Unmanned aerial vehicle cluster path planning method and system based on A* and Dubins

Abstract

This invention discloses a UAV swarm path planning method based on A* and Dubins curves, comprising: acquiring user-defined scene information, including UAV swarm parameters, mission scenario, takeoff point, target point, and obstacle avoidance area information; using the A* algorithm to complete initial path planning for the UAV swarm based on the scene information, obtaining an initial flight path; using the initial flight path output by the A* algorithm as a basis, smoothing and optimizing the initial flight path using Dubins curves to generate optimized trajectory information that conforms to the UAV's flight attitude; and controlling the UAV to execute flight tasks according to the optimized trajectory information. By integrating the A* algorithm and Dubins curves, the A* algorithm first completes the global optimal path search, and then the Dubins curves smoothly connect waypoints, solving the problem of abrupt curvature changes at inflection points in traditional paths, planning a path that better conforms to the UAV's flight attitude, and significantly improving the path's practicality and flight stability.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, specifically to a method, system, device, and medium for UAV swarm path planning based on A* and Dubins. Background Technology

[0002] In recent years, unmanned aerial vehicles (UAVs) have gained widespread recognition for their importance in various fields such as target reconnaissance, emergency rescue, agricultural and forestry plant protection, and logistics transportation due to their advantages of low operating costs and high maneuverability. With the development of modern warfare technology, single UAVs can no longer meet battlefield demands, making UAV swarm collaborative combat technology a research hotspot. Mission planning, including task allocation and trajectory planning, determines the effectiveness of UAV swarm operations and is one of the key technologies for UAV swarm collaborative combat, thus possessing significant research value.

[0003] In the execution of missions by multiple unmanned aerial vehicle (UAV) systems, path planning plays a crucial role, ensuring that the UAV fleet can efficiently and systematically achieve its predetermined objectives. Currently, UAV path planning technology is widely used in various fields. For example, in large-scale inspection missions, this technology ensures comprehensive and effective coverage and detection of the entire area; in farmland monitoring, path planning provides more scientifically sound flight paths for monitoring work, thereby efficiently collecting crop growth data; in urban express delivery scenarios, path planning technology plans optimal routes for UAVs, enabling the completion of more package delivery tasks within a limited time; and in search and rescue operations at disaster sites, path planning technology can quickly locate victims, gaining valuable time for rescue efforts.

[0004] When using drone swarms to complete various tasks, it's also necessary to consider the dynamic adjustments required for different obstacle avoidance zones and tasks in different scenarios. For example, in forest fire monitoring, drones need to flexibly adjust their flight paths based on the direction and speed of fire spread to ensure real-time capture of the latest fire dynamics. In maritime search and rescue operations, drones need to comprehensively consider dynamic environmental factors such as ocean currents and wind direction changes to plan their flight routes, aiming to improve the efficiency and effectiveness of search and rescue operations. If dynamic adjustments to the task are required during execution, it's also necessary to consider the distance between drones in the swarm and the target, as well as whether the drones themselves have sufficient battery power to complete the remaining tasks.

[0005] To address these challenges, multi-drone path planning is evolving towards intelligence, autonomy, and collaboration. Leveraging advanced algorithms and AI, drones can make real-time decisions, avoid obstacles, and dynamically adjust routes in complex environments, improving mission success rates and safety. Information sharing and collaborative operations among drones further optimize path planning efficiency and reliability. However, research shows that while existing technologies have made significant progress, many problems remain to be solved.

[0006] The disadvantages of existing technologies are as follows:

[0007] (1) Most of the research focuses on the generation of waypoints during flight, without considering the flight attitude of the UAV, such as the altitude of climb and the turning radius of flight, which makes the planned path not quite consistent with the real-time flight path of the UAV.

[0008] (2) If the task needs to be dynamically adjusted during the execution of the task by the drone swarm, the distance between the drone and the target point and whether the remaining power of the drone can support the task to continue to be completed in formation are not considered. Summary of the Invention

[0009] The purpose of this invention is to provide a method, system, device, and medium for UAV swarm path planning based on A* and Dubins. By fusing the A* path planning algorithm and the Dubins path planning algorithm, a path that is more in line with the flight attitude of the UAV is planned, which greatly improves the practicality of the path and the flight stability.

