Unmanned aerial vehicle group flight path optimization method and device, computer equipment and medium

Abstract

The embodiment of the invention discloses an unmanned aerial vehicle group flight path optimization method and device, computer equipment and a medium. The method comprises the following steps: configuring current position information in a preset acceleration calculation strategy to calculate an instantaneous acceleration value of each unmanned aerial vehicle; judging whether the instantaneous acceleration value of each unmanned aerial vehicle is greater than a preset maximum acceleration limit value or not; and if the instantaneous acceleration value of the unmanned aerial vehicle is greater than a preset maximum acceleration limit value, according to a trajectory optimization strategy, merging trajectory point coordinate information after acceleration optimization of the related unmanned aerial vehicle to generate flight path trajectory information conforming to an acceleration limit condition. According to the method, through automatic unmanned aerial vehicle group trajectory optimization, the requirement for manually adjusting the flight path is reduced, the acceleration peak value of the unmanned aerial vehicle is reduced, and the safety and stability of the flight process are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent aircraft technology, and in particular to a method, device, computer equipment and medium for optimizing the flight path of a swarm of unmanned aerial vehicles. Background Art

[0002] The technical background of drone swarm performances is the result of the combination of the advancement of drone technology and the needs of artistic performances. With the maturity of drone technology, drone swarm performances, as an emerging modern art form, can create stunning visual effects and complex aerial patterns through the precise control and formation flight of large-scale drones. This form of performance not only shows the charm of science and technology, but also provides a new means of artistic expression. In order to realize drone swarm performances, researchers and technicians have developed a variety of technical solutions, such as optimization solutions and non-optimization solutions. The optimization solution uses advanced algorithms to find the optimal path and smoothes the path through convex optimization methods to adapt to the dynamic characteristics of the drone. However, these solutions are computationally intensive and time-consuming, and in some cases there is no solution. The non-optimization solution uses a particle swarm algorithm or RVO algorithm, which has a shorter calculation time, but does not involve acceleration control, which may cause the generated trajectory to exceed the acceleration physical parameter limit of the drone, resulting in uncontrollable safety of the drone flight process. Summary of the invention

[0003] The embodiments of the present application provide a method, apparatus, computer equipment and medium for optimizing the flight path of a drone swarm, which are intended to reduce the peak acceleration of the drones to improve the safety and stability of the flight process.

[0004] In a first aspect, an embodiment of the present application provides a method for optimizing the flight path of a swarm of unmanned aerial vehicles, the method comprising receiving flight data of unmanned aerial vehicles, the flight data comprising trajectory point coordinate information of the unmanned aerial vehicles; parsing the flight data to obtain initial position information, target position information, flight speed value and flight duration of the unmanned aerial vehicles, wherein the current position information is position information recorded in the form of three-dimensional coordinates; configuring the current position information to a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each unmanned aerial vehicle; determining whether the instantaneous acceleration value of each unmanned aerial vehicle is greater than a preset maximum acceleration limit value; if the instantaneous acceleration value of the unmanned aerial vehicle is greater than the preset maximum acceleration limit value, merging the trajectory point coordinate information of the relevant unmanned aerial vehicles after acceleration optimization according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration limit conditions; and sending a corresponding flight path control signal to each unmanned aerial vehicle to adjust the flight path of the relevant unmanned aerial vehicle.

[0005] In the second aspect, an embodiment of the present application also provides a flight path optimization device for a drone swarm, which includes a data receiving unit for receiving flight data of drones, the flight data including trajectory point coordinate information of the drones; a parsing unit for parsing the flight data to obtain initial position information, target position information, flight speed value and flight duration of the drones, where the current position information is position information recorded in the form of three-dimensional coordinates; a first calculation unit for configuring the current position information to a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone; a first judgment unit for judging whether the instantaneous acceleration value of each drone is greater than a preset maximum acceleration limit value; a signal production unit for merging the trajectory point coordinate information of the relevant drones after acceleration optimization according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration limit condition if the instantaneous acceleration value of the drone is greater than the preset maximum acceleration limit value.

