A cooperative task-oriented unmanned aerial vehicle formation simulation method

Abstract

The application relates to a kind of unmanned aerial vehicle formation simulation methods for collaborative task, which is based on component modeling mechanism, constructs multiple types of platform components, load components, can assemble various models through assembly method, and configures the attributes and load parameters of each model, can obtain unmanned aerial vehicle formation configuration parameters through simulation platform, then simulation platform can automatically calculate the real-time position of each unmanned aerial vehicle in unmanned aerial vehicle formation, automatically control unmanned aerial vehicle to fly from real-time position to target position, and in the task execution process of unmanned aerial vehicle formation, according to the change of environment, the change of task, the change of target state and the state of resource, self-adaptive adjustment is carried out, dynamic loading and control of unmanned aerial vehicle model and simulation process are realized, the adaptive ability of system to complex environment and task is improved, with good universality, expansibility and practical value, effectively improving task efficiency.

Description

TECHNICAL FIELD

[0001] The present application mainly relates to the field of unmanned aerial vehicle simulation, and particularly relates to a simulation method for unmanned aerial vehicle formation aiming at cooperative tasks. BACKGROUND

[0002] Unmanned aerial vehicles have the characteristics of low cost, simple equipment, easy operation, flexibility and high reliability, can be close to the ground to investigate targets in a targeted manner, and can provide timely and reliable information, and thus are widely used. The perception, decision-making and learning ability of a single unmanned aerial vehicle are limited, and multi-unmanned aerial vehicle cooperation can break through the limitations of a single unmanned aerial vehicle. Unmanned aerial vehicles cooperate with each other and maintain appropriate formation shapes to advance, which is more helpful to complete complex tasks.

[0003] Unmanned aerial vehicle formation cooperative task refers to a mode in which multiple unmanned aerial vehicles cooperate with each other and work together to complete complex tasks according to a specified formation in a task. This mode utilizes the diversified task execution capability, efficient information sharing and cooperative action of unmanned aerial vehicle formation, and realizes rapid and accurate target positioning. At the same time, unmanned aerial vehicle formation can reduce the risk of failure or falling of a single unmanned aerial vehicle through decentralized deployment and mutual support. Unmanned aerial vehicle formation can share resource information through a data link, greatly widening the detection range of each unmanned aerial vehicle and enhancing the information accuracy and reliability, which enables the unmanned aerial vehicle group to perform tasks in a wider area.

[0004] However, actual flight experiment of unmanned aerial vehicle formation has a high cost and is restricted by environmental factors such as weather, and may cause the unmanned aerial vehicle body to fall. Simulation technology can greatly reduce the cost and save time by simulating the flight of unmanned aerial vehicle formation to perform tasks on a computer. With the rapid development and diversified application of unmanned aerial vehicle technology, the demand for unmanned aerial vehicle formation simulation is increasing, which requires the development of a perfect unmanned aerial vehicle formation simulation platform and the provision of a convenient simulation method. SUMMARY

[0005] Therefore, it is necessary to provide a simulation method for unmanned aerial vehicle formation aiming at cooperative tasks in view of the above technical problems.

[0006] In one aspect, the present application provides a simulation method for unmanned aerial vehicle formation aiming at cooperative tasks, which comprises:

[0007] obtaining configuration parameters of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation comprises at least one unmanned aerial vehicle;

[0008] determining the real-time positions of the unmanned aerial vehicles in the unmanned aerial vehicle formation, and respectively controlling the unmanned aerial vehicles to fly from the real-time positions to target positions;

[0009] judging whether the unmanned aerial vehicles reach the target positions;

[0010] If each drone in the drone formation reaches the target position, then control the drone formation to maintain the target formation flight;

[0011] During the mission execution of the drone formation, the current environment, mission type, target status, and resource status of each drone in the drone formation are acquired in real time.

[0012] If the current environment of the drone formation changes, and / or the mission type of the drone formation changes, and / or the target state of the drone formation changes, and / or the resource state of the drones changes, then according to the changed current environment and / or mission type and / or target state and / or resource state, the formation of the drone formation is changed, the roles of each drone in the drone formation are reassigned, and the payload configuration of each drone in the drone formation is modified according to the reassigned roles.

[0013] The configuration parameters of the drone formation include formation configuration parameters; obtaining the configuration parameters of the drone formation includes:

[0014] Get the task objective, current environment, and objective status of the current task;

[0015] Based on the current mission objective, current environment, and target status, select the target formation for the UAV formation from the formation library; wherein, the formation library includes, but is not limited to, diamond formation, "V" formation, dense tracking formation, circular formation, "I" formation, and "V" formation;

[0016] Based on the acquired formation setting signal, the formation configuration parameters of the UAV formation of the target formation are determined.

[0017] The configuration parameters of the drone formation also include individual drone configuration parameters; obtaining the configuration parameters of the drone formation further includes:

[0018] Acquire the individual configuration signals of each UAV in the target formation;

[0019] The individual configuration parameters of each drone are determined based on the individual setting signals of each drone.

[0020] The single-machine configuration parameters include the physical and motion characteristics of the UAV.

[0021] The method further includes:

[0022] The drone platform components are determined based on the physical and motion characteristics of the drone.

[0023] determining, according to at least one device model, an airborne device component of the UAV;

[0024] automatically generating a simulation model of the UAV according to the UAV platform component and the airborne device component of the UAV.

