CN113551682A - Path planning method of amphibious unmanned war chariot considering influence of terrain and topography - Google Patents

Path planning method of amphibious unmanned war chariot considering influence of terrain and topography Download PDF

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CN113551682A
CN113551682A CN202110811903.8A CN202110811903A CN113551682A CN 113551682 A CN113551682 A CN 113551682A CN 202110811903 A CN202110811903 A CN 202110811903A CN 113551682 A CN113551682 A CN 113551682A
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CN113551682B (en
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王昕炜
鲍锦秋
彭海军
张盛
李云鹏
陈飙松
刘洁
吕琛
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Dalian University of Technology
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3446Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30Map- or contour-matching
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3407Route searching; Route guidance specially adapted for specific applications
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Abstract

本发明提供一种考虑地形与地势影响的两栖无人战车的路径规划方法,属于机械自动化领域。首先,将战场环境离散为二维栅格。第二,根据战场环境,建立相应的障碍矩阵、地形矩阵与地势矩阵。第三,利用地形矩阵与地势矩阵改进传统A*算法中估值函数。第四,根据改进的估值函数,使用A*算法生成最优路径。第五,评价生成路径的质量。本发明综合利用了战场环境的障碍、地形与地势信息,可以实现两栖无人战车的高质量运动路径的快速规划,生成的路径具有等效路径短、累积坡度低等优点;本发明具有一定的实用性,能为两栖无人战车的路径规划问题提供新的求解思路。

Figure 202110811903

The invention provides a path planning method for an amphibious unmanned combat vehicle considering the influence of terrain and terrain, and belongs to the field of mechanical automation. First, the battlefield environment is discretized into a two-dimensional grid. Second, according to the battlefield environment, establish the corresponding obstacle matrix, terrain matrix and terrain matrix. Third, the evaluation function in the traditional A* algorithm is improved by using the terrain matrix and the terrain matrix. Fourth, according to the improved evaluation function, the A* algorithm is used to generate the optimal path. Fifth, evaluate the quality of the generated path. The invention comprehensively utilizes the obstacles, terrain and terrain information of the battlefield environment, and can realize the rapid planning of the high-quality motion path of the amphibious unmanned combat vehicle, and the generated path has the advantages of short equivalent path and low cumulative gradient; the invention has certain advantages. The practicality of the method can provide a new solution for the path planning problem of amphibious unmanned combat vehicles.

Figure 202110811903

Description

Path planning method of amphibious unmanned war chariot considering influence of terrain and topography
Technical Field
The invention belongs to the field of mechanical automation, and relates to a path planning method of an amphibious unmanned chariot considering influence of terrain and topography.
Background
The unmanned chariot has good flexibility, maneuverability and environmental adaptability, and the firepower striking module of the mission load has the outstanding characteristics of various configurations, quick response and the like. Due to the fact that the sensor can provide urgent field perception capability and diversified breakthrough means for task executing personnel, the sensor is attracted by more and more extensive attention and application in recent years. In order to realize efficient execution of tasks, an optimal traveling route needs to be planned in advance before the unmanned war chariot moves out, so that the unmanned war chariot can quickly reach a target site while avoiding natural or artificial obstacles (such as ravines which cannot be crossed, regions with strong enemy vigor and the like) in a working environment. The study of the path planning of unmanned combat vehicles is an important aspect of their intelligent behavior.
The ground unmanned equipment path planning algorithm is mostly to solve under a two-dimensional framework (such as A, RRT, artificial potential field and the like), the data volume and the complexity are lower than those of a real map, and the influence of the actual topography cannot be considered. The operation environment of the amphibious unmanned war chariot is quite complex, and the movement speeds of the amphibious unmanned war chariot on different terrains have obvious difference. Only by fully and comprehensively utilizing information such as obstacles, terrain, topography and the like in the operation environment, a high-quality motion path can be generated for the amphibious unmanned combat vehicle, so that the combat efficiency is improved.
Disclosure of Invention
In order to solve the problems, the invention provides an improved A-star algorithm considering the influence of terrain and topography to solve the path planning problem of the unmanned combat tank. The algorithm is based on consideration of real geographic environment, and introduces a terrain matrix and a terrain matrix on the basis of a classical two-dimensional A-x algorithm. The path equivalent distance generated by the improved algorithm has the advantages of short equivalent distance, small climbing gradient and the like, and the algorithm has higher calculation efficiency.
In order to achieve the purpose, the invention adopts the technical scheme that:
a path planning method for an amphibious unmanned war chariot considering influence of terrain and topography comprises the steps of firstly, dispersing a battlefield environment into a two-dimensional grid. Secondly, establishing a corresponding obstacle matrix, a terrain matrix and a terrain matrix according to the battlefield environment. Thirdly, the evaluation function in the traditional A-star algorithm is improved by using the terrain matrix and the terrain matrix. Fourth, an optimal path is generated using the a-algorithm based on the improved valuation function. Fifth, the quality of the generated path is evaluated. The calculation flow chart of the method is shown in fig. 1, and comprises the following steps:
step 1: discretizing a battlefield environment into a two-dimensional grid
And establishing a plane rectangular coordinate system xOy for describing the geographic position, and determining the starting position and the target position of the unmanned fighting vehicle.
