Unmanned aerial vehicle cluster path planning control method and storage medium

Abstract

The invention provides an unmanned aerial vehicle cluster path planning control method, which comprises the following steps: obtaining initial position information of an unmanned aerial vehicle cluster and geometric requirements of target configuration, determining a target position of each unmanned aerial vehicle, the initial position information of each unmanned aerial vehicle in the unmanned aerial vehicle cluster comprising latitude and longitude information and height information, the height information is obtained by each unmanned aerial vehicle through a barometer parameter of the unmanned aerial vehicle, and the latitude and longitude information is obtained by signal interaction between the unmanned aerial vehicle and the ultra-wideband positioning system; generating a smooth path of the unmanned aerial vehicle cluster from the current configuration to the target configuration by adopting a dynamic path planning algorithm according to the target position and the initial position information; in the path execution process, the position change of the unmanned aerial vehicle is monitored in real time, whether a potential collision risk exists or not is judged, and the path is adjusted to ensure flight safety; if the unmanned aerial vehicle completes the target configuration transformation, stopping path adjustment, and keeping the current configuration stable; and new morphological transformation is completed according to task requirements.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for controlling the path planning of an unmanned aerial vehicle (UAV) cluster and a storage medium. Background Art

[0002] In the UAV cluster flight control system, the transformation of the cluster configuration is the core challenge for achieving task adaptability and functional diversity. Specifically, this technical problem involves how to enable a group of UAVs to efficiently and stably complete the switching of different geometric configurations, including linear, matrix, spherical, and functional shapes required for specific tasks, in a complex dynamic environment. The primary difficulty in this process lies in how to design an accurate relative position control algorithm to ensure that each UAV can adjust its own position in real time during the configuration transformation to meet the geometric requirements of the target configuration. Due to the large number of UAVs and the coupled motion states of each UAV, how to avoid the distortion of the overall configuration caused by local errors becomes a key issue.

[0003] Secondly, dynamic path planning is another core challenge for configuration transformation. During the switching process, the UAV group needs to generate and execute a smooth path within a limited time to minimize the time and energy consumption of the configuration transformation. However, the paths between different configurations vary greatly, and how to balance the physical constraints of the UAVs, such as the maximum speed and acceleration, while ensuring the continuity of the path, is a difficult point in the planning algorithm.

[0004] Finally, the optimization of the configuration transformation process needs to comprehensively consider energy consumption and collision risks. Since there are frequent position adjustments during the configuration transformation of UAVs, how to real-time monitor and avoid potential collision risks to ensure flight safety is also a key difficulty in technical implementation. These problems together constitute the core contradictions in the UAV cluster configuration transformation technology and need to be solved one by one in algorithm design and system implementation. Summary of the Invention

[0005] Aiming at the deficiencies in the prior art, the purpose of the present invention is to propose a method for controlling the path planning of an unmanned aerial vehicle (UAV) cluster and a storage medium to solve the problems mentioned in the above background art section.

[0006] A method for path planning and control of an unmanned aerial vehicle (UAV) cluster, characterized in that it is implemented based on an Ultra WideBand (UWB) positioning system. The UWB positioning system includes a plurality of UWB base stations, and the plurality of UWB base stations are arranged in the projection plane of the three-dimensional space where the UAV path is located on the ground. One of the UWB base stations is defined as a reference base station, and the other UWB base stations are non-reference base stations. Synchronize the longitude and latitude information of the reference base station to the reference base station, and perform positioning of other non-reference base stations according to the longitude and latitude information of the reference base station to obtain the longitude and latitude information of other non-reference base stations. The method includes: S1. Obtain the initial position information of the UAV cluster and the geometric requirements of the target configuration, and determine the target position of each UAV. Among them, the initial position information of each UAV in the UAV cluster includes longitude and latitude information and altitude information. The altitude information is obtained by each UAV through its own barometer parameters, and the longitude and latitude information is obtained by the UAV through signal interaction with the UWB positioning system; S2. According to the target position and the initial position information, adopt a dynamic path planning algorithm to generate a smooth path for the UAV cluster from the current configuration to the target configuration; S3. During the execution of the path, monitor the position change of the UAV in real time, judge whether there is a potential collision risk, and adjust the path to ensure flight safety; S4. If the UAV completes the target configuration transformation, stop path adjustment and maintain the stability of the current configuration; S5. According to the task requirements, dynamically adjust the target configuration, repeat the above steps, and complete the new configuration transformation.

