Unmanned aerial vehicle path planning method for urban intelligent cooperative combat

Abstract

The application relates to a UAV flight path planning method for urban intelligent cooperative combat. In a target city three-dimensional model, a UAV flyable channel three-dimensional grid is obtained according to a starting point coordinate and a target point coordinate and under flight constraint conditions, each grid is subjected to dangerous coefficient assignment, a first preset selection planning method is adopted to select a network center point coordinate in a selectable range as an intermediate node of a flight path, a second preset selection planning method is adopted to generate a two-dimensional plane with the intermediate node as the center and expand the two-dimensional plane, a three-dimensional space is generated, an expansion part is selected as a selectable range, the next node of the flight path is selected in the range, until the horizontal distance between the intermediate node and the target point coordinate is less than a preset distance, then the flight path of the UAV is generated according to all the intermediate nodes, and the UAV flight path planning is completed. The method can efficiently and accurately plan the UAV flight path.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence science and technology, and in particular to a method for planning drone flight paths for intelligent collaborative operations in cities. Background Technology

[0002] Due to their high flexibility, maneuverability, low safety risk, and low cost, drones are widely used in various fields such as search and patrol, reconnaissance and surveillance, disaster relief, logistics and distribution, and agricultural irrigation, showing great promise for future applications. Particularly in the military field, drones can perform high-risk missions such as reconnaissance and strike operations, effectively reducing the risk of casualties among combat personnel. In recent years, drone-based intelligent collaborative urban warfare has attracted considerable attention.

[0003] Flight path planning is a key technology for intelligent collaborative urban operations using unmanned aerial vehicles (UAVs). It refers to determining the optimal flight path from the starting point to the target point for a UAV under specific conditions, taking into account factors such as safety, flight distance, and time cost. Flight path planning directly impacts the time efficiency, economic efficiency, and success rate of intelligent collaborative urban operations, making it of significant research importance.

[0004] However, common UAV trajectory planning methods are categorized into two types based on the degree of environmental information available: global and local. Global trajectory planning methods rely on all information from the environmental map to plan the trajectory. While aiming for the globally optimal path, they cannot handle unexpected obstacles. Local trajectory planning methods can collect environmental information in real time for planning, but they are prone to getting trapped in local optima, and are not globally optimal. Summary of the Invention

[0005] Therefore, it is necessary to provide a highly efficient and accurate UAV trajectory planning method for intelligent collaborative urban operations, addressing the aforementioned technical issues.

[0006] A method for planning the trajectory of unmanned aerial vehicles (UAVs) for intelligent collaborative urban operations, characterized in that the method includes:

[0007] Obtain the 3D model of the target city, the coordinates of the starting point and the target point of the flight path, and the flight constraints of the UAV;

[0008] In the city's 3D model, the flight path of the UAV is obtained based on the starting point coordinates, the target point coordinates, and the flight constraints. The flight path is then divided into a 3D mesh to obtain the 3D mesh of the flight path.

[0009] Based on the hazard information data, a hazard coefficient is assigned to each grid in the three-dimensional grid of the flightable passage;

[0010] According to the preset first selection planning method, the selectable range of the next node of the track is generated with the starting point coordinates as the center, and the center point coordinates of the three-dimensional network are selected within the selectable range as the next node of the track, i.e., the intermediate node.

[0011] According to the preset second selection planning method, a two-dimensional plane is generated with the intermediate node as the center, and it is expanded according to the direction of the target point coordinates. A three-dimensional solid space is generated according to the expanded two-dimensional plane, and the expanded part is used as the selectable range. The next node of the track is selected within the selectable range.

[0012] Until the horizontal distance between the intermediate node and the target point coordinates is less than a preset distance, the next node is set to the target point coordinates, and the drone's trajectory is generated based on all intermediate nodes to complete the drone trajectory planning.

[0013] In one embodiment, the flight constraints include: minimum turning radius constraints and flight altitude constraints.

