Large-scale unmanned aerial vehicle cluster formation reconstruction method based on event triggering mechanism

Abstract

The invention discloses a large-scale unmanned aerial vehicle cluster formation reconstruction method based on an event triggering mechanism, and the method comprises the steps: constructing an unmanned aerial vehicle formation model, and obtaining the fault time and fault type of a fault unmanned aerial vehicle in an unmanned aerial vehicle formation; obtaining a force between each unmanned aerial vehicle and a neighbor unmanned aerial vehicle, and a force between each unmanned aerial vehicle and an obstacle; according to the fault type of the fault unmanned aerial vehicle, obtaining a predicted position of the fault unmanned aerial vehicle or the neighbor unmanned aerial vehicle; calculating a distance error between the actual position and the predicted position of the fault unmanned aerial vehicle, and abandoning the fault unmanned aerial vehicle whose distance error exceeds a preset control threshold value; and according to the fault type and the predicted position of the fault unmanned aerial vehicle, obtaining a control expression of the fault unmanned aerial vehicle and the neighbor unmanned aerial vehicle so as to control the unmanned aerial vehicle. According to the method, the convergence speed and direction of the unmanned aerial vehicles can be more finely adjusted, the efficiency between the unmanned aerial vehicles is improved, and the dynamic response of the unmanned aerial vehicle formation can be flexibly adjusted according to task requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV formation, and in particular relates to a large-scale UAV cluster formation reconstruction method based on an event triggering mechanism. Background Art

[0002] Since the 1990s, with the rapid development of communication, automatic control, computer and other technologies, a research boom on drones has been stimulated, which has promoted the development of drones and related products and accelerated the development of drone technology. In particular, the research on drone formations and related issues has aroused the interest of researchers from various countries.

[0003] Drone formation refers to the use of control strategies and formation technologies to make multiple drones fly in a desired formation. If the mission environment changes or changes occur within the drones themselves, the drones will also adjust their formation accordingly.

[0004] However, during missions, drones are inevitably subject to interference due to internal communication failures or harsh, changing mission environments, impacting formation effectiveness and mission performance. Furthermore, despite extensive research on time-varying drone formation and swarm control, formation maintenance is often overlooked. Therefore, it is essential to reconfigure the formation and manage environmental obstacles during missions. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a large-scale UAV swarm formation reconstruction method based on an event trigger mechanism. The technical problem to be solved by the present invention is achieved through the following technical solutions: The present invention provides a large-scale UAV swarm formation reconstruction method based on an event trigger mechanism, comprising: S1: Build a UAV formation model and obtain the time and fault type of the faulty UAVs in the UAV formation. The fault types include UAV signal transmitter failure, UAV signal receiver failure, and UAV signal transmitter and signal receiver failure. S2: Obtain the force between each UAV and its neighboring UAVs, and the force between each UAV and obstacles; S3: Obtain the predicted location of the faulty UAV or neighboring UAVs based on the fault type of the faulty UAV; S4: Calculating the distance error between the actual position and the predicted position of the faulty UAV, and discarding the faulty UAVs whose distance error exceeds a preset control threshold; S5: According to the fault type and predicted location of the retained faulty UAV, control expressions of the faulty UAV and the neighboring UAVs are obtained to control the faulty UAV and the neighboring UAVs.

[0006] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a large-scale drone swarm formation reconstruction method based on an event-triggered mechanism. Compared with traditional formation aggregation technology, the present invention can more finely adjust the convergence speed and direction of drones, not only improving the efficiency between drones, but also flexibly adjusting the dynamic response of the formation according to mission requirements. By introducing a time-triggered mechanism, the present invention enables drone formations to flexibly adjust and quickly change formations when faced with different types of faults. Through precise time control, drones can make optimal adjustments in a timely manner, improving the formation's fault tolerance during mission execution and enhancing its adaptability in different environments. In terms of obstacle avoidance, the present invention enables each drone to maintain a desired distance from other drones, and drones always maintain a safe distance from obstacles.