[0006] In a third aspect, an embodiment of the present application further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and wherein the program instructions can implement the above method when executed by a processor.

[0008] The embodiment of the present application provides a method, device, computer equipment and medium for optimizing the flight path of a drone group. The method includes receiving the flight data of a drone, the flight data includes the coordinate information of the trajectory points of the drone; parsing the flight data to obtain the initial position information, target position information, flight speed value and flight duration of the drone, and the current position information is the position information recorded in the form of three-dimensional coordinates; configuring the current position information in a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone; judging whether the instantaneous acceleration value of each drone is greater than the preset maximum acceleration limit value; if the instantaneous acceleration value of the drone is greater than the preset maximum acceleration limit value, merging the trajectory point coordinate information of the relevant drones after acceleration optimization according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration limit condition; correspondingly sending a flight path control signal to each drone to adjust the flight path of the relevant drone. By accurately calculating and optimizing the instantaneous acceleration of the drone, it is ensured that the flight of the drone will not exceed its physical parameter limit, thereby greatly reducing the safety risk in flight. This is crucial to avoid damage to the drone due to overload during high-speed maneuvers. The optimized flight path keeps the acceleration of the drone within a safe range, reduces abrupt acceleration or deceleration, and makes the flight smoother, which is especially important for drone swarms performing delicate tasks or complex performances. This method reduces the need for manual adjustment of the flight path through automated trajectory optimization, improving the efficiency of the drone swarm's mission execution and the efficiency of the performance production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a method for optimizing a flight path of a drone swarm provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a sub-process of a method for optimizing a flight path of a drone swarm provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of another sub-process of the method for optimizing the flight path of a swarm of drones provided in an embodiment of the present application;

[0013] Figure 4 A schematic block diagram of a drone swarm flight path optimization device provided in an embodiment of the present application;

[0014] Figure 5A schematic block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0016] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0017] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0018] It should be further understood that the term “and / or” used in the specification and appended claims refers to and includes any and all combinations of one or more of the associated listed items.

[0019] Embodiments of the present application provide a method, apparatus, computer equipment, and medium for optimizing the flight path of a swarm of unmanned aerial vehicles.

[0020] The executor of the drone swarm flight path optimization method may be the simulation control system provided in the embodiment of the present application, or a computer device integrated with the simulation control system. The control method applied to the simulation control system may be formed as software running on the simulation control system, wherein the simulation control system may be implemented in hardware, the computer device may be a terminal or a server, and the terminal may be a smart phone, a tablet computer, a PDA, or a laptop computer, etc.

[0021] The UAV swarm flight path optimization method is applied to Figure 5 In the computer device 500.

[0022] Figure 1 It is a flow chart of the UAV swarm flight path optimization method provided in an embodiment of the present application, and the method includes the following steps S110-S150.

[0023] S110: Receive flight data of the UAV.

[0024] The flight data contains the coordinate information of the drone's trajectory points.

[0025] S120, analyzing the flight data to obtain the initial position information, target position information, flight speed value and flight duration of the UAV, and the current position information is the position information recorded in the form of three-dimensional coordinates.

[0026] S130, configuring the current position information to a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone.

[0027] S140: Determine whether the instantaneous acceleration value of each UAV is greater than a preset maximum acceleration limit value.

[0028] S150: If the instantaneous acceleration value of the UAV is greater than the preset maximum acceleration limit value, the trajectory point coordinate information of the relevant UAVs after acceleration optimization is merged according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration limit condition.