[0010] This invention is achieved through the following technical solution:

[0011] In a first aspect, the first embodiment of the present invention provides a method for unmanned aerial vehicle (UAV) swarm path planning based on A* and Dubins, comprising:

[0012] Obtain user-defined scene information, including drone cluster parameters, mission scenario, takeoff point, target point, and obstacle avoidance area information;

[0013] Based on the scenario information, the A* algorithm is used to complete the initial path planning for the UAV swarm, and the initial flight path is obtained.

[0014] Based on the initial path output by the A* algorithm, the initial path is smoothed and optimized using the Dubins curve to generate optimized path information that conforms to the flight attitude of the UAV.

[0015] Control the drone to perform flight missions according to optimized flight path information.

[0016] Furthermore, after the step of controlling the UAV to perform the flight mission according to the optimized flight path information, the following is also included:

[0017] After receiving the dynamic adjustment command, the distance of each drone from the new target point and its remaining battery power are obtained. A weighted evaluation is performed based on the distance and remaining battery power to determine whether a return to home is supported, and the evaluation result is obtained.

[0018] The drones were reordered according to priority based on the evaluation results;

[0019] Obtain the drone selected by the user to perform the new mission, and replan the flight path for the selected drone.

[0020] Furthermore, the step of using the A* algorithm to complete the initial path planning of the drone swarm based on the scenario information specifically includes:

[0021] Initialize the open and closed lists, and add the starting node to the open list;

[0022] Select the node with the smallest f value from the open list as the current node;

[0023] If the current node is the target node, then construct the path;

[0024] Otherwise, move the current node to the closed list and evaluate its neighboring nodes;

[0025] For each neighboring node, calculate the actual cost from the starting point to the current node and the heuristically estimated cost from the current node to the target node, and update the open list until the target node is reached and the initial waypoint is generated.

[0026] Furthermore, the Dubins curves employ RSL, LSR, LSL, and RSR.

[0027] Furthermore, the smoothing optimization of the initial route using the Dubins curve specifically includes:

[0028] By calculating the starting center, target center, arc angle, and straight line segment length, and with the goal of minimizing the track length, the waypoints generated by the A* algorithm are connected smoothly in sequence.

[0029] Furthermore, the formula for calculating whether a return trip is supported based on a weighted evaluation of the distance and remaining battery power is as follows:

[0030] ;

[0031] in, This indicates the distance of the drone from the new target point. This indicates the remaining battery power of the drone. The weights representing the distance of the drone from the new target point This indicates the weight of the drone's remaining battery power. .

[0032] Secondly, another embodiment of the present invention provides a UAV swarm path planning system based on A* and Dubins, used to implement the method described in the above embodiments, including: a scene loading module, a path planning module, a data link transmission module and a flight control module, wherein the scene loading module is connected to the path planning module and is used to configure scene information, wherein the scene information includes UAV swarm parameters, mission scene, take-off point, target point and obstacle avoidance area information;

[0033] The path planning module is used to complete the initial path planning of the UAV cluster using the A* algorithm based on the scene information, to obtain the initial route, and to use the initial route output by the A* algorithm as a basis to smooth and optimize the initial route using the Dubins curve to generate optimized trajectory information that conforms to the flight attitude of the UAV.

[0034] The data link transmission module is connected to the path planning module and is used to receive the optimized trajectory information sent by the path planning module, encapsulate the optimized trajectory information, and transmit it to the flight control module.

[0035] After receiving the optimized flight path information, the flight control module controls the UAV to fly along the planned route.

[0036] Furthermore, it also includes a dynamic adjustment module. After receiving a dynamic adjustment instruction, the dynamic adjustment module is used to obtain the current distance and remaining battery power of each UAV from the new target point, perform a weighted evaluation based on the distance and remaining battery power to determine whether a return to home is supported, obtain the evaluation result, rearrange the UAVs according to priority based on the evaluation result, obtain the UAVs selected by the user to perform the new task, and replan the route for the selected UAVs.

[0037] Thirdly, another embodiment of the present invention provides an electronic device comprising: a processor, an input device, an output device, and a memory, wherein the processor, the input device, the output device, and the memory are interconnected, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the method described in the first embodiment above.

