Field path planning method and system based on topographic factors

Abstract

The invention provides a field path planning method and system based on topographic factors, and relates to the technical field of path planning. According to the method provided by the invention, a first grid map and a second grid map are determined, and each grid included in the first grid map corresponds to a plurality of sub-grids included in the second grid map, so that a first path comprising a plurality of target grids of the first grid map is firstly determined; according to the method provided by the invention, the field path can be quickly and accurately determined, the data calculation amount in the path determination process can be reduced, the path planning efficiency can be improved, and the traffic efficiency can be improved. Moreover, according to the method provided by the invention, path planning is carried out based on topographic factors, the planned target path can realize effective obstacle avoidance, and the rationality of field path planning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a field path planning method and system based on terrain factors. Background Art

[0002] When performing field exploration or rescue operations, it is usually necessary to perform path planning based on complex multi-terrain field environments to determine the vehicle movement path of technicians so that relevant personnel can quickly reach the target point and improve traffic efficiency.

[0003] In the related technologies, path planning is usually performed based on the intelligent search method of the optimal algorithm, such as the ant colony algorithm, particle swarm algorithm, genetic algorithm, probabilistic roadmap and rapid exploration random tree algorithm. Or path planning is performed based on the path planning method of the geometric model, such as the Dijkstra algorithm, A* algorithm, D* algorithm, Field D* algorithm, etc. However, both of these methods have some problems. In complex multi-terrain field environments, the field path planning performed by the related technologies usually requires processing a large amount of environmental information, such as slope restrictions, surface coverage and ground undulation, etc. When faced with the field path planning problem in a large area, the amount of calculated data is high, the field path planning requires a lot of time, and there is a problem of low path planning efficiency, which leads to low traffic efficiency.

[0004] Therefore, there is an urgent need for a field path planning method and system based on terrain factors, which can quickly and accurately determine the field path, reduce the amount of data calculation in the path determination process, and thus improve the path planning efficiency, thereby improving the traffic efficiency. Summary of the Invention

[0005] The embodiments of the present invention provide a method and system for outdoor path planning based on terrain factors, which can quickly and accurately determine outdoor paths, reduce the amount of data calculation during the path determination process, and thus improve path planning efficiency, thereby improving traffic efficiency.

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

[0007] In a first aspect, a field path planning method based on terrain factors is provided, the method comprising: receiving a path planning request, the path planning request being used to request 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 comprising high-resolution images, hyperspectral images and digital elevation model images; determining a first raster map and a second raster map of the target area according to the image data of the target area, the first raster map comprising a plurality of grids of the same size, and coordinates, categories and slope levels corresponding to each grid; the second raster map comprising a plurality of sub-grids corresponding to each grid in the plurality of grids, and coordinates, categories and slope levels corresponding to each sub-grid, the categories comprising obstacle terrain, road terrain or non-road obstacle terrain; determining a first path according to the first raster map based on a first cost function, the first path being the path with the smallest cost value among the plurality of paths between the starting point and the target point, the first cost function being used to determine a first path; The cost value of the cost function is used to represent the path distance cost from each first grid to each second grid; the first grid is one of multiple grids, and the second grid is a grid adjacent to the first grid; based on the heuristic function and the second cost function, the second path corresponding to each target grid is determined according to the second grid map, and the second path is the path with the lowest cost value among multiple paths between the entry point and the exit point of each target grid of the first path; the heuristic function is used to represent the path distance and yaw angle from each first grid to each second grid, and the cost value of the second cost function is used to represent the terrain cost from each first grid to each second grid; the cost value of each path is the sum of the heuristic function value and the second cost function value obtained by traversing the multiple sub-grids arranged in sequence included in each path, and the target grid is the grid passed by the first path; the second paths corresponding to each target grid are connected in sequence to obtain the target path from the starting point of the target area to the target point.

[0008] In a possible implementation of the first aspect, the first cost function Cost is:

[0009] Cost=d(n,g)+Cost terrain1 +Cost slope1 ;

[0010]

[0011] Among them, the coordinates of the second grid n are (x n ,y n ); the coordinates of the grid g where the target point is located are (x g ,y g ), d(n,g) is the Euclidean distance from the second grid n to the grid g where the target point is located, Cost terrain1 Represents the terrain cost coefficient of the second grid n corresponding to the first grid map. The terrain cost coefficient is associated with the grid category. Costslope1 Indicates the slope cost coefficient of the second grid n corresponding to the first grid map. The slope cost coefficient is associated with the slope level of the grid.

