmented paths of the segments are communicated to obtain a final path.
The beneficial effects of the technical scheme are as follows: under the condition that the distance between a starting point and a terminal point is far, firstly, carrying out merging operation on grid pixels in an off-road environment area to reduce the processing amount of overall calculation and improve the processing speed, carrying out path planning after the merging operation to plan a superior path, then carrying out segmentation processing on the planned superior path, carrying out high-precision path planning by taking two terminal points of each segment of segmented path as starting and ending points to plan a superior segmented path, completing the planning of each segment of segmented path under a high-precision grid, and finally communicating the superior segmented paths of each segment to form a final path, thereby meeting the requirement of off-road path planning precision and improving the operation efficiency.
Further, in step 3), after the merging operation, a Dijkstra algorithm is adopted between the starting point and the end point to perform path planning.
Further, in step 4), in order to make the planned path more gentle, the two end points of each segment of the segmented path are taken as starting and ending points, and an improved a-algorithm is adopted to carry out path planning again; the cost function of the modified a-algorithm is f=g+h; g is the compensation value from the initial node to the current node; h is a composite value and is determined by Euclidean distance and elevation difference.
Further, if the determination result in the step 2) is that the distance between the start point and the end point is greater than the set distance, the path planning is directly performed between the start point and the end point.
Further, adopting an improved A-type algorithm to directly conduct path planning between a starting point and an end point; the cost function of the modified a-algorithm is f=g+h; g is the compensation value from the initial node to the current node; h is a composite value and is determined by Euclidean distance and elevation difference.
Further, in step 2), the following method is adopted to determine whether the distance between the starting point and the end point is greater than the set distance:
calculating the product of the absolute value of the starting and ending point row number difference value and the absolute value of the starting and ending point column number difference value;
judging whether the product is larger than a set value: if the distance between the starting point and the end point is larger than the set value, the distance between the starting point and the end point is larger than the set distance; otherwise, the distance between the start point and the end point is less than or equal to the set distance.
Further, in step 4), the rule of extracting a plurality of reference points is as follows: the number of grid pixels between adjacent reference points is equal.
Drawings
FIG. 1 is a flow chart of a four-corner grid high-precision long-distance off-road path planning method of the invention;
fig. 2 is a flow chart of a modified a algorithm used in the present invention;
FIG. 3 is a schematic diagram of the present invention after combining 64 x 64 grid pixels into 1 pixel;
FIG. 4 is a schematic view of a region;
FIG. 5 is a schematic diagram of an extracted sample point and segmented path derived using the sample point of the present invention;
FIG. 6-1 is a 90 degree top view schematic of a planned path (prior to closing the terrain) for a region being planned using the method of the present invention;
FIG. 6-2 is a 90 degree top view schematic of a planned path (after closing the terrain) for a region planned by the method of the present invention;
6-3 are three-dimensional side views of a planned path (prior to closing the terrain) for planning a path for an area using the method of the present invention;
fig. 6-4 are three-dimensional side views of a planned path (after closing the terrain) for a region planned by the method of the present invention.
Detailed Description
The embodiment of the high-precision long-distance off-road path planning method for the four-corner grid is shown in fig. 1, and the specific process is as follows:
step one, rasterizing the off-road environment needing path planning, converting the geographic coordinates of the off-road environment area between the starting point and the end point into grid row numbers, and calculating the multiplication absolute value product n of the starting point row numbers and the end point row numbers.
Step two, judging whether the n value calculated in the step one is larger than a set value (for example 300000):
if the value of n is larger than the set value, the distance between the starting point and the end point is far, and the step III is executed;
if the value of n is not greater than the set value, indicating that the distance between the starting point and the end point is not far, directly planning a path between the starting point and the end point by using an improved A-type algorithm in the off-road environment area. The flow of the modified a-algorithm is shown in fig. 2, and the modified a-algorithm adds an influence factor of the elevation difference, so that the path can be preferentially selected to be a gentle path. In addition, the algorithm is different from the existing heuristic A-type algorithm, does not store excessive intermediate data, can reduce memory pressure, and improves operation efficiency. The whole process is as follows:
1) An open table and a closed table are established first, and each table is initialized. Wherein the open table contains all pixels to be searched, and all the pixels are initialized to 0; the closed table records that the pixels which have been read are marked as the pixels which cannot be passed, and the two types of pixels are marked as 1.
2) A vector container m_openlist is created to accommodate the points on the path.