[0007] As a further improvement, in step S1, the obtaining of the initial position information of the UAV cluster and the geometric requirements of the target configuration, and determining the target position of each UAV specifically includes: Obtain the initial position of the UAV cluster through the UWB positioning system, and store the position information in the position database; Read the initial position information from the position database, combine the pre-set geometric requirements of the target configuration, and extract the configuration parameters; According to the configuration parameters and the initial position information, use a spatial geometry algorithm to calculate the center point coordinates of the target configuration; Based on the center point of the target configuration, combine the configuration parameters, and use a position distribution algorithm to calculate the target position of each UAV.

[0008] As a further improvement, the obtaining of the longitude and latitude information by the UAV through signal interaction with the UWB positioning system specifically includes: Obtain the distances between the UAV and the two UWB base stations closest to it in the UWB positioning system, and obtain the projection position of the UAV on the projection plane, that is, its longitude and latitude information, according to the altitude of the UAV from the projection plane.

[0009] As a further improvement, the dynamic path planning algorithm is adopted to generate a smooth path for the UAV cluster from the current configuration to the target configuration, including: Obtain the initial configuration and target configuration of the UAV cluster, and determine the cluster state and geometric requirements; Set the constraint conditions of the dynamic programming algorithm according to physical constraints and geometric requirements; Adopt the dynamic programming algorithm to generate a set of candidate paths from the initial configuration to the target configuration; For the set of candidate paths, calculate the smooth path through the path optimization algorithm; According to the objective function and algorithm parameters, judge whether the smooth path meets the physical constraints and geometric requirements; If the path meets the conditions, determine the smooth path as the final output; If the path does not meet the conditions, adjust the algorithm parameters and regenerate the smooth path.

[0010] As a further improvement, the calculation of the smooth path by the path optimization algorithm specifically includes: Evaluate the path through the smoothness evaluation function C(p), and determine the smooth path as the final output for the path with C(p) less than the set threshold: ; where p(t) represents the path parameter, α and β are weight coefficients, T is the total path time, the first derivative represents the speed, and the second derivative represents the acceleration.

[0011] As a further improvement, during the path execution, the position change of the UAV is monitored in real time to judge whether there is a potential collision risk, and the path is adjusted to ensure flight safety, including: Judge whether there is a potential collision risk through the repulsive potential field function V between UAVs. The repulsive potential field function V between UAVs is: ; where d ij represents the distance between the i-th and j-th UAVs, d safe represents the safety distance threshold, and k1 and k2 are adjustment coefficients.

[0012] The present invention further provides a computer-readable storage medium, which stores a computer program that can be executed by a processor of the device where the computer-readable storage medium is located to implement the method described above.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: The present invention uses an ultra-wideband positioning system to achieve high-precision positioning, with a positioning accuracy of up to centimeter level, providing a reliable guarantee for the precise control of unmanned aerial vehicle (UAV) swarms. Through the signal interaction of multiple ultra-wideband base stations, the system can accurately obtain the initial position information of UAVs, including longitude, latitude, and altitude, ensuring the accuracy of the starting point of path planning. In addition, the present invention adopts a dynamic path planning algorithm, which can generate a smooth path from the current configuration to the target configuration according to the initial positions and target configurations of UAV swarms. This algorithm fully considers the physical constraints and geometric requirements of UAVs, such as maximum speed, acceleration, etc., to ensure the feasibility and safety of the path. In addition, the present invention introduces a repulsive potential field function to quantitatively evaluate the collision risk between UAVs. When the distance between UAVs is less than the safety threshold, the function value increases, indicating an increased collision risk. This method can judge potential collision threats in real time and accurately, providing a scientific basis for path adjustment. Description of the Drawings

[0014] Figure 1 It is a flowchart of a method for path planning and control of a UAV swarm according to the present invention.

