Unmanned aerial vehicle cluster operation task re-planning method and device

Abstract

The invention discloses an unmanned aerial vehicle cluster operation task re-planning method and device, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: obtaining a current operation task path of an unmanned aerial vehicle cluster; performing deviation correction on the current path of each unmanned aerial vehicle individual in the unmanned aerial vehicle cluster by using the predetermined path; performing path simulation by using the situation information data of the unmanned aerial vehicle cluster after deviation correction; performing deviation analysis on the simulation path by using a deviation analysis model, and outputting a deviation analysis result; the deviation analysis model is used for analyzing the path planning effect and the flight performance of the unmanned aerial vehicle according to the difference between the simulated path and the predetermined path; the deviation analysis result comprises whether the simulation path is qualified or not; when the simulation path is qualified, generating task data of the unmanned aerial vehicle cluster and the target task by using a path simulation result; and re-planning future tasks of the unmanned aerial vehicle cluster by using the task data. The unmanned aerial vehicle cluster task re-planning method can improve the efficiency and accuracy of unmanned aerial vehicle cluster task re-planning.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) path planning, and in particular to a method and device for replanning UAV cluster operation tasks. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] In recent years, drones, due to their high flexibility, strong maneuverability, low safety risk, and low cost, have been widely used in search and patrol, reconnaissance and surveillance, disaster relief, logistics and distribution, power inspection, and agricultural irrigation. Consequently, swarm intelligence has become a key future development direction for drones. Unmanned swarms that achieve economies of scale can address the limitations of existing drones, such as their limited functionality, poor reliability, and low intelligence, allowing them to accomplish more complex tasks.

[0004] Prior art approaches to assigning or planning UAV swarm tasks typically involve sending task instructions to the swarm, planning the swarm's future missions, calculating the swarm's future path information, and then using optimization models to assign tasks. While these methods offer good real-time performance, they cannot guarantee the efficiency and accuracy of collaborative task allocation within the swarm. Summary of the Invention

[0005] An embodiment of the present invention provides a method for replanning a UAV swarm operation task, which is used to improve the efficiency and accuracy of UAV swarm task replanning. The method includes:

[0006] Get the current task path of the drone cluster;

[0007] Using the predetermined path, the current path of each individual drone in the drone swarm is corrected for deviation;

[0008] Obtaining situational information data of the UAV cluster after deviation correction, using the situational information data to perform path simulation and obtain path simulation results; the path simulation results include simulated paths, UAV flight parameters, and UAV performance parameters; performing cluster analysis on the situational information data, and using the various types of situation data obtained from the cluster analysis to simulate future paths;

[0009] Using a deviation analysis model, performing deviation analysis on the simulated path and outputting a deviation analysis result; the deviation analysis model is used to analyze the path planning effect and the flight performance of the UAV based on the difference between the simulated path and the predetermined path; the deviation analysis result includes whether the simulated path is qualified;

[0010] When the simulated path is qualified, the path simulation results are used to generate the mission data of the UAV cluster and the target mission;

[0011] Leverage mission data to re-plan future missions for drone swarms.

[0012] An embodiment of the present invention further provides a device for replanning a UAV swarm task, for improving the efficiency and accuracy of UAV swarm task replanning, the device comprising:

[0013] The deviation correction module is used to obtain the current operation task path of the drone cluster; use the predetermined path to perform deviation correction on the current path of each individual drone in the drone cluster;

[0014] The path simulation module is used to obtain the situation information data of the UAV cluster after deviation correction, and use the situation information data to perform path simulation to obtain path simulation results. The path simulation results include simulated paths, UAV flight parameters, and UAV performance parameters. Among them, cluster analysis is performed on the situation information data, and the various types of situation data obtained by cluster analysis are used to simulate future paths.

