Flight simulation method and apparatus for unmanned aerial vehicle swarm

Abstract

The present application relates to the technical field of unmanned aerial vehicle simulation. Disclosed are a flight simulation method and apparatus for an unmanned aerial vehicle swarm. The method comprises: acquiring flight requirement data of an unmanned aerial vehicle swarm, and on the basis of the flight requirement data of the unmanned aerial vehicle swarm, constructing a three-dimensional unmanned aerial vehicle model; performing collision detection on the three-dimensional unmanned aerial vehicle model, so as to generate damage state information, and on the basis of the damage state information, calculating optimal flight data; and using the optimal flight data to simulate a flight task of the unmanned aerial vehicle swarm, so as to obtain a flight simulation result of the unmanned aerial vehicle swarm. The present application realizes the simulation of a real scenario where an unmanned aerial vehicle is detected and struck, and the real-time feedback of effects of the unmanned aerial vehicle being detected and struck, and a model collision detection technique is used for determining and adjusting the flight attitude of the unmanned aerial vehicle, such that the flight simulation of an unmanned aerial vehicle swarm is more accurate.

Description

Flight simulation method and device of unmanned aerial vehicle cluster

[0001] The present application claims priority to the Chinese patent application No. 202410456912.3, filed on April 16, 2024, and entitled "Flight simulation method and device of unmanned aerial vehicle cluster", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of unmanned aerial vehicle simulation, in particular to a flight simulation method and device of unmanned aerial vehicle cluster. BACKGROUND

[0003] With the advancement of artificial intelligence, distributed systems, networking communication and other technologies, and the significant improvement of airborne hardware level, unmanned aerial vehicle cluster has been highly concerned and vigorously developed. However, the flight simulation platform of related unmanned aerial vehicle cluster lacks simulation of real scene, the simulation method is not intuitive, and the real-time feedback of the detected attack effect cannot be achieved, which leads to the inability to adjust the flight attitude.

[0004] SUMMARY

[0005] Therefore, the present application provides a flight simulation method and device of unmanned aerial vehicle cluster to solve the problem that the flight simulation platform of related unmanned aerial vehicle cluster lacks simulation of real scene and cannot achieve real-time feedback of the detected attack effect, which leads to the inability to adjust the flight attitude.

[0006] In a first aspect, the present application provides a flight simulation method of unmanned aerial vehicle cluster, which comprises:

[0007] Obtaining unmanned aerial vehicle cluster flight demand data, and constructing a three-dimensional model of unmanned aerial vehicle based on the unmanned aerial vehicle cluster flight demand data;

[0008] Performing collision detection on the three-dimensional model of unmanned aerial vehicle, generating damage state information, and calculating optimal flight data based on the damage state information;

[0009] Simulating the flight task of the unmanned aerial vehicle cluster by using the optimal flight data to obtain the flight simulation result of the unmanned aerial vehicle cluster.

[0010] The flight simulation method of unmanned aerial vehicle cluster provided in the embodiment performs collision detection on the three-dimensional model of unmanned aerial vehicle, generates damage state information, and calculates optimal flight data based on the damage state information, and then simulates the flight task of the unmanned aerial vehicle cluster by using the optimal flight data, which realizes the simulation of the real scene of the unmanned aerial vehicle being detected and attacked and the real-time feedback of the detected attack effect. The model collision detection technology is used to judge and adjust the flight attitude of the unmanned aerial vehicle, so that the flight simulation of the unmanned aerial vehicle cluster is more accurate.

[0011] In an optional implementation, the UAV three-dimensional model is constructed based on the UAV cluster flight demand data, comprising:

[0012] The initial model of the UAV in different flight environments is constructed based on the UAV cluster flight demand data;

[0013] The initial model of the UAV in different flight environments is rendered in real time to obtain the UAV three-dimensional model.

