Unmanned aerial vehicle formation flight performance collaborative path planning method and system based on artificial path potential energy field

Abstract

The invention discloses an unmanned aerial vehicle formation flight performance collaborative path planning method and system based on an artificial path potential energy field, and belongs to the technical field of collaborative path planning. The method comprises the steps of 1, extracting feature points of an image, and adjusting the number of the feature points to a target value by using a recursive algorithm; step 2, distributing pattern feature points for each unmanned aerial vehicle based on the target numerical value of the image obtained in the step 1; and step 3, based on the pattern feature points distributed in the step 2, performing cooperative path planning for the unmanned aerial vehicle group flying from the starting point to the ending point, and realizing unmanned aerial vehicle formation flight performance cooperative path planning based on the artificial path potential energy field. The method is used for solving the problems that an existing unmanned aerial vehicle formation collides during flight performance and the path length of the unmanned aerial vehicle reaching a specific position point is not optimal.

Description

Technical Field

[0001] This invention belongs to the field of collaborative path planning technology, specifically relating to a collaborative path planning method and system for UAV formation flight performance based on artificial path potential energy fields. Background Technology

[0002] A drone swarm represents a functional cluster of multiple aircraft that operate autonomously without human pilots. Unlike a single drone, a drone swarm can perform more complex tasks by cooperating with each other, and therefore has found wide application in various fields, including reconnaissance, data collection, and aerial performances.

[0003] Cooperative path planning is a crucial task for unmanned aerial vehicle (UAV) systems, responsible for planning efficient, collision-free flight paths for each UAV, and therefore essential for achieving efficient formation flying. However, as the number of UAVs and waypoints increases, the computational complexity of path planning grows exponentially, posing a challenge to cooperative path planning for large-scale UAV swarms.

[0004] Traditional algorithms such as A * Methods like RRT and Dijkstra's algorithm discretize the space into a grid, then use heuristic search or dynamic programming to achieve path planning. However, as the number of grid cells and the number of drones increase, the computational complexity of these methods increases dramatically. Furthermore, these algorithms are overly idealistic, failing to consider the dynamic characteristics and constraints of drones, thus rendering them impractical.

[0005] Global optimization algorithms such as particle swarm optimization, gray wolf optimization, and genetic algorithms are also used for path planning, treating it as an optimization problem with path points as independent variables. However, due to their slow convergence rate, their running time is often too long, making them unsuitable for large-scale computer clusters.

[0006] Artificial Potential Field (APF) algorithms allow UAVs to move within a potential energy field, forming a path. This type of method reduces computation by foregoing direct path optimization, but it also sacrifices accuracy and can lead to more collisions on large-scale swarms. Variants of APF, such as IAPF, HAPF, FAPF, and Theta*-APF, attempt to improve APF by incorporating formation control theory, rotating potential fields, heuristic search, and local optimization algorithms; however, they also introduce higher computational costs and have limited contributions to solution accuracy. Summary of the Invention

[0007] This invention provides a collaborative path planning method and system for drone formation flight performances based on artificial path potential energy fields, in order to solve the problem that collisions occur in existing drone formations during flight performances and the path length of drones to specific locations is not optimal.

[0008] This invention is achieved through the following technical solution: A collaborative path planning method for UAV formation flight performances based on artificial path potential energy fields, the method comprising the following steps: Step 1: Extract feature points from the image and use a recursive algorithm to adjust the number of feature points to the target value; Step 2: Based on the target values ​​of the image obtained in Step 1, assign pattern feature points to each drone; Step 3: Based on the pattern feature points allocated in Step 2, perform collaborative path planning for the drone swarm from the starting point to the destination, realizing collaborative path planning for drone formation flight performance based on artificial path potential energy field.

[0009] Furthermore, step 1 specifically includes the following steps: Step 1.1: Convert image I to a grayscale image; Step 1.2: Apply Gaussian filtering to the grayscale image to remove noise; Step 1.3: Perform dilation and erosion operations sequentially on the grayscale image; Step 1.4: Binarize image I by thresholding (0-1); Step 1.5: Continuously increase or decrease the resolution of image I until the number of pixels with a value of 1 in image I is slightly greater than n; Step 1.6: Randomly change the values ​​of several pixels with a value of 1 to 0, so that the number of pixels with a value of 1 in image I is equal to n; Step 1.7: Treat each pixel with a value of 1 as a pattern feature point, according to... , , Calculate the three-dimensional coordinates of each pattern feature point. .