[0012] In a possible implementation of the first aspect, the heuristic function h (n1) for:

[0013]

[0014] Where α, β, and d0 are preset constants, d(n1,g) is the Euclidean distance from subgrid n1 to the grid where the target point is located, and d(n1,obs) is the Euclidean distance between subgrid n1 and the grid obs closest to subgrid n1, which is classified as an obstacle terrain.

[0015] In a possible implementation of the first aspect, the second cost function g (n1) for:

[0016]

[0017] Among them, Cost terrain2 Represents the terrain cost coefficient of the sub-grid n1 corresponding to the second grid map. The terrain cost coefficient is associated with the category of the sub-grid. Cost slope1 It represents the slope cost coefficient of the sub-grid n1 corresponding to the second grid map. The slope cost coefficient is associated with the slope level of the sub-grid.

[0018] The beneficial effects of the present invention are as follows: the method provided by the present invention determines a first grid map and a second grid map. Since each grid included in the first grid map corresponds to multiple sub-grids included in the second grid map, a first path including multiple target grids of the first grid map is first determined according to a first cost function. Then, a second path including multiple sub-grids corresponding to each target grid is determined according to a heuristic function and a second cost function. The second paths are then connected to obtain a target path. Therefore, the method provided by the present invention can quickly and accurately determine a field path, reduce the amount of data calculation in the path determination process, and thus improve the efficiency of path planning, thereby improving traffic efficiency. In addition, the second cost function of the method provided by the present invention is to perform path planning based on the dimension of terrain factors on the path. The planned target path can effectively avoid obstacles, increasing the rationality of the field path planning results.

[0019] That is to say, the embodiment of the present invention takes into account the terrain slope (slope level) and the passability (category) of the terrain in the problem of field road planning. By proposing the concept of hierarchical planning (first grid map and second grid map), the path-finding efficiency is improved. Based on the high-resolution path planning layer (second grid map), by integrating the artificial potential field method, the improved A* algorithm provides flexible local obstacle avoidance capabilities, allowing the vehicle to avoid dynamic obstacles in real time during movement. The embodiment of the present invention has high path planning efficiency, can combine the global and local environment, can better cope with dynamic changes in the environment, and ensure the real-time and robustness of field path planning.

[0020] In a second aspect, an embodiment of the present invention provides a field path planning system based on terrain factors, the system comprising: a request receiving unit for receiving a path planning request, the path planning request being used to request 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 comprising high-resolution images, hyperspectral images and digital elevation model images; a map determining unit for determining a first raster map and a second raster map of the target area based on the image data of the target area, the first raster map comprising a plurality of grids of the same size, and coordinates, categories and slope levels corresponding to each grid; the second raster map comprising a plurality of sub-grids corresponding to each grid in the plurality of grids, and coordinates, categories and slope levels corresponding to each sub-grid, the categories comprising obstacle terrain, road terrain or non-road obstacle terrain; a path determining unit for determining a first path based on the first raster map based on a first cost function, the first path being the path with the smallest cost value among the plurality of paths between the starting point and the target point. A path, wherein the cost value of the first cost function is used to represent the path distance cost from each first grid to each second grid; the first grid is one of multiple grids, and the second grid is a grid adjacent to the first grid; the path determination unit is further used to determine, based on the heuristic function and the second cost function, a second path corresponding to each target grid according to the second grid map, the second path being the path with the lowest cost value among multiple paths between the entry point and the exit point of each target grid of the first path; the heuristic function is used to represent the path distance and yaw angle from each first grid to each second grid, and the cost value of the second cost function is used to represent the terrain cost from each first grid to each second grid; the cost value of each path is the sum of the heuristic function values ​​and the second cost function values ​​obtained by traversing multiple sub-grids arranged in sequence included in each path, and the target grid is the grid passed by the first path; the path determination unit is further used to sequentially connect the second paths corresponding to each target grid to obtain a target path from the starting point of the target area to the target point.

[0021] In a possible implementation of the second aspect, the first cost function Cost is:

[0022] Cost=d(n,g)+Cost terrain1 +Cost slope1 ;

[0023]

[0024] Among them, the coordinates of the second grid n are (x n ,y n ); the coordinates of the grid g where the target point is located are (x g ,y g ), d(n,g) is the Euclidean distance from the second grid n to the grid g where the target point is located, Cost terrain1 Represents the terrain cost coefficient of the second grid n corresponding to the first grid map. The terrain cost coefficient is associated with the grid category. Cost slope1 Indicates the slope cost coefficient of the second grid n corresponding to the first grid map. The slope cost coefficient is associated with the slope level of the grid.

