A Method and System for UAV Path Planning and Navigation Based on Hierarchical Grid Maps

Abstract

This invention discloses a method and system for UAV path planning and navigation based on a hierarchical grid map. The method includes: S1, constructing a gridded navigation map, on which avoidance factor data affecting UAV flight are marked; S2, setting the mission objective from the UAV's flight start point to its destination in the gridded navigation map, wherein the gridded navigation map is constructed with an m-layer subgrid system built hierarchically, and the first m-1 layers of the subgrid system sequentially perform grid route chain search for the cumulative heuristic mission objective of the preceding and following layers and output the route chain; S3, using the route chain to perform optimal path search and optimization to obtain a finely planned path for the UAV. This invention realizes the search guidance of the cumulative heuristic mission objective of the preceding and following layers and obtains a finely planned route chain through a multi-layer subgrid system, reducing the search computation and complexity, improving the efficiency of UAV flight path planning, and enhancing the UAV's safe navigation capability and planning decision-making capability.

Description

Technical Field

[0001] This invention relates to the field of safe flight route planning for unmanned aerial vehicles (UAVs), and more particularly to a method and system for UAV path planning and navigation based on a hierarchical grid map. Background Technology

[0002] Drones, with their advantages of high flexibility, wide coverage, and strong environmental adaptability, have been widely used in fields such as inspection, logistics delivery, surveying, and search and rescue. For example, in inspection, drones equipped with sensors perform automated, high-precision detection of specific targets (such as infrastructure and natural environments, including power facilities, oil and gas facilities, and forest fire prevention), replacing or assisting manual inspections and improving efficiency and safety. In logistics delivery, drones accurately and quickly deliver goods (such as commodities, medicines, and emergency supplies) from origin to destination. In search and rescue, drones rapidly search for trapped individuals or targets in emergency rescue scenarios, providing real-time information support to assist in rescue decision-making and actions. However, when performing inspection, delivery, and search and rescue tasks, and when executing flight missions from origin to destination, drones face numerous challenges, including flight safety, in large-scale, complex terrain or obstacle environments (such as water, mountains, and buildings). Drones may also intrude into no-fly zones or areas with weak signals or magnetic interference, becoming vulnerable to unsafe flight maneuvers.

[0003] Therefore, when performing mission objectives, UAVs need to plan routes to avoid or bypass the aforementioned avoidance areas in order to ensure safe flight navigation. Current technology uses map navigation, and the path search method for UAV flight path planning calculates the path cost from the starting point pixel by pixel and progressively finds the shortest (i.e., optimal) path. Existing path search methods perform pixel-by-pixel search, employing a one-time global search algorithm. When dealing with large-scale areas, this lacks macroscopic guidance at spatial scale layers, resulting in difficulties in processing massive amounts of data in a timely manner and low search efficiency. Summary of the Invention

[0004] The purpose of this invention is to solve the technical problems pointed out in the background art, and to provide a method and system for UAV path planning and navigation based on a hierarchical grid map. This method utilizes a multi-layer sub-grid system to search for grid route chains at progressively higher resolutions, obtaining progressively more detailed route chains. The route chains of the upper-layer sub-grid system provide search guidance for the grid route chains of the lower-layer sub-grid system. The multi-layer sub-grid system implements cumulative heuristic search guidance for task objectives across layers, improving the efficiency of grid route chain search. The final sub-grid system uses the refined route chains to search for and optimize the optimal path, quickly obtaining a refined UAV route plan, providing the UAV with a safe flight path to avoid avoidance factor areas.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A method for UAV path planning and navigation based on a hierarchical grid map, the method comprising:

[0007] S1. Construct a gridded navigation map and mark the flight avoidance factor data that affects the flight of the UAV on the gridded navigation map;

[0008] S2. Set the mission objective from the UAV's flight start point to its destination in the gridded navigation map. The gridded navigation map is constructed with an m-layer subgrid system built hierarchically. The first m-1 layers of the subgrid system sequentially perform grid route chain search based on the cumulative heuristic mission objective of the preceding and following layers. The (m-1)th layer of the subgrid system outputs the route chain. ;

[0009] S3, utilizing route chains in the m-th layer subgrid system. The optimal path is searched and optimized to obtain a finely planned path for the UAV.