[0010] Furthermore, step 2 specifically includes the following steps: Step 2.1: Establish the objective function: Replacement ; Step 2.2: Solve the above objective function using the branch and bound method.

[0011] Furthermore, step 3 specifically includes the following steps: Step 3.1: Initialize the position X of each drone at any given time, the time represented by each waypoint, and the position to insert a new waypoint; Step 3.2: Based on the data initialized in Step 3.1, initialize the new waypoints and the times represented by the new waypoints; Step 3.3: Calculate the magnitude of the artificial path potential field of the new path point and the gradient of the artificial path potential field of the new path point, and update the new path point using gradient descent; Step 3.4: Repeat steps 3.1-3.3 until the required number of path points is reached.

[0012] Furthermore, step 3.1 specifically includes the following steps: Step 3.1.1: Initialize X: ; Step 3.1.2: Initialize the time represented by each path point: ; Step 3.1.3: Locate the position to insert the new path point: ; Step 3.2 specifically includes the following steps: Step 3.2.1: Initialize the new waypoint: ; Step 3.2.2: Initialize the new pathpoint representation at the following time: .

[0013] Furthermore, step 3.3 specifically includes the following steps: Step 3.3.1: Calculation The magnitude of the potential energy field E of the artificial path; Step 3.3.2: Calculation The gradient of the artificial path potential field is obtained and updated using gradient descent. ; Step 3.3.3: Repeat steps 3.3.1-3.3.2 until convergence.

[0014] Furthermore, the formula for calculating the artificial path potential field E used in step 3.3.1 is as follows:

[0015]

[0016] in, It is the path length of drone i. These are the coordinates of the t-th path point of UAV i. It is the potential energy field of the artificial path length. It is the potential energy field coefficient of the artificial path length. It is the additional potential field applied along the longest path. It is a coefficient. It is a repulsive potential energy field. It is the repulsive potential energy field coefficient, and T is the number of path points. The distance between drones at time t and i and j. It is a distance threshold. It is the repulsive potential energy field of the drone at adjacent moments. It is a coefficient. yes arrive The sum of the distances of drones i and j within the time period.

[0017] A collaborative path planning system for UAV formation flight performances based on an artificial path potential energy field, the system employing the aforementioned collaborative path planning method for UAV formation flight performances based on an artificial path potential energy field, the system comprising: Image target numerical extraction module: Extracts feature points from the image and uses a recursive algorithm to adjust the number of feature points to the target value; Pattern Feature Point Assignment Module: Based on the target values ​​of the image obtained by the image target value extraction module, pattern feature points are assigned to each UAV. The collaborative path planning module of the artificial path potential energy field: Based on the pattern feature points allocated by the pattern feature point allocation module, it performs collaborative path planning for the drone swarm from the starting point to the destination, realizing collaborative path planning for drone formation flight performance based on the artificial path potential energy field.

[0018] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method described above.

[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0020] The beneficial effects of this invention are: This invention can perform collaborative path planning for drone formation flight performances, obtaining the path for each drone, and reducing the length of each path while avoiding collisions.

[0021] When the starting coordinates of each drone in a drone formation and the image of the pattern to be presented in the air are known, this invention can plan a flight path for each drone; when each drone flies along the planned path, the target pattern can be presented in the air without collision. Attached Figure Description

[0022] Figure 1This is a schematic diagram of the path potential field gradient of the present invention.

[0023] Figure 2 This is a schematic diagram of the recursive planning process of the present invention. Figure 3 These are schematic diagrams of the path planning results of the present invention, wherein (a) is a schematic diagram of the path planning results for 100 UAVs, (b) is a schematic diagram of the path planning results for 400 UAVs, (c) is a schematic diagram of the path planning results for 900 UAVs, and (d) is a schematic diagram of the path planning results for 1600 UAVs.