[0025] In a possible implementation of the second aspect, the heuristic function h (n1) for:

[0026]

[0027] Where α, β, and d0 are preset constants, d(n1,g) is the Euclidean distance from subgrid n1 to the grid where the target point is located, and d(n1,obs) is the Euclidean distance between subgrid n1 and the grid obs closest to subgrid n1, which is classified as an obstacle terrain.

[0028] In a possible implementation of the second aspect, the second cost function g (n1) for:

[0029]

[0030] Among them, Cost terrain2 Represents the terrain cost coefficient of the sub-grid n1 corresponding to the second grid map. The terrain cost coefficient is associated with the category of the sub-grid. Cost slope1 It represents the slope cost coefficient of the sub-grid n1 corresponding to the second grid map. The slope cost coefficient is associated with the slope level of the sub-grid.

[0031] In a third aspect, an electronic device is provided, comprising a memory and one or more processors; the memory is coupled to the processor; wherein computer program code is stored in the memory, and the computer program code comprises computer instructions, and when the computer instructions are executed by the processor, the electronic device executes a field path planning method based on terrain factors as in any implementation of the first aspect.

[0032] In a fourth aspect, a computer-readable storage medium is provided, comprising computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes a field path planning method based on terrain factors as in any implementation of the first aspect.

[0033] In a fifth aspect, a computer program product is provided. When the computer program product is run on a computer, the computer is enabled to execute the field path planning method based on terrain factors as in any implementation of the first aspect.

[0034] It can be understood that the beneficial effects that can be achieved by the terrain-based field path planning system of the second aspect, the electronic device of the third aspect, the computer-readable storage medium of the fourth aspect, and the computer program product of the fifth aspect provided above can be referred to the beneficial effects of the first aspect and any possible design method thereof, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0036] Figure 2 A flowchart of a method for outdoor path planning based on terrain factors provided by an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of the hardware structure of a path planning system provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention. In the description of the present invention, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the present invention is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, in the description of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items.

[0039] In addition, to facilitate a clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or execution order, and the words "first" and "second" do not necessarily mean different.

[0040] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0041] When performing field exploration or rescue operations, it is usually necessary to perform path planning based on complex multi-terrain field environments to determine the vehicle movement path of technicians so that relevant personnel can quickly reach the target point and improve traffic efficiency.

[0042] In the related technologies, path planning is usually performed based on the intelligent search method of the optimal algorithm, such as the ant colony algorithm, particle swarm algorithm, genetic algorithm, probabilistic roadmap and rapid exploration random tree algorithm. Or path planning is performed based on the path planning method of the geometric model, such as the Dijkstra algorithm, A* algorithm, D* algorithm, Field D* algorithm, etc. However, both of these methods have some problems. In complex multi-terrain field environments, the field path planning performed by the related technologies usually requires processing a large amount of environmental information, such as slope restrictions, surface coverage and ground undulation, etc. When faced with the field path planning problem in a large area, the amount of calculated data is high, the field path planning requires a lot of time, and there is a problem of low path planning efficiency, which leads to low traffic efficiency.

[0043] Therefore, there is an urgent need for a field path planning method and system based on terrain factors, which can quickly and accurately determine the field path, reduce the amount of data calculation in the path determination process, and thus improve the path planning efficiency, thereby improving the traffic efficiency.

[0044] In view of this, an embodiment of the present invention provides a field path planning method based on terrain factors, the method comprising: receiving a path planning request, the path planning request being used to request 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 comprising high-resolution images, hyperspectral images and digital elevation model images; determining a first raster map and a second raster map of the target area according to the image data of the target area, the first raster map comprising a plurality of grids of the same size, and coordinates, categories and slope levels corresponding to each grid; the second raster map comprising a plurality of sub-grids corresponding to each grid in the plurality of grids, and coordinates, categories and slope levels corresponding to each sub-grid, the categories comprising obstacle terrain, road terrain or non-road non-obstructive terrain; determining a first path according to the first raster map based on a first cost function, the first path being the path with the smallest cost value among the plurality of paths between the starting point and the target point, and the second raster map comprising a plurality of sub-grids corresponding to the plurality of grids, and coordinates, categories and slope levels corresponding to the plurality of sub-grids, A cost value of a cost function is used to represent the path distance cost from each first grid to each second grid; the first grid is one of multiple grids, and the second grid is a grid adjacent to the first grid; based on the heuristic function and the second cost function, a second path corresponding to each target grid is determined according to the second grid map, the second path being the path with the lowest cost value among multiple paths between the entry point and the exit point of each target grid of the first path; the heuristic function is used to represent the path distance and yaw angle from each first grid to each second grid, and the cost value of the second cost function is used to represent the terrain cost from each first grid to each second grid; the cost value of each path is the sum of the heuristic function values ​​and the second cost function values ​​obtained by traversing multiple sub-grids arranged in sequence included in each path, and the target grid is the grid passed by the first path; the second paths corresponding to each target grid are sequentially connected to obtain a target path from the starting point of the target area to the target point.