[0029] In a more specific implementation process, the system obtains flight data from the UAV, and the flight data includes but is not limited to the initial position (starting point), target position (end point), flight speed, and expected flight duration of the UAV. The system uses an algorithm to parse the received flight data to extract the current position information of the UAV. The current position information is expressed in the form of three-dimensional coordinates (X, Y, Z), ensuring the accurate positioning of the UAV in space. The system applies the current position information to a preset acceleration calculation strategy, which usually involves mathematical modeling and numerical analysis. The instantaneous acceleration value of the UAV is calculated by differentiating the time series of the position information. The system compares the calculated instantaneous acceleration value with the preset maximum acceleration limit value. If the instantaneous acceleration value exceeds the limit, the system will identify the UAV that needs to be optimized. For UAVs whose instantaneous acceleration exceeds the limit, the system adjusts according to the trajectory optimization strategy. Identify UAVs with acceleration exceeding the limit and mark them as optimization objects. Analyze the reasons for the exceeding limit and set optimization goals, such as minimizing acceleration or jerk. Construct an optimization model, including objective functions and constraints. Execute the optimization algorithm to adjust the position and time distribution of trajectory points to reduce acceleration. The trajectory points are merged to ensure the continuity and smoothness of the trajectory. A new flight path control signal is generated. The system sends the new flight path control signal. After receiving the signal, the drone adjusts its flight path to ensure operation within a safe range. After sending the control signal, the system processes the drone's response offline to verify whether the optimization result meets expectations. If the optimization result is not ideal, the system needs to re-execute the optimization process. The system records the optimization process and results for subsequent analysis and improvement. Feedback is provided to the operator for manual adjustment or further optimization. Through these steps, drone swarms can perform safer, more efficient and more precise flight missions, especially in complex performance or operation scenarios.

[0030] In summary, by accurately calculating and optimizing the instantaneous acceleration of the drone, it is ensured that the flight of the drone does not exceed its physical parameter limits, thus greatly reducing the safety risks during flight. This is crucial to avoid damage to the drone due to overload during high-speed maneuvers.

[0031] The optimized flight path keeps the acceleration of the drone within a safe range, reduces abrupt acceleration or deceleration, and makes the flight smoother, which is especially important for drone swarms performing delicate tasks or complex performances. This method reduces the need for manual adjustment of the flight path through automated trajectory optimization, improving the efficiency of the drone swarm's mission execution and the efficiency of the performance production process.

[0032] By using efficient optimization algorithms and strategies, the time and resources required for calculations are reduced, allowing the method to quickly adapt to the operational requirements of large-scale drone swarms. The optimized smooth trajectory enables the drone swarm to perform more complex and precise formation movements, enhancing the artistic expression and visual effects, which is particularly important in the field of drone performances. Through modular system design, the method is easy to expand and adapt to different drone models and application scenarios, increasing the flexibility and versatility of the system. By recording the optimization process and results, the system provides valuable data support for subsequent performance analysis and improvements. In general, the present invention provides a method that can effectively control the acceleration of a drone swarm while maintaining high computing efficiency and flight safety, which has significant technical effects on improving the operational performance and artistic expression of a drone swarm.

[0033] like Figure 2 As shown, in a more specific embodiment, executing method S130 further specifically includes executing steps S131-S136.

[0034] S131. Extract multiple continuous trajectory points of the corresponding UAV according to the flight data.

[0035] S132, integrating the continuous trajectory points into a position-time parameter equation, wherein the continuous trajectory points are in a three-dimensional coordinate form.

[0036] S133. Perform three-dimensional axial acceleration calculation on the position-time parameter equation to obtain the instantaneous acceleration value of the UAV.

[0037] S134: Determine whether the instantaneous acceleration value meets the preset physical parameters of the drone.

[0038] S135: If the instantaneous acceleration value does not exceed the preset physical parameters of the UAV, the results corresponding to the continuous trajectory points are combined and output.

[0039] S136. If the instantaneous acceleration value exceeds the preset UAV physical parameters, the flight data of the relevant UAV is sent to a trajectory optimizer to obtain a reduced instantaneous acceleration value by adjusting the spatial position information and time distribution information of the relevant UAV trajectory points.

[0040] Specifically, in a more specific embodiment, when executing method S130, it can be further refined into the following steps S131-S136: extract multiple continuous trajectory points of the drone during flight from the flight data. These trajectory points record the position information of the drone at different time points and provide basic data for subsequent acceleration calculations. The extracted continuous trajectory points are integrated into position-time parameter equations, which are expressed in three-dimensional coordinate form. The parameter equation forms such as (xt), (yt) and (zt) respectively represent the change of the position of the drone in the X, Y and Z axis directions over time. Perform three-dimensional axial acceleration calculation on the position-time parameter equation. The instantaneous acceleration value of the drone in the X, Y and Z axis directions at each time point is obtained by quadratic differential calculation. The calculated instantaneous acceleration value is compared with the preset drone physical parameters. Determine whether the acceleration value is within the setting range of the drone's physical parameters. If the instantaneous acceleration value does not exceed the preset drone physical parameters, directly merge the continuous trajectory points and output the merged result. This means that the flight path of the drone meets the established safety standards.