[0025] wherein the method further comprises:

[0026] determining distances between each of the UAVs and the obstacles according to real-time positions of each of the UAVs in the UAV formation and positions of the obstacles;

[0027] determining flight behaviors of the UAVs according to the distances between the UAVs and the obstacles; wherein the flight behaviors include obstacle avoidance behaviors and collision avoidance behaviors.

[0028] wherein the method further comprises:

[0029] if there is at least one UAV in the UAV formation that has not reached a target position, returning to perform: determining real-time positions of each of the UAVs in the UAV formation, and respectively controlling each of the UAVs to fly from the real-time positions to the target positions until each of the UAVs in the UAV formation reaches the target position.

[0030] wherein the method further comprises:

[0031] judging whether a target formation of the UAV formation is disrupted;

[0032] if the target formation of the UAV formation is disrupted, re-determining a position of a lead UAV and restoring the target formation;

[0033] if the formation of the UAV formation is not disrupted, controlling the UAV formation to maintain the target formation and advance to a target position.

[0034] wherein the method further comprises:

[0035] respectively controlling each of the UAVs in the UAV formation to perform a designated task when the UAV formation reaches a designated position.

[0036] wherein the method further comprises:

[0037] initializing initial positions, flight positions, and target positions of each of the UAVs in the UAV formation.

[0038] wherein the method further comprises:

[0039] displaying a flight trajectory of the UAV formation on a display device.

[0040] The application provides a UAV formation simulation method for a cooperative task, which can obtain UAV formation configuration parameters through a simulation platform, and then the simulation platform can automatically calculate real-time positions of each UAV in the UAV formation, automatically control the UAVs to fly from the real-time positions to target positions, and control the UAV formation to keep flying in a target formation when each UAV in the UAV formation reaches the target position. The UAV formation simulation method for the cooperative task can complete the gathering of UAVs from single machines to formations, and further, the UAV formation simulation method for the cooperative task can also control the formation to complete complex cooperative tasks, thereby reducing experimental costs. Meanwhile, the UAV formation simulation method for the cooperative task can set various configuration parameters in the UAV formation, which can meet most experimental scenes of formation flight, without the need to develop new formation arrays. In addition, the UAV formation simulation method for the cooperative task can adaptively adjust according to changes in the environment, changes in the task, changes in the target state and the resource state during the execution of the task of the UAV formation, realize dynamic loading and control of the UAV model and the simulation process, improve the adaptability of the system to complex environments and tasks, has good universality, expansibility and practical value, and effectively improves the task efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 FIG. 1 is a flowchart of a UAV formation simulation method for a cooperative task according to an embodiment of the application;

[0042] Figure 2 FIG. 2 is a flowchart of a formation configuration parameter determination process of a UAV formation according to an embodiment of the application;

[0043] Figure 3 FIG. 3 is a schematic diagram of a general UAV model according to an embodiment of the application;

[0044] Figure 4 FIG. 4 is a configuration file structure of the general UAV model shown in FIG. 3; Figure 3

[0045] Figure 5 FIG. 5 is a schematic diagram of a UAV formation simulation platform realized by using a game engine such as UE5 and Unity;

[0046] Figure 6 FIG. 6 is a flowchart of a UAV formation simulation method for a cooperative task according to another embodiment of the application;

[0047] Figure 7 FIG. 7 is an architecture diagram corresponding to the simulation method in FIG. 6; Figure 6

[0048] Figure 8 ​​A relationship diagram among various modules in a simulation platform of an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0050] The unmanned aerial vehicle has the characteristics of low cost, simple equipment, simple operation, flexibility and high reliability, can approach the ground to detect the target, and can provide timely and reliable information, and therefore is widely used. The single unmanned aerial vehicle has limited sensing, decision-making and learning ability, and the multi-unmanned aerial vehicle cooperation can break through the limitation of the single unmanned aerial vehicle. The unmanned aerial vehicles cooperate with each other and maintain a proper formation to advance, which is more helpful to complete complex tasks.

[0051] The unmanned aerial vehicle formation cooperative task refers to a mode in which multiple unmanned aerial vehicles cooperate with each other according to a specified formation in a task to jointly complete a complex task. This mode utilizes the diversified task execution capability, efficient information sharing and cooperative action of the unmanned aerial vehicle formation to realize rapid and accurate target positioning. At the same time, the unmanned aerial vehicle formation can reduce the risk of failure or falling of the single unmanned aerial vehicle through decentralized deployment and mutual support. The unmanned aerial vehicle formation can share resource information through a data link, which greatly widens the detection range of each unmanned aerial vehicle and enhances the information accuracy and reliability, which enables the unmanned aerial vehicle group to perform tasks in a wider area.

[0052] However, the actual flight experiment of the unmanned aerial vehicle formation has a high cost and is restricted by environmental factors such as weather, which may cause the unmanned aerial vehicle body to fall. The simulation technology can greatly reduce the cost and save time by simulating the unmanned aerial vehicle formation flight to perform tasks on a computer. With the rapid development and diversification of unmanned aerial vehicle technology, the demand for unmanned aerial vehicle formation simulation is increasing, especially in supporting complex cooperative tasks, in which the unmanned aerial vehicles in the formation perform their respective tasks to meet the detection, attack and evaluation of the target area of the formation. To solve these problems, it is necessary to develop a perfect unmanned aerial vehicle formation simulation platform and provide a convenient simulation method.