[0010] To better implement the present invention, in method S1, the flight avoidance factor data includes obstacle area data affecting the flight of the UAV, no-fly zone data, water area data affecting the flight of the UAV, navigation conditions affecting the flight area of ​​the UAV, and meteorological conditions affecting the flight area of ​​the UAV.

[0011] Preferably, the m-layer subgrid system is divided into subgrid systems according to the upper and lower levels. ~ Subgrid system ~ The grid is constructed sequentially according to the hierarchical structure.

[0012] Preferably, in method S2, the subgrid system ~ Each corresponds to a density threshold with a flight avoidance factor. ~ The method for performing cumulative heuristic grid route chain search before and after the first m-1 layer subgrid system to achieve the task objective is as follows:

[0013] S21, in the subgrid system Perform a grid search on the target of the mission and filter for flight avoidance factors with a density less than the density threshold. The optimal grid connection route is the route chain. ;

[0014] S22, Subgrid System By route chain The cumulative heuristic grid route chain guides the grid search for the mission objective and filters for avoidance factor densities below a density threshold. The optimal grid connection route is the route chain. ;

[0015] S23, Subgrid System By route chain The cumulative heuristic grid route chain guides the grid search for the mission objective and filters for avoidance factor densities below a density threshold. The optimal grid connection route is the route chain. ;

[0016] S24. Following methods S22 to S23, the subgrid system is obtained in this manner. route chain .

[0017] Preferably, subgrid system The grid search for achieving the mission objective employs a subgrid system. The path search method, which uses internal grid cells as nodes, searches for the optimal grid-connected route from the starting point to the ending point in the task objective, as a path chain. Subgrid system ~ The following methods are used in all cases:

[0018] The upper-level subgrid system route chain As a subgrid system of this layer Macro-level route grid search uses a cumulative heuristic grid route chain to guide the sub-grid system at this layer. Guided by the cumulative heuristic grid route chain of the previous layer, perform a grid search for the mission objective and filter for avoidance factor densities less than a density threshold. The optimal grid connection route is the route chain. , For the hierarchy of the subgrid system, .

[0019] Preferably, from the subgrid system Mesh density to subgrid system The grid density increases sequentially; the upper-level subgrid system A given grid corresponds to a mapping that includes the next layer of subgrid systems. Several grids in the middle.

[0020] Preferably, in method S3, the corresponding projected route chain is located in the m-th layer subgrid system of the rasterized navigation map. Serial mesh , grid The m-th layer subgrid system contains several grids. The m-th layer subgrid system performs a grid search for the mission objective and filters out grids that avoid flight avoidance factor data. The optimal grid connection route is the route chain. .

[0021] Preferably, route chains are displayed in a gridded navigation map. A preliminary path is obtained by connecting lines. The preliminary path is then optimized by processes including redundancy elimination, path smoothing, and Bresenham line algorithm to obtain the fine-planned path for the UAV.

[0022] Preferably, the subgrid system Prior to the path search method, in the subgrid system A virtual straight line is drawn from the starting point to the end point of the task objective as a path search guide; the upper-level subgrid system is utilized. route chain Define a virtual polyline from the starting point to the ending point of the task objective as the subgrid system of this layer. Path search guide lines; connecting the upper-level subgrid system route chain grid Corresponding projection of this layer's subgrid system In the middle, grid In the A layered subgrid system contains several grids. In each grid The system employs a distributed parallel processing approach, utilizing cumulative heuristic grid route chains and / or path search guide lines to perform subgrid system processing at this layer. Grid search.

[0023] A UAV path planning and navigation system based on a hierarchical grid map includes a gridded navigation map, a flight avoidance data input module, a mission objective setting module, a subgrid system, a grid route chain search and processing module, and a fine-planning path processing module. The flight avoidance data input module is used to input and label flight avoidance factor data into the gridded navigation map. The mission objective setting module is used to set the mission objective from the UAV's flight start point to its destination in the gridded navigation map. The subgrid system is used to construct m-layer subgrid systems hierarchically on the gridded navigation map. The grid route chain search and processing module is used to sequentially perform grid route chain searches for the cumulative heuristic mission objective of the preceding and following layers in the first m-1 layers of subgrid systems, and the (m-1)th layer of subgrid systems outputs the route chain. The grid route chain search processing module is also used to utilize route chains in the m-th layer subgrid system. The optimal path search result is generated and input into the fine-planning path processing module, which is used to optimize the process using the optimal path search result to obtain the fine-planning path of the UAV.

