A method and system for path determination for outdoor multi-terrain scenarios

Abstract

The application provides a path determination method and system for a wild multi-terrain scene, and relates to the technical field of path planning. The method provided by the application determines a plurality of connection point grids, determines a first path by determining a path with minimum cost value between a target connection point grid and a target point, determines a second path by determining a path with minimum cost value between a starting point and the target connection point grid, and then combines the first path and the second path to obtain a target path from the starting point to the target point. In this way, the second path can include a plurality of grids of a road terrain category. Compared with an action path determined by an A-star algorithm in the related art, the path determined by the embodiment of the application can effectively reduce the travel time from the starting point to the target point by passing through a plurality of road terrain grids, thereby improving the travel efficiency.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a path determination method and system for multi-terrain scenarios in the field. Background Technology

[0002] In scenarios involving field environmental exploration or rescue, it is often necessary to plan routes based on complex, multi-terrain environments to determine the vehicle movement paths of technical personnel, enabling them to quickly reach the target point and improve traffic efficiency.

[0003] In related technologies, path planning is typically based on intelligent search methods using optimal algorithms, such as ant colony optimization, particle swarm optimization, genetic algorithms, probabilistic route maps, and fast exploratory random tree algorithms. Alternatively, path planning methods are based on geometric models, such as Dijkstra's algorithm, A* algorithm, D* algorithm, and Field D* algorithm. However, both of these approaches have some drawbacks. In complex, multi-terrain environments, these technologies often rely on existing road networks or terrains without roads for path planning. The resulting paths require significant time for personnel to traverse, leading to low path efficiency.

[0004] Therefore, there is an urgent need for a path determination method and system for multi-terrain scenarios in the wild, which can determine the movement paths of vehicles with less time to travel and improve traffic efficiency. Summary of the Invention

[0005] This invention provides a path determination method and system for multi-terrain scenarios in the wild. Compared with the action path determined by the A-star algorithm in related technologies, the path determined by this invention can effectively reduce the travel time from the starting point to the target point, thereby improving travel efficiency.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] Firstly, a path determination method for multi-terrain scenarios in the field is provided. The method includes: receiving a path planning request, the path planning request requesting planning of a target path from a starting point to a target point in a target area, the path planning request carrying image data of the target area, the image data including high-resolution imagery, hyperspectral imagery, and digital elevation model imagery; determining a raster map of the target area based on the image data of the target area, the raster map including multiple square grids of the same size, and the coordinates, category, and slope level corresponding to each grid, the category including obstacle terrain, road terrain, or non-obstacle terrain without roads; establishing a heuristic function and a cost function, wherein the heuristic function is used to characterize the path distance and yaw angle from each first grid to each second grid, and the cost function is used to characterize the path distance from each first grid to each second grid. The cost of the second grid; the first grid is one of multiple grids, and the second grid is the grid adjacent to the first grid; the connection point grids among the multiple grids are determined according to the category of each grid, wherein there are grids of category "road terrain" among the multiple grids adjacent to the connection point grids; based on the A-star algorithm, the target path is determined according to the heuristic function and the cost function. The target path includes the first path and the second path. The first path is the path with the minimum cost value among the multiple paths between the multiple connection point grids and the target point. The second path is the path with the minimum cost value among the multiple paths between the starting point and the target connection point grid. The cost value of each path is the sum of the heuristic function value and the cost function value obtained by traversing the multiple grids included in the sequential arrangement of each path. The target connection point grid is the connection point grid corresponding to the starting point of the first path.

[0008] In one possible implementation of the first aspect, the heuristic function includes a first heuristic function and a second heuristic function. When the second grid is a grid adjacent to the first grid in the horizontal or vertical direction, the expression of the first heuristic function is:

[0009] h(n) = d*h d (n)+d*[h s (n)-2h s (n)];

[0010] When the second grid cell is diagonally adjacent to the first grid cell, the expression for the first heuristic function is:

[0011]

[0012] h d (n)=min(|x n -x g |,|y n -y g |);

[0013] hs (n)=(|x n -x g |+|y n -y g |);

[0014] Where n is the identifier of the second grid cell, and the coordinates of the second grid cell are (x... n ,y n The coordinates of the target grid g where the target point is located are (x...). g ,y g ); d is the length of each grid cell, h d (n) represents the number of steps that can be moved diagonally, h s (n) represents the number of linearly movable steps.

[0015] In one possible implementation of the first aspect, the expression for the second heuristic function is:

[0016]

[0017] Where θ is the yaw angle. It is the vector from the first grid cell to the target grid cell. It is a vector from the first grid cell to the second grid cell.

[0018] In one possible implementation of the first aspect, when the second grid is classified as obstacle terrain, the expression for the cost function is:

[0019]

[0020] pass(n-1,n)=α(n))*β(n);

[0021] n-1 is the identifier of the first grid cell, α(n) is the slope weight coefficient of the second grid cell, which is determined according to the slope level of each grid cell; β(n) is the surface attribute weight coefficient of the second grid cell, which is determined according to the category of each grid cell; D is the horizontal distance from the first grid cell to the second grid cell, and Δh is the vertical distance from the first grid cell to the second grid cell.