[0014] In one embodiment, the flightable passageway is represented as:

[0015] TD={(x,y,z)|t1≤x≤t2,t3≤y≤t4,H min ≤z≤H max}

[0016] in,

[0017] In the above formula, α represents a pre-set parameter, H min and H max Let (x0, y0) represent the minimum and maximum values ​​of the flight altitude in the flight constraints, respectively, and let (x0, y0) represent the coordinates of the starting point. N ,y N () represents the coordinates of the target point.

[0018] In one embodiment, the first selection planning method includes:

[0019] The selectable range is generated with the starting point coordinates as the center and according to the preset size;

[0020] Calculate the first-generation value of the three-dimensional mesh that satisfies the flight constraints within the selectable range;

[0021] Choose the center coordinates of the 3D grid corresponding to the minimum first-generation value as the next node of the track, i.e., the intermediate node.

[0022] In one embodiment, the first-generation value is calculated using the formula:

[0023]

[0024] In the above formula, β∈(0,1) and ε0∈(0,0.1) are preset parameters, GN represents the three-dimensional grid where the endpoint DTN(xN,yN) is located, the function D(,) represents the distance between two three-dimensional grids, WX(G) represents the danger coefficient of the three-dimensional grid G, and G0 represents the center coordinate of the starting point coordinates.

[0025] In one embodiment, the second selection planning method includes:

[0026] An initial two-dimensional plane is generated with the position of the intermediate node as the center and according to a preset size;

[0027] The expansion direction is determined based on the starting point coordinates and the target point coordinates. The initial two-dimensional plane is then expanded according to the expansion direction to obtain the expanded two-dimensional plane.

[0028] A three-dimensional solid space is generated from the expanded two-dimensional plane, and the expanded part is used as the selectable range.

[0029] Calculate the second-generation value of the three-dimensional mesh that satisfies the flight constraints within the selectable range, and select the center coordinates of the three-dimensional mesh corresponding to the minimum second-generation value as the next node of the track.

[0030] In one embodiment, determining the expansion direction based on the starting point coordinates and the target point coordinates includes: determining two expansion directions based on the relationship between the starting point coordinates and the target point coordinates.

[0031] In one embodiment, when the initial two-dimensional plane is expanded according to the expansion direction:

[0032] A new two-dimensional plane is formed by adding a grid row to the initial two-dimensional plane in two expansion directions.

[0033] Calculate the difference between the new two-dimensional plane and the initial two-dimensional plane. If the difference is less than a preset parameter, continue to expand on the new two-dimensional plane.

[0034] Until the difference is greater than the preset parameter, the newly generated two-dimensional plane is taken as the expanded two-dimensional plane.

[0035] In one embodiment, the second-generation value is calculated using the formula:

[0036]

[0037] In the above formula, β∈(0,1) and ε0∈(0,0.1) are preset parameters, GN represents the three-dimensional grid where the endpoint DTN(xN,yN) is located, the function D(,) represents the distance between two three-dimensional grids, WX(G) represents the danger coefficient of the three-dimensional grid G, G0 represents the center coordinates of the starting point coordinates, and G m-1 This indicates the position coordinates of the previous intermediate node.

[0038] The aforementioned UAV trajectory planning method for intelligent collaborative urban operations, based on the starting point coordinates, target point coordinates, and flight constraints, obtains the UAV's flight path within a 3D model of the target city. This path is then divided into a 3D mesh. Based on hazard information data, a hazard coefficient is assigned to each mesh within the flight path. Following a pre-defined first selection planning method, a selectable range for the next node of the trajectory is generated, centered on the starting point coordinates. Within this selectable range, the center point coordinates of the 3D network are selected as the next node, i.e., the intermediate node. Following a pre-defined second selection planning method, a 2D plane is generated centered on the intermediate node and expanded according to the direction of the target point coordinates. A 3D space is generated from the expanded 2D plane, and the expanded portion is used as the selectable range. The next node is selected within this range until the horizontal distance between the intermediate node and the target point coordinates is less than a preset distance. At this point, the next node is designated as the target point coordinates. The UAV trajectory is then generated based on all intermediate nodes to complete the UAV trajectory planning. This method enables efficient and highly accurate UAV trajectory planning. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a UAV trajectory planning method for intelligent collaborative combat in urban areas, as shown in one embodiment.