[0038] Fourthly, another embodiment of the present invention provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method described in the first embodiment above.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0040] The first embodiment of this invention provides a drone swarm path planning method based on A* and Dubins curves. It integrates the A* algorithm with Dubins curves, first using the A* algorithm to search for the globally optimal path, and then using Dubins curves to smoothly connect waypoints. This solves the problem of abrupt curvature changes at inflection points in traditional paths, fully matching the drone's turning radius, climb altitude, and flight attitude constraints, generating a smooth and safe flight path that can be directly executed by flight control, significantly improving path practicality and flight stability. When dynamically adjusting tasks, the method considers the distance of each drone in the swarm from the task target point, its current remaining battery power, and whether the minimum and maximum number of drones required to execute the task are sufficient for the user to choose the appropriate method. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0042] Figure 1 A flowchart illustrating a drone swarm path planning method based on A* and Dubins, provided in the first embodiment of the present invention;

[0043] Figure 2 A flowchart of a drone swarm path planning method based on A* and Dubins is provided for another embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the structure of a drone swarm path planning system based on A* and Dubins, provided for another embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of a UAV swarm path planning system based on A* and Dubins, provided as another embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0047] like Figure 1As shown, the first embodiment of the present invention provides a UAV swarm path planning method based on A* and Dubins, applicable to the backend server of a UAV swarm path planning system based on A* and Dubins. The method includes the following steps:

[0048] Obtain user-defined scene information, including drone cluster parameters, mission scenario, takeoff point, target point, and obstacle avoidance area information;

[0049] Based on the scenario information, the A* algorithm is used to complete the initial path planning for the UAV swarm, and the initial flight path is obtained.

[0050] Based on the initial path output by the A* algorithm, the initial path is smoothed and optimized using the Dubins curve to generate optimized trajectory information that conforms to the flight attitude of the UAV.

[0051] Control the drone to perform flight missions according to optimized flight path information.

[0052] The above steps combine the A* path planning algorithm and the Dubins path planning algorithm to plan a path that better matches the flight attitude of the UAV.

[0053] Users can set the number of drones in the current drone swarm, as well as the parameters of each drone, such as flight speed, turning radius, climb altitude, and the location of areas to be avoided in the scene. After obtaining the scene information set by the user, the backend server uses the A* algorithm to perform initial path planning for the drone swarm.

[0054] Users can set the number of drones in the current drone swarm, as well as the parameters of each drone, such as flight speed, turning radius, climb altitude, and the location of areas to be avoided in the scene. After obtaining the scene information set by the user, the backend server uses the A* algorithm to perform initial path planning for the drone swarm.

[0055] First, simplify the obstacle areas in the scene into circular areas, and establish a two-dimensional spatial coordinate system with a certain point on the simplified map as the origin O. Map the task space onto a two-dimensional plane:

[0056] ,

[0057] In the formula: A represents the task space; (x, y) are the coordinates of any point in the task space; 0 ≤ x ≤ x max and 0≤y≤y max This defines the scope of the task space. Assume the obstacle region within this space is: Z represents the set of obstacle regions. Indicates different obstacle areas.

[0058] The A* algorithm is a graph search algorithm widely used in path planning. Its basic idea is to find the optimal path by evaluating the total cost from the starting point to the target node. The A* algorithm combines the advantages of Dijkstra's algorithm with heuristic search, using the following formula to calculate the estimated cost of a node:

[0059] ,

[0060] in, This represents the actual cost from the starting node to the current node n. This represents the heuristic cost estimate from the current node n to the target node.

[0061] When planning a path using the A* algorithm, first initialize the open list (nodes to be evaluated) and the closed list (nodes already evaluated). Add the starting node to the open list.

[0062] Repeat the following steps until the target node is found or the open list is empty:

[0063] (1) Select the node with the smallest f value from the open list as the current node.

[0064] (2) If the current node is the target node, construct the path and return.

[0065] (3) Otherwise, move the current node to the closed list and evaluate its neighboring nodes.

[0066] (4) For each neighbor node, calculate g(n) and h(n) and update the open list.