[0014] The flight simulation method of the UAV cluster provided in this embodiment realizes the description of the flight posture of the UAV cluster by constructing the initial model of the UAV in different flight environments and rendering the initial model of the UAV in different flight environments in real time, and lays a foundation for subsequent adjustment of the UAV cluster.

[0015] In an optional implementation, the UAV three-dimensional model is subjected to collision detection to generate damage state information, and the optimal flight data is calculated based on the damage state information, comprising:

[0016] The simulation control information is acquired, and the UAV three-dimensional model is subjected to detection and attack based on the simulation control information;

[0017] The flight state of the UAV after the detection and attack is acquired, and the target discovery probability and damage probability are calculated based on the flight state of the UAV;

[0018] Based on the damage probability, the damage situation is marked in the UAV three-dimensional model by using different colors to obtain the damage state information, and the damage state information is displayed on the simulation interface;

[0019] The position of the attack is determined based on the damage state information, and the optimal flight data is calculated based on the position of the attack.

[0020] The flight simulation method of the UAV cluster provided in this embodiment judges the distance in the adjustment of the UAV flight by using the model collision detection technology, updates the target discovery probability and damage probability of the UAV in a timely manner by using the ray detection technology, real-time feedbacks the damage state information of the UAV, displays the damage state information on the simulation interface, realizes the visual simulation and real-time interaction of the UAV cluster being detected and attacked, calculates the optimal flight data based on the position of the attack, provides a data source for subsequent evaluation of the cluster flight model, and realizes real-time feedback of the effect of being detected and attacked.

[0021] In an optional implementation, the target discovery probability and damage probability are calculated based on the flight state of the UAV, comprising:

[0022] The flight speed of the UAV, the ray detection area, the average number of target hits of the ammunition, and the hit probability of single ammunition are determined based on the flight state of the UAV;

[0023] Calculate the target discovery probability based on the flight speed of the unmanned aerial vehicle and the ray detection area;

[0024] Calculate the damage probability based on the average number of target hits and the single-shot ammunition hit probability.

[0025] The flight simulation method of the unmanned aerial vehicle cluster provided in this embodiment realizes real-time feedback of the detection and attack effect of the unmanned aerial vehicle cluster by calculating the target discovery probability and the damage probability.

[0026] In an optional implementation, the attacked position is determined based on the damaged state information, and the optimal flight data is calculated based on the attacked position, including:

[0027] The attacked position is determined based on the damaged state information, and the maneuvering performance constraint condition and the tangential angle change constraint condition are set at the attacked position;

[0028] The unmanned aerial vehicle cluster is controlled based on the maneuvering performance constraint condition and the tangential angle change constraint condition to obtain the optimal flight data; wherein the optimal flight data includes the optimal maneuvering direction, the optimal attitude, and the optimal path.

[0029] The flight simulation method of the unmanned aerial vehicle cluster provided in this embodiment controls the unmanned aerial vehicle cluster based on the maneuvering performance constraint condition and the tangential angle change constraint condition to obtain the optimal flight data, which realizes timely adjustment of the flight attitude of the unmanned aerial vehicle, and makes the flight simulation of the unmanned aerial vehicle cluster more accurate.

[0030] In an optional implementation, the flight task of the unmanned aerial vehicle cluster is simulated by using the optimal flight data to obtain the flight simulation result of the unmanned aerial vehicle cluster, including:

[0031] The flight environment model is established based on the flight task, and the flight task of the unmanned aerial vehicle cluster is simulated in combination with the flight environment model to obtain the flight simulation result of the unmanned aerial vehicle cluster.

[0032] In a second aspect, the application provides a flight simulation device of an unmanned aerial vehicle cluster, which comprises:

[0033] The construction module is configured to obtain the flight demand data of the unmanned aerial vehicle cluster, and construct a three-dimensional model of the unmanned aerial vehicle based on the flight demand data of the unmanned aerial vehicle cluster;

[0034] The detection module is configured to perform collision detection on the three-dimensional model of the unmanned aerial vehicle, generate damaged state information, and calculate optimal flight data based on the damaged state information;

[0035] The simulation module is configured to simulate the flight task of the unmanned aerial vehicle cluster by using the optimal flight data to obtain the flight simulation result of the unmanned aerial vehicle cluster.