[0024] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0025] (1) The present invention constructs a multi-layer sub-grid system on a gridded navigation map. The multi-layer sub-grid system is used to perform grid route chain search at progressively higher resolutions to obtain progressively more detailed route chains. The route chains of the upper-layer sub-grid system provide search guidance for the grid route chains of the lower-layer sub-grid system. The multi-layer sub-grid system realizes the search guidance of the cumulative heuristic task objectives of the front and rear layers, which improves the efficiency of grid route chain search. The last layer sub-grid system uses the refined route chains to perform optimal path search and optimization, which can quickly obtain refined UAV fine planning paths and provide UAVs with safe flight planning routes to avoid avoidance factor areas.

[0026] (2) The grid hierarchical construction of the m-layer subgrid system of the present invention accumulates search guidance and obtains route chains layer by layer from macro route to fine route, realizes the search guidance of the cumulative heuristic task target of the front and back layers, and obtains fine route chains quickly through the multi-layer subgrid system, reduces the search computation and complexity, improves the efficiency of UAV flight path planning, and improves the safe navigation capability and planning decision capability of UAV. Attached Figure Description

[0027] Figure 1 This is a flowchart of the UAV path planning and navigation method of the present invention;

[0028] Figure 2 This is a schematic diagram illustrating how a preliminary path is obtained in a certain region based on a search descent of the preceding and following grid layers, as exemplified in the embodiment.

[0029] Figure 3 for Figure 2 A schematic diagram of the finely planned path of the UAV is obtained after the initial path processing. Detailed Implementation

[0030] The present invention will be further described in detail below with reference to embodiments:

[0031] Example

[0032] like Figure 1 As shown, a UAV path planning and navigation method based on a hierarchical grid map is described, the method comprising:

[0033] S1. Construct a gridded navigation map, marking flight avoidance factor data that affects UAV flight. In this embodiment, the flight avoidance factor data includes obstacle area data, no-fly zone data, water area data affecting UAV flight, navigation conditions affecting UAV flight area, and weather conditions affecting UAV flight area. Obstacle area data includes obstacles such as mountains (especially mountains higher than the UAV's flight altitude) and canyons. No-fly zone data includes airports, military zones, and sensitive areas. Weather conditions affecting UAV flight area refers to areas where severe weather (strong winds, heavy rain, dense fog, etc.) affects UAV flight. Water area data affecting UAV flight includes water areas that may cause strong reflections on the water surface, interfering with the visual sensors on the UAV. Navigation conditions affecting UAV flight area includes areas with weak navigation signals and magnetic field interference (such as the airspace above a primeval forest).

[0034] S2. Set the mission objective (the drone flight mission from start to finish) in the rasterized navigation map. The rasterized navigation map is constructed with an m-layer subgrid system built hierarchically, with each layer representing a subgrid system at different levels. ~ Subgrid system ~ The grid is constructed sequentially according to its hierarchical structure. Specifically, from the subgrid system... Mesh density to subgrid system The grid density increases sequentially while the spatial scale of the grid decreases sequentially. Upper-level subgrid system ( In a hierarchy of subgrid systems, a mapping of a grid to the next subgrid level contains the subgrid system of the next level. There are several grids in the middle. Taking the mapping of one grid in the upper-level subgrid system to include 10×10 grids (i.e., 100 grids) in the lower-level subgrid system as an example, if the subgrid system... If the grid space scale is 10m × 10m, then the upper-level subgrid system The grid space scale is 100m × 100m, and the upper-level subgrid system The mesh mapping includes 100 next-level sub-mesh systems. The grid (100 grids in the next subgrid system correspond to one grid in the previous subgrid system, i.e., constructed sequentially according to the hierarchical affiliation), is the subgrid system. ~ The grid spatial scale decreases sequentially in increments of 100; if the subgrid system The grid spatial scale is 100m × 100m, and so on, to obtain the subgrid system. The grid spatial scale. Taking a four-layer subgrid system as an example, in an example where the reduction factor of the grid spatial scale between upper and lower levels is 100, the grid spatial scale of the first layer subgrid system is 10km × 10km, the grid spatial scale of the second layer subgrid system is 1km × 1km, the grid spatial scale of the third layer subgrid system is 100m × 100m, and the grid spatial scale of the fourth layer subgrid system is 10m × 10m. The subgrid system hierarchy and the reduction factor of the grid spatial scale between upper and lower subgrid systems constructed in the rasterized navigation map of this invention are specifically set in actual practice.