[0045] The method provided by an embodiment of the present invention determines a first grid map and a second grid map. Since each grid included in the first grid map corresponds to multiple sub-grids included in the second grid map, a first path including multiple target grids of the first grid map is first determined based on a first cost function. Then, a second path including multiple sub-grids corresponding to each target grid is determined based on a heuristic function and a second cost function. The second paths are then connected to obtain a target path. Therefore, the method provided by the present invention can quickly and accurately determine a path in the wild, reduce the amount of data calculations during the path determination process, and thus improve the efficiency of path planning, thereby improving traffic efficiency. In addition, the second cost function of the method provided by an embodiment of the present invention performs path planning based on the dimension of terrain factors on the path. The planned target path can effectively avoid obstacles, increasing the rationality of the results of the field path planning.

[0046] That is to say, the embodiment of the present invention takes into account the terrain slope (slope level) and the passability (category) of the terrain in the problem of field road planning. By proposing the concept of hierarchical planning (first grid map and second grid map), the path-finding efficiency is improved. Based on the high-resolution path planning layer (second grid map), by integrating the artificial potential field method, the improved A* algorithm provides flexible local obstacle avoidance capabilities, allowing the vehicle to avoid dynamic obstacles in real time during movement. The embodiment of the present invention has high path planning efficiency, can combine the global and local environment, can better cope with dynamic changes in the environment, and ensure the real-time and robustness of field path planning.

[0047] In some embodiments, the terrain-based outdoor path planning method provided in embodiments of the present invention can be executed by a terrain-based outdoor path planning system 100 (hereinafter referred to as path planning system 100). As an example, path planning 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. The specific implementation of path planning system 100 is not limited herein.

[0048] Figure 1 The electronic device 200 includes a processor 210 , a memory 220 , and a communication interface 230 .

[0049] The processor 210 may include one or more processing cores. The processor 210 uses various interfaces and lines to connect various parts of the electronic device 200, and executes various functions and processes data of the electronic device 200 by running or executing instructions, programs, code sets or instruction sets stored in the memory 220, and calling data stored in the memory 220. Optionally, the processor 210 can be implemented in the form of at least one hardware of a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA).

[0050] The memory 220 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 220 includes a non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 220 may include a program storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a request receiving function, a path confirmation function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.

[0051] The communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing equipment or Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0052] In physical implementation, the aforementioned components (e.g., processor 210, memory 220, and communication interface 230) may be components within the same device (e.g., a laptop). Alternatively, at least two of the components may be provided within the same device, i.e., as different components within a single device, similar to the deployment of devices or components in a distributed system.

[0053] It should be 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 shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0054] The following describes a method for outdoor path planning based on terrain factors provided by an embodiment of the present invention in conjunction with the accompanying drawings.

[0055] Figure 2 A flowchart of a method for outdoor path planning based on terrain factors provided by an embodiment of the present invention. Optionally, the method can be executed by the path planning system 100. The method can include the following steps:

[0056] S1. Receive a path planning request, where the path planning request is used to request planning of a target path from a starting point to a target point in a target area.

[0057] Specifically, the path planning request carries image data of the target area, including high-resolution images, hyperspectral images, and digital elevation model images;

[0058] Among them, high-resolution images are high-resolution satellite images, which refer to images of the earth's surface taken by high-resolution cameras carried on satellites.

[0059] Hyperspectral imaging is an imaging technology that combines the advantages of imaging technology and spectral technology and can obtain continuous, narrow-band image data of actual scenes.

[0060] A Digital Elevation Model (DEM) is a geographic information model that mathematically represents the elevation of each point on the Earth's surface. Rather than presenting it directly as an image, it constructs a three-dimensional representation of the Earth's surface using a series of digital data. This data is typically stored as a raster or point cloud, and includes the elevation value of each point or grid cell.

[0061] S2. Determine a first grid map and a second grid map of the target area according to the image data of the target area.

[0062] The first grid map includes multiple grids of the same size, and the coordinates, categories, and slope levels corresponding to each grid; the second grid map includes multiple subgrids corresponding to each grid in the multiple grids, and the coordinates, categories, and slope levels corresponding to each subgrid, where the categories include obstacle terrain, road terrain, or non-road obstacle terrain;

[0063] Specifically, the categories include obstacle terrain, road terrain, and non-road non-obstruction terrain. Obstacle terrain includes impassable terrain such as rivers, forests, or lakes. Road terrain refers to terrain where vehicles can quickly pass through roads. Non-road non-obstruction terrain refers to terrain where vehicles can slowly pass through roads, such as saline-alkali land, sand, gravel, and grassland. Different categories correspond to different terrain cost coefficients.