According to the mission requirement of the unmanned chariot, a rectangular area M is adopted to describe the range of motion of the unmanned chariot, and the range of motion is recorded as M { (x, y) | xmin≤x≤xmax,ymin≤y≤ymax}. Wherein [ x ]min,xmax]Represents the minimum and maximum values of the region M in the x direction, [ y ]min,ymax]Representing the minimum and maximum values of the region M in the y direction, where X is equal to Xmax-xmin,Y=ymax-ymin. From the terrain information, the elevation information for each location within the area M may be described by a function z ═ z (x, y). Considering that R terrain exists in the area M, the driving speed of the unmanned war chariot on each terrain has difference, and the formal speed of the unmanned war chariot on the R-th terrain is recorded as vr(R ═ 1, 2.., R). Remember that unmanned war chariot is on
Figure BDA0003168513870000021
The ground shape has the fastest moving speed, and the speed is recorded as
Figure BDA0003168513870000022
Discretizing a two-dimensional area into nx×nyA rectangular grid of which rx=X/nxAnd ry=Y/nyDiscrete resolution in the x-direction and y-direction, respectively, typically let rx=ry. Let the notation "grid (i, j)" denote a grid region located within the following coordinate range
Mij={(x,y)|xmin+(i-1)rx≤x≤xmax+irx,ymin+(j-1)ry≤y≤ymax+jry} (1)
Wherein, i is 1,2x,j=1,2,...,ny
To achieve an accurate description of the geographical environment, the discrete resolution rxAnd ryThe selection should satisfy the following three conditions as much as possible simultaneously: the proportion of the area occupied by the obstacles in the specific grid is close to 0 or 1; secondly, only one type of terrain exists in the specific grid; and thirdly, in the specific grid, the terrain change is not obvious.
The starting position of the unmanned war chariot corresponds to the grid (i)#,j#) The target position corresponds to the grid (i)*,j*)。
Step 2: establishing an obstacle matrix, a terrain matrix and a terrain matrix according to the battlefield environment
Establishing an obstacle matrix according to the obstacle distribution condition in the area M
Figure BDA0003168513870000023
Each element of which has a value of 0 or 1. If Q (i, j) is 0, representing that no obstacle exists in the grid (i, j), and allowing the unmanned combat vehicle to pass through; if Q (i, j) is 1, it represents that there is an obstacle in the grid (i, j), and the unmanned vehicle cannot pass through.
Establishing a terrain matrix according to the terrain distribution condition in the region M
Figure BDA0003168513870000024
If the grid (i, j) belongs to the r-th terrain, then the elementE (i, j) has a value vmax/vrWhich describes over the terrain
Figure BDA0003168513870000025
To the time respectively spent on the terrain r over the same distance.
Establishing a terrain matrix according to the altitude distribution condition in the area M
Figure BDA0003168513870000026
The value of the element W (i, j) is the maximum value of the elevation within the grid (i, j), i.e.
Figure BDA0003168513870000027
And step 3: method for improving estimation function in traditional A-matrix algorithm by using terrain matrix and terrain matrix
The expansion of the nodes is realized in the A-algorithm by the following evaluation function
F(n)=G(n)+H(n) (2)
Wherein n represents the nth node in the path and corresponds to a certain grid generated in the step 1; f (n) is from the initial grid (i)#,j#) Via node n to the target grid (i)*,j*) The minimum cost estimate of (2); g (n) is formed by the initial grid (i)#,j#) Minimum cost to node n; h (n) is from node n to the target grid (i)*,j*) The minimum cost estimate of (2), i.e. the heuristic function. In step 3, the setting of g (n) and h (n) is improved by using the terrain matrix E and the terrain matrix W generated in step 2.
Firstly, the calculation formula of G (n) is improved as follows:
Figure BDA0003168513870000031
wherein, assuming that the node n and the node (n-1) correspond to the grid (i, j) and the grid (i ', j'), respectively, the calculation method of each parameter in the formula (3) is as follows: l ═ E (i, j) + E (i ', j'))/2, representing the effect of terrain on travel time; Δ h — W (i ', j') -W (i, j), representing the relative elevation between two nodes; t is the linear distance between the center points of the grid (i, j) and the grid (i ', j').
The calculation formula of the heuristic function H (n) is then improved. Let node n correspond to grid (i, j), consider the following two grids (i, j) to (i)*,j*) The following path:
route 1: the unmanned war chariot firstly moves from the grid (i, j) to the grid (i, j) along the y direction*) (ii) a Then along the x direction, by the grid (i, j)*) Move to grid (i)*,j*)。
Route 2: the unmanned war chariot firstly moves from the grid (i, j) to the grid (i) along the x direction*J); and then along the y direction, by the grid (i)*J) movement to grid (i)*,j*)。
It is apparent that paths 1 and 2 pass through the same number of grids (excluding the current grid (i, j)), which is denoted as m ═ i-i*|+|j-j*L. For path c (c is 1 or 2), the estimated cost is
Figure BDA0003168513870000032
Wherein g (k) represents the space between two adjacent grids in the path c (the previous and subsequent grids are respectively denoted as grid (a)-,b-) Move to the grid (a)+,b+) Path cost estimate, which may be expressed as
Figure BDA0003168513870000033
Two terms are in the right end bracket of the medium number in the formula (5). Wherein the first item
Figure BDA0003168513870000034
The meaning of (2) is the same as that of the second term at the right end of the equation (3), and the term indirectly reflects the time taken by the unmanned chariot to run; the second term Q reflects the path cost (i.e., the effect of path slope) of adjacent grids with respect to elevation, and is defined as follows:
Figure BDA0003168513870000035
wherein p is1< 0 and p2The coefficient more than 0 corresponds to the two conditions of climbing and descending. When coefficient piThe larger the absolute value of (i ═ 1,2), the greater the weight to consider for the path gradient. The coefficients q in equation (5) are selected from the set { q }1,q2According to the grid (a) selected from+,b+) Whether or not a change occurs for the barrier grid, which satisfies
Figure BDA0003168513870000041
Wherein q is1And q is2Satisfy q1>q2Is greater than 0. H (n) is H1And H2The smaller of these, H (n) min { H }1,H2}
And 4, step 4: and (4) generating an optimal path by using an A-x algorithm according to the evaluation function improved in the step 3.