[0035] The first m-1 sub-grid systems sequentially perform grid route chain search for the cumulative heuristic task objective of the preceding and following layers, and the (m-1)th sub-grid system outputs the route chain. In some embodiments, the subgrid system ~ Each of these corresponds to a set threshold for the density of the flight avoidance factor (the density of the flight avoidance factor is the area ratio of the corresponding pixel in the grid of the subgrid system). ~ From subgrid system ~ The grid search is a cumulative heuristic guided search that progresses sequentially from macroscopic coarse to fine. Therefore, generally speaking, the set density threshold... ~ The order is reduced sequentially. The method for cumulative heuristic mesh path chain search of the first m-1 layers of the subgrid system to achieve the task objective is as follows:

[0036] S21, in the subgrid system The task objective is to perform a grid search and filter for avoidance factor densities (the density of the avoidance factor is the area ratio of the corresponding pixel in the grid of the subgrid system) that are less than a density threshold. The optimal grid connection route is the route chain. Subgrid system The grid space scale is the largest, which facilitates macroscopic searches at large grid scales, in the subgrid system. The optimal search for a macro-grid-connected route is performed based on the starting point to the ending point of the mission objective, forming a route chain. The density of the flight avoidance factor in each grid connected in series is less than the density threshold. Density threshold A larger threshold can be set (the density threshold of the subgrid system can be gradually reduced) to facilitate macro-route selection.

[0037] S22, Subgrid System By route chain The cumulative heuristic grid route chain guides the grid search for the mission objective and filters for avoidance factor densities below a density threshold. The optimal grid connection route is the route chain. By route chain The grid concatenation route serves as a cumulative heuristic grid route chain guide in the subgrid system. The optimal route chain is obtained by performing a grid-connected route search from the starting point to the ending point of the task objective. .

[0038] S23, Subgrid System By route chain The cumulative heuristic grid route chain guides the grid search for the mission objective and filters for avoidance factor densities below a density threshold. The optimal grid connection route is the route chain. By route chain The grid concatenation route serves as a cumulative heuristic grid route chain guide in the subgrid system. The optimal route chain is obtained by performing a grid-connected route search from the starting point to the ending point of the task objective. .

[0039] S24. Following methods S22 to S23, the subgrid system is obtained in this manner. route chain .

[0040] In some embodiments, subgrid systems The grid search for achieving the mission objective employs a subgrid system. The path search method, which uses internal grid cells as nodes, searches for the optimal grid-connected route from the starting point to the ending point in the task objective, as a path chain. Subgrid system ~ The following methods are used in all cases:

[0041] The upper-level subgrid system route chain As a subgrid system of this layer Macro-level route grid search uses a cumulative heuristic grid route chain to guide the sub-grid system at this layer. Guided by the cumulative heuristic grid route chain of the previous layer, perform a grid search for the mission objective and filter for avoidance factor densities less than a density threshold. The optimal grid connection route is the route chain. , For the hierarchy of the subgrid system, .

[0042] In some embodiments, subgrid systems Prior to the path search method, in the subgrid system A virtual straight line is drawn from the starting point to the end point of the task objective as a path search guide. The upper-level subgrid system is then utilized. route chain Define a virtual polyline from the starting point to the ending point of the task objective as the subgrid system of this layer. Path search guide lines. This will guide the upper-level subgrid system. route chain grid Corresponding projection of this layer's subgrid system In the middle, grid In the A layered subgrid system contains several grids. In each grid The system employs a distributed parallel processing approach, utilizing cumulative heuristic grid route chains and / or path search guide lines to perform subgrid system processing at this layer. Grid search, that is, in each grid The system utilizes a combination of cumulative heuristic grid route chains and path search guidance lines to facilitate rapid deployment of the subgrid system at this layer. The grid search is performed simultaneously with the subgrid system at this layer. During grid search, each grid Distributed parallel processing (i.e., grid processing) can be used. In the corresponding mapped grids Medium grid search, each grid (Processing in parallel) to accelerate processing efficiency.