[0022] When the second grid cell is classified as either road terrain or non-road, non-obstacle terrain, the cost function is expressed as follows:

[0023] g(n) = k road *[g(n-1)+cost(n,n-1)];

[0024] cost(n,n-1)=d(n,n-1)(1+αS slope +βk road );

[0025] Where, k road S represents the passability coefficient for different types of rasters. slope Here, α and β represent the cost coefficients corresponding to different slope grades, and α and β are adjustment coefficients.

[0026] In one possible implementation of the first aspect, the target path is determined based on the A-Star algorithm, according to a heuristic function and a cost function, including: determining the path with the minimum cost value among multiple paths between multiple connection point grids and the target point as the first path based on the A-Star algorithm; determining the connection point grid corresponding to the starting point of the first path as the target connection point grid; determining the path with the minimum cost value between the starting point and the target connection point grid as the second path based on the A-Star algorithm; and merging the first path and the second path to obtain the target path.

[0027] In one possible implementation of the first aspect, based on the A-star algorithm, the path with the minimum cost among multiple paths between multiple connection point grids and the target point is determined as the first path. This includes: taking each connection point grid as the starting point, determining the center point of the grid with the minimum cost and a yaw angle less than a preset angle among multiple adjacent grids of each connection point grid as a path point, wherein the yaw angle is determined based on a second heuristic function; based on each path point, determining the center point of the grid with the minimum cost among multiple adjacent grids of the grid corresponding to the path point as the next path point corresponding to each path point; sequentially determining multiple path points, and when the path point is located in the grid where the target point is located, obtaining the path corresponding to each connection point grid, wherein the path is a line connecting multiple sequentially arranged path points starting from each connection point grid; and determining the path with the minimum cost among the paths corresponding to each connection point grid as the first path.

[0028] In one possible implementation of the first aspect, based on the A-star algorithm, the path with the minimum cost among multiple paths between multiple connection point grids and the target point is determined as the first path. This includes: merging multiple grids of the same category arranged in a preset shape in the grid map to obtain grid groups corresponding to multiple grids, the preset shape being a square; taking each connection point grid as the starting point, determining the center point of the grid or grid group with the minimum cost and a yaw angle less than a preset angle among multiple adjacent grids or grid groups of each connection point grid as the path point, wherein the yaw angle is based on a second heuristic function. The cost of a grid group is determined, where the cost of the grid group is the maximum cost among the multiple grids included in the grid group. Based on each path point, the center point of the grid or grid group with the smallest cost among the multiple grids or grid groups adjacent to the grid corresponding to the path point is determined as the next path point for each path point. Multiple path points are determined sequentially, and when the path point is located in the grid where the target point is located, the path corresponding to each connection point grid is obtained. The path is a line connecting multiple sequentially arranged path points starting from each connection point grid. The path with the smallest cost among the paths corresponding to each connection point grid is determined as the first path.

[0029] The beneficial effects of this invention are as follows: The method provided by this invention determines multiple connection point grids, identifies the path with the minimum cost between the target connection point grid and the target point as the first path, and identifies the path with the minimum cost between the starting point and the target connection point grid as the second path. Then, the first path and the second path are merged to obtain the target path from the starting point to the target point. In this way, the second path can include multiple grids of the road terrain category. Compared to the action path determined by the A-star algorithm in related technologies, the path determined by this invention, by passing through multiple road terrain grids, can effectively reduce the travel time from the starting point to the target point and improve traffic efficiency.

[0030] Secondly, embodiments of the present invention provide a path determination system for multi-terrain scenarios in the field. The system includes: a request receiving unit for receiving a path planning request, the path planning request requesting the planning of a target path from a starting point to a target point in a target area, the path planning request carrying image data of the target area, the image data including high-resolution imagery, hyperspectral imagery, and digital elevation model imagery; an image processing unit for determining a raster map of the target area based on the image data of the target area, the raster map including multiple square grids of the same size, and coordinates, category, and slope level corresponding to each grid, the category including obstacle terrain, road terrain, or non-obstacle terrain without roads; and a function construction unit for establishing a heuristic function and a cost function, wherein the heuristic function is used to characterize the path distance and yaw angle from each first grid to each second grid, and the cost function is used to characterize the path distance and yaw angle from each first grid to each second grid. The cost value from the first grid to each second grid; the first grid is one of multiple grids, and the second grid is the grid adjacent to the first grid; the grid determination unit is used to determine the connection point grids among multiple grids according to the category of each grid, wherein there are grids of category road terrain among the multiple grids adjacent to the connection point grids; the path determination unit is used to determine the target path based on the A-star algorithm, according to the heuristic function and the cost function, the target path includes a first path and a second path, the first path is the path with the minimum cost value among multiple paths between multiple connection point grids and the target point, the second path is the path with the minimum cost value among multiple paths between the starting point and the target connection point grid, the cost value of each path is the sum of the heuristic function value and the cost function value obtained by traversing the multiple grids included in the sequential arrangement of each path, and the target connection point grid is the connection point grid corresponding to the starting point of the first path.