[0053] Based on this, the present application provides a kind of unmanned aerial vehicle formation simulation method for cooperative task, which can be realized by a simulation platform, which can be realized by software or hardware. For example, the above-mentioned unmanned aerial vehicle formation simulation method can be applied to UE5, Unity Such as game engine realizes the unmanned aerial vehicle formation simulation method for cooperative task. As shown in the figure, the unmanned aerial vehicle formation simulation method can include: Figure 1

[0054] ​S110, obtain configuration parameters of the UAV formation, wherein the UAV formation comprises at least one UAV, and at least one UAV in the UAV formation can gather to form a specified formation.

[0055] In the embodiment of the application, the user can set the configuration parameters of the UAV formation through the interactive interface provided by the simulation platform. The configuration parameters of the UAV formation can comprise formation configuration parameters and single-machine configuration parameters of each UAV in the UAV formation. The formation configuration parameters of the UAV formation comprise, but are not limited to, the formation of the UAV formation, the number of UAVs in the UAV formation, the flight speed of the UAV formation, etc. The single-machine configuration parameters of each UAV in the UAV formation comprise, but are not limited to, the physical characteristics of the UAV, the motion characteristics of the UAV, the airborne equipment of the UAV, etc.

[0056] S120, determine the real-time positions of each UAV in the UAV formation in the UAV formation, and respectively control each UAV to fly from the real-time position to the target position;

[0057] After the simulation platform receives the configuration parameters of the UAV formation input by the user, the simulation platform automatically selects the formation algorithm corresponding to the target formation to perform the gathering of the formation, thereby automatically realizing the gathering and flight control of the UAV formation. Specifically, after receiving the configuration parameters of the UAV formation input by the user, the simulation platform can calculate the real-time positions of each UAV in the UAV formation in the UAV formation, and respectively control each UAV to fly from the real-time position to the target position of the UAV in the UAV formation, so as to form the target formation of the UAV formation. The target position of each UAV can refer to the target position of the UAV in the UAV formation, which can be determined according to the target formation of the UAV formation, and the target formation can be selected by the user.

[0058] Optionally, after the simulation platform obtains the configuration parameters of the UAV formation, the simulation platform can initialize the initial positions, flight positions and target positions of each UAV in the UAV formation in the UAV formation according to the configuration parameters of the UAV formation. The initial position refers to the position of the UAV in the UAV formation at the initial moment, and the target position of the UAV refers to the position of the UAV in the UAV formation at the moment when the target formation is formed.

[0059] S130, respectively determine whether each UAV reaches the target position;

[0060] The simulation platform can also determine whether the UAV reaches the target position in the UAV formation based on the real-time motion trajectory of each UAV, so as to automatically determine whether the UAV formation is completed. If all the UAVs in the UAV formation reach the corresponding target positions, the simulation platform can automatically execute step S140 to control the UAV formation to fly in the target formation.

[0061] Further, if there is at least one UAV in the UAV formation that has not reached the target position, the simulation platform can return to perform the above step S120 to correct the position of the at least one UAV in the UAV formation until each UAV in the UAV formation reaches the target position, and the automatic gathering of each UAV in the UAV formation is completed, at which time the simulation platform can control the UAV formation to maintain the target formation and fly.

[0062] Further, the simulation platform can also control the UAV formation to perform corresponding tasks. Specifically, when the UAV formation flies to the specified position according to the target formation, the simulation platform can control each UAV in the UAV formation to perform a specified task. The specified position refers to the position of the UAV formation when performing the task. For example, the UAV formation can include a detection UAV, an attack UAV, and a damage evaluation UAV, the simulation platform can control the detection UAV in the UAV formation to perform a detection task, control the attack UAV in the UAV formation to attack a specified target according to the information provided by the detection UAV, control the damage evaluation UAV in the UAV formation to evaluate the damage of the target, and so on.

[0063] During the performance of the task by the UAV formation, the simulation platform can also adaptively dynamically adjust the UAV formation according to the real-time situation, and this dynamic adjustment mechanism can enhance the task robustness, cooperation efficiency, and survival ability of the UAV formation in a complex and uncertain environment.

[0064] Specifically, S150, during the performance of the task by the UAV formation, the current environment of the UAV formation, the task type of the current task, the target state, and the resource state of each UAV in the UAV formation are acquired in real time.

[0065] The current environment includes but is not limited to a terrain environment, a meteorological environment, and a human environment.

[0066] The target state includes but is not limited to a target size and a target threat level.

[0067] The task type of the current task includes communication relay, wide-area search, target tracking, target identification, etc.

[0068] The resource state of each UAV in the UAV formation includes but is not limited to a device sudden failure or a resource state update, such as a low battery level or a damaged key sensor.

[0069] S160, if the current environment of the UAV formation changes, and / or the task type of the current task of the UAV formation changes, and / or the target state of the UAV formation changes, and / or the resource state of the UAV changes, according to the changed current environment and / or task type and / or target state and / or resource state of the UAV, the formation shape of the UAV formation is changed, the role allocation of each UAV in the UAV formation is re-performed, and the load configuration of each UAV in the UAV formation is modified according to the re-allocated role.

[0070] For example, the above-mentioned change of the current environment can be that the UAV formation encounters bad weather, sudden obstacles or electromagnetic interference; when the current environment of the UAV formation changes and the change affects the normal execution of the current task, the simulation platform can reconstruct the UAV formation in real time, including but not limited to changing the formation shape of the UAV formation, re-performing the role allocation of each UAV in the UAV formation, and modifying the load configuration of each UAV in the UAV formation according to the re-allocated role.