[0015] a deviation analysis module for performing deviation analysis on a simulated path using a deviation analysis model and outputting deviation analysis results; the deviation analysis model is used to analyze the path planning effect and the flight performance of the UAV based on the difference between the simulated path and the predetermined path; the deviation analysis results include whether the simulated path is qualified;

[0016] The mission re-planning module is used to generate mission data for the UAV cluster and the target mission using the path simulation results when the simulation path is qualified; and use the mission data to re-plan the future missions of the UAV cluster.

[0017] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for replanning the drone cluster operation task is implemented.

[0018] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for replanning the drone cluster operation task.

[0019] An embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for replanning the drone cluster operation task.

[0020] In an embodiment of the present invention, a current operation task path of a drone cluster is obtained; a predetermined path is used to perform deviation correction on the current path of each individual drone in the drone cluster; situation information data of the drone cluster after deviation correction is obtained, and the situation information data is used to perform path simulation to obtain a path simulation result; the path simulation result includes a simulated path, drone flight parameters, and drone performance parameters; wherein, a cluster analysis is performed on the situation information data, and future path simulation is performed using multiple types of situation data obtained by the cluster analysis; a deviation analysis model is used to perform deviation analysis on the simulated path, and a deviation analysis result is output; the deviation analysis model is used to analyze the path planning effect and the drone flight performance based on the difference between the simulated path and the predetermined path; the deviation analysis result includes whether the simulated path is qualified; when the simulated path is qualified, the path simulation result is used to generate task data of the drone cluster and the target task; and the task data is used to replan the future tasks of the drone cluster. In the embodiment of the present invention, deviation correction is performed on the drone cluster, and path simulation is performed using the situation information data of the drone cluster after deviation correction. Then, path simulation is performed on the corrected path, deviation analysis is performed on the simulated path, and task data of the drone cluster and the target task are generated based on the deviation analysis results. Finally, the future tasks of the drone cluster are replanned. Through the overall framework of deviation correction-simulation-deviation analysis-replanning, the efficiency and accuracy of drone cluster task replanning are effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0022] Figure 1 Schematic diagram of the process of re-planning a UAV swarm operation task in an embodiment of the present invention;

[0023] Figure 2 This is a diagram showing a specific example of a method for replanning a UAV swarm operation task according to an embodiment of the present invention;

[0024] Figure 3 FIG2 is another specific example diagram of the method for re-planning a UAV swarm operation task in an embodiment of the present invention;

[0025] Figure 4 Schematic diagram of a re-planning device for a UAV swarm operation task according to an embodiment of the present invention;

[0026] Figure 5 Schematic diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0028] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order.

[0029] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0030] First, the technical terms involved in the embodiments of the present invention are explained.

[0031] Drone swarm technology: a technology that allows multiple drones to perform tasks together through self-organization and collaboration.

[0032] Cluster optimization: Improve cluster performance by adjusting and improving cluster configuration.

[0033] Path planning: The process of calculating one or more safe and efficient paths within a given environment based on the starting and target locations of a mobile entity. This process typically involves steps such as environment modeling, obstacle detection, path search, and path optimization.

[0034] Task replanning: During the execution of a task, due to changes in the external environment, changes in the internal system state, or the emergence of new task requirements, the process of adjusting the execution order, resource allocation, or behavior pattern of the original tasks that are no longer applicable or inefficient is carried out.

[0035] Through research, the inventors discovered that path planning is crucial for drone mission execution and a major challenge for autonomous drones in engineering applications. Existing drone path planning algorithms are primarily classified into classical and metaheuristic algorithms. However, these algorithms suffer from low search efficiency and slow convergence in complex environments, and their accuracy needs to be improved. Therefore, the inventors proposed a replanning method for drone swarm operations.