[0031] Thirdly, an electronic device is provided, the electronic device including a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs a path determination method for multi-terrain scenarios in the field as in any implementation of the first aspect.

[0032] Fourthly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform a path determination method for multi-terrain scenarios in the field as described in any implementation of the first aspect.

[0033] Fifthly, a computer program product is provided that, when the computer program product is run on a computer, causes the computer to execute a path determination method for multi-terrain scenarios in the field, as in any implementation of the first aspect.

[0034] Understandably, the beneficial effects of the path determination system for multi-terrain scenarios in the second aspect, the electronic device in the third aspect, the computer-readable storage medium in the fourth aspect, and the computer program product in the fifth aspect provided above can be referred to the beneficial effects of the first aspect and any of its possible design embodiments, which will not be repeated here. Attached Figure Description

[0035] Figure 1 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown;

[0036] Figure 2 A flowchart illustrating a path determination method for multi-terrain scenarios in the field, provided by an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of a grid map provided in an embodiment of the present invention;

[0038] Figure 4 This is a flowchart illustrating another path determination method for multi-terrain scenarios in the field, as shown in an embodiment of the present invention.

[0039] Figure 5 This is a schematic diagram of another raster map shown in an embodiment of the present invention;

[0040] Figure 6 A schematic diagram of the hardware structure of a determination system provided by an embodiment of the present invention is shown. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0042] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0043] In this embodiment of the invention, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this embodiment of the invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0044] In scenarios involving field environmental exploration or rescue, it is often necessary to plan routes based on complex, multi-terrain environments to determine the vehicle movement paths of technical personnel, enabling them to quickly reach the target point and improve traffic efficiency.

[0045] In related technologies, path planning is typically based on intelligent search methods using optimal algorithms, such as ant colony optimization, particle swarm optimization, genetic algorithms, probabilistic route maps, and fast exploratory random tree algorithms. Alternatively, path planning methods are based on geometric models, such as Dijkstra's algorithm, A* algorithm, D* algorithm, and Field D* algorithm. However, both of these approaches have some drawbacks. In complex, multi-terrain environments, these technologies often rely on existing road networks or terrains without roads for path planning. The resulting paths require significant time for personnel to traverse, leading to low path efficiency.

[0046] Therefore, there is an urgent need for a path determination method and system for multi-terrain scenarios in the wild, which can determine the movement paths of vehicles with less time to travel and improve traffic efficiency.

[0047] In view of this, embodiments of the present invention provide a path determination method for multi-terrain scenarios in the field. The method includes: receiving a path planning request, the path planning request being used to request planning a target path from the starting point of the target area to the target point, the path planning request carrying image data of the target area, the image data including high-resolution imagery, hyperspectral imagery, and digital elevation model imagery; determining a raster map of the target area based on the image data of the target area, the raster map including multiple square grids of the same size, and the coordinates, category, and slope level corresponding to each grid, the category including obstacle terrain, road terrain, or non-obstacle terrain without road; establishing a heuristic function and a cost function, wherein the heuristic function is used to characterize the path distance and yaw angle from each first grid to each second grid, and the cost function is used to characterize the path distance from each first grid to each second grid. The cost value of each second grid cell; the first grid cell is one of multiple grid cells, and the second grid cell is the grid cell adjacent to the first grid cell; the connection point grid cells among the multiple grid cells are determined according to the category of each grid cell, wherein there is a grid cell of category "road terrain" among the multiple grid cells adjacent to the connection point grid cell; based on the A-star algorithm, the target path is determined according to the heuristic function and the cost function. The target path includes a first path and a second path. The first path is the path with the minimum cost value among the multiple paths between the multiple connection point grid cells and the target point. The second path is the path with the minimum cost value among the multiple paths between the starting point and the target connection point grid cell. The cost value of each path is the sum of the heuristic function value and the cost function value obtained by traversing the multiple grid cells included in the sequential arrangement of each path. The target connection point grid cell is the connection point grid cell corresponding to the starting point of the first path.

[0048] The method provided in this embodiment of the invention determines multiple connection point grids, identifies the path with the minimum cost between the target connection point grid and the target point as the first path, and identifies the path with the minimum cost between the starting point and the target connection point grid as the second path. The first and second paths are then merged to obtain the target path from the starting point to the target point. In this way, the second path can include multiple grids of the road terrain category. Compared to the action path determined by the A-star algorithm in related technologies, the path determined in this embodiment of the invention, by traversing multiple road terrain grids, can effectively reduce the travel time from the starting point to the target point and improve traffic efficiency.

[0049] In some embodiments, the path determination method for multi-terrain scenarios in the wild provided by the present invention can be executed by a path determination system 100 for multi-terrain scenarios in the wild (hereinafter referred to as determination system 100). As an example, determination system 100 can be any electronic device 200 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer, etc. The specific implementation of determination system 100 is not limited here.

[0050] Figure 1 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown. The electronic device 200 includes a processor 210, a memory 220, and a communication interface 230.