[0036] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are connected with each other for communication, the memory stores computer instructions, and the processor executes the computer instructions to perform the flight simulation method of the UAV cluster according to the first aspect or any one of the corresponding embodiments.

[0037] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for making a computer execute the flight simulation method of the UAV cluster according to the first aspect or any one of the corresponding embodiments.

[0038] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions for making a computer execute the flight simulation method of the UAV cluster according to the first aspect or any one of the corresponding embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the present application or prior art, the following will briefly introduce the drawings needed in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0040] Fig. 1 is a schematic diagram of information interaction between a flight control computer and a Unity three-dimensional engine according to an embodiment of the present application;

[0041] Fig. 2 is a flowchart of a flight simulation method of a UAV cluster according to an embodiment of the present application;

[0042] Fig. 3 is a flowchart of another flight simulation method of a UAV cluster according to an embodiment of the present application;

[0043] Fig. 4 is a flowchart of still another flight simulation method of a UAV cluster according to an embodiment of the present application;

[0044] Fig. 5 is a structural block diagram of a flight simulation device of a UAV cluster according to an embodiment of the present application;

[0045] Fig. 6 is a hardware structure schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0047] Most of the design of the unmanned aerial vehicle simulation platform is based on matlab (a commercial mathematical software) and simulink (a visual simulation tool). The visual effect of this simulation method is not friendly to ordinary users, lacks simulation of real scenes, is too limited to a certain model of unmanned aerial vehicle, and focuses on the bottom layer, resulting in a large cost of modification and expansion.

[0048] The embodiments of the present application provide a flight simulation method of an unmanned aerial vehicle cluster, which is applied to a server-type device. As shown in FIG. 1, the server-type device can include a flight control computer and a Unity (a real-time 3D interactive content creation and operation platform) three-dimensional engine. The Unity three-dimensional engine includes a built unmanned aerial vehicle cluster and a detection and attack equipment. The above technical problems are solved through three-dimensional virtual simulation.

[0049] According to the embodiments of the present application, a flight simulation method of an unmanned aerial vehicle cluster is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0050] In the present embodiment, a flight simulation method of an unmanned aerial vehicle cluster is provided, which can be used in the above-mentioned server-type device. FIG. 2 is a flowchart of a flight simulation method of an unmanned aerial vehicle cluster according to an embodiment of the present application. As shown in FIG. 2, the flowchart includes the following steps:

[0051] In step S201, unmanned aerial vehicle cluster flight demand data is acquired, and a three-dimensional model of an unmanned aerial vehicle is constructed based on the unmanned aerial vehicle cluster flight demand data.

[0052] Specifically, a 3dmax software (a three-dimensional animation rendering and production software) is used to construct models of a rotary-wing unmanned aerial vehicle, a fixed-wing unmanned aerial vehicle, a detection radar, a fire attack weapon, a building, an environment, etc.

[0053] In step S202, collision detection is performed on the three-dimensional model of the unmanned aerial vehicle, damaged state information is generated, and optimal flight data is calculated based on the damaged state information.

[0054] Specifically, the unmanned aerial vehicle cluster model feeds back the ray detection area and damage in real time according to the camera and the collision box mounted on the three-dimensional model of the unmanned aerial vehicle, calculates and updates the probability of being detected and shot down of the unmanned aerial vehicle, and then prompts the optimal maneuvering information for avoiding the attack and receives the operation information to update the damage in real time.

[0055] In step S203, the flight task of the unmanned aerial vehicle cluster is simulated by using the optimal flight data, and flight simulation results of the unmanned aerial vehicle cluster are obtained.

[0056] Specifically, the flight environment model is established based on the flight task, and the flight task of the unmanned aerial vehicle cluster is simulated in combination with the flight environment model, and flight simulation results of the unmanned aerial vehicle cluster are obtained.