[0007] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of a large-scale UAV swarm formation reconstruction method based on an event trigger mechanism provided by an embodiment of the present invention; Figure 2 Schematic diagram of three types of neighborhoods of a drone provided by an embodiment of the present invention; Figure 3 This is a flow chart of an event triggering mechanism in a communication failure provided by an embodiment of the present invention; Figure 4 1 is a schematic diagram of simulation results of a UAV with a launch failure provided by an embodiment of the present invention; Figure 5 1 is a schematic diagram of simulation results of a UAV with a receiving failure provided by an embodiment of the present invention; Figure 6 This is a schematic diagram of simulation results of a UAV with transmission and reception failures provided by an embodiment of the present invention; Figure 7 This is a schematic diagram of a formation effect when a UAV fails but fault-tolerant control is not introduced, provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of the large-scale UAV cluster formation reconstruction method based on the event trigger mechanism proposed by the present invention in combination with the accompanying drawings and specific implementation methods.

[0010] The aforementioned and other technical contents, features, and effects of the present invention are clearly presented in the following detailed description of the specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a deeper and more specific understanding of the technical means and effects adopted by the present invention to achieve the intended purpose can be obtained. However, the accompanying drawings are provided for reference and illustration purposes only and are not intended to limit the technical solutions of the present invention.

[0011] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations are intended to cover non-exclusive inclusion, such that an article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or device comprising the element.

[0012] Example 1 See Figure 1 , Figure 1 This is a flow chart of a large-scale UAV swarm formation reconstruction method based on an event trigger mechanism provided by an embodiment of the present invention, the method comprising the following steps: S1: Build a UAV formation model and obtain the time and type of failure of the faulty UAVs in the UAV formation.

[0013] Assume that the drone swarm consists of N The drone swarm is limited by unpredictable external disturbances. In order to realize the formation and obstacle avoidance of drone formation, the drones randomly distributed in a specific area are N In a drone swarm consisting of 100 drones, each drone is assigned neighbors based on the proximity principle, and topological and interactive relationships are established between drones and their neighbors. A topological graph is used to represent the communication relationships between drones in the swarm, with each drone being considered a node in the topological graph and the information exchanges between drones being considered edges. The interaction topology between drones in a drone formation is established using the following undirected graph model: , in, represents the vertices in the undirected graph, i.e., drones, N represents the number of drones in the drone formation, represents a list of edge sets, Indicates drone i With drones j For a pair of neighbors, there is an interactive topology and they can exchange information with each other.i The neighbor drones are defined as follows: , in, Indicates drone i A collection of neighbor drones, Indicates drone i With drones j The edge between exist.

[0014] Furthermore, the adjacency matrix of the undirected graph model is obtained as follows: , in, Indicates the existence of an edge , namely drones i With drones j There is a communication relationship between them, and ; Indicates that there is no edge , namely drones i With drones j There is no communication relationship between the drones. j Not a drone i Neighbor drone.

[0015] See Figure 2 , Figure 2 This is a schematic diagram of three types of neighborhoods of a drone provided by an embodiment of the present invention. i All neighboring drones of a drone are divided into three categories: 、 and express: , , , in, 、 and Respectively represent the locations of drones i The set of neighbor drones in the exclusion area, desired area and attraction area, Indicates drone i Its neighbor drones j The distance between 、 and denote the radii of the repulsive region, the desired region, and the attractive region, respectively. In other words, when When the neighbor drone j Located in drone iThe exclusion zone, when When the neighbor drone j Located in drone i The expected area, when When the neighbor drone j Located in drone i attraction area.

[0016] It is known that drones i The dynamics of is governed by the following equations: , in, Indicates drone i The location coordinates of x i , y i , z i Respectively represent drones i On the three vertical axes xyz The position coordinate components on , Indicates drone i The velocity scalar in the absence of disturbance, Indicates location coordinates About time t The derivative of , that is, Indicates drone i The three-dimensional velocity, 、 and Represented on three vertical coordinate axes xyz On the drone i The velocity component of and Respectively represent drones i Track angle and heading angle.