[0041] like Figure 3 As shown, in a more specific embodiment, before executing method S150, it also specifically includes executing steps S1501-S1502.

[0042] S1501. Analyze and extract the flight data of the relevant UAV after acceleration optimization to obtain the optimized reference position and optimized reference speed value.

[0043] S1502, optimizing the optimized reference position and the optimized reference speed as input information of the trajectory optimizer at the same time to obtain optimized acceleration data.

[0044] Specifically, in a more specific embodiment, before executing method S150, the following steps S1501-S1502 may be included: analyzing the UAV flight data after acceleration optimization. After the UAV trajectory is optimized by the trajectory optimizer, the acceleration of the UAV still exceeds the setting range of the physical parameters. Secondary processing refers to using the result of the first optimization as the input of the second optimization, that is, using the result of the previous optimization as the input of the trajectory optimizer. Re-optimize the trajectory to reduce the acceleration, and repeat this operation until the acceleration meets the setting range of the physical parameters. Through these steps, the system can perform more refined acceleration optimization on the UAV swarm before generating a new flight path control signal. This helps to ensure that the flight path of the UAV swarm provides a smoother and more stable flight experience while meeting safety and efficiency requirements.

[0045] In a more specific embodiment, the execution method S150 further specifically includes the execution steps of: identifying relevant drones whose instantaneous acceleration values ​​exceed the preset maximum acceleration limit value from the flight data and marking them as objects to be optimized; generating information on reasons for the drones to be optimized and setting optimization targets according to the information on reasons for the drones to be optimized; constructing an optimization model, the optimization model including an objective function and constraints; determining an optimization algorithm configured by the optimization model, and performing acceleration optimization calculations on relevant drones according to the optimization algorithm, objective function and constraints to generate new flight path control signals.

[0046] The above technical solution of the present invention optimizes the acceleration of the initial trajectory of the drone cluster through a series of steps to ensure that the drone does not exceed the physical parameter limit during flight, thereby improving the safety and performance of the flight. The three-dimensional trajectory is organized into a parametric equation about position-time, that is, (xt), (yt) and (zt) form, which respectively represent the spatial displacement of the drone at different time points. Further, taking the trajectory optimization of the parametric equation of xt as an example, the parametric equation of xt uses the objective function of the trajectory optimizer as follows:

[0047]

[0048] The optimization variables are x, x′ i ,x″ i ,x″′ i is the optimization variable (where i = 0, 1, 2, ...); n is the total number of segments; w x-ref represents the weight of the position; w x′-ref represents the weight of speed; w x″ represents the weight of acceleration; w x″′ Indicates the weight of the acceleration; x x-ref represents the reference position (i.e. the position on the x-axis after splitting); x′ x′-ref Indicates the reference speed (i.e. the speed on the x-axis after splitting). endref , x′ endref , x′ e ' ndref is the position, velocity, and acceleration of the end point of the initial trajectory; w end-x w end-x′ w end-x″ is the position, velocity, and acceleration weight of the corresponding end point. Further, its constraint condition is the position constraint: x∈(x min ,x max ), speed constraint: x′∈(x′ min ,x′ max ), acceleration constraint: x″∈(x″ min ,x″ max ), continuity constraints:

[0049]