[0036] Figure 1 FIG. 1 is a flow chart of a method for replanning a UAV swarm task in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0037] Step 101: Obtain the current task path of the drone cluster;

[0038] Step 102: Using the predetermined path, perform deviation correction on the current path of each individual drone in the drone cluster;

[0039] Step 103: Obtaining situation information data of the deviation-corrected UAV cluster, performing path simulation using the situation information data, and obtaining a path simulation result; the path simulation result includes a simulated path, UAV flight parameters, and UAV performance parameters; wherein, cluster analysis is performed on the situation information data, and future path simulation is performed using multiple types of situation data obtained by the cluster analysis;

[0040] Step 104: Utilize a deviation analysis model to perform deviation analysis on the simulated path and output a deviation analysis result; the deviation analysis model is used to analyze the path planning effect and the flight performance of the UAV based on the difference between the simulated path and the predetermined path; the deviation analysis result includes whether the simulated path is qualified;

[0041] Step 105: When the simulated path is qualified, the path simulation results are used to generate mission data of the UAV cluster and the target mission;

[0042] Step 106: Use the mission data to re-plan future missions of the drone cluster.

[0043] from Figure 1 As can be seen from the process shown, the method of the embodiment of the present invention can effectively improve the efficiency and accuracy of drone cluster task re-planning by focusing on correction, simulating the correction results, and re-executing the planning logic based on the simulation results.

[0044] In step 101, the current operation task path of the drone cluster is obtained.

[0045] For example, the current operation task path of the drone cluster is obtained based on the drone's own positioning system, or based on the wireless communication link, the drone transmits its own position, posture and other information to the control center in real time.

[0046] In step 102, deviation correction is performed on the current path of each individual drone in the drone cluster using the predetermined path.

[0047] During implementation, the drone cluster sets a predetermined path, target mission, etc. before taking off, and monitors each individual drone in the drone cluster at the control center. When an individual drone deviates, deviation correction is performed.

[0048] Figure 2 FIG. 1 is a specific example diagram of a method for replanning a UAV swarm operation task according to an embodiment of the present invention. Figure 2 As shown, the predetermined path is used to perform deviation correction on the current path of each individual drone in the drone cluster, including:

[0049] Step 201: Compare the predetermined path with the current task path of each UAV in the UAV cluster;

[0050] Step 202: When the current operation task path of any UAV deviates from the predetermined path, the deviation time point, deviation angle, and deviation distance of the UAV are obtained;

[0051] Step 203: Monitor the actual position of the drone, and continuously adjust the flight direction and speed of the drone using the predetermined path and the deviation time point, deviation angle, and deviation distance of the drone until the deviation distance between the actual position of the drone and the predetermined path is less than a first preset deviation value.

[0052] For example, the current operation task path of the acquired target drone cluster is compared with the planned path. When the comparison shows a deviation from the planned path, the deviation time point and deviation angle are recorded; based on the recorded data, the deviation distance is determined in combination with the planned flight speed of the drone cluster; the deviation distance is reported to the drone cluster control center; and the control center performs deviation correction.

[0053] The deviation distance refers to the straight-line distance between the actual position of the drone during flight and the corresponding position point on the planned path. For example, suppose the planned position of the drone at time point t is , the actual location is , then the deviation distance The deviation distance can be reflected from three angles: lateral deviation, which is the deviation perpendicular to the planned path (such as the left or right deviation of the route); longitudinal deviation, which is the advance or lag along the planned path (such as caused by speed abnormality); comprehensive deviation, which is calculated through the deviation analysis model (such as formula ) quantify the total deviation impact.

[0054] Specifically, when the comparison shows a deviation from the predetermined path, the deviation time and angle are promptly counted. After obtaining the corresponding data, the deviation distance is determined based on the predetermined flight speed. The product of speed and time is the distance, and it can also be directly calculated by obtaining the drone positioning coordinates in real time.

[0055] When the deviation distance between the actual position of the UAV and the predetermined path is less than or equal to a first preset deviation value, the deviation correction is stopped.

[0056] After the deviation is corrected, step 103 continues to obtain the situation information data of the drone cluster after the deviation is corrected.