[0051] Processor 210 may include one or more processing cores. Processor 210 connects to various parts within electronic device 200 using various interfaces and lines, and performs various functions and processes data of electronic device 200 by running or executing instructions, programs, code sets, or instruction sets stored in memory 220, and by calling data stored in memory 220. Optionally, processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0052] The memory 220 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 220 may include a non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a stored program area. The stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a request / receive function, a path confirmation function, etc.), and instructions for implementing the various method embodiments described above.

[0053] The communication interface 230 is used to communicate with other devices, equipment, or communication networks, such as data storage devices, image processing devices, or Ethernet, wireless access networks (RAN), wireless local area networks (WLAN), etc.

[0054] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, and communication interface 230) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be located within the same device, i.e., as different devices within a single device, similar to the deployment of devices or components in a distributed system.

[0055] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 200. In other embodiments of the present invention, the electronic device 200 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0056] The following description, in conjunction with the accompanying drawings, illustrates the path determination method for multi-terrain scenarios in the field provided by embodiments of the present invention.

[0057] Figure 2 This is a flowchart illustrating a path determination method for multi-terrain scenarios in the field, provided by an embodiment of the present invention. Optionally, this method can be executed by a determination system 100. The method may include the following steps:

[0058] S1. Receive a path planning request. The path planning request is used to request the planning of a target path from the starting point of the target area to the target point.

[0059] Specifically, the route planning request carries image data of the target area, including high-resolution imagery, hyperspectral imagery, and digital elevation model imagery.

[0060] High-resolution imagery refers to high-resolution satellite imagery, which is an image of the Earth's surface taken by a high-resolution camera mounted on a satellite.

[0061] Hyperspectral imaging is an imaging technology that combines the advantages of imaging and spectral technologies to acquire continuous, narrow-band image data of real-world scenes.

[0062] A Digital Elevation Model (DEM) is a geographic information model that uses a mathematical model to represent the elevation information of points on the Earth's surface. It is not presented directly as an "image," but rather uses a series of digital data to construct the three-dimensional shape of the Earth's surface. This data is typically stored in the form of raster or point clouds and contains the elevation value of each point or each raster cell.

[0063] S2. Determine the raster map of the target area based on the image data of the target area. The raster map includes multiple square grids of the same size, as well as the coordinates, category and slope level of each grid.

[0064] Specifically, the categories include obstacle terrain, road terrain, or non-roadable non-obstacle terrain. Obstacle terrain includes terrain that is impassable by vehicles, such as rivers, forests, or lakes. Road terrain refers to terrain where vehicles can pass quickly, and non-roadable non-obstacle terrain refers to terrain where vehicles can pass slowly, such as sandy areas or grasslands, where there are no roads.

[0065] For example, see Figure 3 , Figure 3 This is a schematic diagram of a grid map provided in an embodiment of the present invention. The image data of the target area is divided into 9 grids, namely grids 1-9. Grids 1, 2, and 3 are classified as obstacle terrain (river), grids 4, 5, and 6 as road terrain (mountain road), and grids 7, 8, and 9 as non-road, non-obstacle terrain (grassland). The coordinates of grid 1 in the grid map are (1, 1), and the coordinates of grid 9 in the grid map are (3, 3).

[0066] The slope grade is determined based on the elevation data of the area corresponding to the grid, with different slopes corresponding to different slope grades. Different slope grades also correspond to different slope weighting coefficients.

[0067] For example, the slope grade is 0 when the slope is 0° to 5°. The slope grade is 1 when the slope is 5° to 10°. The slope grade is 2 when the slope is 10° to 15°. The slope grade is 3 when the slope is 15° to 20°. The slope grade is 4 when the slope is 20° to 25°. The slope grade is 5 when the slope is 25° to 30°. The slope grade is 6 when the slope is greater than 31°. The slope weight coefficient corresponding to slope grade 0 is 1. The slope weight coefficient corresponding to slope grade 1 is 0.8. The slope weight coefficient corresponding to slope grade 2 is 0.6. The slope weight coefficient corresponding to slope grade 3 is 0.4. The slope weight coefficient corresponding to slope grade 4 is 0.2. The slope weight coefficient corresponding to slope grade 5 is 0.1. The slope weight coefficient corresponding to slope grade 6 is 0.

[0068] S3. Establish a heuristic function and a cost function, where the heuristic function is used to characterize the path distance and yaw angle from each first grid cell to each second grid cell, and the cost function is used to characterize the cost from each first grid cell to each second grid cell.

[0069] Specifically, the first grid is one of multiple grids, and the second grid is the grid adjacent to the first grid. This can also be understood as follows: when the path is from grid 1 to grid 2, the heuristic function represents the path distance and yaw angle from grid 1 to grid 2, and the cost function represents the cost of the path from grid 1 to grid 2.

[0070] It should be understood that adjacent grids refer to the four grids that share an edge with the first grid, and the four grids that share a vertex with the first grid. In other words, it refers to the eight grids in eight directions surrounding the first grid. The angle between the line connecting the center point of the first grid to the target point and the line connecting the center points of the first grid and the second grid is the yaw angle.

[0071] In one possible implementation, the heuristic function includes a first heuristic function and a second heuristic function.