[0024] Figure 4 Figure 1 is a schematic diagram of the running time of the present invention. (a) is a comparison of the running time of the present invention (R-ATPF) and other algorithms on a small-scale cluster, and (b) is a comparison of the running time of the present invention (R-ATPF) and other runnable algorithms on a larger-scale cluster.

[0025] Figure 5 This is a flowchart of the method of the present invention. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0027] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0028] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0029] The following is in conjunction with the appendix to this application specification. Figure 1-5 The technical solutions in the embodiments of this application are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0030] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0031] Implementation Method 1 This embodiment provides a collaborative path planning method for UAV formation flight performance based on artificial path potential energy fields. It extracts pattern feature points that the UAVs need to reach from image I, assigns pattern feature points to each UAV, obtains the destination position of each UAV, and performs collaborative path planning for the UAV swarm from the starting point to the destination. The method is as follows: Figure 1 As shown, it includes the following steps: Step 1: Extract feature points from the image and use a recursive algorithm to adjust the number of feature points to the target value; Step 2: Based on the target values ​​of the image obtained in Step 1, assign pattern feature points to each drone; Step 3: Based on the pattern feature points allocated in Step 2, perform collaborative path planning for the drone swarm from the starting point to the destination, realizing collaborative path planning for drone formation flight performance based on artificial path potential energy field.

[0032] Furthermore, step 1 specifically includes the following steps: the input is image I, and the spacing between adjacent drones on the pattern. The coordinates of the center point of the plane in which the pattern is located in the air. The plane normal vector of the pattern The number of drones is n, and the output is the three-dimensional coordinates of each feature point. ; Step 1.1: Convert image I to a grayscale image; Step 1.2: Apply Gaussian filtering to the grayscale image to remove noise; Step 1.3: Perform dilation and erosion operations on the grayscale image sequentially to remove excessively small pattern points; Step 1.4: Binarize image I by thresholding (0-1); Step 1.5: Continuously increase or decrease the resolution of image I until the number of pixels with a value of 1 in image I is slightly greater than n; Step 1.6: Randomly change the values ​​of several pixels with a value of 1 to 0, so that the number of pixels with a value of 1 in image I is equal to n; Step 1.7: Treat each pixel with a value of 1 as a pattern feature point, according to... , , Calculate the three-dimensional coordinates of each pattern feature point. ;

[0033] in, These are the three-dimensional coordinates of feature point i. These are the pixel coordinates of feature point i in the image. It is a vector product.

[0034] The coordinates of n three-dimensional feature points can be obtained, and these feature points can form a target pattern in the air.

[0035] Furthermore, while we have identified the locations where drones need to reach, we haven't yet planned the specific location each drone should arrive at. Therefore, this step requires planning the correspondence between drones and feature points, that is, specifying which feature point each drone should fly to, and thus planning the start and end points for each drone.

[0036] This step mainly uses integer programming techniques to assign feature points, which is achieved by minimizing the average displacement of the UAV.

[0037] Step 2 specifically includes the following steps, where the input is the starting position of each drone. 3D coordinates of each pattern feature point Output the destination position of each drone. ; Step 2.1: Establish the objective function: Replacement ; Step 2.2: Solve the above objective function using the branch and bound method.

[0038] By obtaining a one-to-one correspondence between drones and pattern feature points, a destination can be planned for each drone.

[0039] Furthermore, based on the previously assigned start and end points for each drone, the flight paths of the drones are planned, requiring the drone swarm to reach the end point as quickly as possible and simultaneously without any collisions. The path planning is mainly achieved using artificial path potential fields and recursive solutions.

[0040] The input is a list of drones and the starting position of each drone. End position Output the position X of each drone at any given time; step 3 specifically includes the following steps: Step 3.1: Initialize the position X of each drone at any given time, the time represented by each waypoint, and the position to insert a new waypoint; Step 3.2: Based on the data initialized in Step 3.1, initialize the new waypoints and the times represented by the new waypoints; Step 3.3: Calculate the magnitude of the artificial path potential field of the new path point and the gradient of the artificial path potential field of the new path point, and update the new path point using gradient descent; Step 3.4: Repeat steps 3.1-3.3 until the required number of path points is reached.