3) Converting the geographic coordinates of the starting point and the end point into row and column numbers, and taking out the values of the corresponding pixels in the data according to the row and column numbers. The planning starting coordinate is used as a starting node m_startpos, and the planning ending coordinate is used as a target node m_endpos.
4) A mobile pointer is created to the current node.
5) Storing the starting point into an m_openlist in the form of a row number, taking the starting point as a starting father node, moving a pointer to the node, and sequentially judging the trafficability of eight points around the father node and the F value. The trafficability is judged by whether the child node is in the closed table, and F is composed of two parts, namely a G value and an H value on the premise of trafficability. The calculation formula of the F value is as follows:
F＝G+H
the G value is the compensation value from the starting node m_startPos to the current node, if the adjacent child node is the condition that the weight value does not exist, the G value is calculated equidistantly by 10 or 14 respectively, and if the adjacent child node is the condition that the weight value exists, the weight value is used as a judgment standard; the H value is a composite value and is determined by Euclidean distance and elevation difference, the Euclidean distance is the distance from the current point to the end point, the point pointing function is mainly achieved, the elevation difference is the difference between the elevation of the father point and the current point, the difference is mainly used for selecting relatively flat topography, and the F value of the current point is finally calculated.
6) And respectively comparing F values of eight points, selecting the minimum value, adding the minimum value into the m_openList, taking the minimum value as the current point of the road, moving a pointer to the point, marking the point as 1, and representing the point on the road.
7) And searching the conditions of eight surrounding direction nodes by taking the current pixel as a father node, firstly judging whether the nodes are marked, judging the position on the path if the nodes are marked, directly skipping if the nodes are not found, indicating the nodes as non-passable points, and if the positions are found. The paths described on the road overlap, and a compromise process is performed, in which a cross path occurs, and the cross portion is removed. If the parent node is passable and not on the road, a storage comparison is performed, and a node with the minimum F value in eight surrounding direction child nodes is selected as a next node of the path.
8) If the eight directions of the parent node are not feasible, carrying out rollback, searching the minimum value point in the rest child points in the parent node at the upper level again, and if the parent node is rolled back to the starting point, indicating that no path is found, and ending the algorithm.
9) Repeating steps 4) to 7) until the final node is added into the m_openlist, indicating that the path has been found.
It should be noted that, the improved a-x algorithm is that the H-complex value is added with a factor of elevation difference, and the path of auxiliary planning is smoother. The improvement of the algorithm makes the calculated path not optimal, but can improve the efficiency of the operation, and the breakdown is not calculated even under huge data volume. When testing, it was found that the conventional a-algorithm requires seven or eight minutes for planning a path of ten kilometers, and that even more (e.g., approximately thirty kilometers) one hour may not result, and that the improved a-algorithm requires less than ten seconds. Therefore, the practical application value of the improved A-x algorithm under a higher-precision map is higher.
And step three, carrying out merging operation on the grid pixels under the condition that the n value is larger than a set value. The merging operation is to perform merging operation processing on all grid pixels, namely, change the resolution of the grid image. For example, the size of the combined grid pixels is 64×64, as shown in fig. 3, the entire square is one combined grid pixel, and the small grid in the square is the initial grid pixel. Typically, n will not exceed 300000 when combined at 64 x 64. However, if the combined n value still exceeds the set 300000, the combination operation is performed again, and the combination is 128×128, and so on.
And step four, after the merging operation, planning a path between the starting point and the end point by using a Dijkstra algorithm, and planning a better path. The Dijkstra algorithm is as follows:
1) A distance matrix distance from start of the node and the starting point is created, the value of the starting point is set to 0, and the rest elements are set to infinity inf.
2) And creating a parent node matrix parent of the node, initializing to 0, and indicating no parent node.
3) When each iteration is updated, finding out the node with the lowest total evaluation cost in the uncompleted traversal nodes in the distance from start, and setting the node as the current node; setting current_node.dist=inf, (preventing re-traversal), and marking as completed traversal; and judging the non-traversed node in the four adjacent nodes of the current_node, judging whether the adjacent node N.g is larger than the current_node.g+edge.cost, and if so, N.g =current_node.g+edge.cost and N.parent_node=current_node.
4) Repeating the step 3) until the current_node is the target point.
5) And backtracking the optimal path according to the parent node matrix parent.
And fifthly, extracting a plurality of reference points on the planned preferred path according to the established rule, and dividing the preferred path planned in the fourth step into a plurality of sections of segmented paths by the reference points, the starting point and the ending point. In this embodiment, the rule formulated is: starting from the start point, a reference point is taken every time a number of (e.g. 20) grid pixels pass until the end point is reached.