[0072] The first heuristic function is used to characterize the path distance from each first grid cell to each second grid cell, and the second heuristic function is used to characterize the yaw angle from each first grid cell to each second grid cell.

[0073] Furthermore, when the second grid cell is adjacent to the first grid cell in the horizontal or vertical direction, the expression for the first heuristic function is:

[0074] h(n) = d*h d (n)+d*[h s (n)-2h s (n)];

[0075] When the second grid cell is diagonally adjacent to the first grid cell, the expression for the first heuristic function is:

[0076]

[0077] h d (n)=min((|x n -x g |,|y n -y g |);

[0078] h s (n)=(|x n -x g |+|y n -y g |);

[0079] Where n is the identifier of the second grid cell, and the coordinates of the second grid cell are (x... n ,y n The coordinates of the target grid g where the target point is located are (x...). g ,y g ); d is the length of each grid cell, h d (n) represents the number of steps that can be moved diagonally, h s (n) represents the number of linearly movable steps.

[0080] In another possible implementation, the expression for the second heuristic function is:

[0081]

[0082] Where θ is the yaw angle. It is the vector from the first grid cell to the grid cell containing the target point. It is a vector from the first grid cell to the second grid cell.

[0083] In another possible implementation, when the second grid is classified as obstacle terrain, the cost function is expressed as follows:

[0084]

[0085] pass(n-1,n) = α(n) * β(n);

[0086] n-1 is the identifier of the first grid cell, α(n) is the slope weight coefficient of the second grid cell, which is determined according to the slope level of each grid cell; β(n) is the surface attribute weight coefficient of the second grid cell, which is determined according to the category of each grid cell; D is the horizontal distance from the first grid cell to the second grid cell, and Δh is the vertical distance from the first grid cell to the second grid cell.

[0087] Among them, the closer the slope weight coefficient α(n) and surface attribute weight coefficient β(n) of the second grid are to 1, the better the accessibility of the second grid. The slope weight coefficient α(n) and surface attribute weight coefficient β(n) of the second grid indicate the higher the access risk of the second grid.

[0088] Construction of the weight matrix: In a 3D raster map, each raster is assigned a weight value between 0 and 1. The weight matrix is ​​constructed using a slope weight coefficient α(n) and a surface attribute weight coefficient β(n).

[0089] In one example, the surface attribute weight coefficient for a raster classified as road terrain is 1. The surface attribute weight coefficient for a raster classified as obstacle terrain is 0. The surface attribute weight coefficient for a raster classified as non-road, non-obstacle terrain is 0.5.

[0090] In one possible implementation, system 100 further subdivides the non-roadable, non-barrier terrain into more granular categories, including saline-alkali land, grassland, gravel, and sandy terrain. Specifically, the surface attribute weighting coefficient for saline-alkali land is 0.8, for grassland it is 0.7, for gravel it is 0.4, and for sandy terrain it is 0.2.

[0091] When the second grid cell is classified as either road terrain or non-road, non-obstacle terrain, the cost function is expressed as follows:

[0092] g(n) = k road *[g(n-1)+cost(n,n-1)];

[0093] cost(n,n-1)=d(n,n-1)(1+αS slope +βk road );

[0094] Where, k road S represents the passability coefficient for different types of rasters. slope Here, α and β represent the cost coefficients corresponding to different slope grades, and α and β are adjustment coefficients.

[0095] S4. Determine the connection point grids among multiple grids according to the category of each grid, wherein there are grids of category "road terrain" among multiple adjacent grids of the connection point grids.

[0096] For example, in combination Figure 3 Since grids 4, 5, and 6 are classified as road terrain (mountain road), and grids 1, 2, 3, 7, 8, and 9 are adjacent to road grids, grids 1, 2, 3, 7, 8, and 9 are connection point grids.

[0097] In one possible implementation, the process of determining the connection point grid of system 100 can be achieved by the following algorithm.

[0098] Specifically, the system initializes an empty list "connection_points" to store all connection point grids; it iterates through each row and column of grids in the road map; if the current grid (x, y) is classified as road terrain, it further checks all adjacent grids (the eight grids in the surrounding eight directions); for each of the eight grids in the eight directions (d... x ,d y ), calculate the new raster coordinates (n) x ,n y The raster coordinates (n) are checked using an auxiliary function "is_road". x ,n yThe system checks whether the corresponding raster category is road terrain. If not, then the raster is a connection point raster. The system then adds the connection point raster to the "connection_points" table and stops checking other directions around the current raster (x,y). After all the rasters included in the raster map have been checked, the system returns the "connection_points" list, which contains the coordinates of all connection point rasters, thus determining the connection point rasters among multiple rasters.

[0099] S5. Based on the A-star algorithm, the target path is determined according to the heuristic function and the cost function. The target path includes a first path and a second path. The first path is the path with the minimum cost value among multiple paths between multiple connection point grids and the target point. The second path is the path with the minimum cost value among multiple paths between the starting point and the target connection point grid.

[0100] Specifically, the cost value of each path is the sum of the heuristic function value and the cost function value obtained by traversing the multiple grids arranged in order for each path, and the target connection point grid is the connection point grid corresponding to the starting point of the first path.