[0017] Then, get the drone i Dynamic expression: , in, , and Respectively represent drones i Velocity scalar under no disturbance , drones i Heading angle and drones i Track angle Relative to time t The derivative of Indicates drone i quality, grepresents the acceleration due to gravity, Indicates drone i The tilt angle, 、 and Respectively represent drones i thrust, drag and lift.

[0018] Through the feedback linearization process, the UAV i The linear time-invariant form of can be expressed as follows: , in, x i , y i , z i Respectively represent drones i On the vertical axis xyz Position coordinate components in three directions, 、 and Respectively represent drones i On the vertical axis xyz Velocity components in three directions; , , Respectively 、 and In drones i The derivatives with respect to time in three directions, i.e., the drone i On the vertical axis xyz The acceleration in three directions, 、 and Indicates drone i The control variables correspond to the drones i Acceleration in three directions.

[0019] Get the actual control input variables, 、 and It can be calculated as follows:

[0020]

[0021]

[0022] Therefore, the UAV formation system can be described as: , in, is the position vector of the UAV formation system, is the velocity vector, , represents the control input, , Respectively represent drones i Control input in three directions.

[0023] Further, the i Non-neighboring drones within the influence range (exclusion area) of a drone and obstacles Described as:

[0024]

[0025] It should be noted that in this embodiment, it is assumed that when a communication failure occurs among UAVs, at most only one UAV fails at the same time, it is assumed that the influence of the size and shape of the UAVs participating in the formation control is ignored, it is assumed that the initial states of all UAVs are randomly distributed in the required area, UAVs are able to share information with their neighbors, and the environment (spatial constraints) is uniform (time-invariant) throughout the mission.

[0026] The control goal of this embodiment is to detect and abandon uncontrollable faulty drones while automatically forming and maintaining the required formation, changing the formation while meeting the obstacle avoidance requirements.

[0027] S2: Get the forces between each drone and its neighbor drones, and the forces between each drone and its obstacles.

[0028] In the actual execution of missions, individual drones often experience communication failures. When a drone encounters a communication failure, the faulty drone can detect the time and type of failure through its own fault detector. The failure types can be divided into the following three types: (1) drone signal transmitter failure, (2) drone signal receiver failure, and (3) drone signal transmitter and receiver failure. For the above three situations, three controllers need to be designed respectively.

[0029] In this step, the potential function is first designed according to the artificial potential field method as follows: , in, Indicates drone i With drones j The potential function between and Indicates a positive coefficient. Specifically, the first line on the right side of the equation indicates that when the drone j UAVi When the neighbor drones i With drones j The second line indicates that when the UAV j Not a drone i Neighbor drones and not drones i When the drone is within the exclusion zone i With drones j The potential function between is 0, and the third line indicates that when the drone j Not a drone i Neighbor drones and drones j In drones i When the drone is within the exclusion zone i With drones j The potential function between .

[0030] From the above formula, the corresponding force between drones can be obtained as: , in, Indicates drone i With drones j The force between Indicates drone i With drones j The distance between them is similar. The first line on the right side of the equation indicates that when the drone j UAV i When the neighbor drones i With drones j The second line shows the force between the drone and j Not a drone i Neighbor drones and not drones i When the drone is within the exclusion zone i With drones j The force between them is 0, and the third line indicates that when the drone j Not a drone i Neighbor drones and drones j In drones i When the drone is within the exclusion zone i With drones j The force between.

[0031] Similarly, the UAV is obtained according to the potential function i The force between the obstacle : , in, Indicates drone i The distance to the obstacle, Indicates the norm, Indicates drone i The radius of the exclusion zone.

[0032] S3: Obtain the predicted location of the faulty drone or neighboring drone based on the drone’s fault type.

[0033] It should be noted that during the actual mission execution, if there is no faulty drone in the drone formation, each drone can collect and record its own location information in real time and transmit its own location information to neighboring drones.