[0043] S3, utilizing route chains in the m-th layer subgrid system. Optimal path search and optimization are performed to obtain a finely planned path for the UAV. Specifically, the corresponding projected route chain is determined in the m-th layer sub-grid system of the rasterized navigation map. Serial mesh , grid The m-th layer subgrid system contains several grids. (In the example where the grid space scale reduction factor is 100, there are 100 grids.) The m-th layer subgrid system performs a grid search for the mission objective and filters out grids that avoid flight avoidance factor data. The optimal grid connection route is the route chain. Route chains in a rasterized navigation map A preliminary path is obtained by connecting the lines, such as... Figure 2 As shown, this embodiment takes a task objective from the starting point to the ending point in a certain area with many obstacles as an example. This embodiment obtains the route chain based on a grid search with the spatial scale decreasing step by step (referring to the grid spatial scale decreasing step by step). For route chains By performing connection processing, Figure 2 The preliminary path (i.e., the initial planned route) for this area is shown. Figure 2 The initial path (marked by the red line) avoids various obstacles (i.e., the flight avoidance factor area) in this complex environment. Then, the initial path undergoes optimization processes including redundancy elimination, path smoothing, and the Bresenham line algorithm to obtain the finely planned path for the UAV (e.g., [path name missing]). Figure 3 As shown, Figure 3 The blue line represents the optimized, finely planned path for the drone. The redundancy elimination method involves: if the straight line segment between two points does not pass through any flight avoidance factor data (e.g., ...), ... Figure 2 If there are obstacles, all intermediate points between them will be removed, and redundant point clearing will be carried out step by step to reduce the number of critical flight points; when processing the fine planning path of the UAV, avoidance factor data should be set as a constraint condition.

[0044] A UAV path planning and navigation system based on a hierarchical grid map includes a gridded navigation map, a flight avoidance data input module, a mission objective setting module, a subgrid system, a grid route chain search and processing module, and a fine-grained path planning and processing module. The flight avoidance data input module inputs and labels flight avoidance factor data into the gridded navigation map. The mission objective setting module sets the mission objective from the UAV's flight start point to its destination in the gridded navigation map. The subgrid system constructs m layers of subgrids hierarchically on the gridded navigation map. The grid route chain search and processing module sequentially performs grid route chain searches based on the cumulative heuristic mission objective of the preceding and following layers in the first m-1 layers of subgrids, outputting the route chain from the (m-1)th layer of subgrids. The grid route chain search processing module is also used to utilize route chains in the m-th layer subgrid system. The optimal path search result is generated and input into the fine-planning path processing module. The fine-planning path processing module is used to optimize and process the optimal path search result to obtain the fine-planned path of the UAV (i.e., the fine-planned flight route of the UAV).

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for UAV path planning and navigation based on a hierarchical grid map, characterized in that: The methods include: S1. Construct a gridded navigation map and mark the flight avoidance factor data that affects the flight of the UAV on the gridded navigation map; S2. Set the mission objective from the UAV's flight start point to its destination in the gridded navigation map. The gridded navigation map is constructed with an m-layer subgrid system built hierarchically. The first m-1 layers of the subgrid system sequentially perform grid route chain search based on the cumulative heuristic mission objective of the preceding and following layers. The m-layer subgrid system is divided into subgrid systems according to the upper and lower levels. ~ Subgrid system ~ The grid is constructed sequentially according to its hierarchical structure, and the subgrid system... ~ The following task objective is to perform a cumulative heuristic grid route chain search method between previous and next layers: [The method involves] the upper-layer subgrid system... route chain As a subgrid system of this layer Macro-level route grid search uses a cumulative heuristic grid route chain to guide the sub-grid system at this layer. Guided by the cumulative heuristic grid route chain of the previous layer, perform a grid search for the mission objective and filter for avoidance factor densities less than a density threshold. The optimal grid connection route is the route chain. , For the hierarchy of the subgrid system, Subgrid system Prior to the path search method, in the subgrid system A virtual straight line is drawn from the starting point to the end point of the task objective as a path search guide; the upper-level subgrid system is utilized. route chain Define a virtual polyline from the starting point to the ending point of the task objective as the subgrid system of this layer. Path search guide lines; connecting the upper-level subgrid system route chain grid Corresponding projection of this layer's subgrid system In the middle, grid In the A layered subgrid system contains several grids. In each grid The system employs a distributed parallel processing approach, utilizing cumulative heuristic grid route chains and / or path search guide lines to perform subgrid system processing at this layer. The grid search outputs the route chain of the (m-1)th layer subgrid system. ; S3, utilizing route chains in the m-th layer subgrid system. The optimal path is searched and optimized to obtain a finely planned path for the UAV.