[0040] Figure 2 This is a schematic diagram of the four scalable directions of the mesh in one embodiment;

[0041] Figure 3 This is a schematic diagram illustrating the expansion of the grid range in two directions in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] To address the problems of existing UAV trajectory planning methods, such as the inability to select an appropriate search range based on the actual environment, high algorithm complexity, and low accuracy, [further solutions are needed]. Figure 1As shown, a method for UAV trajectory planning for intelligent collaborative operations in cities is provided, which specifically includes the following steps:

[0044] Step S100: Obtain the 3D model of the target city, the coordinates of the starting point and the target point of the flight path, and the flight constraints of the UAV.

[0045] Step S110: In the city 3D model, based on the starting point coordinates, target point coordinates, and flight constraints, the flight path of the UAV is obtained, and the flight path is divided into 3D meshes to obtain the 3D mesh of the flight path.

[0046] Step S120: Based on the hazard information data, assign a hazard coefficient value to each grid in the three-dimensional grid of the flightable passage.

[0047] Step S130: According to the preset first selection planning method, generate the selectable range of the next node of the track with the starting point coordinates as the center, and select the center point coordinates of the three-dimensional network within the selectable range as the next node of the track, i.e., the intermediate node.

[0048] Step S140: According to the preset second selection planning method, a two-dimensional plane is generated with the intermediate node as the center, and it is expanded according to the direction of the target point coordinates. A three-dimensional solid space is generated according to the expanded two-dimensional plane, and the expanded part is used as the selectable range. The next node of the track is selected within the selectable range.

[0049] Step S150: Until the horizontal distance between the intermediate node and the target point coordinates is less than the preset distance, the next node is set to the target point coordinates, and the drone's trajectory is generated based on all intermediate nodes to complete the drone trajectory planning.

[0050] In this embodiment, the starting and ending points of the flight path are first determined on a 3D model corresponding to the specified target city. Next, the flight constraints for the UAV are determined, and the flight path of the UAV is divided into a 3D mesh. Each mesh is assigned a risk coefficient. Then, starting from the starting point, the next node is determined based on the cost value 1 of each mesh within the selected range. An adaptive method is then used to determine the mesh selection range of the current node, and the next node is determined based on the cost value 2 of the meshes within that range. Finally, the distance relationship between the current node and the ending point is determined. If the condition is met, the flight path composed of all nodes is output. If the condition is not met, the iterative process of determining the next node using the adaptive mesh selection method and cost value 2 continues until the distance relationship with the ending point is satisfied. This method can select an appropriate search range according to the actual environment, has low cost calculation complexity, and high accuracy, making it highly practical.

[0051] In step S100, in the city 3D model, the starting point coordinates and the target point coordinates of the trajectory are represented as DT0(x0,y0) and DTN(xN,yN), respectively.

[0052] Specifically, flight constraints include minimum turning radius constraints and flight altitude constraints.

[0053] Furthermore, assume the minimum turning radius of the drone is R. min If the turning radius at any point on the planned trajectory is r, then the minimum turning radius constraint can be expressed as: r ≥ R min .

[0054] Furthermore, assuming the maximum flight altitude of the drone is H... min Minimum flight altitude is H min Then the constraint condition for flight altitude h can be expressed as: H max ≥h≥H min .

[0055] In step S110, the flyable channel is represented as follows:

[0056] TD={(x,y,z)|t1≤x≤t2,t3≤y≤t4,H min ≤z≤H max} (1)

[0057] in,

[0058] In formula (1), α represents a pre-set parameter, H min and H max Let x and y represent the minimum and maximum values ​​of the flight altitude under the flight constraints, respectively, and (x0, y0) represent the coordinates of the starting point. N ,y N () represents the coordinates of the target point.

[0059] Furthermore, the flyable passage represented by formula (1) is divided into a three-dimensional mesh, with each mesh having a size of A×A×B, where B represents the height of the mesh, and A and B are pre-set parameters. Three-dimensional mesh of the flyable passage.

[0060] In step S120, based on the existing hazard information, each grid G ​​is assigned a hazard coefficient WX(G). The value range of the hazard coefficient is [0,1]. When the hazard coefficient is 0, it means that the grid is very safe. When the hazard coefficient is 1, it means that the grid is prohibited from passage. The larger the hazard coefficient, the more dangerous it is to pass through the grid.