[0057] Optionally, the step of simulating the flight task of the unmanned aerial vehicle cluster comprises: receiving the flight task; receiving flight control information sent by a flight control computer and receiving flight control operation joystick and throttle control data, and calculating and outputting longitude, latitude, height, yaw, pitch and roll six-degree-of-freedom data of the aircraft in real time to update the flight position and attitude of the unmanned aerial vehicle cluster; transmitting key position image information by using a camera carried by the unmanned aerial vehicle; detecting the unmanned aerial vehicle group for detection and fire attack; automatically retracting and replacing the formation aircraft according to the damage condition to maintain the stability of the flight formation; and returning the unmanned aerial vehicle.

[0058] Optionally, the flight simulation results of the unmanned aerial vehicle cluster are verified, and then the optimal flight data is adjusted according to the verification results to ensure the accuracy of the flight simulation results.

[0059] The flight simulation method of the unmanned aerial vehicle cluster provided in this embodiment performs collision detection on the three-dimensional model of the unmanned aerial vehicle, generates damage state information, calculates optimal flight data based on the damage state information, and then simulates the flight task of the unmanned aerial vehicle cluster by using the optimal flight data, thereby realizing the simulation of the real scene of the unmanned aerial vehicle being detected and attacked and the real-time feedback of the detection and attack effect, adjusting the flight attitude of the unmanned aerial vehicle by using the model collision detection technology, and making the flight simulation of the unmanned aerial vehicle cluster more accurate.

[0060] In this embodiment, a flight simulation method of an unmanned aerial vehicle cluster is provided, which can be used for the server-type device described above. FIG. 3 is a flowchart of a flight simulation method of an unmanned aerial vehicle cluster according to an embodiment of the present application. As shown in FIG. 3, the flowchart comprises the following steps:

[0061] In step S301, flight demand data of the unmanned aerial vehicle cluster are obtained, and a three-dimensional model of the unmanned aerial vehicle is constructed based on the flight demand data of the unmanned aerial vehicle cluster.

[0062] Specifically, the step S301 comprises:

[0063] Step S3011, constructing an initial model of the UAV in different flight environments based on the UAV cluster flight demand data.

[0064] Specifically, different environments such as mountains, deserts, oceans, etc. are made by using 3dmax software, and different texture environments are pasted; and according to the body structure of the UAV, an initial model of the UAV is constructed, and the UAV skeleton animation is bound.

[0065] Step S3012, real-time rendering the initial model of the UAV in different flight environments to obtain a three-dimensional model of the UAV.

[0066] Specifically, the above model constructed by the 3dmax software is imported, the terrain selection and the setting of environmental factors such as wind, rain, snow, etc. are performed, and the model material and the collision type are added to the model; finally, the environment of the initial model of the UAV is rendered in real time by using the shaderlab language (a language system invented or created by Unity).

[0067] Step S302, performing collision detection on the three-dimensional model of the UAV to generate damage state information, and calculating optimal flight data based on the damage state information. For details, please refer to step S202 of the embodiment shown in FIG. 2, which will not be repeated here.

[0068] Step S303, simulating the flight task of the UAV cluster by using the optimal flight data to obtain the flight simulation result of the UAV cluster. For details, please refer to step S203 of the embodiment shown in FIG. 2, which will not be repeated here.

[0069] The flight simulation method of the UAV cluster provided in this embodiment realizes the description of the flight attitude of the UAV cluster by constructing the initial model of the UAV in different flight environments and performing real-time rendering on the initial model of the UAV in different flight environments, which lays a foundation for subsequent adjustment of the UAV cluster.

[0070] In this embodiment, a flight simulation method of a UAV cluster is provided, which can be used for the above-mentioned server-type device. FIG. 4 is a flowchart of a flight simulation method of a UAV cluster according to an embodiment of the present application. As shown in FIG. 4, the flowchart includes the following steps:

[0071] Step S401, obtaining UAV cluster flight demand data, and constructing a three-dimensional model of the UAV based on the UAV cluster flight demand data. For details, please refer to step S301 of the embodiment shown in FIG. 3, which will not be repeated here.