Step 4-1: two table data structures OPENLIST and close are created, where the OPENLIST table holds all nodes that have been generated but not explored and the close table holds nodes that have been explored.
Step 4-2: will initiate the grid (i)#,j#) Add OPENLIST table.
Step 4-3: calculating the costs G and H of all NODEs in OPENLIST, and selecting the NODE with the lowest F value as a NODE NODE according to a formula (2); NODE was removed from the OPENLIST table and placed in the CLOSELIST table.
Step 4-4: if the target grid (i)*,j*) If the path exists in the CLOSELIST table, the path planning is successful. Stopping calculation, connecting the target nodes with each father node in sequence to form a final path, and recording the final path as P*. Note P*Is composed of N nodes (including initial grid and target grid), and the k-th node is grid (i)k,jk) Apparently there is (i)#,j#)=(i1,j1),(i*,j*)=(iN,jN)。
And 4-5: for each NODE that is adjacent to the NODE and not in the close list table, it is determined whether it is in the open list table. If not, adding the product into OPENLIST table; if so, updating the minimum G cost and the parent node.
And 4-6: and 4, repeating the step 4-3.
And 5: evaluating quality of generated paths
Two path-related indicators, equivalent distance S and accumulated slope OSlope, are defined as follows. For path P*S and OSlope are defined as shown in equations (8) and (9), respectively
Figure BDA0003168513870000042
Figure BDA0003168513870000043
Wherein the meaning of each variable is the same as in equation (3).
The quality of the path is evaluated using the index S and the index OSlope.
The invention has the beneficial effects that:
a terrain matrix and a terrain matrix are introduced on the basis of a traditional two-dimensional A-star algorithm to realize accurate description of the geographic environment, so that a path planning method of the amphibious unmanned chariot considering the influence of the terrain and the terrain is developed. The method considers the complex operation environment of the amphibious chariot and comprehensively utilizes the obstacles and the geographic information in the environment. Compared with the traditional three-dimensional A-x algorithm, the path generated by the method has the advantages of short equivalent distance, gradual climbing gradient and the like, and has higher calculation efficiency. The method has certain practicability and can provide a new solution thought for the path planning problem of the amphibious unmanned chariot.
Drawings
FIG. 1 is a flow chart of the calculation of the present invention.
Fig. 2 is a schematic diagram of a battlefield environment in an embodiment of the invention.
FIG. 3 is a schematic diagram of an obstacle according to an embodiment of the present invention.
Fig. 4 is a path planning result obtained by using the method of the present invention in the embodiment of the present invention.
Fig. 5 shows the comparison result obtained by using the conventional three-dimensional a-x algorithm in the embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following specific examples.
The battlefield map is assumed to have a specification of 10km × 12km, which comprises three mountains with a relatively slow slope and a relatively steep slope, and the battlefield comprises 4 kinds of terrains including water, grassland, rock 1 and rock 2. In fig. 2, a schematic diagram of the terrain and topography of the simulation environment is given. The speeds of the unmanned chariot under four terrains are different, the rock ground 2 is selected as a standard, and the terrain matrix element values corresponding to the four terrains are shown in table 1. The distribution of obstacles in a battlefield environment is given in fig. 3.
TABLE 1 speed of unmanned vehicles under terrain and value of terrain matrix elements
Figure BDA0003168513870000051
A path planning method of an amphibious unmanned war chariot considering terrain and topography influence comprises the following steps:
step 1: discretizing a battlefield environment into a two-dimensional grid
To describe the geographic position, a plane rectangular coordinate system xOy is established, and the starting position (0.05km ) and the target position (7.75km,100.05km) of the unmanned chariot are determined.
According to the mission requirement of the unmanned chariot, the activity range is described by a rectangular area M which is written as M { (x, y) xmin≤x≤xmax,ymin≤y≤ymax}. Wherein [ x ]min,xmax]=[0,10]Represents the minimum and maximum values of the region M in the x direction, [ y ]min,ymax]=[0,12]The representative region M is in the y directionMinimum and maximum values of (c), let X be Xmax-xmin=10,Y=ymax-y min12. From the terrain information, the elevation information for each location within the area M may be described by a function z ═ z (x, y). Considering 4 terrains in the area M, the driving speed of the unmanned war chariot on each terrain has difference, and the formal speed of the unmanned war chariot on the r-th terrain is recorded as vr(R ═ 1, 2.., R). Remember that unmanned war chariot is on
Figure BDA0003168513870000061
The ground shape has the fastest moving speed, and the speed is recorded as
Figure BDA0003168513870000062
Let n bex=100,nyDiscretizing the two-dimensional region into n 120x×nyA rectangular grid of which rx=X/nx0.1km and ry=Y/ny0.1km is the discrete resolution in the x direction and the y direction respectively, and r is satisfiedx=ry. Let the notation "grid (i, j)" denote a grid region located within the following coordinate range
Mij={(x,y)|xmin+(i-1)rx≤x≤xmax+irx,ymin+(j-1)ry≤y≤ymax+jry} (10)
Wherein, i is 1,2x,j=1,2,...,ny
Discrete resolution rxAnd ryThe selection of (A) satisfies the following three conditions: the proportion of the area occupied by the obstacles in the specific grid is close to 0 or 1; secondly, only one type of terrain exists in the specific grid; and thirdly, in the specific grid, the terrain change is not obvious. Therefore, the adopted dispersion can accurately describe the geographic environment corresponding to the battlefield.