[0101] It should be understood that the A-star algorithm, or A-star algorithm, is a heuristic search algorithm that combines the features of best-first search and Dijkstra's algorithm to find the shortest path from the starting point to the target point in a graph.

[0102] In some embodiments, see Figure 4 The above S5 specifically includes the following steps:

[0103] S51. Determine the path with the lowest cost value among multiple paths between multiple connection point grids and the target point as the first path;

[0104] In one possible implementation, S51 specifically includes the following steps: Based on the A-star algorithm, starting from each connection point grid, the center point of the grid with the smallest cost value and a yaw angle less than a preset angle among the multiple adjacent grids of each connection point grid is determined as a path point, wherein the yaw angle is determined based on a second heuristic function; based on each path point, the center point of the grid with the smallest cost value among the multiple adjacent grids of the grid corresponding to the path point is determined as the next path point corresponding to each path point; multiple path points are determined sequentially, and when the path point is located in the grid where the target point is located, the path corresponding to each connection point grid is obtained, and the path is a line connecting multiple sequentially arranged path points starting from each connection point grid; the path with the smallest cost value among the paths corresponding to each connection point grid is determined as the first path.

[0105] In one example, the default angle is 90°.

[0106] In another possible implementation, S51 specifically includes the following steps: merging multiple grids of the same category arranged in a preset shape in the grid map to obtain grid groups corresponding to multiple grids, the preset shape being a square; based on the A-star algorithm, taking each connection point grid as the starting point, determining the center point of the grid or grid group with the smallest cost value and a yaw angle less than a preset angle among the multiple adjacent grids or grid groups of each connection point grid as the path point, wherein the yaw angle is determined based on a second heuristic function, and the cost value of the grid group is... The maximum cost value among the multiple grids included in the grid group is used; based on each path point, the center point of the grid or grid group with the smallest cost value among the multiple grids or grid groups adjacent to the grid corresponding to the path point is determined as the next path point corresponding to each path point; multiple path points are determined in sequence, and when the path point is located in the grid where the target point is located, the path corresponding to each connection point grid is obtained, and the path is the connection of multiple path points arranged in order starting from each connection point grid; the path with the smallest cost value among the paths corresponding to each connection point grid is determined as the first path.

[0107] For example, Figure 5 This is a schematic diagram of another grid map according to an embodiment of the present invention. The grid map includes grids A, B, C, and D, which are classified as roadless, non-obstacle-free terrain. Grids A, B, C, and D are arranged in a preset shape. Grids A, B, C, and D are merged into grid group 1. When grid E is a waypoint, the cost value from grid E to grids A, B, C, or D is determined respectively. The cost value from grid E to grids A, B, C, or D with the smallest cost value is determined as the cost value from grid E to grid group 1. Then, the cost value from grid E to each grid F is determined. If the cost value from grid E to grid group 1 is smaller than the cost value from grid E to each adjacent grid, the center point of grid group 1 is determined as the next waypoint.

[0108] S52. Determine the connection point grid corresponding to the starting point of the first path as the target connection point grid.

[0109] S53. The path with the lowest cost between the starting point and the target connection point grid is determined as the second path.

[0110] It should be understood that the process of determining the second path from the starting point is the same as the method of determining multiple paths from the connection point grid mentioned above. In both cases, the center point of the grid with the smallest cost value and a yaw angle less than the preset angle among multiple adjacent grids is determined as the next path point, until the path point and the endpoint are located in the same grid. This will not be elaborated here.

[0111] S54. Merge the first path and the second path to obtain the target path.

[0112] Specifically, the first path starts at the target connection point grid and ends at the target point. The second path starts at the initial point and ends at the target connection point grid. Therefore, connecting the first and second paths through the target connection point grid results in a target path with the initial point as the starting point and the target point as the ending point.

[0113] In one example, the process of determining the target path based on the A-star algorithm, using heuristic and cost functions, can be implemented using the following algorithm.

[0114] Specifically, the function "is_diagonal_move" is constructed to determine the movement from the first grid cell (x n-1 y n-1 ) to the second grid (x n y n Is it a diagonal movement? Set the slope weight coefficient α(n) and surface attribute weight coefficient β(n) of the second grid, and set the special weight coefficient ω of the connection point. connect The code creates two direction lists: "directions" and "extended_directions". "directions" contains the eight regular directions (up, down, left, right, and their diagonals), while "extended_directions" includes four long-distance directions (each two grid units away). Based on the current location's "slope_map", "terrain_map", and "road_map" information, it determines the current location's category. It then determines whether the current second grid cell is a road terrain or a roadless terrain, adding "extended_directions" to "directions". For each direction in "directions", it calculates the coordinates of each second grid cell and checks if each second grid cell is within the map's valid area (i.e., not exceeding the map's boundaries). For each potential next waypoint, it again determines the category of each second grid cell using the grid's elevation information. If a second grid cell is classified as an obstacle terrain, it is skipped as needed and not added to the neighbor list.