[0041] Furthermore, step 3.1 specifically includes the following steps: Step 3.1.1: Initialize X: ; Step 3.1.2: Initialize the time represented by each path point: ; Step 3.1.3: Locate the position to insert the new path point: ; Step 3.2 specifically includes the following steps: Step 3.2.1: Initialize the new waypoint: ; Step 3.2.2: Initialize the new pathpoint representation at the following time: .

[0042] Furthermore, step 3.3 specifically includes the following steps: Step 3.3.1: Calculation The magnitude of the potential energy field E of the artificial path; Step 3.3.2: Calculation The gradient of the artificial path potential field is obtained and updated using gradient descent. ; Step 3.3.3: Repeat steps 3.3.1-3.3.2 until convergence.

[0043] Furthermore, the formula for calculating the artificial path potential field E used in step 3.3.1 is as follows:

[0044]

[0045] in, It is the path length of drone i. These are the coordinates of the t-th path point of UAV i. It is the potential energy field of the artificial path length. It is the potential energy field coefficient of the artificial path length. It is the additional potential field applied along the longest path. It is a coefficient. It is a repulsive potential energy field. It is the repulsive potential energy field coefficient, and T is the number of path points. The distance between drones at time t and i and j. It is a distance threshold. It is the repulsive potential energy field of the drone at adjacent moments. It is a coefficient. yes arrive The sum of the distances of drones i and j within the time period.

[0046] The gradient of the artificial path potential field can ensure that the UAV path converges to the optimal value, as illustrated in the diagram. Figure 1 As shown. Step 3.4 represents the recursive addition of path points, which can obtain any number of path points with limited computing power, such as... Figure 2 As shown.

[0047] This invention can plan paths for swarms of drones numbering in the thousands, such as... Figure 3 As shown. By calculating the distance between any two adjacent path points, it can be confirmed that the path planned by this invention can completely avoid collisions in most cases.

[0048] The experimental results of this invention are shown in Tables 1 and 2. The experimental results show that this invention has significant advantages over other algorithms in terms of solution accuracy (fewer collisions, shorter path length) and running time, confirming that this invention is a cooperative path planning algorithm suitable for use on large-scale computer clusters.

[0049] The running time of this invention and other algorithms is as follows: Figure 4 As shown, the running time of this invention is better than most algorithms, so it can also complete the solution within a specified time on a large-scale cluster.

[0050] Table 1 Comparison of the present invention with other algorithms

[0051] Table 2 Comparative experiments of the present invention and APF in ultra-large-scale fleets

[0052] Implementation Method 2 This embodiment provides a collaborative path planning system for UAV formation flight performances based on an artificial path potential energy field. The system uses a collaborative path planning method for UAV formation flight performances based on an artificial path potential energy field as described in Embodiment 1. The system includes: Image target numerical extraction module: Extracts feature points from the image and uses a recursive algorithm to adjust the number of feature points to the target value; Pattern Feature Point Assignment Module: Based on the target values ​​of the image obtained by the image target value extraction module, pattern feature points are assigned to each UAV. The collaborative path planning module of the artificial path potential energy field: Based on the pattern feature points allocated by the pattern feature point allocation module, it performs collaborative path planning for the drone swarm from the starting point to the destination, realizing collaborative path planning for drone formation flight performance based on the artificial path potential energy field.

[0053] Implementation Method 3 This invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. The memory stores software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and processor are connected via a bus. Specifically, the processor implements any step in Embodiment 1 by running the computer program stored in the memory.

[0054] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0055] Memory may include read-only memory, flash memory, and random access memory, and provides instructions and data to the processor. Some or all of the memory may also include non-volatile random access memory.

[0056] It should be understood that if the integrated modules / units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods described above can also be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0057] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0058] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the above device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0059] It should be noted that the methods and detailed examples provided in the above embodiments can be incorporated into the apparatus and devices provided in the embodiments for mutual reference, and will not be repeated here.