The starting position of the unmanned war chariot corresponds to the grid (i)#,j#)(i#=0,j#0), the target position corresponds to the grid (i)*,j*)(i*=100,j*=77)。
Step 2: establishing an obstacle matrix, a terrain matrix and a terrain matrix according to the battlefield environment
Establishing an obstacle matrix according to the obstacle distribution condition in the area M
Figure BDA0003168513870000063
Each element of which has a value of 0 or 1. If Q (i, j) is 0, representing that no obstacle exists in the grid (i, j), and allowing the unmanned combat vehicle to pass through; if Q (i, j) is 1, it represents that there is an obstacle in the grid (i, j), and the unmanned vehicle cannot pass through.
Establishing a terrain matrix according to the terrain distribution condition in the region M
Figure BDA0003168513870000064
If the grid (i, j) belongs to the r-th terrain, the value of the element E (i, j) is vmax/vrWhich describes over the terrain
Figure BDA0003168513870000065
To the time respectively spent on the terrain r over the same distance.
Establishing a terrain matrix according to the altitude distribution condition in the area M
Figure BDA0003168513870000066
The value of the element W (i, j) is the maximum value of the elevation within the grid (i, j), i.e.
Figure BDA0003168513870000067
And step 3: method for improving estimation function in traditional A-matrix algorithm by using terrain matrix and terrain matrix
Firstly, the calculation formula of G (n) is improved as follows:
Figure BDA0003168513870000068
wherein, assuming that the node n and the node (n-1) correspond to the grid (i, j) and the grid (i ', j'), respectively, the calculation method of each parameter in the formula (11) is as follows: l ═ E (i, j) + E (i ', j'))/2, representing the effect of terrain on travel time; Δ h — W (i ', j') -W (i, j), representing the relative elevation between two nodes; t is the linear distance between the center points of the grid (i, j) and the grid (i ', j').
The calculation formula of the heuristic function H (n) is then improved. Let node n correspond to grid (i, j), consider the following two grids (i, j) to (i)*,j*) The following path:
route 1: the unmanned war chariot firstly moves from the grid (i, j) to the grid (i, j) along the y direction*) (ii) a Then along the x direction, by the grid (i, j)*) Move to grid (i)*,j*)。
Route 2: the unmanned war chariot firstly moves from the grid (i, j) to the grid (i) along the x direction*J); and then along the y direction, by the grid (i)*J) movement to grid (i)*,j*)。
It is apparent that paths 1 and 2 pass through the same number of grids (excluding the current grid (i, j)), which is denoted as m ═ i-i*|+|j-j*L. For path c (c is 1 or 2), the estimated cost is
Figure BDA0003168513870000071
Wherein g (k) represents the space between two adjacent grids in the path c (the previous and subsequent grids are respectively denoted as grid (a)-,b-) Move to the grid (a)+,b+) Path cost estimate, which may be expressed as
Figure BDA0003168513870000072
Two terms are in the right end bracket of the medium number in the formula (5). Wherein the first item
Figure BDA0003168513870000073
Has the same meaning with the second term at the right end of the equation (11), and the term indirectly reflects the time taken by the unmanned chariot to run; the second term Q reflects the adjacent grid phaseThe path cost for elevation (i.e., the effect of path slope) is defined as follows:
Figure BDA0003168513870000074
wherein p is1-70 and p2The coefficients for both the uphill and downhill slope are 70. The coefficients q in equation (13) are selected from the set { q }1,q2According to the grid (a) selected from+,b+) Whether or not a change occurs for the barrier grid, which satisfies
Figure BDA0003168513870000075
Wherein q is1=10,q21. H (n) is H1And H2The smaller of these, H (n) min { H }1,H2}。
And 4, step 4: and (4) generating an optimal path by using an A-x algorithm according to the evaluation function improved in the step 3.
Step 4-1: two table data structures OPENLIST and close are created, where the OPENLIST table holds all nodes that have been generated but not explored and the close table holds nodes that have been explored.
Step 4-2: will initiate the grid (i)#,j#) Add OPENLIST table.
Step 4-3: calculating the costs G and H of all NODEs in OPENLIST, and selecting the NODE with the lowest F value as a NODE NODE according to a formula (2); NODE was removed from the OPENLIST table and placed in the CLOSELIST table.
Step 4-4: if the target grid (i)*,j*) If the path exists in the CLOSELIST table, the path planning is successful. Stopping calculation, connecting the target nodes with each father node in sequence to form a final path, and recording the final path as P*
And 4-5: for each NODE that is adjacent to the NODE and not in the close list table, it is determined whether it is in the open list table. If not, adding the product into OPENLIST table; if so, updating the minimum G cost and the parent node.