[0072] Step S402, performing collision detection on the three-dimensional model of the UAV to generate damage state information, and calculating optimal flight data based on the damage state information.

[0073] Specifically, the above step S402 includes:

[0074] Step S4021, obtain simulation control information, and detect and attack the unmanned aerial vehicle three-dimensional model based on the simulation control information.

[0075] Specifically, the position of the detection and attack equipment is set, the unmanned aerial vehicle three-dimensional model is accessed into the simulation platform, and the control instructions and position information of the simulation platform are received in real time; the detection and attack equipment sends a detection line in real time, the weapon is launched according to the real-time feedback of the detection line, and then the damage state information of the unmanned aerial vehicle three-dimensional model is obtained.

[0076] Step S4022, obtain the flight state of the unmanned aerial vehicle after detection and attack, and calculate the target discovery probability and damage probability based on the flight state of the unmanned aerial vehicle.

[0077] Specifically, different collision detection bodies are arranged at the middle parts of each unmanned aerial vehicle three-dimensional model, and different damage upper limit values are arranged at each part, and the flight state of the unmanned aerial vehicle is fed back in real time when it is attacked by the weapon.

[0078] In some optional embodiments, the above step S4022 comprises:

[0079] Step a1, determining the unmanned aerial vehicle flight speed, ray detection area, average number of hit target bullets, and single bullet hit probability based on the flight state of the unmanned aerial vehicle.

[0080] Step a2, calculating the target discovery probability based on the unmanned aerial vehicle flight speed and the ray detection area.

[0081] Specifically, it is assumed that the unmanned aerial vehicle appears in the region with equal probability, the detection and attack equipment adopts a random distribution search mode, and the calculation formula of the target discovery probability P(t) is as follows:

[0082] In the above formula, v represents the flight speed of the unmanned aerial vehicle, w represents the search width in the ray detection area, S1 represents the total area of the target region, and t represents time.

[0083] Step a3, calculating the damage probability based on the average number of hit target bullets and the single bullet hit probability.

[0084] Specifically, it is assumed that the single bullet hit probability of the detection and attack equipment is P k , the damage is subject to an exponential damage rate, and the average number of hit target bullets is k, then the damage target probability of one bullet is P k / k, and in the actual attack process, the target can be damaged only by continuously launching accurate guided bullets, therefore the calculation formula of the damage probability P(h) is as follows:

[0085] In the above formula, N represents the number of continuously launched bullets.

[0086] At step S4023, the damage situation is marked in the UAV three-dimensional model by using different colors based on the damage probability, the damaged state information is obtained, and the damaged state information is displayed on the simulation interface.

[0087] Specifically, the damage probability is mapped to RGB (red, green, and blue) colors. When the damage probability is 1, the corresponding component of the UAV three-dimensional model is red. When the damage probability is 0, the corresponding component of the UAV three-dimensional model is green. The specific representation is: Color = newColor (255*P(h), 255*(1-P(h)), 0, 1) (3)

[0088] Optionally, the UAV three-dimensional model with different colors marking the damaged state information is displayed on the operation simulation interface.

[0089] At step S4024, the hit position is determined based on the damaged state information, and the optimal flight data is calculated based on the hit position.

[0090] In some optional embodiments, the above step S4024 includes:

[0091] Step b1, determining the hit position based on the damaged state information, and setting the maneuverability constraint condition and the tangential angle change constraint condition at the hit position.