[0061] In step S130, when planning the next node of the starting point coordinates, the first selection planning method includes: generating a selectable range with the starting point coordinates as the center according to a preset size, calculating the first generation value of the three-dimensional mesh that satisfies the flight constraints within the selectable range, and selecting the center coordinates of the three-dimensional mesh corresponding to the minimum first generation value as the next node of the track, i.e., the intermediate node.

[0062] In this embodiment, the UAV starts from the starting point DT0(x0,y0). Let m=1. Using the grid G0 where DT0(x0,y0) is located as the center, and following a preset size (3×3×3 as an example), the generated neighborhood Ω0(G0) serves as the selection range for the next intermediate node position. Within this selection range, there are 3×3×3-1=26 grids to choose from.

[0063] Furthermore, the 26 grids in Ω0(G0) are re-evaluated based on the flight constraints, and the cost value of the grids that meet the flight constraints is calculated to obtain the first generation value. The center point of the grid with the smallest generation value is selected as the next node, that is, the middle node of the entire trajectory.

[0064] In this embodiment, the first-generation value is calculated using the following formula:

[0065]

[0066] In formula (2), β∈(0,1) and ε0∈(0,0.1) are preset parameters, GN represents the three-dimensional grid where the endpoint DTN(xN,yN) is located, the function D(,) represents the distance between two three-dimensional grids, WX(G) represents the danger coefficient of the three-dimensional grid G, and G0 represents the center coordinate of the starting point coordinates.

[0067] Specifically, the function D(,) calculates the Euclidean distance between the center pixels of two grids.

[0068] Specifically, select the grid with the lowest cost, i.e., G. m =minJ1(G), let the grid G m The center pixel is used as the currently selected new node DT m (x m ,y m ,z m That is, the position coordinates of the next node, and finally let m = m + 1.

[0069] In step 140, when the next node of any intermediate node other than the next node of the planning starting point coordinates is known (i.e., m > 1), the current intermediate node DT is known. m-1 (x m-1,y m-1 ,z m-1 The grid where ) is located is G m-1 If we take G m-1 The centered, three-dimensional region of size 3×3×3 is located in the domain Ω. m-1 (G m-1 The initial selection range is the node planning method in step S130. However, in order to speed up the algorithm, an adaptive grid selection range method, namely the second selection planning method, is adopted in this method. In this method, the height of the selection range is not considered at first. It is only expanded on the two-dimensional plane, and then the height is superimposed on the expanded two-dimensional plane.

[0070] In this embodiment, the second selection planning method includes: generating an initial two-dimensional plane with the position of the intermediate node as the center and according to a preset size; determining the expansion direction based on the coordinates of the starting point and the target point; expanding the initial two-dimensional plane according to the expansion direction to obtain an expanded two-dimensional plane; generating a three-dimensional solid space based on the expanded two-dimensional plane; taking the expanded part as the selectable range; calculating the second-generation value of the three-dimensional mesh that satisfies the flight constraints within the selectable range; and selecting the center coordinates of the three-dimensional mesh corresponding to the minimum second-generation value as the next node of the trajectory.

[0071] Specifically, based on the position DT of the intermediate node m-1 (x m-1 ,y m-1 ,z m-1 Centered on δ0, an initial two-dimensional plane is generated according to a preset size. In this embodiment, taking 3×3 as an example, the 3×3 generated area δ0(G) is used as the center. m-1 Let Ψ0 be the initial two-dimensional plane.

[0072] In this embodiment, determining the expansion direction based on the starting point coordinates and the target point coordinates includes: determining two expansion directions based on the relationship between the starting point coordinates and the target point coordinates.

[0073] Specifically, on the initial two-dimensional plane Ψ0, there are four directions that can be extended, denoted as direction 1, direction 2, direction 3, and direction 4, respectively, as follows: Figure 3 As shown.