[0034] If the current drone is faulty and the fault type is a signal transmitter failure (i.e., the signal transmitter is faulty but the signal receiver is functioning properly), the faulty drone itself can receive location information from neighboring drones, while neighboring drones communicating with it cannot. At the same time, since the faulty drone's signal receiver is functioning properly, the current drone can obtain the speed and location information of its neighboring drones.

[0035] Because the neighboring drone cannot obtain the speed and position information of the current faulty drone, it uses the predicted position of the faulty drone instead of the actual position to participate in the control. i Over time t The predicted position is realized by the following formula: , in, Indicates drone i The time when the failure occurred, Indicates drone i The actual location when the fault occurred, Indicates the current faulty drone calculated by the neighbor drones i In time t The predicted position at time .

[0036] When the fault type of the faulty drone is a signal receiver failure, that is, the signal receiver fails but the signal transmitter is normal, the current faulty drone can transmit its own real location information to neighboring drones, but cannot receive the real location information from neighboring drones.

[0037] Because the neighboring drones cannot transmit their own speed and position information to the faulty drone in time, the faulty drone uses the predicted position of the neighboring drone instead of the actual position to participate in the control. Specifically, the faulty drone calculates the neighboring drones j Over time t The predicted position is expressed by the following formula: , in, Indicates drone i The time when the failure occurred, Indicates drone i When a malfunction occurs, its neighbor drone j The real location, Indicates the neighbor drones calculated by the faulty drone j In time t The predicted position at time .

[0038] Furthermore, when the fault type of the faulty drone is that both the signal receiver and the signal transmitter are faulty, the faulty drone can neither transmit its own real position information to the neighboring drones nor receive the real position information from the neighboring drones. When the faulty drone and the neighboring drones are controlling themselves, they both need to use the predicted position instead of the real position information to participate in the control. Specifically, the faulty drone needs to calculate the neighboring drones j Over time t The predicted location of the neighboring drone also needs to calculate the faulty drone i Over time t The predicted positions, and the respective predicted position calculation formulas are as described above and will not be repeated here.

[0039] S4: Calculate the distance error between the actual position and the predicted position of the faulty UAV, and discard the faulty UAVs whose distance error exceeds the preset control threshold.

[0040] See Figure 3 , Figure 3 This is a flow chart of a communication fault event triggering mechanism provided by an embodiment of the present invention. Regardless of the fault type of the current faulty drone, the faulty drone itself can calculate its predicted position based on the above-mentioned expression of the predicted position of the faulty drone, and determine whether the distance error between the actual position of the current faulty drone and the predicted position is greater than a preset control threshold on the faulty drone: , in, Indicates a faulty drone i In time t The predicted position at time Indicates a faulty drone i In time t The real position at the time a Indicates preset parameters, which can be set according to actual task needs.

[0041] Faulty drones whose distance error between their actual position and predicted position is greater than a control threshold are discarded from the drone formation, and the formation is changed at the same time.

[0042] S5: According to the fault type and predicted location of the retained faulty UAV, control expressions of the faulty UAV and the neighboring UAVs are obtained to control the faulty UAV and the neighboring UAVs.

[0043] It should be noted that the drone formation control in this embodiment adopts a hierarchical control method. Specifically, the first layer treats each drone as an individual, the second layer treats each group in the first layer as an individual, the third layer treats each group in the second layer as an individual, and so on.

[0044] For the second layer, N drones can be divided into Subgroups , oi Indicates the upper limit of neighbors assigned to each group. Each group consists of an individual and its neighbors. Once the upper limit is reached, the excess drones will participate in the control of other subgroups. These subgroups are regarded as independent individuals and controlled similarly to the first layer. Similarly, for the third layer, the subgroups of the second layer are regarded as independent individuals. Each individual and its neighbors form the subgroups of the third layer, and so on. Layer, where .