[0071] For another example, the change of the resource state of the UAV can be a sudden failure of the device or an update of the resource state (such as the battery level of the UAV being lower than a safety threshold or a critical sensor being damaged); when the resource state of the UAV in the UAV formation changes and the change affects the normal execution of the current task, the simulation platform can reconstruct the UAV formation in real time, including but not limited to changing the formation shape of the UAV formation, re-performing the role allocation of each UAV in the UAV formation, and modifying the load configuration of each UAV in the UAV formation according to the re-allocated role.

[0072] For another example, the change of the task type of the current task can be the task switching of the UAV formation, for example, the current task changes from a wide-area search task to a target tracking task; the simulation platform can reconstruct the UAV formation in real time, including but not limited to changing the formation shape of the UAV formation, re-performing the role allocation of each UAV in the UAV formation, and modifying the load configuration of each UAV in the UAV formation according to the re-allocated role.

[0073] Optionally, the adaptive reconfiguration logic of the UAV formation includes: firstly, intelligently filtering the candidate formation most suitable for the current scenario from the preset formation library (for example, automatically switching to a dense tracking formation when the target accelerates to escape); secondly, dynamically reassigning roles and tasks based on the real-time capability state of each UAV platform component and on-board equipment (for example, when the infrared sensor of a certain machine fails, automatically assigning another UAV in the formation that is closer and has this capability to take over the detection task); finally, ensuring the consistency of behavior decision-making within the formation through a distributed negotiation algorithm, effectively avoiding single point failure risks, and achieving smooth formation switching or role redistribution in fault conditions. This closed-loop dynamic adjustment mechanism significantly enhances the task robustness, collaboration efficiency and survivability of the formation in complex and uncertain environments.

[0074] Optionally, the simulation platform can also automatically select a formation algorithm corresponding to the target formation to control the flight behavior of each UAV in the UAV formation according to the configuration parameters of the UAV formation described above. Specifically, the simulation platform can also determine the distance between each UAV and the obstacle according to the real-time position of each UAV in the UAV formation and the position of the obstacle; and automatically determine the flight behavior of the UAV according to the distance between the UAV and the obstacle; wherein the flight behavior includes obstacle avoidance behavior and collision avoidance behavior.

[0075] Further optionally, when the simulation platform determines that the UAV formation has been assembled according to the real-time position of each UAV and its target position in the UAV formation, the simulation platform can also determine whether the formation of the UAV formation changes in real time when controlling the flight of the UAV formation. Specifically, the simulation platform can also determine whether the target formation of the UAV formation is disturbed; if the target formation of the UAV formation is disturbed, the simulation platform can automatically determine the position of the lead UAV and restore the target formation. If the formation of the UAV formation is not disturbed, the simulation platform controls the UAV formation to maintain the target formation and advance to the target position.

[0076] The simulation method for the unmanned aerial vehicle formation facing the cooperative task can obtain the unmanned aerial vehicle formation configuration parameters through the simulation platform, and then the simulation platform can automatically calculate the real-time positions of each unmanned aerial vehicle in the unmanned aerial vehicle formation, automatically control the unmanned aerial vehicles to fly from the real-time positions to the target positions, and control the unmanned aerial vehicle formation to keep the target formation flying when each unmanned aerial vehicle in the unmanned aerial vehicle formation reaches the target position. The simulation method for the unmanned aerial vehicle formation facing the cooperative task can complete the gathering of the unmanned aerial vehicles from single machines to formations, and further, the simulation method for the unmanned aerial vehicle formation facing the cooperative task can also control the formation to complete complex cooperative tasks, thereby reducing the experimental cost. Meanwhile, the simulation method for the unmanned aerial vehicle formation facing the cooperative task can set various configuration parameters in the unmanned aerial vehicle formation, which can meet most experimental scenarios of formation flying, without the need to develop new formation arrays. Moreover, the simulation method for the unmanned aerial vehicle formation facing the cooperative task can adaptively adjust according to the changes of the environment, the changes of the task, the changes of the target state and the resource state during the execution of the task of the unmanned aerial vehicle formation, realize the dynamic loading and control of the unmanned aerial vehicle model and the simulation process, improve the adaptability of the system to complex environments and tasks, have good universality, expansibility and practical value, and effectively improve the task efficiency.

[0077] Optionally, before the unmanned aerial vehicle formation flies, the target formation of the unmanned aerial vehicle formation is first determined according to the current task, the current environment and the target state and the like. The unmanned aerial vehicle formation needs to complete formation building and switch to a suitable formation according to the real-time changes of the current environment during flight. Whether the selected formation is suitable has great significance for whether the unmanned aerial vehicle formation can successfully complete the task. The task type of the current task can be communication relay, wide-area search, target tracking, target identification and the like. The task target of the current task can be rapid search, rapid identification, efficient identification, wide-area search, precise tracking, accurate identification, search-tracking-identification task chain and the like. The current environment can be a terrain environment, a meteorological environment, a human environment and the like. The target state includes but is not limited to target size and target threat level. The simulation platform provides a formation library for specific task scenarios, task targets of the current task and target state and the like situation information.