[0064] The slope grade determines the slope of the area corresponding to each grid or sub-grid based on the elevation data in the area corresponding to each grid or sub-grid, and different slopes correspond to different slope grades. Different slope grades correspond to different slope cost coefficients.

[0065] For example, when the slope is between 0° and 5°, the slope level is 0. When the slope is between 5° and 10°, the slope level is 1. When the slope is between 10° and 15°, the slope level is 2. When the slope is between 15° and 20°, the slope level is 3. When the slope is between 20° and 25°, the slope level is 4. When the slope is between 25° and 30°, the slope level is 5. When the slope is greater than 31°, the slope level is 6.

[0066] S3. Determine a first path based on the first cost function and the first grid map, where the first path is a path with the smallest cost among multiple paths between the starting point and the target point.

[0067] The cost value of the first cost function is used to represent the path distance cost from each first grid to each second grid; the first grid is one of the multiple grids, and the second grid is a grid adjacent to the first grid;

[0068] In a possible implementation, the first cost function Cost is:

[0069] Cost=d(n,g)+Cost terrain1 +Cost slope1 ;

[0070]

[0071] Among them, the coordinates of the second grid n are (x n ,y n ); the coordinates of the grid g where the target point is located are (x g ,y g ), d(n,g) is the Euclidean distance from the second grid n to the grid g where the target point is located, Cost terrain1 Represents the terrain cost coefficient of the second grid n corresponding to the first grid map. The terrain cost coefficient is associated with the grid category. Cost slope1 Indicates the slope cost coefficient of the second grid n corresponding to the first grid map. The slope cost coefficient is associated with the slope level of the grid.

[0072] For example, when the category of the grid or sub-grid is road terrain, the terrain cost coefficient is 0; when the category of the grid or sub-grid is saline-alkali land with no roads and no obstacles, the terrain cost coefficient is 0.2; when the category of the grid or sub-grid is grassland with no roads and no obstacles, the terrain cost coefficient is 0.3; when the category of the grid or sub-grid is gravel land with no roads and no obstacles, the terrain cost coefficient is 0.6; when the category of the grid or sub-grid is sandy land with no roads and no obstacles, the terrain cost coefficient is 0.8.

[0073] The slope weight coefficient for a slope grade of 0 is 0. The slope weight coefficient for a slope grade of 1 is 0.2. The slope weight coefficient for a slope grade of 2 is 0.4. The slope weight coefficient for a slope grade of 3 is 0.6. The slope weight coefficient for a slope grade of 4 is 0.8. The slope weight coefficient for a slope grade of 5 is 0.9. The slope weight coefficient for a slope grade of 6 is 1.

[0074] S4. Based on the heuristic function and the second cost function, determine a second path corresponding to each target grid according to the second grid map, where the second path is a path with the minimum cost value among multiple paths between the entry point and the exit point of each target grid of the first path.

[0075] Specifically, the heuristic function is used to characterize the path distance and yaw angle from each first grid to each second grid, and the cost value of the second cost function is used to characterize the terrain cost from each first grid to each second grid; the cost value of each path is the sum of the heuristic function value and the second cost function value obtained by traversing multiple sub-grids arranged in order included in each path, and the target grid is the grid passed by the first path.

[0076] It should be noted that the entry point of the second path of each target grid is the exit point of the previous target grid, the entry point of the first target grid is the starting point of the target area, and the exit point of the last target grid is the target point of the target area. The field path planning system 100 first determines the second path corresponding to the first target grid, that is, the path with the lowest cost value among multiple paths from the starting point of the target area to the second target grid adjacent to the first target grid. Then, the exit point of the first target grid is used as the entry point of the second target grid. The above operation is repeated until the second path corresponding to each target grid is determined.

[0077] In some embodiments, the heuristic function h (n1) for:

[0078]

[0079] Where α, β, and d0 are preset constants, d(n1,g) is the Euclidean distance from subgrid n1 to the grid where the target point is located, and d(n1,obs) is the Euclidean distance between subgrid n1 and the grid obs closest to subgrid n1, which is classified as an obstacle terrain.