[0057] Since the subsequent route status after the current deviation correction still has two possible states: qualified or unqualified, it is necessary to continue monitoring the drone cluster to obtain the situation information data of the drone cluster after the deviation correction. This situation information data is used to perform path simulation and obtain the path simulation results. The path simulation results include the simulated path, drone flight parameters, and drone performance parameters. Among them, cluster analysis is performed on the situation information data, and the various types of situation data obtained by cluster analysis are used to simulate the future path and obtain the path simulation results.

[0058] During implementation, situational information data of the deviation-corrected drone cluster is obtained, and cluster analysis is performed on the situational information data to obtain at least two types of situational information data. Future path simulation is then performed on the two types of situational information data, which may be situation data of qualified planned routes and situation data of planned routes with reasonable deviations. It will be understood that cluster analysis involves distinguishing and categorizing the data information into different types, and in this method, the preferred classification basis is the degree of overlap of planned routes.

[0059] Various path planning algorithms can be used for path simulation, such as the A algorithm, the Dijkstra algorithm, etc. Taking into account environmental factors and the uncertainty of UAV flight, the Monte Carlo simulation algorithm can be introduced to randomly generate a large number of sample paths for simulation calculations.

[0060] In step 104, a deviation analysis model is used to perform a deviation analysis on the simulated path and output a deviation analysis result; the deviation analysis model is used to analyze the path planning effect and the UAV flight performance based on the difference between the simulated path and the predetermined path; the deviation analysis result includes whether the simulated path is qualified.

[0061] The deviation analysis model is used to analyze the difference between the planned path of the UAV and the simulated path to evaluate the accuracy of the path planning and the flight performance of the UAV.

[0062] In one embodiment, based on error analysis in control theory, fault-tolerant mechanisms in robot path planning, and engineering practice data, the deviation analysis model can be expressed as follows:

[0063] ;

[0064] in, Indicates the deviation value, represents the deviation distance in the simulation path, Indicates the allowed deviation distance. Indicates the nominal deviation value in the planned path.

[0065] In the formula, the first term is the normalization process, which converts the deviation distance ( ) and the allowable deviation distance ( ) is converted into a dimensionless relative value to avoid analysis deviations caused by different units or dimensions; the second item is to introduce inherent system deviations such as hardware errors and environmental noise to ensure that the model is in the ideal path ( =0) can still reflect the actual fault tolerance capability.

[0066] Furthermore, the deviation analysis model is used to perform deviation analysis on the simulation path, and the deviation analysis results are output, which may include:

[0067] When the deviation value is within the set allowable deviation range, the simulation path is determined to be qualified;

[0068] When the deviation value is not within the set allowable deviation range, the simulation path is determined to be unqualified.

[0069] During implementation, when the future paths of the two types of situation information data deviate from the predetermined route, the deviation data information is input into the deviation analysis model for analysis; when the deviation value is within the set allowable deviation range, it means that the simulated path of the drone cluster after correction is qualified and can be input into the control center for execution; when the deviation value is not within the set allowable deviation range, it means that the simulated path of the drone cluster after correction is unqualified and is suspended from entering the control center for execution.

[0070] For example, it is understandable that when 3m, The default value is 1m. When the height is 2.5m, It is about 3.9m. At this time, the ∑ value is compared with the preset ideal deviation distance of the control center or terminal. When the comparison difference is less than 1m, it can be input into the control center to control the drone cluster to execute.

[0071] In step 105, when the simulated path is qualified, the path simulation results are used to generate mission data of the drone cluster and the target mission.

[0072] For example, if the deviation analysis result is qualified, the drone cluster collaborative task execution is triggered, and the drone cluster receives the collaborative task instruction again. After receiving the collaborative task, the drone cluster obtains the status information of the drone cluster and the target task in response to receiving the collaborative task to obtain task data. The task data refers to various information data related to the drone execution task, and different tasks involve different types of data and content.

[0073] In step 106, the mission data is used to replan future missions for the drone cluster.

[0074] Finally, in response to the mission data, future missions of the drone swarm are replanned.