[0092] Specifically, the UAV motion needs maneuverability constraints. The maneuverability constraint conditions mainly include the constraints of the track control quantity, speed, acceleration, heading angle, and minimum turning radius. The maneuverability constraint condition is represented as:

[0093] In the above formula, represents the speed of the UAV in the x direction, represents the speed of the UAV in the y direction, represents the speed of the UAV in the height direction, represents the acceleration of the UAV, represents the pitch angle velocity, represents the yaw angle velocity, represents the change rate of the mass of the UAV, v represents the flight speed of the UAV, γ represents the pitch angle, ψ represents the yaw angle, T represents the thrust of the UAV, α represents the angle of attack, D represents the resistance of the UAV, m represents the mass, g represents the gravitational acceleration, L represents the lift, μ represents the side slip angle, τ represents the tangential force, δ represents the deflection angle of the control surface, represents the velocity vector, h c represents the center position, ρ represents the air density, S represents the wing area, C L represents the lift coefficient, C D represents the resistance coefficient.

[0094] Optionally, to avoid damage to the unmanned aerial vehicle, the unmanned aerial vehicle should avoid obstacles tangentially during flight, and as far as possible from the threat, the tangential angle change range of the unmanned aerial vehicle is:

[0095] In the above formula, indicates the tangential angle of the unmanned aerial vehicle, indicates the yaw angle of the unmanned aerial vehicle.

[0096] wherein,

[0097] In the above formula, Ω(p) represents the roll angle, p represents the probability, x represents the horizontal coordinate of the unmanned aerial vehicle, and y represents the longitudinal coordinate of the unmanned aerial vehicle, indicates the damage probability.

[0098] Step b2, flight control of the unmanned aerial vehicle cluster based on the maneuvering performance constraint condition and the tangential angle change constraint condition to obtain optimal flight data; wherein the optimal flight data includes optimal maneuvering direction, optimal attitude and optimal path.

[0099] Specifically, real-time receiving operation control of the unmanned aerial vehicle, adjusting four inputs (throttle, roll, pitch, yaw) of the unmanned aerial vehicle based on the maneuvering performance constraint condition and the tangential angle change constraint condition to control the attitude and position of the aircraft, and updating the damage situation in real time until the damage situation of the unmanned aerial vehicle cluster is minimized, obtaining optimal maneuvering information that avoids attack, that is, optimal flight data.

[0100] Step S403, simulating the flight task of the unmanned aerial vehicle cluster using the optimal flight data to obtain the flight simulation result of the unmanned aerial vehicle cluster. For details, please refer to step S303 of the embodiment shown in FIG. 3, which will not be repeated here.

[0101] The flight simulation method of the unmanned aerial vehicle cluster provided in this embodiment uses model collision detection technology to judge the distance in the flight of the unmanned aerial vehicle, uses ray detection technology to update the target discovery probability and damage probability of the unmanned aerial vehicle in a timely manner, real-time feedback of the damaged state information of the unmanned aerial vehicle, and the damaged state information is displayed on the simulation interface, realizing the visual simulation and real-time interaction of the unmanned aerial vehicle cluster being detected and attacked, and calculating the optimal flight data based on the position of being attacked, providing a data source for subsequent evaluation of the cluster flight model, and realizing real-time feedback of the detected attack effect.

[0102] The embodiment also provides a flight simulation device of a UAV cluster, which is used for implementing the above-mentioned embodiment and optional implementation manners, and details are not repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiment is preferably implemented in software, implementation of hardware or a combination of software and hardware is also possible and contemplated.

[0103] The embodiment provides a flight simulation device of a UAV cluster, as shown in FIG. 5, which comprises:

[0104] A construction module 501 is configured to acquire UAV cluster flight demand data, and construct a UAV three-dimensional model based on the UAV cluster flight demand data.

[0105] A detection module 502 is configured to perform collision detection on the UAV three-dimensional model, generate damage state information, and calculate optimal flight data based on the damage state information.

[0106] A simulation module 503 is configured to simulate a flight task of the UAV cluster by using the optimal flight data, and obtain a flight simulation result of the UAV cluster.

[0107] In some optional implementation manners, the construction module 501 comprises:

[0108] A construction unit is configured to construct an initial model of the UAV in different flight environments based on the UAV cluster flight demand data.