And step six, respectively taking two end points of each segment of segmented path as starting and ending points, and adopting an improved A-type algorithm to carry out path planning again to obtain a plurality of segments of better segmented paths.
And seventhly, communicating the better segmented paths of the segments obtained in the step six to obtain a final path, and taking the final path as an optimal path planned between a starting point and an end point.
Thus, the path planning can be completed. Under the condition that the distance between the starting point and the end point is judged to be far, firstly, the grid pixels in the cross-country environment area are combined to reduce the processing amount of overall calculation and improve the processing speed, the path planning is carried out after the combination processing to plan a superior path, then the planned superior path is segmented, the high-precision path planning is carried out again by taking the two end points of each segment of segmented path as starting points to plan a superior segmented path, the planning of each segment of segmented path is completed under the high-precision grid, and finally, the superior segmented paths of all segments are communicated to form a final path, so that the operation efficiency is improved while the requirement of the cross-country path planning precision is met, and the problems of low operation efficiency and easy operation collapse in the prior art are solved.
The method of the present invention is applied to specific examples below to illustrate the effectiveness of the method. Image data, DEM data and SHP data (including roads, residential areas, vegetation, geology and the like) of a certain area (shown in fig. 4) are selected for implementation, the off-road environment of the planning area is quantized into raster data first, and then the sectional path planning is carried out by using the method. The partial parameters used are as follows: when the set value is 300000 and the reference points are extracted, one reference point is extracted every 20 grid pixels (as shown in fig. 5). The final planning result is shown in fig. 6-1 to 6-4, the planned length of the path is 32.717 km, and the planning calculation time is 9 seconds.
Claims (4)
1. The high-precision long-distance off-road path planning method for the quadrangle grid is characterized by comprising the following steps of:
1) Performing rasterization treatment on an off-road environment area needing path planning;
2) Judging whether the distance between the starting point and the end point is larger than a set distance; if the distance between the starting point and the end point is not greater than the set distance, directly planning a path under the high-precision grid between the starting point and the end point;
3) If the distance between the starting point and the end point is larger than the set distance, merging the grid pixels in the off-road environment area; after the merging operation is carried out, a Dijkstra algorithm is adopted between a starting point and a terminal point to carry out path planning, so as to obtain a better path;
4) Extracting a plurality of reference points on the superior path, dividing the superior path into a plurality of sections of segmented paths by the plurality of reference points, starting points and end points, respectively carrying out high-precision path planning under a high-precision grid by taking two end points of each section of segmented path as starting and ending points, and carrying out path planning again by adopting an improved A-type algorithm by taking two end points of each section of segmented path as starting and ending points; the cost function of the modified a-algorithm is f=g+h; g is the compensation value from the initial node to the current node; h is a composite value, and is determined by Euclidean distance and elevation difference to obtain a plurality of sections of better segmented paths;
5) The preferred segmented paths of the segments are communicated to obtain a final path.
2. The method for planning a high-precision long-distance off-road path of a four-corner grid according to claim 1, wherein a modified a-algorithm is adopted to directly plan the path between a starting point and an ending point; the cost function of the modified a-algorithm is f=g+h; g is the compensation value from the initial node to the current node; h is a composite value and is determined by Euclidean distance and elevation difference.
3. The method for planning a high-precision long-distance off-road path of a four-corner grid according to claim 1, wherein in the step 2), the following method is adopted to determine whether the distance between the starting point and the ending point is greater than a set distance:
calculating the product of the absolute value of the starting and ending point row number difference value and the absolute value of the starting and ending point column number difference value;
judging whether the product is larger than a set value: if the distance between the starting point and the end point is larger than the set value, the distance between the starting point and the end point is larger than the set distance; otherwise, the distance between the start point and the end point is less than or equal to the set distance.
4. The method for planning a high-precision long-distance off-road path of a four-corner grid according to any one of claims 1 to 3, wherein in the step 4), a rule of extracting a plurality of reference points is as follows: the number of grid pixels between adjacent reference points is equal.
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| CN114562998A (en) * | 2022-01-27 | 2022-05-31 | 北京四象爱数科技有限公司 | Multi-target-point path planning method based on DEM (digital elevation model) under non-road condition in mountainous area |
| CN116625378B (en) * | 2023-07-18 | 2023-10-31 | 上海仙工智能科技有限公司 | Cross-regional path planning method and system and storage medium |
| CN116734862B (en) * | 2023-08-09 | 2023-11-21 | 常熟理工学院 | Directional off-road route selection method, device and computer storage medium |
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