[0078] As shown in Figure 8 , the user can input the current task to be executed by the unmanned aerial vehicle formation, the task scenario and the target state and the like situation information through the interactive interface of the simulation platform. The simulation platform determines the formation of the unmanned aerial vehicle formation by calling the formation library. The user can set the formation configuration parameters of the unmanned aerial vehicle formation through the interactive interface of the simulation platform to initialize the formation configuration parameters and the like of the unmanned aerial vehicle formation. As shown in Figure 2 , the above simulation method can include the following steps:

[0079] S210, acquire a task target of a current task, a current environment, and a target state;

[0080] As shown in the following table, the task target can include fast search, fast identification, efficient identification, search-tracking-identification task chain, etc. The environment in which the current task is executed can be a terrain environment such as sea, desert, city, forest, etc., and a meteorological environment such as wind speed, humidity, temperature, visibility, etc. The target state can include the size of the target and the threat level of the target, etc. Figure 8

[0081] S220, select a target formation of the UAV formation from a formation library according to the task target of the current task, the current environment, and the target state;

[0082] The simulation platform can select a target formation of the UAV formation from the formation library according to the task target of the current task, the current environment, and the target state input by the user. The target formation can be a "one" shape, a "person" shape, a diamond shape, a "V" shape, a ring shape, a dense tracking formation, etc. Each formation has different advantages and can be suitable for different tasks to achieve the task target more efficiently and economically. For example, the diamond formation can cover a larger detection range, the "V" shape formation can keep the target in the center of the sensor field of view, and the dense tracking formation can track an accelerating target. The environment formation can achieve 360° signal coverage. Further, the target formation of the UAV formation can be visually displayed through the interactive interface of the simulation platform. For example, the target formation can be displayed in the form of a picture through the interactive interface.

[0083] S230, determine the formation configuration parameters of the UAV formation of the target formation according to the acquired formation setting signal.

[0084] After selecting the target formation of the UAV formation, the user can also configure each UAV in the UAV formation through the simulation platform. All UAVs in a formation can be of one type, or there can be multiple types of UAVs, thereby setting the formation configuration parameters of the UAV formation. The simulation platform can provide at least one formation configuration item, each formation configuration item corresponding to a formation configuration parameter, and the above-mentioned formation setting signal can be a modification or selection signal of the corresponding formation configuration item by the user.

[0085] The related parameters of the UAV formation are shown in the following table 1. The formation configuration parameters can include but are not limited to: formation speed, formation shape, number of UAVs, UAV offset, UAV type, UAV task and target, UAV formation number, migration coefficient, separation coefficient, aggregation coefficient, escape angle. Each configuration parameter in the table can correspond to at least one formation configuration item of the simulation platform, so that the user can set the formation configuration parameters through the interactive interface of the simulation platform.

[0086] ​Table 1 Formation configuration parameters of UAV formation

[0087]

[0088] Optionally, the types of UAVs in the UAV formation can be configured through a Json file, and a user can select the same and different Json files to configure each UAV in the UAV formation. After the above parameters are configured, the simulation platform can control the UAVs in the UAV formation to automatically form a formation array according to a formation algorithm and an obstacle avoidance algorithm to perform a task, so that the simulation platform can simulate the entire process of the UAV formation, that is, simulate the processes of building the UAV formation, maintaining the formation, transforming the formation, avoiding obstacles in the formation, avoiding collisions between UAVs in the formation, and performing tasks, and can reflect the UAV formation and obstacle avoidance behaviors.

[0089] Further, the simulation platform can be used to configure each UAV in the UAV formation to determine the single-machine configuration parameters of each UAV in the UAV formation. Optionally, the simulation platform can provide at least one single-machine configuration item, and a user can modify or select the at least one single-machine configuration item of the simulation platform, so as to configure the parameters of a single UAV in the UAV formation. The simulation platform can obtain single-machine setting signals of each UAV in the target formation through an interactive interface, the single-machine setting signals can be the modification or selection operations of the user on the single-machine configuration item, and then the simulation platform can determine the single-machine configuration parameters of each UAV according to the single-machine setting signals of each UAV, and further, the simulation platform can automatically generate a UAV model according to the single-machine configuration parameters of each UAV. Further optionally, the interactive interface of the simulation platform can visually display the at least one single-machine configuration item, and the interactive interface of the simulation platform can also visually display a single UAV model and a UAV formation formed by the single UAV model. The single-machine configuration parameters include a UAV platform component and an airborne device component, the UAV platform component includes a set of physical characteristic attributes and motion characteristics of the UAV, and the airborne device component includes at least one device model.

[0090] In the embodiment of the present application, in order to facilitate the parameter configuration of a single UAV in the UAV formation, the simulation platform can provide a general UAV model, which can realize modeling of different types and models of UAVs, so that the simulation platform can quickly generate a corresponding UAV model according to the single-machine configuration parameters input by a user. The general UAV model can be obtained based on a component-based modeling method. The simulation platform can disassemble the inherent functions of a UAV, encapsulate the inherent functions of a single UAV into component models respectively, and combine different component models according to the same modeling specification to obtain the general UAV model.

[0091] Optionally, asFigure 3 As shown, the general unmanned aerial vehicle model can include an unmanned aerial vehicle platform component and an airborne device component, the unmanned aerial vehicle platform component is a mandatory model, and each unmanned aerial vehicle should contain the unmanned aerial vehicle platform component; the airborne device component is a model of airborne devices that can be carried by the unmanned aerial vehicle, and is an optional function model.

[0092] In order to ensure the generality of the unmanned aerial vehicle model, the general unmanned aerial vehicle model should contain as few functions as possible. Therefore, in the general unmanned aerial vehicle model, the unmanned aerial vehicle platform component only contains necessary physical characteristic attributes and motion characteristics. Among them, the physical characteristic attribute set is a description of the shape characteristics and basic capabilities of the unmanned aerial vehicle, and these attributes include the type, size, specification, fuel capacity of the unmanned aerial vehicle, and the specific attribute names are shown in Table 2. The embodiment of the application can generate an unmanned aerial vehicle with a certain type and size by configuring these attributes of a single unmanned aerial vehicle, and these attributes also indicate the maximum value of some capabilities of the unmanned aerial vehicle.