[0109] A rendering unit is configured to perform real-time rendering on the initial model of the UAV in different flight environments, and obtain the UAV three-dimensional model.

[0110] In some optional implementation manners, the detection module 502 comprises:

[0111] A detection and attack unit is configured to acquire simulation control information, and perform detection and attack on the UAV three-dimensional model based on the simulation control information.

[0112] A first calculation unit is configured to acquire a UAV flight state after the detection and attack, calculate a target discovery probability and a damage probability based on the UAV flight state.

[0113] A marking unit is configured to mark damage conditions in the UAV three-dimensional model by using different colors based on the damage probability, obtain the damage state information, and display the damage state information on a simulation interface.

[0114] A second calculation unit is configured to determine a position of the attack based on the damage state information, and calculate the optimal flight data based on the position of the attack.

[0115] In some optional implementation manners, the first calculation unit comprises:

[0116] determine the flight speed of the UAV, the ray detection area, the average number of hits of the target, and the single-shot ammunition hit probability based on the flight state of the UAV;

[0117] The first calculation sub-unit is configured to calculate the target discovery probability based on the flight speed of the UAV and the ray detection area.

[0118] The second calculation sub-unit is configured to calculate the damage probability based on the average number of hits of the target and the single-shot ammunition hit probability.

[0119] In some optional embodiments, the second calculation unit comprises:

[0120] The setting sub-unit is configured to determine the hit position based on the damage state information, and set the maneuverability constraint condition and the change constraint condition of the tangent angle at the hit position.

[0121] The control sub-unit is configured to perform flight control on the UAV cluster based on the maneuverability constraint condition and the change constraint condition of the tangent angle, to obtain optimal flight data, wherein the optimal flight data comprises an optimal maneuvering direction, an optimal attitude, and an optimal path.

[0122] In some optional embodiments, the simulation module 503 is specifically configured to establish a flight environment model based on the flight task, simulate the flight task of the UAV cluster in combination with the flight environment model, and obtain the flight simulation result of the UAV cluster.

[0123] Further function descriptions of the above-mentioned various modules and units are the same as those of the corresponding embodiments, and will not be described here.

[0124] The flight simulation device of the UAV cluster in this embodiment is presented in the form of a functional unit. The unit herein refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory executing one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0125] The embodiments of the present application also provide a computer device with the flight simulation device of the UAV cluster shown in FIG. 5.

[0126] Referring to FIG. 6, FIG. 6 is a structural diagram of a computer device according to an optional embodiment of the present application. As shown in FIG. 6, the computer device includes one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components communicate with each other using different buses, and can be mounted on a common main board or mounted in other manners as needed. The processor can process instructions executed in the computer device, including instructions stored in the memory or on the memory to display graphical information of a GUI on an external input / output device, such as a display device coupled to the interface. In some optional embodiments, multiple processors and / or buses can be used with multiple memories and multiple memory banks, if needed. Also, multiple computer devices can be connected, each providing part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). One processor 10 is taken as an example in FIG. 6.

[0127] The processor 10 can be a central processor, a network processor, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic device, a general array logic, or any combination thereof.

[0128] The memory 20 stores instructions executable by the at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0129] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system and application programs required by at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some optional embodiments, the memory 20 can optionally include a memory remotely arranged with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0130] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state disk; and the memory 20 can further include a combination of the above kinds of memories.

[0131] The computer device further includes a communication interface 30 for communication of the computer device with other devices or communication networks.

[0132] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded through a network and stored in a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Alternatively, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, processor, microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, processor, or hardware, the method shown in the above embodiments is implemented.