[0067] The UAV path generated by the A* algorithm does not take into account the UAV's turning radius and the curvature smoothness of the path. There is a problem of curvature abrupt change at the inflection point. At this time, multiple Dubins curves are used sequentially based on the waypoints planned by the A* algorithm. The waypoints are connected in a head-to-tail manner to optimize the pose and curve radius at the curve connection point. With the goal of minimizing the UAV track length, a smooth and safe shortest track is obtained.

[0068] Dubins curves include six types: {RSL, LSR, LSL, RSR, RLR, LRL}. "R" represents a right turn, represented as a clockwise arc segment on the unit circle; "L" represents a left turn, represented as a counter-clockwise arc segment on the unit circle; and "S" represents a straight line segment. Taking the LSR type Dubins curve as an example... Indicates the starting point. The target point is represented by the circle; the radius of both circles is R. , These are the angles between the tangent directions at the starting and target points and the X-axis, respectively. Since RLR and LRL type Dubins curves do not contain straight line segments and the curvature of circular segments is too large, only the four types of Dubins curves (RSL, LSR, LSL, and RSR) are considered. The formula for calculating the length of a Dubins curve is as follows:

[0069] ,

[0070] in, These are the turning parameters for the starting arc and the target arc, respectively. A value of 1 is used for a right turn, and a value of -1 is used for a left turn. , and These represent the lengths of the initial arc segment, the straight line segment, and the target arc segment, respectively. , These are the initial arc angle and the target arc angle, respectively. This represents the length of the straight line segment between the end point of the starting arc segment and the start point of the target arc segment. and These represent the distances along the X and Y axes between the end point of the initial arc segment and the start point of the target arc segment, respectively. Calculation , First, the starting center and the target center of the circle must be determined using the following formula:

[0071] ,

[0072] in, and Use the starting center of the circle. Calculate the arc angle using the following formula. , :

[0073] ,

[0074] in, The distance between the starting center and the target center; Let be the unit vectors in the directions of the centers of the two circles.

[0075] ,

[0076] ,

[0077] The initial arc angle can be calculated using the above formula. and the angle of the target arc Then, the corresponding tangent point can be calculated, and then based on the steering parameters... The lengths of the four types of Dubins curves were calculated for different combinations of values.

[0078] By simplifying the obstacle area into a circle and establishing a two-dimensional coordinate system, the complexity of scene modeling is reduced; four types of efficient Dubins curves, RSL, LSR, LSL and RSR, are selected to reduce the amount of computation while ensuring the shortest path, thus achieving fast, stable and low-cost cluster path pre-planning.

[0079] like Figure 2 As shown in the figure, the UAV swarm path planning method based on A* and Dubins provided in this embodiment of the invention further includes:

[0080] After receiving the dynamic adjustment command, the distance of each drone from the new target point and its remaining battery power are obtained. A weighted evaluation is performed based on the distance and remaining battery power to determine whether a return to home is supported, and the evaluation result is obtained.

[0081] The drones were reordered according to priority based on the evaluation results;

[0082] Obtain the drone selected by the user to perform the new mission, and replan the flight path for the selected drone.

[0083] Specifically, after setting the new target point The formula for comprehensively judging the distance of each drone in the drone swarm from the new target point and whether the remaining battery power of each drone supports returning to home is as follows:

[0084] ,

[0085] in, This indicates that the drone is close to the new target point. distance, This indicates the remaining battery power of the drone. The weights representing the distance of the drone from the new target point This indicates the weight of the drone's remaining battery power. The weight values ​​can be set by the user. Drone numbers are ranked according to priority based on calculations, and the user can manually select which drone will travel to the new target point. After selecting the drone, the flight path will be replanned for the selected drone.

[0086] In the dynamic replanning of the cluster, the two core constraints of distance between the drone and the new target point and remaining power are comprehensively considered. The execution priority is sorted by weighted scoring to provide users with scientifically selectable drone execution solutions, avoid mission failure due to excessive distance or insufficient power, and improve the success rate of dynamic missions.