[0050] Where Δt is the sampling time; x min ,x max is the minimum and maximum value of the position constraint, that is, the maximum deformable variable; x′ min ,x′ max is the minimum and maximum value of the speed constraint; x′ m ' in ,x′ m ' ax is the minimum and maximum value of the acceleration constraint. The acceleration of the (xt), (yt) and (zt) parametric equations is calculated in different axes. Through quadratic differentiation, the acceleration values ​​of the drone in the X, Y and Z axes at each time point are obtained. The calculated acceleration values ​​are compared with the preset drone physical parameters. If the acceleration value is within the setting range of the drone's physical parameters, the axes are directly merged. If the acceleration value of any axis exceeds the setting range of the drone's physical parameters, the trajectory optimizer is used to adjust and optimize the acceleration of the three axes of the drone. The optimizer adjusts the position and time distribution of the trajectory points to reduce the acceleration value so that it meets the physical parameter restrictions of the drone again. When optimizing the trajectory of the parametric equation of xt, specific objective functions and constraints are used, including position constraints, velocity constraints, acceleration constraints and continuity constraints. After being processed by the optimizer, the acceleration of each axis is recalculated and verified. If it is found that the optimized acceleration does not meet the requirements, the result processed by the optimizer is used as the input of the next optimization and optimized again. Continue this process until the accelerations in all axes meet the physical parameter settings of the drone. When the accelerations in all axes meet the requirements, merge the optimization results of each axis to generate new parametric equations (x(t)), (y(t)) and (z(t)), and output smoother trajectory points with lower acceleration. The optimized trajectory data can be used for actual flight missions to improve the performance and reliability of the drone. Through the above steps, the technical solution of the present invention can not only process the collision-free initial trajectories generated by algorithms such as the particle swarm algorithm (PSO) and the RVO algorithm, but also process the collision-free initial trajectories generated by 3D dynamic performance animations, thereby ensuring the safety and efficiency of drone clusters when performing various tasks.

[0051] Figure 4 1 is a schematic block diagram of a drone swarm flight path optimization device provided in an embodiment of the present application. As shown in the figure, corresponding to the above drone swarm flight path optimization method, the present application also provides a drone swarm flight path optimization device 100. The drone swarm flight path optimization device includes a unit for executing the above drone swarm flight path optimization method, and the device can be configured in a terminal such as a desktop computer, a tablet computer, a laptop computer, etc. For details, please refer to Figure 4 The drone swarm flight path optimization device 100 includes a data receiving unit 110, which is used to receive the flight data of the drone, and the flight data includes the trajectory point coordinate information of the drone; a parsing unit 120, which is used to parse the flight data to obtain the initial position information, target position information, flight speed value and flight duration of the drone, and the current position information is the position information recorded in the form of three-dimensional coordinates; a first calculation unit 130, which is used to configure the current position information to a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone; a first judgment unit 140, which is used to judge whether the instantaneous acceleration value of each drone is greater than the preset maximum acceleration limit value; a signal production unit 150, which is used to merge the trajectory point coordinate information of the relevant drones after acceleration optimization according to the trajectory optimization strategy if the instantaneous acceleration value of the drone is greater than the preset maximum acceleration limit value to generate flight data that the drone can accurately execute, thereby improving the stability and safety of the flight process.

[0052] It should be noted that technical personnel in the relevant field can clearly understand that the specific implementation process of the above-mentioned drone swarm flight path optimization device and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0053] The above-mentioned drone group flight path optimization device can be implemented in the form of a computer program, which can be used in Figure 5 Runs on the computer device shown.

[0054] See also Figure 5 , which shows a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 can be a terminal or a server, wherein the terminal can be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server can be an independent server or a server cluster composed of multiple servers.

[0055] The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0056] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a method for optimizing the flight path of a drone group.

[0057] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0058] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for optimizing the flight path of a swarm of drones.

[0059] The network interface 505 is used to communicate with other devices over the network. Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0060] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0061] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0062] Therefore, the present application also provides a storage medium. The storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by the processor, the processor executes the following steps: receiving the flight data of the drone, the flight data includes the trajectory point coordinate information of the drone; parsing the flight data to obtain the initial position information, target position information, flight speed value and flight duration of the drone, and the current position information is the position information recorded in the form of three-dimensional coordinates; configuring the current position information to a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone; judging whether the instantaneous acceleration value of each drone is greater than the preset maximum acceleration limit value; if the instantaneous acceleration value of the drone is greater than the preset maximum acceleration limit value, merging the trajectory point coordinate information of the relevant drones after acceleration optimization according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration limit condition; correspondingly sending a flight path control signal to each drone to adjust the flight path of the relevant drone.