[0075] Figure 3FIG. 1 is another specific example of a method for replanning a UAV swarm operation task in an embodiment of the present invention, as shown in FIG. Figure 3 As shown in Figure 2, using mission data to replan future missions for a drone swarm can include:

[0076] Step 301: clustering and analyzing the task data to obtain task data of different groups; the task data of different groups include emergency search tasks, routine inspection tasks, and may also include classification tasks and attack tasks;

[0077] Step 302: Input task data of different groups into a pre-built collaborative task processing model and output a UAV cluster replanning plan; the UAV cluster replanning plan includes the task sequence, path, and resource allocation information of each UAV; the collaborative task processing model uses minimizing total task completion time, maximizing resource utilization, and balancing load as objective functions, and uses UAV endurance and communication range as constraints to perform path planning.

[0078] For example, they first perform a cluster analysis of mission data, using drone status, mission objectives, and environmental information as input. Based on pre-set classification rules (task priority), they cluster the mission data into different groups (emergency search missions, routine inspection missions). Next, they construct a collaborative task processing model, including three objective functions: minimizing total task completion time, maximizing resource utilization, and balancing load, and applying constraints such as drone endurance and communication range. Finally, they perform a replanning output, outputting the mission sequence, path, and resource allocation plan for each drone.

[0079] The present invention also provides a device for replanning UAV swarm missions, as described in the following embodiments. Because the principles underlying the device are similar to those of the method for replanning UAV swarm missions, the implementation of the device can be referenced to the implementation of the method for replanning UAV swarm missions, and any repetitions will not be repeated.

[0080] Figure 4 FIG. 1 is a schematic diagram of a re-planning device for a UAV swarm operation task according to an embodiment of the present invention. Figure 4 As shown, the apparatus 400 includes:

[0081] The deviation correction module 401 is used to obtain the current operation task path of the drone cluster; perform deviation correction on the current path of each individual drone in the drone cluster using the predetermined path;

[0082] Path simulation module 402 is used to obtain situation information data of the UAV cluster after deviation correction, perform path simulation using the situation information data, and obtain path simulation results; the path simulation results include simulated paths, UAV flight parameters, and UAV performance parameters; wherein, cluster analysis is performed on the situation information data, and future path simulation is performed using multiple types of situation data obtained by cluster analysis;

[0083] Deviation analysis module 403 is used to perform deviation analysis on the simulated path using a deviation analysis model and output a deviation analysis result; the deviation analysis model is used to analyze the path planning effect and the flight performance of the UAV based on the difference between the simulated path and the planned path; the deviation analysis result includes whether the simulated path is qualified;

[0084] The mission re-planning module 404 is used to generate mission data of the UAV cluster and the target mission using the path simulation results when the simulated path is qualified; and use the mission data to re-plan the future missions of the UAV cluster.

[0085] In one embodiment, the deviation correction module 401 is specifically configured to:

[0086] Compare the planned path with the current task path of each UAV in the UAV swarm;

[0087] When the current operation task path of any UAV deviates from the planned path, the deviation time point, deviation angle, and deviation distance of the UAV are obtained;

[0088] The actual position of the drone is monitored, and the flight direction and speed of the drone are continuously adjusted using the predetermined path and the deviation time point, deviation angle, and deviation distance of the drone until the deviation distance between the actual position of the drone and the predetermined path is less than a first preset deviation value.

[0089] In one embodiment, the multiple types of situation data include qualified planned route situation data and reasonable deviation planned route situation data.

[0090] In one embodiment, the deviation analysis model is expressed as follows:

[0091] ;

[0092] in, Indicates the deviation value, represents the deviation distance in the simulation path, Indicates the allowed deviation distance. Indicates the nominal deviation value in the predetermined path;

[0093] The deviation analysis module 403 is specifically used for:

[0094] When the deviation value is within the set allowable deviation range, the simulation path is determined to be qualified;

[0095] When the deviation value is not within the set allowable deviation range, the simulation path is determined to be unqualified.