And 4-6: and 4, repeating the step 4-3.
And 5: evaluating quality of generated paths
Optimal path P generated in step 4*As shown in fig. 4, it is composed of N147 nodes. Settlement path P*And the accumulated slope OSlope. To show the superiority of the algorithm, the problem was solved using the conventional three-dimensional a-algorithm for comparison (the planned path is shown in fig. 5). From visual analysis of the effect graph, the traditional three-dimensional A-star algorithm does not fully consider map information, and a dangerous walking path is generated. The optimal path generated by considering the terrain and the topography of the amphibious chariot is avoided, the amphibious chariot is prevented from climbing over a steep hill, and the amphibious chariot is allowed to climb over a relatively slow hill. Further, the performance indicators of the present invention and the conventional three-dimensional a-algorithm are summarized in table 2. The equivalent distance and the accumulated gradient of the path generated by the method are superior to those of the traditional three-dimensional A-star algorithm, and the method has obvious advantages in calculation efficiency.
Table 2 comparison of the performance of the inventive method with the performance of the conventional three-dimensional a-x algorithm
Figure BDA0003168513870000081
The method is based on a two-dimensional A-star algorithm, the description of the complex geographic environment is realized by constructing the terrain, the terrain and the obstacle matrix, the valuation function is reasonably constructed, and the path planning of the amphibious unmanned chariot in the complex environment is realized. Meanwhile, a path evaluation method taking the equivalent distance and the accumulated gradient as indexes is established. Compared with the optimal path obtained by the basic A-star algorithm, the path distance equivalent distance generated by the method is shorter and more gradual, the calculation efficiency is higher, and a new solution thought is provided for the path planning of the amphibious unmanned chariot in the complex geographic environment.
The above-mentioned embodiments only express the embodiments of the present invention, but not should be understood as the limitation of the scope of the invention patent, it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the concept of the present invention, and these all fall into the protection scope of the present invention.

Claims (1)

1.一种考虑地形与地势影响的两栖无人战车的路径规划方法,其特征在于,包括以下步骤:1. a path planning method for amphibious unmanned combat vehicles considering terrain and terrain influence, is characterized in that, comprises the following steps: 步骤1:将战场环境离散为二维栅格Step 1: Discrete the battlefield environment into a two-dimensional grid 建立平面直角坐标系xOy,用于描述地理位置,确定无人战车的起始位置与目标位置;Establish a plane rectangular coordinate system xOy to describe the geographic location and determine the starting position and target position of the unmanned combat vehicle; 根据无人战车的任务需求,采用矩形区域M描述其活动范围,记作M={(x,y)|xmin≤x≤xmax,ymin≤y≤ymax};其中[xmin,xmax]代表区域M在x方向上的最小值与最大值,[ymin,ymax]代表区域M在y方向上的最小值与最大值,令X=xmax-xmin,Y=ymax-ymin;根据地势信息,采用函数z=z(x,y)描述区域M内各位置的海拔信息;考虑区域M中存在R种地形,无人战车在每种地形上的行驶速度具有差异,记无人车在第r中地形上的形式速度为vr(r=1,2,...,R);记无人战车在第R种地形上移动速度最快,并记该速度为vmax=vRAccording to the mission requirements of the unmanned combat vehicle, a rectangular area M is used to describe its activity range, which is denoted as M={(x,y)|x min ≤x≤x max ,y min ≤y≤y max }; where [x min ,x max ] represents the minimum and maximum values of the region M in the x direction, [y min ,y max ] represents the minimum and maximum values of the region M in the y direction, let X=x max -x min , Y= y max -y min ; According to the terrain information, the function z=z(x, y) is used to describe the altitude information of each position in the area M; Considering that there are R types of terrain in the area M, the driving of the unmanned combat vehicle on each terrain There are differences in speed, and the formal speed of the unmanned vehicle on the rth terrain is v r (r=1,2,...,R); the unmanned combat vehicle moves the fastest on the Rth terrain, And record the speed as v max =v R ; 将二维区域离散为nx×ny个矩形栅格,其中rx=X/nx与ry=Y/ny分别为x方向与y方向上的离散分辨率,通常令rx=ry;令符号“栅格(i,j)”表示位于如下坐标范围内的栅格区域:Discrete the two-dimensional area into n x × ny rectangular grids, where r x =X/n x and ry =Y/ ny are the discrete resolutions in the x and y directions respectively, usually let r x = r y ; let the notation "raster(i,j)" denote the region of the grid that lies within the following coordinate ranges: Mij={(x,y)|xmin+(i-1)rx≤x≤xmax+irx,ymin+(j-1)ry≤y≤ymax+jry} (1)M ij ={(x,y)|x min +(i-1)r x ≤x≤x max +ir x ,y min +(j-1)r y ≤y≤y max +jr y } (1 ) 其中,i=1,2,...,nx,j=1,2,...,nyWherein, i=1,2,...,n x , j=1,2,..., ny ; 为实现对于地理环境的准确描述,离散分辨率rx与ry的选取应尽量同时满足如下三个条件:①特定栅格内障碍物所占区域的比例,接近于0或1;②特定栅格内,只有一种地形;③特定栅格内,地势变化不明显;In order to achieve an accurate description of the geographical environment, the selection of discrete resolutions r x and r y should meet the following three conditions as far as possible: (1) the proportion of the area occupied by obstacles in a specific grid is close to 0 or 1; (2) a specific grid In the grid, there is only one type of terrain; ③In a specific grid, the terrain changes are not obvious; 无人战车的起始位置对应于栅格(i#,j#),目标位置对应于栅格(i*,j*);The starting position of the unmanned combat vehicle corresponds to the grid (i # ,j # ), and the target position corresponds to the grid (i * ,j * ); 步骤2:根据战场环境,建立障碍矩阵、地形矩阵与地势矩阵Step 2: According to the battlefield environment, establish the obstacle matrix, terrain matrix and terrain matrix 根据区域M内的障碍分布情况,建立障碍矩阵
Figure FDA0003168513860000011
其每个元素的值为0或者1;若Q(i,j)=0,代表栅格(i,j)内无障碍,允许无人战车通过;若Q(i,j)=1,代表栅格(i,j)内存在障碍,无人战车无法通过;
According to the distribution of obstacles in the area M, establish the obstacle matrix
Figure FDA0003168513860000011
The value of each element is 0 or 1; if Q(i,j)=0, it means that there is no obstacle in the grid (i,j), allowing unmanned vehicles to pass; if Q(i,j)=1, It means that there is an obstacle in the grid (i, j), and the unmanned combat vehicle cannot pass;
根据区域M内的地形分布情况,建立地势矩阵
Figure FDA0003168513860000012
若栅格(i,j)属于第r种地形,则元素E(i,j)的值为vmax/vr,用于描述在地形R与地形r上通过相同距离时分别消耗的时间的比值;
According to the terrain distribution in the area M, establish a terrain matrix
Figure FDA0003168513860000012
If the grid (i,j) belongs to the rth type of terrain, the value of the element E(i,j) is v max /v r , which is used to describe the time spent on the terrain R and the terrain r passing the same distance respectively. ratio;
根据区域M内的海拔分布情况,建立地势矩阵
Figure FDA0003168513860000013
元素W(i,j)的值为栅格(i,j)内海拔的最大值,即
Figure FDA0003168513860000014
According to the altitude distribution in the area M, establish a terrain matrix
Figure FDA0003168513860000013
The value of the element W(i,j) is the maximum altitude in the grid (i,j), that is
Figure FDA0003168513860000014
步骤3:利用地形矩阵与地势矩阵改进传统A*算法中估值函数Step 3: Use the terrain matrix and the terrain matrix to improve the evaluation function in the traditional A* algorithm 所述的A*算法采用如下的估值函数来实现节点的扩展:The described A* algorithm uses the following evaluation function to achieve node expansion: F(n)=G(n)+H(n) (2)F(n)=G(n)+H(n) (2) 其中,n代表路径中的第n个节点,对应于步骤1中生成的某一个栅格;F(n)是从初始栅格(i#,j#)经由节点n到目标栅格(i*,j*)的最小代价估计;G(n)是由初始栅格(i#,j#)到节点n的最小代价;H(n)是由节点n到目标栅格(i*,j*)的最小代价估计,即启发式函数;在步骤3中利用步骤2中生成的地形矩阵E和地势矩阵W改进G(n)和H(n)的设定;Among them, n represents the nth node in the path, corresponding to a grid generated in step 1; F(n) is from the initial grid (i # ,j # ) via node n to the target grid (i * , j * ) minimum cost estimate; G(n) is the minimum cost from the initial grid (i # , j # ) to node n; H(n) is the minimum cost from node n to the target grid (i * , j * ) minimum cost estimation, i.e. heuristic function; in step 3, the terrain matrix E and terrain matrix W generated in step 2 are used to improve the setting of G(n) and H(n); 首先,改进G(n)的计算公式如下:First, the calculation formula of the improved G(n) is as follows:
Figure FDA0003168513860000021
Figure FDA0003168513860000021
其中,设节点n和节点(n-1)分别对应于栅格(i,j)和栅格(i′,j′),则公式(3)中各参数的计算方法如下:l=(E(i,j)+E(i′,j′))/2,代表地形对于行驶时间的影响;Δh=W(i′,j′)-W(i,j),代表两节点和之间的相对高程;t为栅格(i,j)和栅格(i′,j′)中心点的直线距离;Among them, suppose that node n and node (n-1) correspond to grid (i, j) and grid (i', j') respectively, then the calculation method of each parameter in formula (3) is as follows: l=(E (i,j)+E(i',j'))/2, representing the influence of terrain on the travel time; Δh=W(i',j')-W(i,j), representing the distance between two nodes and The relative elevation of ; t is the straight-line distance between grid (i, j) and grid (i', j') center; 然后,改进启发式函数H(n)的计算公式;设节点n对应于栅格(i,j),考虑如下两条由栅格(i,j)到栅格(i*,j*)的路径:Then, improve the calculation formula of the heuristic function H(n); let node n correspond to grid (i, j), consider the following two equations from grid (i, j) to grid (i * , j * ) path: 路径1:无人战车首先沿着y方向,由栅格(i,j)运动到栅格(i,j*);再沿着x方向,由栅格(i,j*)运动至栅格(i*,j*);Path 1: The unmanned vehicle first moves from grid (i, j) to grid (i, j * ) along the y direction; then moves from grid (i, j * ) to grid along the x direction lattice(i * ,j * ); 路径2:无人战车首先沿着x方向,由栅格(i,j)运动到栅格(i*,j);再沿着y方向,由栅格(i*,j)运动至栅格(i*,j*);Path 2: The unmanned combat vehicle first moves from grid (i,j) to grid (i * ,j) along the x direction; then moves from grid (i * ,j) to grid along the y direction lattice(i * ,j * ); 所述路径1和路径2所经过栅格的数目相同,且不包括当前栅格(i,j),记作m=|i-i*|+|j-j*|;对于路径c来说,其预估代价为:The number of grids passed by the path 1 and path 2 is the same, and does not include the current grid (i, j), denoted as m=|ii * |+|jj * |; for path c, its estimated The cost is:
Figure FDA0003168513860000022
Figure FDA0003168513860000022
其中,c=1或2;g(k)代表路径c中相邻两个栅格之间的路径代价估计,其可以表示为:where c=1 or 2; g(k) represents the path cost estimate between two adjacent grids in path c, which can be expressed as:
Figure FDA0003168513860000023
Figure FDA0003168513860000023
其中,第一项
Figure FDA0003168513860000024
的意义与公式(3)中等式右端的第二项具有相同的意义,用于间接反映无人战车行驶所用的时间;第二项Q反映相邻栅格相对高程所带来的路径代价,其定义如下:
Among them, the first
Figure FDA0003168513860000024
The meaning is the same as the second term on the right-hand side of equation (3), which is used to indirectly reflect the time spent by the unmanned combat vehicle; the second term Q reflects the path cost caused by the relative elevation of adjacent grids, It is defined as follows:
Figure FDA0003168513860000031
Figure FDA0003168513860000031
其中,p1<0和p2>0为爬坡和下坡两种情况对应的系数;当系数pi(i=1,2)的绝对值越大,对于路径坡度考虑的权重就越大;公式(5)中的系数q从集合{q1,q2}中选取的根据栅格(a+,b+)是否为障碍栅格而发生变化,其满足:Among them, p 1 <0 and p 2 >0 are the coefficients corresponding to the two situations of climbing and descending; when the absolute value of the coefficient p i (i=1, 2) is larger, the weight considered for the path gradient is larger. ; The coefficient q in formula (5) is selected from the set {q 1 , q 2 } and changes according to whether the grid (a + , b + ) is an obstacle grid, and it satisfies:
Figure FDA0003168513860000032
Figure FDA0003168513860000032
其中,q1与q2满足q1>q2>0;H(n)取H1和H2中较小者,即H(n)=min{H1,H2};Wherein, q 1 and q 2 satisfy q 1 >q 2 >0; H(n) takes the smaller of H 1 and H 2 , that is, H(n)=min{H 1 ,H 2 }; 步骤4:按照步骤3中改进的估值函数,使用A*算法生成最优路径;Step 4: According to the improved evaluation function in Step 3, use the A* algorithm to generate the optimal path; 步骤4-1:创建两个表数据结构OPENLIST和CLOSELIST,其中OPENLIST表保存所有已经生成而未探索的节点,CLOSELIST表保存已经探索过的节点;Step 4-1: Create two table data structures, OPENLIST and CLOSELIST, where the OPENLIST table saves all nodes that have been generated but not explored, and the CLOSELIST table saves the nodes that have been explored; 步骤4-2:将初始栅格(i#,j#)加入OPENLIST表;Step 4-2: Add the initial grid (i # , j # ) to the OPENLIST table; 步骤4-3:计算OPENLIST中所有节点的代价G和代价H,根据公式(2)挑选其中F值最低的节点记作节点NODE;将NODE移除OPENLIST表,并将其放入CLOSELIST表中;Step 4-3: Calculate the cost G and cost H of all nodes in OPENLIST, select the node with the lowest F value according to formula (2) and record it as node NODE; remove NODE from the OPENLIST table and put it into the CLOSELIST table; 步骤4-4:若目标栅格(i*,j*)存在于CLOSELIST表中,则代表路径规划成功;计算停止,由目标节点依次连接各父节点形成最终的路径,记作P*;记P*由N个节点构成(包含起始栅格和目标栅格),设其中第k个节点为栅格(ik,jk),显然有(i#,j#)=(i1,j1),(i*,j*)=(iN,jN);Step 4-4: If the target grid (i * ,j * ) exists in the CLOSELIST table, it means that the path planning is successful; the calculation stops, and the target node connects each parent node in turn to form the final path, denoted as P * ; P * consists of N nodes (including the starting grid and the target grid), and the kth node is set as grid (i k , j k ), obviously (i # , j # )=(i 1 , j 1 ), (i * , j * )=(i N , j N ); 步骤4-5:对于每一个和节点NODE相邻并不在CLOSELIST表中的节点,判断其是否在OPENLIST表中;若不在,将其加入OPENLIST表中;若在,更新其最小的G代价与父节点;Step 4-5: For each node that is adjacent to the node NODE and is not in the CLOSELIST table, determine whether it is in the OPENLIST table; if not, add it to the OPENLIST table; if so, update its minimum G cost with the parent. node; 步骤4-6:重复步骤4-3;Step 4-6: Repeat steps 4-3; 步骤5:评价生成路径的质量Step 5: Evaluate the quality of the generated paths 定义如下的两个路径相关的指标,分别为等效距离S和累加坡度OSlope,采用指标S和指标OSlope评价路径的质量;The following two path-related indicators are defined, which are the equivalent distance S and the cumulative slope OSlope respectively, and the indicator S and the indicator OSlope are used to evaluate the quality of the path; 对于路径P*,S和OSlope的定义分别如公式(8)和(9)所示:For the path P * , S and OSlope are defined as equations (8) and (9), respectively:
Figure FDA0003168513860000033
Figure FDA0003168513860000033