[0015] Figure 2 It is an architecture diagram of an ultra-wideband positioning system in a method for path planning and control of a UAV swarm according to the present invention. Detailed Embodiments

[0016] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0017] As Figure 1 , a method for path planning and control of a UAV swarm in this embodiment may specifically include: The method is implemented based on an ultra-wideband positioning system, and the ultra-wideband positioning system includes multiple ultra-wideband base stations 1 / 2. The multiple ultra-wideband base stations are arranged in the projection plane of the three-dimensional space where the UAV path is located on the ground. One of the ultra-wideband base stations is defined as the reference base station 1 (serving as the origin O of the coordinate axes XYZ), and the other ultra-wideband base stations are non-reference base stations 2-1 / 2-2 / 2-3... / 2-n. Synchronize the longitude and latitude information of the reference base station 1 to the reference base station 1, and perform positioning on other non-reference base stations 2-1 / 2-2 / 2-3... / 2-n according to the longitude and latitude information of the reference base station 1 to obtain the longitude and latitude information of other non-reference base stations. The method includes: S1. Obtain the initial position information of the UAV cluster and the geometric requirements of the target configuration, and determine the target position of each UAV. Among them, the initial position information of each UAV in the UAV cluster includes longitude and latitude information and altitude information. The altitude information is obtained by each UAV through its own barometer parameters, and the longitude and latitude information is obtained by the UAV through signal interaction with the ultra-wideband positioning system; S2. According to the target position and the initial position information, use the dynamic path planning algorithm to generate a smooth path for the UAV cluster to transform from the current configuration to the target configuration; S3. During the execution of the path, continuously monitor the position change of the UAV, judge whether there is a potential collision risk, and adjust the path to ensure flight safety; S4. If the UAV completes the target configuration transformation, stop path adjustment and maintain the stability of the current configuration; S5. According to the mission requirements, dynamically adjust the target configuration, and repeat the above steps to complete the new form transformation.

[0018] In step S1, the obtaining of the initial position information of the UAV cluster and the geometric requirements of the target configuration and the determination of the target position of each UAV specifically include: S11. Obtain the initial position of the UAV cluster through the ultra-wideband positioning system, and store the position information in the position database; S12. Read the initial position information from the position database, and combine the pre-set geometric requirements of the target configuration to extract the configuration parameters; S13. According to the configuration parameters and the initial position information, use the spatial geometry algorithm to calculate the center point coordinates of the target configuration; S14. Based on the center point of the target configuration, combine the configuration parameters, and use the position distribution algorithm to calculate the target position of each UAV.

[0019] Exemplarily, in the acquisition of the initial position information of the UAV cluster, assume that there are 5 UAVs at the same altitude z, and their initial coordinates are (0, 0, z), (10, 0, z), (20, 0, z), (30, 0, z), (40, 0, z). The target configuration requires the UAVs to form a regular pentagon with a height of z and a side length of 15. First, determine the center coordinates of the regular pentagon as (30, 15, z) through geometric calculation, and then calculate the target positions of each UAV according to the vertex formula of the regular pentagon, which are (30, 30, z), (15, 282, z), (49, 119, z), (251, 119, z), (45, 282, z). Then use the region division algorithm based on the Voronoi diagram to divide the entire plane region with a height of Z into 5 independent sub-regions, and each UAV is responsible for one sub-region.