[0093] Table 2 Physical characteristic attribute set of general unmanned aerial vehicle model

[0094]

[0095] The motion characteristics can simulate the whole process of take-off, cruising and landing of the unmanned aerial vehicle, and can be used to calculate the position, height and attitude of the unmanned aerial vehicle at a certain time point in the motion process. The embodiment of the application summarizes the related parameters of the motion characteristics of the unmanned aerial vehicle by studying the formulas related to the kinematics of the unmanned aerial vehicle, as shown in Table 3.

[0096] Table 3 Related parameters of motion characteristics of a single unmanned aerial vehicle

[0097]

[0098] Some safety values of the unmanned aerial vehicle flight are also given in Table 3, such as the maximum flight speed and the maximum climbing speed. At the same time, the take-off and landing modes of the unmanned aerial vehicle can also be configured. Since there are many types of unmanned aerial vehicles, there are also many take-off and landing modes. The take-off modes include taxi take-off, catapult take-off, vertical take-off, air drop, and load take-off, and the landing modes include taxi landing, arresting cable landing, vertical landing, and parachute landing. In the configuration parameters of the motion characteristics of a single unmanned aerial vehicle, mode 1 can be set as taxi take-off, mode 2 can be set as catapult take-off, and so on. These configuration parameters ensure the generality and diversity of the unmanned aerial vehicle model.

[0099] The airborne device component can include one or more of a perception device model, a communication device model, and an ECM device model. Since the airborne device models have different categories, the airborne device model configuration files are also organized according to different categories. The airborne device model configuration file records the name, type, number, and the like of the airborne device. The UAV has different functions by mounting different device models, and can perform different tasks. The mounted device component parameters are shown in Table 4.

[0100] Table 4 Mounted device component parameters of a single UAV

[0101]

[0102] In order to facilitate the generation and analysis of the UAV model, the configuration file of the UAV platform component can be implemented in Json language. When the UAV model is initialized, the simulation platform can obtain and analyze the configuration file of the UAV platform component, and write the data in the configuration file of the UAV platform component into the UAV model. Thus, the simulation platform can configure the parameters such as physical characteristics and motion characteristics in the general UAV model through the configuration file of the UAV platform component. As shown in Figure 4 The configuration file of the UAV platform component is organized according to the characteristic parameter data structure, and is divided into two parts according to the physical characteristic attribute set and the motion characteristic. Each part contains its own parameter data structure and attribute details, and the attribute details include name, data type, and value.

[0103] Figure 5 An interactive interface diagram of the simulation platform is provided, as shown in Figure 5 The user can set the formation configuration parameters of the UAV formation (i.e., the formation of the UAV formation), the speed of the UAV formation, the number of UAVs in the UAV formation, the type of each UAV in the UAV formation, the offset of each UAV in the UAV formation, the formation number of each UAV in the UAV formation, the leader of the UAV formation, the task of each UAV in the UAV formation, and the obstacle avoidance behavior of each UAV in the UAV formation, and the like through the interactive interface of the simulation platform. Thus, the simulation platform can obtain the formation configuration parameters set by the user, and automatically implement the assembly and control of the UAV formation according to the formation configuration parameters. Further, the user can also set the single-machine configuration parameters of each UAV in the UAV formation through the interactive interface of the simulation platform. The single-machine configuration parameters can be implemented according to the general UAV model provided by the simulation platform, and the single-machine configuration parameters include but are not limited to the physical characteristics of a single UAV, the motion characteristics of a single UAV, and the airborne device of a single UAV, and the like.

[0104] Figure 6 is a flow diagram of a UAV formation simulation method according to another embodiment of the present application,Figure 7 An architecture diagram of the simulation method is shown. As shown in Figure 7 The simulation platform can provide a componentized modeling module, an entity construction module, a formation cooperative dynamic control simulation module, and a simulation execution module. The simulation platform can provide a resource library including a component model library, a task library, and a formation library. The relationship and execution process between the above modules and the resource library can be seen from Figure 8 The componentized modeling module can realize the selection and switching of the current task by calling the task library, and realize the parameter configuration of a single unmanned aerial vehicle by constructing a general unmanned aerial vehicle model. The construction of the general unmanned aerial vehicle model can be seen from Figure 3 The entity construction module is used to generate an unmanned aerial vehicle entity model on the simulation platform according to the parameter configuration of the unmanned aerial vehicle. The formation cooperative dynamic control simulation module is used to dynamically adjust the strategy of the unmanned aerial vehicle formation during the task execution process of the unmanned aerial vehicle formation, realize the formation switching, role allocation and reorganization of the unmanned aerial vehicle formation, and can be seen from Figure 8 The trigger conditions of the dynamic adjustment of the unmanned aerial vehicle formation include but are not limited to the current environmental change, the task type change of the current task, the target state change, and the resource abnormal change. The simulation execution module is used to show the simulation execution and cooperative dynamic control of the unmanned aerial vehicle formation, and output the task efficiency.

[0105] As shown in Figure 6 The unmanned aerial vehicle formation simulation method of an embodiment of the present application can include:

[0106] S610, obtaining configuration parameters of an unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation includes at least one unmanned aerial vehicle, and at least one unmanned aerial vehicle in the unmanned aerial vehicle formation can be assembled to form a target formation. The configuration parameters of the unmanned aerial vehicle formation can be input by a user through a simulation platform. The target formation of the unmanned aerial vehicle formation can be determined according to the real-time situation of the unmanned aerial vehicle formation. For example, the user can input the task target, the target state, and the task environment corresponding to the current task through the simulation platform. The simulation platform can call the formation library and select the target formation from the formation library based on the task target, the target state, and the current task environment. Then, the simulation platform can obtain the configuration parameters of each unmanned aerial vehicle in the target formation to construct each unmanned aerial vehicle entity.