Figure FDA0003168513860000034
Figure FDA0003168513860000034
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114578798A (en) * 2022-02-24 2022-06-03 苏州驾驶宝智能科技有限公司 Autonomous driving system of air-ground amphibious aerodyne
CN115077556A (en) * 2022-07-26 2022-09-20 中国电子科技集团公司第二十八研究所 Unmanned vehicle field operation path planning method based on multi-dimensional map
CN117949001A (en) * 2024-02-04 2024-04-30 北京中维数通软件有限公司 A path planning and point surveying system for engineering drawings

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5187667A (en) * 1991-06-12 1993-02-16 Hughes Simulation Systems, Inc. Tactical route planning method for use in simulated tactical engagements
CN103038605A (en) * 2010-06-15 2013-04-10 通腾比利时公司 Detecting location, timetable and travel time estimations for barrier crossings in a digital map
CN106441303A (en) * 2016-09-30 2017-02-22 哈尔滨工程大学 Path programming method based on A* algorithm capable of searching continuous neighborhoods
CN107063255A (en) * 2017-01-09 2017-08-18 北京工业大学 A kind of three-dimensional Route planner based on improvement drosophila optimized algorithm
WO2017173990A1 (en) * 2016-04-07 2017-10-12 北京进化者机器人科技有限公司 Method for planning shortest path in robot obstacle avoidance
CN109405836A (en) * 2017-08-17 2019-03-01 维布络有限公司 For determine pilotless automobile can driving navigation path method and system
CN109597385A (en) * 2018-12-26 2019-04-09 芜湖哈特机器人产业技术研究院有限公司 A kind of grating map and more AGV dynamic path planning methods based on grating map
CN111397622A (en) * 2020-03-26 2020-07-10 江苏大学 Local path planning method for smart car based on improved A* algorithm and Morphin algorithm
CN112034836A (en) * 2020-07-16 2020-12-04 北京信息科技大学 Mobile robot path planning method for improving A-x algorithm
CN112325892A (en) * 2020-10-10 2021-02-05 南京理工大学 Similar three-dimensional path planning method based on improved A-x algorithm
CN112857384A (en) * 2021-01-18 2021-05-28 西安电子科技大学 Mobile robot path planning method based on A-algorithm of improved heuristic function
CN113044029A (en) * 2021-03-19 2021-06-29 北京理工大学 Motion planning method for ensuring safe driving of unmanned vehicle on three-dimensional terrain

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5187667A (en) * 1991-06-12 1993-02-16 Hughes Simulation Systems, Inc. Tactical route planning method for use in simulated tactical engagements
CN103038605A (en) * 2010-06-15 2013-04-10 通腾比利时公司 Detecting location, timetable and travel time estimations for barrier crossings in a digital map
WO2017173990A1 (en) * 2016-04-07 2017-10-12 北京进化者机器人科技有限公司 Method for planning shortest path in robot obstacle avoidance
CN106441303A (en) * 2016-09-30 2017-02-22 哈尔滨工程大学 Path programming method based on A* algorithm capable of searching continuous neighborhoods
CN107063255A (en) * 2017-01-09 2017-08-18 北京工业大学 A kind of three-dimensional Route planner based on improvement drosophila optimized algorithm
CN109405836A (en) * 2017-08-17 2019-03-01 维布络有限公司 For determine pilotless automobile can driving navigation path method and system
CN109597385A (en) * 2018-12-26 2019-04-09 芜湖哈特机器人产业技术研究院有限公司 A kind of grating map and more AGV dynamic path planning methods based on grating map
CN111397622A (en) * 2020-03-26 2020-07-10 江苏大学 Local path planning method for smart car based on improved A* algorithm and Morphin algorithm
CN112034836A (en) * 2020-07-16 2020-12-04 北京信息科技大学 Mobile robot path planning method for improving A-x algorithm
CN112325892A (en) * 2020-10-10 2021-02-05 南京理工大学 Similar three-dimensional path planning method based on improved A-x algorithm
CN112857384A (en) * 2021-01-18 2021-05-28 西安电子科技大学 Mobile robot path planning method based on A-algorithm of improved heuristic function
CN113044029A (en) * 2021-03-19 2021-06-29 北京理工大学 Motion planning method for ensuring safe driving of unmanned vehicle on three-dimensional terrain

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114578798A (en) * 2022-02-24 2022-06-03 苏州驾驶宝智能科技有限公司 Autonomous driving system of air-ground amphibious aerodyne
CN115077556A (en) * 2022-07-26 2022-09-20 中国电子科技集团公司第二十八研究所 Unmanned vehicle field operation path planning method based on multi-dimensional map
CN115077556B (en) * 2022-07-26 2022-11-18 中国电子科技集团公司第二十八研究所 Unmanned vehicle field operation path planning method based on multi-dimensional map
CN117949001A (en) * 2024-02-04 2024-04-30 北京中维数通软件有限公司 A path planning and point surveying system for engineering drawings

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