[0020] Please refer to Figure 2As described above, further, the process of obtaining the longitude and latitude information through signal interaction between the drone and the ultra-wideband positioning system specifically includes: Obtain the distances L1 and L2 between the drone 5 and the two closest ultra-wideband base stations 1 / 2 - 3 in the ultra-wideband positioning system, and based on the height H1 of the drone 5 from the projection plane, obtain the projection position of the drone 5 on the projection plane, which is its longitude and latitude information. Among them, the height H1 of the drone 5 from the projection plane is obtained by acquiring the altitude through the barometer on the drone 5 and then subtracting the altitude of the projection plane. Therefore, through the distances L1 and L2 and the height H1, according to the trigonometric formula, the distances L’1 and L’2 between point A of the drone 5 on the projection plane and the two ultra-wideband base stations 1 / 2 - 3 can be calculated, and respectively, with the ultra-wideband base stations 1 / 2 - 3 as the origin and the distances L’1 and L’2 as the radii, draw circles that intersect at point A within the projection plane (points outside the projection plane are directly removed to reduce the calculation amount), which is its longitude and latitude information. Finally, combined with the height H1, obtain the overall position information of the drone 5. Since the positioning accuracy of the ultra-wideband base station is at the centimeter level, it is possible to eliminate the technical problem of inaccurate positioning accuracy caused by GPS positioning, resulting in a large difference in the overall configuration.

[0021] Generally speaking, the ultra-wideband base stations are arranged at equal intervals. As a further improvement, in other embodiments, in order to reduce blind spots, preferably, ultra-wideband base stations need to be arranged at key nodes and turning points on the drone path. For example, in path planning, the key nodes include: starting point, target point, turning point, speed adjustment point, obstacle avoidance point, and emergency landing point, etc.; and the turning point generally refers to a point with a large curvature change.

[0022] In one of the embodiments, the arrangement of the ultra-wideband base station at the speed adjustment point can be carried out in the following manner: Define the speed of the drone as follows:

[0023] Where: r u (t) is the position vector of the drone at time t; r u0 is the initial position vector of the drone; v u (T) is the speed vector of the drone, which is a function of time.

[0024] And the propagation loss L(d) of the ultra-wideband signal can be obtained through the following formula:

[0025] Where: L(d) is the signal loss at distance d; L0 is the signal loss at the reference distance d0; n is the path loss exponent, which depends on the environmental characteristics.

[0026] and the coverage radius of the base station is R(t):

[0027] Where: L tx is the transmission power of the base station; L rx is the sensitivity of the receiver.

[0028] Finally, the layout spacing D(t) of the base station is dynamically adjusted according to the speed change of the UAV as follows:

[0029] Where θ is the angle between the base stations; k v is the speed adjustment coefficient; L tx0 is the initial transmission power of the base station.

[0030] In other embodiments, the layout density of the base stations at the starting point, the target point, the turning point, the obstacle avoidance point, and the emergency landing point can be about 2 times the general layout spacing density. For example, assuming that the general layout spacing is about 40 meters, the spacing between the base stations at the starting point, the target point, the turning point, the obstacle avoidance point, and the emergency landing point can be appropriately reduced to about 20 meters.

[0031] In one of the embodiments, the layout of the ultra-wideband base stations at the turning point can be carried out in the following manner: Define the flight path of the UAV as a parametric curve r(s), where s is the arc length parameter of the path. Therefore, the curvature κ(s) of its path can be defined as:

[0032] Where the curvature κ(s) describes the degree of bending of the path at point s.

[0033] The propagation loss of the ultra-wideband signal can be obtained by the formula L(d), and the coverage radius of the base station can be obtained by the formula R(t).

[0034] When adjusting the layout spacing of the base stations according to the path curvature, the influence of the curvature on the signal coverage needs to be considered. In the high-curvature area, the layout spacing of the base stations should be reduced to ensure the continuity of the signal coverage; in the low-curvature area, the layout spacing can be increased. The layout spacing D(s) can be dynamically adjusted according to the curvature κ(s) as follows:

[0035] Where R, that is, R(t), is the coverage radius of the base station; θ is the angle between the base stations, and α is the curvature adjustment coefficient, which is used to control the influence degree of the curvature on the layout spacing.