[0133] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, the method and / or technical solutions according to the present application can be invoked or provided. Those skilled in the art should understand that the form of computer program instructions in a computer readable medium includes but is not limited to source files, executable files, installation package files, etc. Correspondingly, the way of executing computer program instructions by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0134] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A method for flight simulation of a UAV swarm, the method comprising: The method comprises: acquiring unmanned aerial vehicle cluster flight demand data, constructing an unmanned aerial vehicle three-dimensional model based on the unmanned aerial vehicle cluster flight demand data; collision detection is performed on the unmanned aerial vehicle three-dimensional model, damaged state information is generated, and optimal flight data is calculated based on the damaged state information; the flight task of the unmanned aerial vehicle cluster is simulated using the optimal flight data, and flight simulation results of the unmanned aerial vehicle cluster are obtained.

2. The method of claim 1, wherein, The unmanned aerial vehicle three-dimensional model is constructed based on the unmanned aerial vehicle cluster flight demand data, which comprises: constructing an initial unmanned aerial vehicle model in different flight environments based on the unmanned aerial vehicle cluster flight demand data; real-time rendering is performed on the initial unmanned aerial vehicle model in different flight environments, and the unmanned aerial vehicle three-dimensional model is obtained.

3. The method of claim 1, wherein, The collision detection is performed on the unmanned aerial vehicle three-dimensional model, the damaged state information is generated, and the optimal flight data is calculated based on the damaged state information, which comprises: acquiring simulation control information, and performing detection and attack on the unmanned aerial vehicle three-dimensional model based on the simulation control information; acquiring the flight state of the unmanned aerial vehicle after detection and attack, calculating the target discovery probability and damage probability based on the flight state of the unmanned aerial vehicle; based on the damage probability, the damage situation is marked in the unmanned aerial vehicle three-dimensional model using different colors to obtain the damaged state information, and the damaged state information is displayed on the simulation interface; determining the attacked position based on the damaged state information, and calculating the optimal flight data based on the attacked position.

4. The method of claim 3, wherein, The target discovery probability and damage probability are calculated based on the flight state of the unmanned aerial vehicle, which comprises: determining the flight speed of the unmanned aerial vehicle, the ray detection area, the average number of hits on the target, and the single-shot ammunition hit probability based on the flight state of the unmanned aerial vehicle; calculating the target discovery probability based on the flight speed of the unmanned aerial vehicle and the ray detection area; calculating the damage probability based on the average number of hits on the target and the single-shot ammunition hit probability.

5. The method of claim 3, wherein, The attacked position is determined based on the damaged state information, and the maneuvering performance constraint condition and the tangential angle change constraint condition are set at the attacked position; flight control is performed on the unmanned aerial vehicle cluster based on the maneuvering performance constraint condition and the tangential angle change constraint condition, and the optimal flight data is obtained; wherein the optimal flight data comprises an optimal maneuvering direction, an optimal attitude, and an optimal path. The flight task of the unmanned aerial vehicle cluster is simulated using the optimal flight data, and flight simulation results of the unmanned aerial vehicle cluster are obtained, which comprises:

6. The method of claim 1, wherein, establishing a flight environment model based on the flight task, simulating the flight task of the unmanned aerial vehicle cluster in combination with the flight environment model, and obtaining the flight simulation results of the unmanned aerial vehicle cluster. The device comprises: a construction module for acquiring unmanned aerial vehicle cluster flight demand data, and constructing an unmanned aerial vehicle three-dimensional model based on the unmanned aerial vehicle cluster flight demand data; 7. A flight simulation device for a drone swarm, characterized in that: a detection module for performing collision detection on the unmanned aerial vehicle three-dimensional model, generating damaged state information, and calculating optimal flight data based on the damaged state information; ​ ​ An emulation module is configured to emulate the flight task of the UAV cluster by using the optimal flight data, and obtain a flight emulation result of the UAV cluster.

8. A computer device, comprising: The method comprises the following steps: A memory and a processor are in communication connection with each other, and the memory stores computer instructions.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to execute the flight emulation method of the UAV cluster.

10. A computer program product, characterised in that, The computer instructions are used for causing a computer to execute the flight emulation method of the UAV cluster. The computer instructions are used for causing a computer to execute the flight emulation method of the UAV cluster.

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