[0087] The first embodiment of this invention provides a drone swarm path planning method based on A* and Dubins curves. It integrates the A* algorithm with Dubins curves, first using the A* algorithm to search for the globally optimal path, and then using Dubins curves to smoothly connect waypoints. This solves the problem of abrupt curvature changes at inflection points in traditional paths, fully matching the drone's turning radius, climb altitude, and flight attitude constraints, generating a smooth and safe flight path that can be directly executed by flight control, significantly improving path practicality and flight stability. When dynamically adjusting tasks, the method considers the distance of each drone in the swarm from the task target point, its current remaining battery power, and whether the minimum and maximum number of drones required to execute the task are sufficient for the user to choose the appropriate method.

[0088] like Figure 3 As shown in another embodiment of the present invention, a UAV swarm path planning system based on A* and Dubins is provided, comprising: a scene loading module, a path planning module, a data link transmission module, and a flight control module. The scene loading module is connected to the path planning module and is used to configure scene information, which includes UAV swarm parameters, mission scenario, takeoff point, target point, and obstacle avoidance area information. The path planning module is used to complete the initial path planning of the UAV swarm using the A* algorithm based on the scene information to obtain an initial route. Based on the initial route output by the A* algorithm, the initial route is smoothed and optimized using a Dubins curve to generate optimized trajectory information that conforms to the UAV's flight attitude. The data link transmission module is connected to the path planning module and is used to receive the optimized trajectory information sent by the path planning module, encapsulate the optimized trajectory information, and transmit it to the flight control module. After receiving the optimized trajectory information, the flight control module controls the UAV to fly according to the planned route.

[0089] The scene loading module consists of a starting point editing submodule, a drone quantity editing submodule, a drone parameter editing submodule, and an obstacle avoidance area editing submodule. The starting point editing submodule can adjust the takeoff point and destination of the drones based on scene information. The drone quantity editing submodule allows setting the number of drones in the current drone cluster. The drone parameter editing submodule allows editing the parameters of each drone, such as flight speed, turning radius, and climb altitude. The obstacle avoidance area editing submodule allows editing the location information of areas in the scene that need to be avoided.

[0090] The path planning module is connected to the scene loading module, dynamic adjustment module, and data link transmission module. After receiving the scene information edited by the user, the path planning module pre-plans the flight path of the UAV swarm based on the scene information. The data link transmission module is connected to the path planning module. After the path planning module completes the waypoint planning for the UAVs in the swarm, it sends the waypoints to the data link transmission module via the network port. The data link transmission module encapsulates the waypoint information. The flight control module is connected to the data link module. The flight control module and the data link module transmit data via serial port. When the flight control module receives the flight path information transmitted by the data link module, it controls the UAVs to fly according to the pre-planned path.

[0091] like Figure 4 As shown, the system also includes a dynamic adjustment module. After receiving a dynamic adjustment command, the dynamic adjustment module obtains the current distance of each UAV from the new target point and its remaining battery power. Based on the distance and remaining battery power, it performs a weighted evaluation to determine whether a return to home is supported, obtains the evaluation result, rearranges the UAVs according to priority based on the evaluation result, replans the flight path for the selected UAV based on the user's selection to perform the new task, and then sends the flight path information to the data link module. The data link module transmits the information to the flight control module to control the UAV to perform the flight task according to the new flight path.

[0092] The execution process of each module can be carried out according to the steps of the UAV swarm path planning method based on A* and Dubins provided in the first embodiment, and will not be described in detail in this embodiment.

[0093] The UAV swarm path planning system based on A* and Dubins provided in this embodiment of the invention and the UAV swarm path planning method based on A* and Dubins are based on the same inventive concept and have the same beneficial effects, and will not be described again here.

[0094] Another embodiment of the present invention provides an electronic device, which includes a processor, an input device, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the method described in the first embodiment above.

[0095] 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.

[0096] Input devices may include touchpads, microphones, etc., and output devices may include displays (LCDs, etc.), speakers, etc.

[0097] The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information about the device type.

[0098] In specific implementations, the processor, input device, and output device described in the embodiments of the present invention can execute the implementation of the method embodiments described in the embodiments of the present invention, or they can execute the implementation of the system embodiments described in the embodiments of the present invention, which will not be repeated here.

[0099] The present invention also provides an embodiment of a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the method described in the first embodiment above.