[0074] Specifically, based on the positional relationship between the starting coordinates DT0(x0,y0) and the ending coordinates DTN(xN,yN), the extended range is further simplified, as shown in Table 1:

[0075] Table 1

[0076] Positional relationship Expansion direction xN≥x0, yN≥y0 Direction 1, Direction 2 xN < x0, yN < y0 Direction 3, Direction 4 xN≥x0, yN<y0 Direction 2, Direction 3 xN<x0, yN≥y0 Direction 1, Direction 4

[0077] As shown in Table 1, based on the positional relationship between the starting point coordinates and the ending point coordinates, two expansion directions can be obtained, which are denoted as direction v1 and direction v2.

[0078] In this embodiment, when expanding the initial two-dimensional plane according to the expansion direction: a grid is added to the initial two-dimensional plane Ψ0 according to the two expansion directions v1 and v2 to form a new two-dimensional plane Ψ1, as follows. Figure 3 As shown. Next, the difference between the new two-dimensional plane Ψ1 and the initial two-dimensional plane Ψ0 is calculated. If the difference is less than the preset parameter, the expansion continues on the new two-dimensional plane Ψ1, that is, Ψ0 = Ψ1 is set, and the expansion continues on Ψ0 until the difference is greater than the preset parameter. Then, the newly generated two-dimensional plane is taken as the expanded two-dimensional plane.

[0079] Specifically, the difference between the new two-dimensional plane Ψ1 and the initial two-dimensional plane Ψ0 is calculated using the following formula:

[0080]

[0081] In formula (3), γ represents a predetermined adjustment parameter.

[0082] Furthermore, after obtaining the expanded two-dimensional plane, only its outer contour mesh set, i.e., the last expanded mesh, is selected. Then, a height range is added to the outer contour mesh, including the height of the upper and lower meshes and the mesh itself, forming a three-dimensional mesh selection area, denoted as newΩ. m-1 (G m-1 () represents the final range of choices.

[0083] Furthermore, based on the current node DT m-1 (x m-1 ,y m-1 ,z m-1 The grid where ) is located is G m-1 And the next step's selection range is newΩ m-1 (G m-1 Then, within this selection range, a judgment is made based on the flight constraints, and the cost value of the grid that satisfies the flight constraints is calculated, which is the second generation value.

[0084] Specifically, the second-generation value is calculated using the following formula:

[0085]

[0086] In formula (4), β∈(0,1) and ε0∈(0,0.1) are preset parameters, GN represents the three-dimensional grid where the endpoint DTN(xN,yN) is located, the function D(,) represents the distance between two three-dimensional grids, WX(G) represents the danger coefficient of the three-dimensional grid G, G0 represents the center coordinates of the starting point coordinates, and G m-1 This indicates the position coordinates of the previous intermediate node.

[0087] Furthermore, select the grid with the lowest value in the second generation, namely G. m =minJ2(G), let the grid G m The center pixel is used as the currently selected new node DT m (x m ,y m ,z m ).

[0088] In step S150, after performing intermediate node planning multiple times in step S140, when DT m (x m ,y m ,z m The horizontal distance between the endpoint DTN(xN,yN) and the endpoint DTN(xN,yN) If the width is less than twice the grid width (i.e., 2A), the termination condition is met, and the next node is directly set as the endpoint. The set of all nodes is {DT1(x1,y1,z1),…,DT…}. m (x m ,y m ,z m The flight path of the UAV is formed by the following steps: After obtaining the next intermediate node, the distance between the newly obtained intermediate node and the target point is calculated. If the termination condition is not met, m = m + 1 is set until the termination condition is met.

[0089] In the aforementioned UAV trajectory planning method for intelligent collaborative urban operations, a flight path for the UAV is obtained from the 3D model of the target city based on the coordinates of the starting point and the target point, under flight constraints. This path is then divided into a 3D mesh. Based on hazard information data, a hazard coefficient is assigned to each mesh within the flight path. Following a pre-defined first selection planning method, a selectable range for the next node of the trajectory is generated, centered on the starting point coordinates. Within this selectable range, the center point coordinates of the 3D network are selected as the next node, i.e., the intermediate node. Following a pre-defined second selection planning method, a 2D plane is generated centered on the intermediate node and expanded according to the direction of the target point coordinates. A 3D space is generated from the expanded 2D plane, and the expanded portion is used as the selectable range. The next node is selected within this selectable range until the horizontal distance between the intermediate node and the target point coordinates is less than a preset distance. At this point, the next node is set as the target point coordinates. The UAV trajectory is then generated based on all intermediate nodes to complete the UAV trajectory planning. This method allows for the selection of an appropriate search range based on the actual environment, has low cost calculation complexity, and high accuracy, demonstrating significant practical value.