[0060] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0061] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units described above is merely a logical functional division, and in actual implementation, it can be divided in other ways. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A collaborative path planning method for UAV formation flight performances based on artificial path potential energy fields, characterized in that, The method includes the following steps: Step 1: Extract feature points from the image and use a recursive algorithm to adjust the number of feature points to the target value; Step 2: Based on the target values ​​of the image obtained in Step 1, assign pattern feature points to each drone; Step 3: Based on the pattern feature points allocated in Step 2, perform collaborative path planning for the drone swarm from the starting point to the destination, realizing collaborative path planning for drone formation flight performance based on artificial path potential energy field.

2. The method according to claim 1, characterized in that, Step 1 specifically includes the following steps: Step 1.1: Convert image I to a grayscale image; Step 1.2: Apply Gaussian filtering to the grayscale image to remove noise; Step 1.3: Perform dilation and erosion operations sequentially on the grayscale image; Step 1.4: Binarize image I by thresholding (0-1); Step 1.5: Continuously increase or decrease the resolution of image I until the number of pixels with a value of 1 in image I is slightly greater than n; Step 1.6: Randomly change the values ​​of several pixels with a value of 1 to 0, so that the number of pixels with a value of 1 in image I is equal to n; Step 1.7: Treat each pixel with a value of 1 as a pattern feature point, according to... , , Calculate the three-dimensional coordinates of each pattern feature point. .

3. The method according to claim 2, characterized in that, Step 2 specifically includes the following steps. Step 2.1: Establish the objective function: Replacement ; Step 2.2: Solve the above objective function using the branch and bound method.

4. The method according to claim 3, characterized in that, Step 3 specifically includes the following steps: Step 3.1: Initialize the position X of each drone at any given time, the time represented by each waypoint, and the position to insert a new waypoint; Step 3.2: Based on the data initialized in Step 3.1, initialize the new waypoints and the times represented by the new waypoints; Step 3.3: Calculate the magnitude of the artificial path potential field of the new path point and the gradient of the artificial path potential field of the new path point, and update the new path point using gradient descent; Step 3.4: Repeat steps 3.1-3.3 until the required number of path points is reached.

5. The method according to claim 4, characterized in that, Step 3.1 specifically includes the following steps: Step 3.1.1: Initialize X: ; Step 3.1.2: Initialize the time represented by each path point: ; Step 3.1.3: Locate the position to insert the new path point: ; Step 3.2 specifically includes the following steps: Step 3.2.1: Initialize the new waypoint: ; Step 3.2.2: Initialize the new pathpoint representation at the following time: .

6. The method according to claim 4, characterized in that, Step 3.3 specifically includes the following steps: Step 3.3.1: Calculation The magnitude of the artificial path potential field E; Step 3.3.2: Calculation The gradient of the artificial path potential field is obtained and updated using gradient descent. ; Step 3.3.3: Repeat steps 3.3.1-3.3.2 until convergence.

7. The method according to claim 6, characterized in that, The formula for calculating the artificial path potential field E used in step 3.3.1 is as follows: in, It is the path length of drone i. These are the coordinates of the t-th path point of UAV i. It is the potential energy field of the artificial path length. It is the potential energy field coefficient of the artificial path length. It is the additional potential field applied along the longest path. It is a coefficient. It is a repulsive potential energy field. It is the repulsive potential energy field coefficient, and T is the number of path points. The distance between drones at time t and i and j. It is a distance threshold. It is the repulsive potential energy field of the drone at adjacent moments. It is a coefficient. yes arrive The sum of the distances of drones i and j within the time period.

8. A collaborative path planning system for UAV formation flight performances based on artificial path potential energy fields, characterized in that, The system uses a collaborative path planning method for UAV formation flight performance based on an artificial path potential energy field as described in any one of claims 1-7, and the system includes: Image target numerical extraction module: Extracts feature points from the image and uses a recursive algorithm to adjust the number of feature points to the target value; Pattern Feature Point Assignment Module: Based on the target values ​​of the image obtained by the image target value extraction module, pattern feature points are assigned to each UAV. The collaborative path planning module of the artificial path potential energy field: Based on the pattern feature points allocated by the pattern feature point allocation module, it performs collaborative path planning for the drone swarm from the starting point to the destination, realizing collaborative path planning for drone formation flight performance based on the artificial path potential energy field.

9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-7.

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