[0036] As a further improvement, in step S2, the use of a dynamic path planning algorithm to generate a smooth path for the UAV swarm from the current configuration to the target configuration includes: S21, obtaining the initial configuration and the target configuration of the UAV swarm, and determining the swarm state and geometric requirements; S22, setting the constraint conditions of the dynamic programming algorithm according to physical constraints and geometric requirements; S23, using the dynamic programming algorithm to generate a set of candidate paths from the initial configuration to the target configuration; S24, for the set of candidate paths, calculating a smooth path through a path optimization algorithm; S25, judging whether the smooth path meets the physical constraints and geometric requirements according to the objective function and algorithm parameters; S26, if the path meets the conditions, determining the smooth path as the final output; S27, if the path does not meet the conditions, adjusting the algorithm parameters and regenerating the smooth path.

[0037] Specifically, in dynamic path planning, it is first necessary to obtain the initial position and the target position of the UAV swarm. For example, the initial position is (0, 0, 0) and the target position is (10, 10, 5), with the unit being meters. Then, an initial path is generated through the Rapidly-exploring Random Tree (RRT) algorithm. The RRT algorithm randomly samples nodes in three-dimensional space and ensures the feasibility of the path through collision detection. For example, the sampling step size is 5 meters, the collision detection uses an octree data structure, and the detection accuracy is 1 meter. Then, spline interpolation is used to smooth the initial path to ensure the continuity and differentiability of the path. For example, a cubic B-spline curve is used, the control point interval is 1 meter, and the curvature of the smoothed path is less than 1. To meet the physical constraints of the UAVs, such as a maximum speed of 2 m / s and a maximum acceleration of 1 m / s², Model Predictive Control (MPC) is used to optimize the path. MPC generates an optimal path that conforms to the dynamic model by solving a constrained quadratic programming problem. For example, the prediction horizon is 5 seconds, the control horizon is 2 seconds, and the optimization objective is to minimize energy consumption. Finally, the planned path is distributed to each UAV in the swarm through a distributed communication protocol to ensure coordinated flight. For example, a communication protocol based on Time Division Multiple Access (TDMA) is used, the communication period is 1 second, and the data transmission delay is less than 10 milliseconds.

[0038] The calculation of the smooth path by the path optimization algorithm specifically includes: Evaluating the path through a smoothness evaluation function C(p): ; Among them, p(t) represents the path parameter, α and β are weight coefficients, T is the total path time, the first derivative represents the velocity, and the second derivative represents the acceleration. When the path is smooth, the changes in velocity and acceleration are relatively small, so the integral value of their squares is also small, resulting in a small C(p) value. On the contrary, if there are drastic changes in velocity or acceleration in the path, such as sharp turns, sudden accelerations or decelerations, etc., the C(p) value will be large. Therefore, the smaller the C(p), the smoother the path is in terms of velocity and acceleration, the more stable and comfortable the flight process is, and it also helps to reduce the energy consumption and mechanical wear of the UAV. In the actual calculation process, in order to simplify the calculation, a threshold can be set, and the path with C(p) less than the set threshold is determined as the smooth path for the final output. This can greatly reduce the amount of calculation, because there are many types of initial paths generated by the Rapidly-exploring Random Tree (RRT) algorithm and it is difficult to exhaust them.

[0039] In step S3, during the execution of the path, the position change of the UAV is monitored in real time to determine whether there is a potential collision risk, and the path is adjusted to ensure flight safety, which specifically includes: Determine whether there is a potential collision risk through the repulsive potential field function V between UAVs. The repulsive potential field function V between UAVs is: ; where d ij represents the distance between the i-th and j-th UAVs, and d safe represents the safety distance threshold, and k1 and k2 are adjustment coefficients. In one embodiment, when the repulsive potential field function V is greater than the set value, it is determined that there is a collision risk.

[0040] The embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program, and the computer program can be executed by the processor of the device where the computer-readable storage medium is located to implement the method as described above.