[0080] Specifically, α*d(n1,g) is the attraction force from subgrid n1 to the target point, which aims to guide the second path toward the target point in the target area. The preset constant α is the attraction coefficient, which is used to adjust the strength of the attraction force. Represents the repulsive force from subgrid n1 to grids classified as obstacle terrain, aiming to prevent the second path from approaching grids with obstacle terrain (obstacle areas). The preset constant β is the repulsive force coefficient, which adjusts the strength of the repulsive force. The preset constant d0 sets the range of influence of the repulsive force. When subgrid n1 approaches an obstacle, the distance d(n1, obs) decreases, and the repulsive force increases. When the distance is large, the repulsive force approaches zero, effectively preventing the path from passing through the obstacle area.

[0081] In some embodiments, the second cost function g (n1) for:

[0082]

[0083] Among them, Cost terrain2 Represents the terrain cost coefficient of the sub-grid n1 corresponding to the second grid map. The terrain cost coefficient is associated with the category of the sub-grid. Cost slope1 It represents the slope cost coefficient of the sub-grid n1 corresponding to the second grid map. The slope cost coefficient is associated with the slope level of the sub-grid.

[0084] It should be understood that the terrain cost coefficient and slope cost coefficient of each sub-grid are determined in the same manner as the terrain cost coefficient and slope cost coefficient of each grid included in the first grid map, and will not be repeated here.

[0085] S5. Connect the second paths corresponding to each target grid in sequence to obtain a target path from the starting point to the target point in the target area.

[0086] As can be seen from S1-S5 above, the method provided by the embodiment of the present invention determines a first grid map and a second grid map. Since each grid included in the first grid map corresponds to multiple sub-grids included in the second grid map, a first path including multiple target grids of the first grid map is first determined according to the first cost function. Then, a second path including multiple sub-grids corresponding to each target grid is determined according to the heuristic function and the second cost function. The second paths are then connected to obtain the target path. Therefore, the method provided by the present invention can quickly and accurately determine the field path, reduce the amount of data calculation in the path determination process, and thus improve the efficiency of path planning, thereby improving the efficiency of traffic flow. In addition, the second cost function of the method provided by the embodiment of the present invention is to perform path planning based on the dimension of terrain factors on the path. The planned target path can effectively avoid obstacles and increase the rationality of the field path planning results.

[0087] That is to say, the embodiment of the present invention takes into account the terrain slope (slope level) and the passability (category) of the terrain in the problem of field road planning. By proposing the concept of hierarchical planning (first grid map and second grid map), the path-finding efficiency is improved. Based on the high-resolution path planning layer (second grid map), by integrating the artificial potential field method, the improved A* algorithm provides flexible local obstacle avoidance capabilities, allowing the vehicle to avoid dynamic obstacles in real time during movement. The embodiment of the present invention has high path planning efficiency, can combine the global and local environment, can better cope with dynamic changes in the environment, and ensure the real-time and robustness of field path planning.

[0088] The above mainly introduces the solution of the embodiment of the present invention from the perspective of method. It can be understood that in order to realize the above functions, the path planning system 100 includes at least one of the hardware structure and software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiment disclosed herein, the embodiment of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiment of the present invention.

[0089] In an embodiment of the present invention, the path planning system 100 can be divided into functional units according to the above method example. For example, the path planning 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 above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present invention is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0090] For example, Figure 3The hardware structure diagram of a path planning system 100 provided by an embodiment of the present invention is shown. The path planning system 100 includes: a request receiving unit 110, for receiving a path planning request, the path planning request is used to request planning of a target path from a starting point to a target point in a target area, and the path planning request carries image data of the target area, and the image data includes high-resolution images, hyperspectral images, and digital elevation model images; a map determining unit 120, for determining a first raster map and a second raster map of the target area based on the image data of the target area, the first raster map includes a plurality of grids of the same size, and coordinates, categories, and slope levels corresponding to each grid; the second raster map includes a plurality of sub-grids corresponding to each grid in the plurality of grids, and coordinates, categories, and slope levels corresponding to each sub-grid, and the categories include obstacle terrain, road terrain, or non-road obstacle terrain; a path determining unit 130, for determining a first path based on the first raster map based on a first cost function, the first path being the path with the smallest cost value among the plurality of paths between the starting point and the target point, and the cost value of the first cost function is the minimum. The value is used to represent the path distance cost from each first grid to each second grid; the first grid is one of the multiple grids, and the second grid is a grid adjacent to the first grid; the path determination unit 130 is further used to determine the second path corresponding to each target grid according to the second grid map based on the heuristic function and the second cost function, the second path being the path with the minimum cost value among the multiple paths between the entry point and the exit point of each target grid of the first path, the heuristic function is used to represent the path distance and yaw angle from each first grid to each second grid, and the cost value of the second cost function is used to represent the terrain cost from each first grid to each second grid; the cost value of each path is the sum of the heuristic function value and the second cost function value obtained by traversing the multiple sub-grids arranged in sequence included in each path, and the target grid is the grid passed by the first path; the path determination unit 130 is further used to connect the second paths corresponding to each target grid in sequence to obtain a target path from the starting point of the target area to the target point.