[0090] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0091] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for UAV trajectory planning for intelligent collaborative urban operations, characterized in that, The method includes: Obtain the 3D model of the target city, the coordinates of the starting point and the target point of the flight path, and the flight constraints of the UAV; In the city's 3D model, the flight path of the UAV is obtained based on the starting point coordinates, the target point coordinates, and the flight constraints. The flight path is then divided into a 3D mesh to obtain the 3D mesh of the flight path. Based on the hazard information data, a hazard coefficient is assigned to each grid in the three-dimensional grid of the flightable passage; According to the preset first selection planning method, the selectable range of the next node of the track is generated with the starting point coordinates as the center, and the center point coordinates of the three-dimensional network are selected within the selectable range as the next node of the track, i.e., the intermediate node. According to the preset second selection planning method, an initial two-dimensional plane of a preset size is generated with the intermediate node as the center, and two expansion directions of the initial two-dimensional plane are determined according to the relationship between the starting point coordinates and the target point coordinates. A grid is added to the initial two-dimensional plane according to the two expansion directions to form a new two-dimensional plane. Calculate the difference between the new two-dimensional plane and the initial two-dimensional plane. If the difference is less than a preset parameter, continue the expansion on the new two-dimensional plane; where the difference is expressed as: in, Represents the initial two-dimensional plane. Represents a new two-dimensional plane. , , This indicates a predetermined adjustment parameter; Until the difference is greater than the preset parameter, the newly generated two-dimensional plane is taken as the expanded two-dimensional plane; A three-dimensional solid space is generated based on the expanded two-dimensional plane, and the expanded part is used as a selectable range. The next node of the track is selected within the selectable range. Until the horizontal distance between the intermediate node and the target point coordinates is less than a preset distance, the next node is set to the target point coordinates, and the drone's trajectory is generated based on all intermediate nodes to complete the drone trajectory planning.

2. The UAV trajectory planning method according to claim 1, characterized in that, The flight constraints include: minimum turning radius constraint and flight altitude constraint.

3. The UAV trajectory planning method according to claim 1, characterized in that, The flyable passageway is represented as follows: in, In the above formula, This indicates pre-set parameters. and These represent the minimum and maximum values ​​of the flight altitude in the aforementioned flight constraints, respectively. Indicates the coordinates of the starting point. Indicates the coordinates of the target point.

4. The UAV trajectory planning method according to claim 2, characterized in that, The first selection planning method includes: The selectable range is generated with the starting point coordinates as the center and according to the preset size; Calculate the first-generation value of the three-dimensional mesh that satisfies the flight constraints within the selectable range; Choose the center coordinates of the 3D grid corresponding to the minimum first-generation value as the next node of the track, i.e., the intermediate node.

5. The UAV trajectory planning method according to claim 4, characterized in that, The first-generation value is calculated using the following formula: In the above formula, and These are preset parameters. Indicates the end point The three-dimensional mesh in which it is located, the function This represents the distance between two 3D grids. Representing a 3D mesh The risk factor, The center coordinates represent the coordinates of the starting point.

6. The UAV trajectory planning method according to claim 1, characterized in that, In the step of selecting the next node of the track within the selectable range, the second-generation value of the three-dimensional mesh that satisfies the flight constraints within the selectable range is calculated, and the center coordinates of the three-dimensional mesh corresponding to the minimum second-generation value are selected as the next node of the track.

7. The UAV trajectory planning method according to claim 6, characterized in that, The second-generation value is calculated using the following formula: In the above formula, and These are preset parameters. Indicates the end point The three-dimensional mesh in which it is located, the function This represents the distance between two 3D grids. Representing a 3D mesh The risk factor, The center coordinates of the starting point are indicated. This indicates the position coordinates of the previous intermediate node.

Images