[0107] S611, initializing the initial position, the flight position, and the target position of each unmanned aerial vehicle in the unmanned aerial vehicle formation according to the configuration parameters of the unmanned aerial vehicle formation.

[0108] S612, determining the real-time position of each unmanned aerial vehicle in the unmanned aerial vehicle formation, and respectively controlling each unmanned aerial vehicle to fly from the real-time position to the target position in the unmanned aerial vehicle formation;

[0109] S613, determine the distance between each UAV and the obstacle according to the real-time position of each UAV in the UAV formation and the position of the obstacle;

[0110] S614, determine the flight behavior of the UAV according to the distance between the UAV and the obstacle; wherein the flight behavior includes obstacle avoidance behavior and collision avoidance behavior.

[0111] When the UAV flies around the obstacle to the safe area, the simulation method of the UAV formation can continue as follows:

[0112] S615, determine whether each UAV reaches the target position; wherein the target position of each UAV in the UAV formation can be determined according to the target formation of the UAV formation.

[0113] If each UAV in the UAV formation reaches its target position in the UAV formation, step S616 is performed to control the UAV formation to maintain the target formation and fly. If there is at least one UAV in the UAV formation that does not reach its target position in the UAV formation, return to step S612 until all UAVs in the UAV formation reach their target positions in the UAV formation.

[0114] S617, determine whether the target formation of the UAV formation is disturbed;

[0115] If the target formation of the UAV formation is disturbed, step S618 is performed to re-determine the position of the lead UAV and restore the target formation; if the formation of the UAV formation is not disturbed, control the UAV formation to maintain the target formation and advance to the target position.

[0116] S619, when the UAV formation reaches the specified position, control each UAV in the UAV formation to perform a specified task, respectively. For example, the UAV formation can include a detection UAV, an attack UAV and a damage evaluation UAV, the simulation platform can control the detection UAV in the UAV formation to perform a detection task, control the attack UAV in the UAV formation to attack a specified target according to the information provided by the detection UAV, control the damage evaluation UAV in the UAV formation to evaluate the damage of the target, etc.

[0117] During the execution of the task by the UAV formation, the simulation platform can also obtain the situation information of the UAV formation in real time to dynamically adjust the configuration parameters of the UAV formation, such as formation switching, role assignment and recombination, etc., to realize the cooperative dynamic control of the UAV formation. The order of steps involved in the dynamic adjustment process of the UAV formation according to the embodiments of the present application is not limited.

[0118] S620, determine whether the task type of the current task is changed, if the task type of the current task is changed, execute S621, change one or more conditions of the formation switching of the UAV formation, the role re-allocation of the UAV in the formation or the load parameter adjustment of each UAV to adapt to the change of the task. For example, the current task is changed from a wide-area search task to a target tracking task, when the current task is changed, the simulation platform can gather the formation of the UAV formation, adopt a "V" shape formation to keep the target in the middle of the sensor, change the focal length of the visible light sensor to increase the accuracy and success rate of tracking.

[0119] If the current task is not changed, the simulation platform can execute S622 to determine whether the current environment of the UAV formation is changed, if the current environment is changed and the current environment has a greater impact on the target, the current environment will have a greater impact on the execution of the current task by the UAV formation, at this time, S623 is executed.

[0120] S623, according to the change of the current environment, change one or more conditions of the current UAV formation, the role re-allocation of the UAV or the load parameter reconfiguration of the UAV to adapt to the current environment. For example, when the wind speed of the current environment is large, the formation is switched to a wind-resistant "swan" formation to reduce power consumption, and the wind disturbance direction angle γ is calculated in real time to dynamically rotate the formation direction (such as I-shaped turning), thereby reducing the windward area. For another example, when the fog concentration of the environment is too large, the UAV will mainly use infrared sensors and adjust the parameters of the sensors.

[0121] Further, if the current environment does not affect the execution of the current task by the UAV formation, the simulation platform continues to execute S624 to determine the change of the current target state. If the target state is changed and the target state has a greater impact on the current task, S625 is executed.

[0122] S625, according to the change of the current target state, change one or more conditions of the current UAV formation, the role re-allocation of the UAV or the load parameter reconfiguration of the UAV to adapt to the change of the target to complete the task more efficiently. For example, when the target situation changes greatly, the target is divided into scattered or the target position changes greatly, the target position is redefined, and the formation is switched according to the current task, wherein the formation of the UAV formation is a diamond formation to cover the largest search range.

[0123] Further, if the current target state is not changed, S626 is executed to determine whether the current UAV resource is abnormal. If the abnormal condition of the UAV will affect the execution of the current task, S627 is executed.

[0124] S627, due to the abnormality of the UAV resources, one or more of the following conditions can be changed to avoid reducing the efficiency of task execution or causing the task execution to fail due to the abnormality of the resources: changing the current UAV formation shape, reassigning the UAV role, or reconfiguring the UAV payload parameters. For example, when the UAV formation is performing a search task, the visible light sensor of a UAV in the formation is abnormal. At this time, the simulation platform can adaptively change the position and role of the UAV with the abnormal visible light sensor in the UAV formation, so that it no longer performs the visible light search task. The visible light search task can be performed by other UAVs (whether other UAVs can perform the visible light search task also needs to be determined, and the role-resource matching matrix is queried and switched, while the principle of proximity should be followed).