[0096] In one embodiment, the task re-planning module 404 is specifically configured to:

[0097] Cluster analysis of task data was performed to obtain task data of different groups; task data of different groups included emergency search tasks and routine inspection tasks;

[0098] The task data of different groups are input into a pre-built collaborative task processing model, and a drone cluster replanning plan is output; the drone cluster replanning plan includes the task sequence, path, and resource allocation information of each drone; the collaborative task processing model uses minimizing the total task completion time, maximizing resource utilization, and balancing the load as the objective function, and uses the drone's flight time and communication range as constraints to perform path planning.

[0099] Figure 5 Schematic diagram of a computer device according to an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention also provides a computer device 500, including a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501, wherein the processor 501 implements the above-mentioned method for replanning the UAV cluster operation task when executing the computer program 503.

[0100] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for replanning the drone cluster operation task.

[0101] An embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for replanning the drone cluster operation task.

[0102] In an embodiment of the present invention, a current operation task path of a drone cluster is obtained; a predetermined path is used to perform deviation correction on the current path of each individual drone in the drone cluster; situation information data of the drone cluster after deviation correction is obtained, and the situation information data is used to perform path simulation to obtain a path simulation result; the path simulation result includes a simulated path, drone flight parameters, and drone performance parameters; wherein, a cluster analysis is performed on the situation information data, and future path simulation is performed using multiple types of situation data obtained by the cluster analysis; a deviation analysis model is used to perform deviation analysis on the simulated path, and a deviation analysis result is output; the deviation analysis model is used to analyze the path planning effect and the drone flight performance based on the difference between the simulated path and the predetermined path; the deviation analysis result includes whether the simulated path is qualified; when the simulated path is qualified, the path simulation result is used to generate task data of the drone cluster and the target task; and the task data is used to replan the future tasks of the drone cluster. In the embodiment of the present invention, deviation correction is performed on the drone cluster, and path simulation is performed using the situation information data of the drone cluster after deviation correction. Then, path simulation is performed on the corrected path, deviation analysis is performed on the simulated path, and task data of the drone cluster and the target task are generated based on the deviation analysis results. Finally, the future tasks of the drone cluster are replanned. Through the overall framework of deviation correction-simulation-deviation analysis-replanning, the efficiency and accuracy of drone cluster task replanning are effectively improved.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may 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, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0107] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for replanning a UAV swarm task, characterized in that: include: Get the current task path of the drone cluster; Using the predetermined path, the current path of each individual drone in the drone swarm is corrected for deviation; Obtaining situational information data of the UAV cluster after deviation correction, using the situational information data to perform path simulation and obtain path simulation results; the path simulation results include simulated paths, UAV flight parameters, and UAV performance parameters; performing cluster analysis on the situational information data, and using the various types of situation data obtained from the cluster analysis to simulate future paths; Using a deviation analysis model, performing deviation analysis on the simulated path and outputting a deviation analysis result; the deviation analysis model is used to analyze the path planning effect and the flight performance of the UAV based on the difference between the simulated path and the predetermined path; the deviation analysis result includes whether the simulated path is qualified; When the simulated path is qualified, the path simulation results are used to generate the mission data of the UAV cluster and the target mission; Leverage mission data to re-plan future missions for drone swarms.

2. The method according to claim 1, wherein Using the predetermined path, the current path of each individual drone in the drone swarm is corrected, including: Compare the planned path with the current task path of each UAV in the UAV swarm; When the current operation task path of any UAV deviates from the planned path, the deviation time point, deviation angle, and deviation distance of the UAV are obtained; The actual position of the drone is monitored, and the flight direction and speed of the drone are continuously adjusted using the predetermined path and the deviation time point, deviation angle, and deviation distance of the drone until the deviation distance between the actual position of the drone and the predetermined path is less than a first preset deviation value.