[0041] The present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0042] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0043] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0045] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for path planning and control of an unmanned aerial vehicle cluster, characterized in that, It is implemented based on an ultra-wideband positioning system, which includes multiple ultra-wideband base stations. The multiple ultra-wideband base stations are arranged in the projection plane on the ground of the three-dimensional space where the UAV path is located. One of the ultra-wideband base stations is defined as the reference base station, and the other ultra-wideband base stations are non-reference base stations. Synchronize the longitude and latitude information of the reference base station to the reference base station, and perform positioning of other non-reference base stations based on the longitude and latitude information of the reference base station to obtain the longitude and latitude information of other non-reference base stations. Further, ultra-wideband base stations are arranged at key nodes and turning points on the UAV path. The key nodes include: starting point, target point, turning point, speed adjustment point, obstacle avoidance point, and emergency landing point; the turning point refers to a point where the curvature exceeds a set threshold; the method includes: S1. Obtain the initial position information of the UAV cluster and the geometric requirements of the target configuration, and determine the target position of each UAV. Among them, the initial position information of each UAV in the UAV cluster includes longitude and latitude information and altitude information. The altitude information is obtained by each UAV through its own barometer parameters, and the longitude and latitude information is obtained by the UAV through signal interaction with the ultra-wideband positioning system; S2. According to the target position and the initial position information, use a dynamic path planning algorithm to generate a smooth path for the UAV cluster from the current configuration to the target configuration; S3. During the execution of the path, monitor the position change of the UAV in real time, judge whether there is a potential collision risk, and adjust the path to ensure flight safety; S4. If the UAV completes the target configuration transformation, stop path adjustment and keep the current configuration stable; S5. According to the mission requirements, dynamically adjust the target configuration, and repeat the above steps to complete a new form transformation.

2. The method according to claim 1, characterized in that, In step S1, the obtaining the initial position information of the UAV cluster and the geometric requirements of the target configuration, and determining the target position of each UAV specifically includes: Obtain the initial position of the UAV cluster through the ultra-wideband positioning system, and store the position information in the position database; Read the initial position information from the position database, and combine the pre-set geometric requirements of the target configuration to extract configuration parameters; According to the configuration parameters and the initial position information, use a spatial geometry algorithm to calculate the center point coordinates of the target configuration; Based on the center point of the target configuration, combine the configuration parameters, and use a position distribution algorithm to calculate the target position of each UAV.

3. The method according to claim 2, wherein The longitude and latitude information is obtained by the UAV through signal interaction with the ultra-wideband positioning system specifically includes: Obtain the distances between the UAV and the two ultra-wideband base stations closest to it in the ultra-wideband positioning system, and obtain the projection position of the UAV on the projection plane, that is, its longitude and latitude information, according to the height of the UAV from the projection plane.

4. The method according to claim 1, wherein The using a dynamic path planning algorithm to generate a smooth path for the UAV cluster from the current configuration to the target configuration includes: Obtain the initial configuration and the target configuration of the UAV cluster, and determine the cluster state and geometric requirements; According to the physical constraints and geometric requirements, set the constraint conditions of the dynamic programming algorithm; Use a dynamic programming algorithm to generate a set of candidate paths from the initial configuration to the target configuration; For the candidate path set, calculate a smooth path through a path optimization algorithm; According to the objective function and algorithm parameters, determine whether the smooth path meets the physical constraints and geometric requirements; If the path meets the conditions, determine the smooth path as the final output; If the path does not meet the conditions, adjust the algorithm parameters and regenerate the smooth path.

5. The method according to claim 1, characterized in that The specific calculation of the smooth path by the path optimization algorithm includes: Evaluate the path through the smoothness evaluation function C(p), and determine the smooth path as the final output for the path where C(p) is less than the set threshold: ； Among them, p(t) represents the path parameter, α and β are weight coefficients, T is the total path time, the first derivative represents the speed, and the second derivative represents the acceleration.

6. The method according to claim 1, wherein During the execution of the path, the position change of the UAV is monitored in real time to determine whether there is a potential collision risk, and the path is adjusted to ensure flight safety, including: Judge whether there is a potential collision risk through the repulsive potential field function V between UAVs, and the repulsive potential field function V between UAVs is: ； where d ij represents the distance between the i-th and j-th drones, and d safe represents the safety distance threshold, and k1 and k2 are adjustment coefficients.

7. A computer-readable storage medium, characterized in that, A computer program is stored, and the computer program can be executed by the processor of the device where the computer-readable storage medium is located to implement the method according to any one of claims 1 to 6.

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