[0115] As described in S1-S5 above, the method provided in this embodiment of the invention determines multiple connection point grids, identifies the path with the minimum cost between the target connection point grid and the target point as the first path, and identifies the path with the minimum cost between the starting point and the target connection point grid as the second path. The first and second paths are then merged to obtain the target path from the starting point to the target point. In this way, the second path can include multiple grids of the road terrain category. Compared to the action path determined by the A-star algorithm in related technologies, the path determined in this embodiment of the invention, by passing through multiple road terrain grids, can effectively reduce the travel time from the starting point to the target point and improve traffic efficiency.

[0116] The foregoing primarily describes the solutions of the embodiments of the present invention from a methodological perspective. It is understood that, to achieve the aforementioned functions, the system 100 includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present invention.

[0117] In this embodiment of the invention, the system 100 can be divided into functional units according to the above method example. For example, the system 100 can be divided into functional units corresponding to various functions, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0118] For example, Figure 6This diagram illustrates the hardware structure of a determination system 100 according to an embodiment of the present invention. The determination system 100 includes: a request receiving unit 610, configured to receive a path planning request, the path planning request requesting to plan a target path from the starting point of a target area to a target point, the path planning request carrying image data of the target area, the image data including high-resolution imagery, hyperspectral imagery, and digital elevation model imagery; an image processing unit 620, configured to determine a raster map of the target area based on the image data of the target area, the raster map including multiple square grids of the same size, and the coordinates, category, and slope level corresponding to each grid, the category including obstacle terrain, road terrain, or non-obstacle terrain without roads; and a function construction unit 630, configured to establish a heuristic function and a cost function, wherein the heuristic function is used to characterize the path distance and yaw angle from each first grid to each second grid, and the cost function is used to characterize the cost from each first grid to each second grid. Value; the first grid is one of multiple grids, and the second grid is the grid adjacent to the first grid; the grid determination unit 640 is used to determine the connection point grids among multiple grids according to the category of each grid, wherein there are grids of category road terrain among the multiple grids adjacent to the connection point grids; the path determination unit 650 is used to determine the target path based on the A-star algorithm, according to the heuristic function and the cost function, the target path includes a first path and a second path, the first path is the path with the minimum cost value among multiple paths between multiple connection point grids and the target point, the second path is the path with the minimum cost value among multiple paths between the starting point and the target connection point grid, the cost value of each path is the sum of the heuristic function value and the cost function value obtained by traversing the multiple grids included in the sequential arrangement of each path, and the target connection point grid is the connection point grid corresponding to the starting point of the first path.

[0119] It should be understood that a detailed description of the above-mentioned optional methods can be found in the foregoing method embodiments, and will not be repeated here. Furthermore, explanations of any of the determination systems 100 provided above, as well as descriptions of their beneficial effects, can be found in the corresponding method embodiments described above, and will not be repeated here.

[0120] This invention also provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of the various embodiments described above. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0121] This invention also provides a chip. This chip integrates a control circuit for implementing the functions of the aforementioned determining system 100 and one or more ports. Optionally, the functions supported by this chip are as described above and will not be repeated here.

[0122] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0123] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0124] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0125] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A path determination method for multi-terrain scenarios in the wild, characterized in that, The method includes: Receive a path planning request, the path planning request is used to request planning a target path from the starting point of the target area to the target point, the path planning request carries image data of the target area, the image data includes high-resolution imagery, hyperspectral imagery and digital elevation model imagery; A raster map of the target area is determined based on the image data of the target area. The raster map includes multiple square grids of the same size, as well as the coordinates, category, and slope level of each grid. The category includes obstacle terrain, road terrain, or non-obstacle terrain without road. Establish a heuristic function and a cost function, wherein the heuristic function is used to characterize the path distance and yaw angle from each first grid cell to each second grid cell, and the cost function is used to characterize the cost from each first grid cell to each second grid cell; the first grid cell is one of a plurality of grid cells, and the second grid cell is a grid cell adjacent to the first grid cell; Based on the category of each grid, connection point grids are determined among multiple grids, wherein among the multiple grids adjacent to the connection point grids, there are grids of the category of road terrain; Based on the A-star algorithm, the target path is determined according to the heuristic function and the cost function. The target path includes a first path and a second path. The first path is the path with the minimum cost value among multiple paths between multiple connection point grids and the target point. The second path is the path with the minimum cost value among multiple paths between the starting point and the target connection point grid. The cost value of each path is the sum of the heuristic function value and the cost function value obtained by traversing the multiple grids included in each path in an ordered manner. The target connection point grid is the connection point grid corresponding to the starting point of the first path. When the category of the second grid is obstacle terrain, the expression for the cost function is: ； ； n-1 This is the identifier for the first grid cell. This is the slope weighting coefficient for the second grid cell. The slope weighting coefficient is based on the slope of each grid cell. is the surface attribute weighting coefficient for the second grid cell, which is determined based on the category of each grid cell; D is the horizontal distance from the first grid cell to the second grid cell. It is the vertical distance from the first grid cell to the second grid cell; When the second grid is classified as road terrain or non-road, non-obstacle terrain, the expression for the cost function is: ； ； in, For different categories of raster, the passability coefficient is... These are the cost coefficients corresponding to different slope grades. and This is the adjustment coefficient.