[0125] The UAV formation simulation method for collaborative tasks provided by the present application can obtain UAV formation configuration parameters through a simulation platform, and then the simulation platform can automatically calculate the real-time positions of each UAV in the UAV formation, automatically control the UAVs to fly from their real-time positions to target positions, and control the UAV formation to maintain the target formation shape when each UAV in the UAV formation reaches the target position. The UAV formation simulation method for collaborative tasks of the present application can complete the assembly of UAVs from single machines to formations. Further, the UAV formation simulation method for collaborative tasks of the present application can also control the formation to complete complex collaborative tasks, thereby reducing the experimental cost. At the same time, the UAV formation simulation method for collaborative tasks of the present application can set various configuration parameters in the UAV formation, which can meet most of the experimental scenarios of formation flight, without the need to develop new formation arrays. Moreover, the UAV formation simulation method for collaborative tasks of the present application can adaptively adjust according to the changes in the environment, the changes in the task, the changes in the target state, and the resource state during the execution of the task of the UAV formation, realize the dynamic loading and control of the UAV model and the simulation process, and improve the adaptability of the system to complex environments and tasks, thereby having good versatility, expansibility, and practical value, and effectively improving the task efficiency.

[0126] It should be understood that, although Figures 1-2 , Figure 6 The steps in the flowchart of the present application are displayed in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figures 1-2 , Figure 6At least one of the steps in the above embodiments can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the order of execution of the sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or at least a part of the sub-steps or stages of other steps.

[0127] The technical features of the above embodiments can be combined in any manner. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, but it is understood that any combination of the technical features is within the scope of the present disclosure as long as there is no contradiction.

[0128] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these are within the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. A method for simulating a UAV formation for a cooperative task, characterized in that, The simulation platform provides a general-purpose UAV model, which includes UAV platform components and airborne equipment components. The UAV platform components include the physical characteristics and motion characteristics of the UAV; the airborne equipment components include one or more of the following: sensing equipment models, communication equipment models, and electronic countermeasures equipment models. For any drone in the drone swarm, the method further includes: Based on the physical and motion characteristics of the UAV, the UAV platform components are determined; Based on at least one device model, determine the airborne equipment components of the UAV; A simulation model of the UAV is automatically generated based on the UAV platform components and the UAV's onboard equipment components. The configuration parameters of the drone formation are obtained, wherein the drone formation includes at least one drone; the configuration parameters of the drone formation also include individual drone configuration parameters. Determine the real-time position of each drone in the drone formation, and control each drone to fly from the real-time position to the target position; Determine whether each of the drones has reached the target location; If each drone in the drone formation reaches the target position, then control the drone formation to maintain the target formation flight; During the mission execution of the drone formation, the current environment, mission type, target status, and resource status of each drone in the drone formation are acquired in real time. If the current environment of the drone formation changes, and / or the mission type of the drone formation changes, and / or the target state of the drone formation changes, and / or the resource state of the drones changes, then the formation of the drone formation is changed according to the changed current environment and / or mission type and / or target state and / or resource state. Based on the real-time capability status of each drone platform component and airborne equipment, the roles of each drone in the drone formation are reassigned, and the payload configuration of each drone in the drone formation is modified according to the reassigned roles.

2. The method of claim 1, wherein, The configuration parameters of the drone formation include formation configuration parameters; obtaining the configuration parameters of the drone formation includes: Get the task objective, current environment, and objective status of the current task; Based on the current mission objective, current environment, and target status, select the target formation for the UAV formation from the formation library; wherein, the formation library includes, but is not limited to, diamond formation, "V" formation, dense tracking formation, circular formation, "I" formation, and "V" formation; Based on the acquired formation setting signal, the formation configuration parameters of the UAV formation of the target formation are determined.

3. The method according to claim 1 or 2, characterized in that, The process of obtaining the configuration parameters of the drone formation also includes: Acquire the individual configuration signals of each UAV in the target formation; The individual configuration parameters of each drone are determined based on the individual setting signals of each drone. The single-machine configuration parameters include the physical and motion characteristics of the UAV.

4. The method of claim 1, wherein, The method further includes: According to real-time positions of each of the UAVs in the UAV formation and positions of obstacles, distances between each of the UAVs and the obstacles are determined respectively; According to the distances between the UAVs and the obstacles, flight behaviors of the UAVs are determined; wherein the flight behaviors include obstacle avoidance behaviors and collision avoidance behaviors.

5. The method of claim 1, wherein, The method further comprises: If there is at least one of the UAVs in the UAV formation that has not reached a target position, the method returns to determining real-time positions of each of the UAVs in the UAV formation and respectively controlling each of the UAVs to fly from the real-time positions to the target positions until each of the UAVs in the UAV formation reaches the target position.

6. The method of claim 1, wherein, The method further comprises: Determining whether a target formation of the UAV formation is disturbed; If the target formation of the UAV formation is disturbed, re-determining a position of a leading UAV and restoring the target formation; If the formation of the UAV formation is not disturbed, controlling the UAV formation to keep the target formation and move to a target position.

7. The method of claim 1, wherein, The method further comprises: When the UAV formation reaches a specified position, respectively controlling each of the UAVs in the UAV formation to perform a specified task.

8. The method of claim 1, wherein, The method further comprises: Initializing initial positions, flight positions and target positions of each of the UAVs in the UAV formation.

9. The method of claim 1, wherein, The method further comprises: Displaying a flight trajectory of the UAV formation on a display device.

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