[0100] The computer-readable storage medium can be an internal storage unit of the terminal described in the foregoing embodiments, such as the terminal's hard drive or memory. The computer-readable storage medium can also be an external storage device of the terminal, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of the terminal. The computer-readable storage medium is used to store the computer program and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0101] 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, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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.

[0102] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the terminals and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for unmanned aerial vehicle (UAV) swarm path planning based on A* and Dubins, characterized in that, include: Obtain user-defined scene information, including drone cluster parameters, mission scenario, takeoff point, target point, and obstacle avoidance area information; Based on the scenario information, the A* algorithm is used to complete the initial path planning for the UAV swarm, and the initial flight path is obtained. Based on the initial path output by the A* algorithm, the initial path is smoothed and optimized using the Dubins curve to generate optimized trajectory information that conforms to the flight attitude of the UAV. Control the drone to perform flight missions according to optimized flight path information.

2. The method according to claim 1, characterized in that, Following the step of controlling the UAV to perform a flight mission according to optimized flight path information, the following is also included: After receiving the dynamic adjustment command, the distance of each drone from the new target point and its remaining battery power are obtained. A weighted evaluation is performed based on the distance and remaining battery power to determine whether a return to home is supported, and the evaluation result is obtained. The drones were reordered according to priority based on the evaluation results; Obtain the drone selected by the user to perform the new mission, and replan the flight path for the selected drone.

3. The method according to claim 1, characterized in that, The step of using the A* algorithm to complete the initial path planning of the drone swarm based on the scenario information specifically includes: Initialize the open and closed lists, and add the starting node to the open list; Select the node with the smallest f value from the open list as the current node; If the current node is the target node, then construct the path; Otherwise, move the current node to the closed list and evaluate its neighboring nodes; For each neighboring node, calculate the actual cost from the starting point to the current node and the heuristically estimated cost from the current node to the target node, and update the open list until the target node is reached and the initial waypoint is generated.

4. The method according to claim 1, characterized in that, The Dubins curves are calculated using RSL, LSR, LSL, and RSR.

5. The method according to claim 4, characterized in that, The specific steps of using Dubins curves to smooth and optimize the initial route include: By calculating the starting center, target center, arc angle, and straight line segment length, and with the goal of minimizing the track length, the waypoints generated by the A* algorithm are connected smoothly in sequence.

6. The method according to claim 2, characterized in that, The formula for determining whether a return trip is supported based on a weighted evaluation of the distance and remaining battery power is as follows: ； in, This indicates the distance of the drone from the new target point. This indicates the remaining battery power of the drone. The weights representing the distance of the drone from the new target point This indicates the weight of the drone's remaining battery power. .

7. A UAV swarm path planning system based on A* and Dubins, characterized in that, The method for implementing the method as described in any one of claims 1-6 includes: a scene loading module, a path planning module, a data link transmission module, and a flight control module. The scene loading module is connected to the path planning module and is used to configure scene information, which includes UAV cluster parameters, mission scene, takeoff point, target point, and obstacle avoidance area information. The path planning module is used to complete the initial path planning of the UAV cluster using the A* algorithm based on the scene information, to obtain the initial route, and to use the initial route output by the A* algorithm as a basis to smooth and optimize the initial route using the Dubins curve to generate optimized trajectory information that conforms to the flight attitude of the UAV. The data link transmission module is connected to the path planning module and is used to receive the optimized trajectory information sent by the path planning module, encapsulate the optimized trajectory information, and transmit it to the flight control module. After receiving the optimized flight path information, the flight control module controls the UAV to fly along the planned route.

8. The system according to claim 7, characterized in that, It also includes a dynamic adjustment module, which, after receiving a dynamic adjustment instruction, obtains the current distance and remaining battery power of each UAV from the new target point, performs a weighted evaluation based on the distance and remaining battery power to determine whether a return to home is supported, obtains the evaluation result, rearranges the UAVs according to priority based on the evaluation result, obtains the UAVs selected by the user to perform the new task, and replans the route for the selected UAVs.

9. An electronic device, comprising: The processor, input device, output device, and memory are interconnected, the memory being used to store a computer program, the computer program including program instructions, characterized in that the processor is configured to invoke the program instructions to perform the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-6.

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