2. The UAV path planning and navigation method based on a hierarchical grid map as described in claim 1, characterized in that: In method S1, the flight avoidance factor data includes obstacle area data affecting the flight of the UAV, no-fly zone data, water area data affecting the flight of the UAV, navigation conditions affecting the flight area of ​​the UAV, and meteorological conditions affecting the flight area of ​​the UAV.

3. The UAV path planning and navigation method based on a hierarchical grid map as described in claim 1, characterized in that: In method S2, the subgrid system ~ Each corresponds to a density threshold with a flight avoidance factor. ~ The method for performing cumulative heuristic grid route chain search before and after the first m-1 layer subgrid system to achieve the task objective is as follows: S21, in the subgrid system Perform a grid search on the target of the mission and filter for flight avoidance factors with a density less than the density threshold. The optimal grid connection route is the route chain. ; S22, Subgrid System By route chain The cumulative heuristic grid route chain guides the grid search for the mission objective and filters for avoidance factor densities below a density threshold. The optimal grid connection route is the route chain. ; S23, Subgrid System By route chain The cumulative heuristic grid route chain guides the grid search for the mission objective and filters for avoidance factor densities below a density threshold. The optimal grid connection route is the route chain. ; S24. Following methods S22 to S23, the subgrid system is obtained in this manner. route chain .

4. The UAV path planning and navigation method based on a hierarchical grid map as described in claim 3, characterized in that: Subgrid system The grid search for achieving the mission objective employs a subgrid system. The path search method, which uses internal grid cells as nodes, searches for the optimal grid-connected route from the starting point to the ending point in the task objective, as a path chain. .

5. The UAV path planning and navigation method based on a hierarchical grid map as described in claim 1, characterized in that: From subgrid system Mesh density to subgrid system The grid density increases sequentially; the upper-level subgrid system A given grid corresponds to a mapping that includes the next layer of subgrid systems. Several grids in the middle.

6. The UAV path planning and navigation method based on a hierarchical grid map as described in claim 1, characterized in that: In method S3, the corresponding projected route chain is located in the m-th layer subgrid system of the rasterized navigation map. Serial mesh , grid The m-th layer subgrid system contains several grids. The m-th layer subgrid system performs a grid search for the mission objective and filters out grids that avoid flight avoidance factor data. The optimal grid connection route is the route chain. .

7. The UAV path planning and navigation method based on a hierarchical grid map as described in claim 6, characterized in that: Route chains in a gridded navigation map A preliminary path is obtained by connecting lines. The preliminary path is then optimized by processes including redundancy elimination, path smoothing, and Bresenham line algorithm to obtain the fine-planned path for the UAV.

8. A UAV path planning and navigation system based on a hierarchical grid map that implements the UAV path planning and navigation method of claim 1, characterized in that: The system includes a rasterized navigation map, a flight avoidance data input module, a mission objective setting module, a sub-grid system module, a grid route chain search and processing module, and a fine-planning path processing module. The flight avoidance data input module is used to input and label flight avoidance factor data into the rasterized navigation map. The mission objective setting module is used to set the mission objective from the UAV's flight start point to its destination in the rasterized navigation map. The sub-grid system module is used to construct an m-layer sub-grid system on the rasterized navigation map, hierarchically. The grid route chain search and processing module is used to sequentially perform grid route chain searches for the cumulative heuristic mission objective of the preceding and following layers in the first m-1 layers of the sub-grid system, and the (m-1)th layer of the sub-grid system outputs the route chain. The grid route chain search processing module is also used to utilize route chains in the m-th layer subgrid system. The optimal path search result is generated and input into the fine-planning path processing module, which is used to optimize the process using the optimal path search result to obtain the fine-planning path of the UAV.

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