[0063] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0064] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0065] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0066] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present application can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0067] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0068] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for optimizing the flight path of a drone swarm, characterized in that: The method is applied in a simulation control system, and the method comprises: Receiving flight data of the UAV, wherein the flight data includes coordinate information of trajectory points of the UAV; The flight data is parsed to obtain the initial position information, target position information, flight speed value and flight duration of the UAV, and the current position information is the position information recorded in the form of three-dimensional coordinates; The current position information is configured in a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone; Determine whether the instantaneous acceleration value of each drone is greater than the preset maximum acceleration limit value; If the instantaneous acceleration value of the UAV is greater than the preset maximum acceleration limit value, the trajectory point coordinate information of the relevant UAVs after acceleration optimization is merged according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration limit conditions.

2. The method for optimizing the flight path of a drone swarm according to claim 1, characterized in that: The configuring the current position information to a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone includes: Extracting a plurality of continuous trajectory points of the corresponding UAV according to the flight data; Integrating the continuous trajectory points into a position-time parameter equation, wherein the continuous trajectory points are in a three-dimensional coordinate form; The three-dimensional axial acceleration is calculated for the position-time parameter equation to obtain the instantaneous acceleration value of the UAV; Determining whether the instantaneous acceleration value meets the preset physical parameters of the drone; If the instantaneous acceleration value does not exceed the preset physical parameters of the drone, the results corresponding to the continuous trajectory points are combined and output; If the instantaneous acceleration value exceeds the preset UAV physical parameters, the flight data of the relevant UAV is sent to the trajectory optimizer to obtain a reduced instantaneous acceleration value by adjusting the spatial position information and time distribution information of the relevant UAV trajectory points.

3. The method for optimizing the flight path of a drone swarm according to claim 1, characterized in that: Before merging the trajectory point coordinate information of the relevant UAVs after acceleration optimization according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration restriction condition, the method includes: Analyze and extract the flight data of the relevant UAV after acceleration optimization to obtain the optimized reference position and optimized reference speed value; The optimized reference position and the optimized reference speed are simultaneously optimized as input information of a trajectory optimizer to obtain optimized acceleration data.

4. The method for optimizing the flight path of a drone swarm according to claim 1, characterized in that: The step of combining the trajectory point coordinate information of the relevant UAVs after acceleration optimization according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration restriction condition includes: Identifying relevant UAVs whose instantaneous acceleration values ​​exceed a preset maximum acceleration limit value from the flight data and marking them as objects requiring optimization; Generate the information of reasons for the optimization of the UAV and set the optimization target according to the information of reasons for the optimization; Constructing an optimization model, wherein the optimization model includes an objective function and constraints; An optimization algorithm for configuring the optimization model is determined, and acceleration optimization calculation is performed on the relevant UAV according to the optimization algorithm, the objective function and the constraint condition to generate a new flight path control signal.

5. A drone swarm flight path optimization device, applying the drone swarm flight path optimization method according to any one of claims 1 to 4, characterized in that: The device comprises: A data receiving unit, used to receive the flight data of the UAV, wherein the flight data includes the coordinate information of the trajectory points of the UAV; A parsing unit, used to parse the flight data to obtain the initial position information, target position information, flight speed value and flight duration of the UAV, wherein the current position information is position information recorded in a three-dimensional coordinate format; A first calculation unit, configured to configure the current position information to a preset acceleration calculation strategy to calculate the instantaneous acceleration value of each drone; The first judgment unit is used to judge whether the instantaneous acceleration value of each UAV is greater than a preset maximum acceleration limit value; The signal production unit is used to merge the trajectory point coordinate information of the relevant drones after acceleration optimization according to the trajectory optimization strategy to generate flight path trajectory information that meets the acceleration limit conditions if the instantaneous acceleration value of the drone is greater than the preset maximum acceleration limit value.

6. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 4 can be implemented.

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