[0091] Optionally, the first cost function Cost is:

[0092] Cost=d(n,g)+Cost terrain1 +Cost slope1 ;

[0093]

[0094] Among them, the coordinates of the second grid n are (x n ,y n ); the coordinates of the grid g where the target point is located are (x g ,y g), d(n,g) is the Euclidean distance from the second grid n to the grid g where the target point is located, Cost terrain1 Represents the terrain cost coefficient of the second grid n corresponding to the first grid map. The terrain cost coefficient is associated with the grid category. Cost slope1 Indicates the slope cost coefficient of the second grid n corresponding to the first grid map. The slope cost coefficient is associated with the slope level of the grid.

[0095] Optional, heuristic function h (n1) for:

[0096]

[0097] Where α, β, and d0 are preset constants, d(n1,g) is the Euclidean distance from subgrid n1 to the grid where the target point is located, and d(n1,obs) is the Euclidean distance between subgrid n1 and the grid obs closest to subgrid n1, which is classified as an obstacle terrain.

[0098] Optionally, the second cost function g (n1) for:

[0099]

[0100] Among them, Cost terrain2 Represents the terrain cost coefficient of the sub-grid n1 corresponding to the second grid map. The terrain cost coefficient is associated with the category of the sub-grid. Cost slope1 It represents the slope cost coefficient of the sub-grid n1 corresponding to the second grid map. The slope cost coefficient is associated with the slope level of the sub-grid.

[0101] It should be understood that the specific description of the above optional methods can refer to the above method embodiments and will not be repeated here. In addition, the explanation of any of the above-mentioned path planning systems 100 and the description of the beneficial effects can refer to the above-mentioned corresponding method embodiments and will not be repeated here.

[0102] An embodiment of the present invention further 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 each of the above embodiments. For explanations of the relevant contents and descriptions of the beneficial effects of any of the above-mentioned computer-readable storage media, reference can be made to the corresponding embodiments described above and will not be repeated here.

[0103] The embodiment of the present invention further provides a chip. The chip integrates a control circuit and one or more ports for implementing the functions of the above-mentioned path planning system 100. Optionally, the functions supported by the chip can be referred to above and will not be repeated here.

[0104] Those skilled in the art will appreciate that all or part of the steps of the above-described embodiments can be implemented by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The aforementioned storage medium can be a read-only memory, a random access memory, etc. The aforementioned processing unit or processor can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (digital signal processor, DSP), a field programmable gate array (FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof.

[0105] An embodiment of the present invention further provides a computer program product comprising 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 comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially 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, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that includes one or more available media. Available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives).

[0106] It should be noted that the above-mentioned devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as but not limited to the above-mentioned memories, computer-readable storage media and communication chips, etc., all have non-transitory properties. Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present invention 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 codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0107] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A field path planning method based on terrain factors, characterized in that: The method comprises: receiving a path planning request, the path planning request being used to request 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 first grid map and a second grid map of the target area based on the image data of the target area, wherein the first grid map includes a plurality of grids of the same size, and coordinates, categories, and slope levels corresponding to each grid; and the second grid map includes a plurality of subgrids corresponding to each grid in the plurality of grids, and coordinates, categories, and slope levels corresponding to each subgrid, wherein the categories include obstacle terrain, road terrain, or roadless non-obstruction terrain. Determining a first path based on the first grid map based on a first cost function, wherein the first path is a path with a minimum cost among multiple paths between a starting point and a destination point, and the cost value of the first cost function is used to represent the path distance cost from each first grid to each second grid; the first grid is one of the multiple grids, and the second grid is a grid adjacent to the first grid; Based on a heuristic function and a second cost function, a second path corresponding to each target grid is determined according to the second grid map, the second path being the path with the lowest cost value among multiple paths between an entry point and an exit point of each target grid of the first path, the heuristic function being used to characterize the path distance and yaw angle from each first grid to each second grid, and the cost value of the second cost function being used to characterize the terrain cost from each first grid to each second grid; the cost value of each path being the sum of the heuristic function values ​​and the second cost function values ​​obtained by traversing a plurality of sequentially arranged subgrids included in each path, and the target grid being the grid traversed by the first path; The second paths corresponding to each target grid are sequentially connected to obtain a target path from the starting point of the target area to the target point.