2. The method according to claim 1, characterized in that, The heuristic function includes a first heuristic function and a second heuristic function. When the second grid is a grid adjacent to the first grid in the horizontal or vertical direction, the expression of the first heuristic function is: ； When the second grid cell is diagonally adjacent to the first grid cell, the expression for the first heuristic function is: ； ； ； in, n This is the identifier for the second grid cell, and the coordinates of the second grid cell are ( The target grid where the target point is located. g The coordinates are ( ); d The length of each grid cell, The number of steps that can be moved diagonally. This represents the number of linearly movable steps.

3. The method according to claim 2, characterized in that, The expression for the second heuristic function is: ； in, For yaw angle, It is the vector from the first grid cell to the target grid cell. It is a vector from the first grid cell to the second grid cell.

4. The method according to claim 1, characterized in that, The determination of the target path based on the A-Star algorithm, according to the heuristic function and the cost function, includes: Based on the A-star algorithm, the path with the minimum cost value among multiple paths between multiple connection point grids and the target point is determined as the first path; The grid corresponding to the starting point of the first path is defined as the target grid. Based on the A-star algorithm, the path with the minimum cost value between the starting point and the target connection point grid is determined as the second path; The first path and the second path are merged to obtain the target path.

5. The method according to claim 4, characterized in that, The method of determining the first path based on the A-star algorithm, which involves selecting the path with the lowest cost among multiple paths between multiple connection point grids and the target point, includes: Starting from each connection point grid, the center point of the grid with the smallest cost value and a yaw angle less than a preset angle among the multiple adjacent grids of each connection point grid is determined as the path point, wherein the yaw angle is determined based on the second heuristic function. Based on each path point, the center point of the grid with the smallest cost value among the multiple grids adjacent to the grid corresponding to the path point is determined as the next path point corresponding to each path point; Multiple path points are determined sequentially. When a path point is located in the grid where the target point is located, a path corresponding to each connection point grid is obtained. The path is a line connecting multiple path points arranged in sequence, with each connection point grid as the starting point. The path with the lowest cost value among the paths corresponding to each connection point grid is determined as the first path.

6. The method according to claim 5, characterized in that, The method of determining the first path based on the A-star algorithm, which involves selecting the path with the lowest cost among multiple paths between multiple connection point grids and the target point, includes: Multiple grids of the same category arranged in a preset shape in the grid map are merged to obtain grid groups corresponding to multiple grids, with the preset shape being a square; Starting from each connection point grid, the center point of the grid or grid group with the smallest cost value and a yaw angle less than a preset angle among the multiple adjacent grids or grid groups of each connection point grid is determined as the path point. The yaw angle is determined based on the second heuristic function. The cost value of the grid group is the maximum value among the cost values ​​of the multiple grids included in the grid group. Based on each path point, the center point of the grid or grid group with the smallest cost value among the multiple grids or grid groups adjacent to the grid corresponding to the path point is determined as the next path point corresponding to each path point. Multiple path points are determined sequentially. When a path point is located in the grid where the target point is located, a path corresponding to each connection point grid is obtained. The path is a line connecting multiple path points arranged in sequence, with each connection point grid as the starting point. The path with the lowest cost value among the paths corresponding to each connection point grid is determined as the first path.

7. A path determination system for multi-terrain scenarios in the field, characterized in that, The path determination method for multi-terrain scenarios in the field, applied to any one of claims 1-6, comprises: The request receiving unit is used to receive a path planning request, which is used to request the planning of a target path from the starting point of the target area to the target point. The path planning request carries image data of the target area, including high-resolution imagery, hyperspectral imagery, and digital elevation model imagery. An image processing unit is used to determine a raster map of the target area based on image data of the target area. The raster map includes multiple square grids of the same size, and coordinates, category and slope level corresponding to each grid. The category includes obstacle terrain, road terrain or non-obstacle terrain without road. A function building unit is used to build a heuristic function and a cost function, wherein the heuristic function is used to characterize the path distance and yaw angle from each first grid to each second grid, and the cost function is used to characterize the cost from each first grid to each second grid; the first grid is one of a plurality of grids, and the second grid is a grid adjacent to the first grid; A grid determination unit is used to determine connection point grids among multiple grids according to the category of each grid, wherein among the multiple grids adjacent to the connection point grids, there are grids of the category of road terrain; The path determination unit is used to determine the target path based on the A-star algorithm, according to the heuristic function and the cost function. The target path includes a first path and a second path. The first path is the path with the minimum cost value among multiple paths between multiple connection point grids and the target point. The second path is the path with the minimum cost value among multiple paths between the starting point and the target connection point grid. The cost value of each path is the sum of the heuristic function value and the cost function value obtained by traversing the multiple grids included in each path in an ordered manner. The target connection point grid is the connection point grid corresponding to the starting point of the first path.

8. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A computer-readable medium, characterized in that, The device stores computer program instructions that can be executed by a processor to implement the method as described in any one of claims 1 to 6.

Images