3. The method according to claim 1, wherein The multiple types of situation data include qualified planned route situation data and reasonable deviation planned route situation data.

4. The method according to claim 1, wherein The deviation analysis model is expressed as follows: ； in, Indicates the deviation value, represents the deviation distance in the simulation path, Indicates the allowed deviation distance. Indicates the nominal deviation value in the predetermined path; Use the deviation analysis model to perform deviation analysis on the simulation path and output the deviation analysis results, including: When the deviation value is within the set allowable deviation range, the simulation path is determined to be qualified; When the deviation value is not within the set allowable deviation range, the simulation path is determined to be unqualified.

5. The method according to claim 1, wherein Use mission data to re-plan future missions for the drone swarm, including: Cluster analysis of task data was performed to obtain task data of different groups; task data of different groups included emergency search tasks and routine inspection tasks; The task data of different groups are input into a pre-built collaborative task processing model, and a drone cluster replanning plan is output; the drone cluster replanning plan includes the task sequence, path, and resource allocation information of each drone; the collaborative task processing model uses minimizing the total task completion time, maximizing resource utilization, and balancing the load as the objective function, and uses the drone's flight time and communication range as constraints to perform path planning.

6. A re-planning device for UAV swarm operation tasks, characterized in that: include: Deviation correction module, used to obtain the current operation task path of the drone cluster; Using the predetermined path, the current path of each individual drone in the drone swarm is corrected for deviation; The path simulation module is used to obtain the situation information data of the UAV cluster after deviation correction, and use the situation information data to perform path simulation to obtain path simulation results. The path simulation results include simulated paths, UAV flight parameters, and UAV performance parameters. Among them, cluster analysis is performed on the situation information data, and the various types of situation data obtained by cluster analysis are used to simulate future paths. a deviation analysis module for performing deviation analysis on a simulated path using a deviation analysis model and outputting deviation analysis results; the deviation analysis model is used to analyze the path planning effect and the flight performance of the UAV based on the difference between the simulated path and the predetermined path; the deviation analysis results include whether the simulated path is qualified; The mission re-planning module is used to generate mission data for the UAV cluster and the target mission using the path simulation results when the simulation path is qualified; and use the mission data to re-plan the future missions of the UAV cluster.

7. The device according to claim 6, characterized in that The deviation correction module is specifically used to: Compare the planned path with the current task path of each UAV in the UAV swarm; When the current operation task path of any UAV deviates from the planned path, the deviation time point, deviation angle, and deviation distance of the UAV are obtained; The actual position of the drone is monitored, and the flight direction and speed of the drone are continuously adjusted using the predetermined path and the deviation time point, deviation angle, and deviation distance of the drone until the deviation distance between the actual position of the drone and the predetermined path is less than a first preset deviation value.

8. The device according to claim 6, wherein The multiple types of situation data include qualified planned route situation data and reasonable deviation planned route situation data.

9. The device according to claim 6, wherein The deviation analysis model is expressed as follows: ； in, Indicates the deviation value, represents the deviation distance in the simulation path, Indicates the allowed deviation distance. Indicates the nominal deviation value in the predetermined path; The deviation analysis module is specifically used to: When the deviation value is within the set allowable deviation range, the simulation path is determined to be qualified; When the deviation value is not within the set allowable deviation range, the simulation path is determined to be unqualified.

10. The device according to claim 6, wherein The mission replanning module is specifically used to: Cluster analysis of task data was performed to obtain task data of different groups; task data of different groups included emergency search tasks and routine inspection tasks; Input different groups of task data into the pre-built collaborative task processing model and output the drone swarm replanning plan; The UAV swarm replanning scheme includes each UAV’s mission sequence, path, and resource allocation information; The collaborative task processing model takes minimizing the total task completion time, maximizing resource utilization and balancing the load as the objective function, and performs path planning with the UAV's flight time and communication range as constraints.

11. A computer device comprising 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, the method according to any one of claims 1 to 5 is implemented.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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