2. The method according to claim 1, characterized in that The first cost function Cost is: Cost=d(n,g)+Cost terrain1 +Cost slope1 ; Among them, the coordinates of the second grid n are (x n ,y n ); the coordinates of the grid g where the target point is located are (x g ,y g ), d(n,g) is the Euclidean distance from the second grid n to the grid g where the target point is located, Cost terrain1 Represents the terrain cost coefficient of the second grid n corresponding to the first grid map. The terrain cost coefficient is associated with the grid category. Cost slope1 Indicates the slope cost coefficient of the second grid n corresponding to the first grid map. The slope cost coefficient is associated with the slope level of the grid.

3. The method according to claim 2, characterized in that The heuristic function h (n1) for: Where α, β, and d0 are preset constants, d(n1,g) is the Euclidean distance from subgrid n1 to the grid where the target point is located, and d(n1,obs) is the Euclidean distance between subgrid n1 and the grid obs closest to subgrid n1, which is classified as an obstacle terrain.

4. The method according to claim 3, characterized in that The second cost function g (n1) for: Among them, Cost terrain2 Represents the terrain cost coefficient of the sub-grid n1 corresponding to the second grid map. The terrain cost coefficient is associated with the category of the sub-grid. Cost slope1 It represents the slope cost coefficient of the sub-grid n1 corresponding to the second grid map. The slope cost coefficient is associated with the slope level of the sub-grid.

5. A field path planning system based on terrain factors, characterized in that: The system comprises: a request receiving unit, configured to receive a path planning request, wherein the path planning request is used to request planning of a target path from a starting point to a target point in a target area, wherein the path planning request carries image data of the target area, wherein the image data includes high-resolution imagery, hyperspectral imagery, and digital elevation model imagery; a map determination unit, configured to determine a first grid map and a second grid map of the target area based on the image data of the target area, wherein the first grid map includes a plurality of grids of equal size, and coordinates, a category, and a slope grade corresponding to each grid; and the second grid map includes a plurality of subgrids corresponding to each grid in the plurality of grids, and coordinates, a category, and a slope grade corresponding to each subgrid, wherein the categories include obstacle terrain, road terrain, or roadless non-obstructive terrain. a path determination unit, configured to determine a first path according to the first grid map based on a first cost function, wherein the first path is a path having the lowest cost value among multiple paths between a starting point and a destination point, wherein the cost value of the first cost function is used to represent the path distance cost from each first grid to each second grid; the first grid is one of the multiple grids, and the second grid is a grid adjacent to the first grid; The path determination unit is further configured to determine, based on a heuristic function and a second cost function, a second path corresponding to each target grid according to the second grid map, wherein the second path is a path having the lowest cost value among multiple paths between an entry point and an exit point of each target grid of the first path, the heuristic function is configured to represent the path distance and yaw angle from each first grid to each second grid, and the cost value of the second cost function is configured to represent the terrain cost from each first grid to each second grid; the cost value of each path is the sum of the heuristic function values ​​and the second cost function values ​​obtained by traversing multiple sub-grids arranged in sequence included in each path, and the target grid is the grid passed by the first path; The path determining unit is further configured to sequentially connect the second paths corresponding to each target grid to obtain a target path from the starting point of the target area to the target point.

6. The system according to claim 5, characterized in that The first cost function Cost is: Among them, the coordinates of the second grid n are (x n ,y n ); the coordinates of the grid g where the target point is located are (x g ,y g ), d(n,g) is the Euclidean distance from the second grid n to the grid g where the target point is located, Cost terrain1 Represents the terrain cost coefficient of the second grid n corresponding to the first grid map. The terrain cost coefficient is associated with the grid category. Cost slope1 Indicates the slope cost coefficient of the second grid n corresponding to the first grid map. The slope cost coefficient is associated with the slope level of the grid.

7. The system according to claim 6, characterized in that The heuristic function h (n1) for: Where α, β, and d0 are preset constants, d(n1,g) is the Euclidean distance from subgrid n1 to the grid where the target point is located, and d(n1,obs) is the Euclidean distance between subgrid n1 and the grid obs closest to subgrid n1, which is classified as an obstacle terrain.

8. The system according to claim 7, characterized in that The second cost function g (n1) for: Among them, Cost terrain2 Represents the terrain cost coefficient of the sub-grid n1 corresponding to the second grid map. The terrain cost coefficient is associated with the category of the sub-grid. Cost slope1 It represents the slope cost coefficient of the sub-grid n1 corresponding to the second grid map. The slope cost coefficient is associated with the slope level of the sub-grid.

9. An electronic device, characterized in that: include: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

10. A computer-readable medium, characterized in that Computer program instructions are stored, which can be executed by a processor to implement the method according to any one of claims 1 to 4.

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