[0045] Specifically, as mentioned above, when the fault type of the faulty drone is a signal transmitter failure and the signal receiver is normal, the faulty drone itself can receive the location information from the neighboring drones. Therefore, the control input of the faulty drone remains unchanged. The neighboring drones communicating with the faulty drone cannot receive the location information from the faulty drone and need to generate control inputs based on the predicted position of the faulty drone. Therefore, the control expression of the neighboring drone communicating with the faulty drone is as follows: , in, Indicates the use of faulty drones i The predicted location of the faulty drone i Its neighbor drones j The force between Indicates a faulty drone i Neighbor Drone j Among its neighboring drones, except for the faulty drone i The sum of the forces of all drones except Indicates the current drone j The sum of the forces of all its obstacles, Indicates the first layer, Indicates the total number of hierarchical control layers, Indicates the Layer Individual Neighbors, From the second layer to the The sum of the forces between individuals in each layer, Indicates the Individual center of the layer j Neighborhood Center k The sum of the forces, For the preset parameters, Indicates drone j speed, represents the expected speed of all drones, which is a set constant.

[0046] Among them, v is the final desired convergence speed of the UAV, which is a preset constant.

[0047] It should be noted that in the control expression of the neighbor drones mentioned above, the first part is the control using the predicted position, the first and second parts are used to maintain the position of the central drone and its neighbors in the first layer to avoid internal collisions; the third part is to adjust the distance between the drone and the obstacle for obstacle avoidance; the fourth part is the force exerted by the virtual center of each group of neighbors in each layer except the first layer on the drones in the group; the fifth part is to adjust the speed v at which each drone finally converges stably.

[0048] It should be noted that when the fault type of the faulty UAV is a signal transmitter failure and the signal receiver is normal, the control expression of the current faulty UAV remains unchanged.

[0049] Furthermore, when the fault type of the faulty drone is a signal receiver failure, while the signal transmitter is normal, the current faulty drone can transmit its own real position information to the neighboring drones, but cannot receive the real position information from the neighboring drones. Therefore, the control input of the neighboring drones of the faulty drone remains unchanged, because its neighboring drones cannot transmit their own speed and position information to the faulty drone in time. Therefore, the faulty drone participates in control with the predicted position of the neighboring drone instead of the actual position. Specifically, the faulty drone i The control expression is:

[0050] in, Indicates the use of neighbor drones j The predicted location of the faulty drone i Its neighbor drones j The force between Indicates a faulty drone i The sum of the forces of all its neighbor drones, Indicates the current faulty drone iThe sum of the forces of all its obstacles, From the second layer to the The sum of the forces between individuals in each layer, Indicates the Individual center of the layer i Neighborhood Center j The sum of the forces, For the preset parameters, Indicates drone i speed.

[0051] The above-mentioned faulty drone i The first part of the control expression is the control using the predicted position, which represents the control of the faulty drone. i Actual location and drone j The predicted position is used to control the first layer, maintaining the position of the central drone and its neighbors to avoid internal collisions. The second part adjusts the distance between the drone and obstacles for obstacle avoidance. The third part controls the force exerted by the virtual center of each group of neighbors on the drones in each layer except the first layer. The fourth part adjusts the speed v at which each drone ultimately converges to stability.

[0052] It should be noted that when the fault type of the faulty UAV is a signal receiver failure and the signal transmitter is normal, the control expression of the neighboring UAVs of the current faulty UAV remains unchanged.

[0053] Furthermore, when both a drone's signal transmitter and receiver fail, it can neither transmit its own location information to its neighbors nor receive location information from neighboring drones. Consequently, neighboring drones cannot receive the faulty drone's location information, requiring them to generate control inputs based on the faulty drone's predicted position. Furthermore, neighboring drones are unable to promptly transmit their own speed and position information to the faulty drone, forcing the faulty drone to use its neighbors' predicted positions instead of its actual position for control.

[0054] For malfunctioning drones i , its control expression is: , For malfunctioning drones i Neighbor Drone j , its control expression is: .

[0055] Subsequently, the faulty UAV and its neighboring UAVs control their own motion according to the control expressions corresponding to each fault type.

[0056] The following simulation experiments are used to verify the effectiveness of the large-scale UAV cluster formation reconstruction method based on the event trigger mechanism of the present invention.

[0057] Specifically, under the control protocol, a group of 18 drones moving in 3D space (3 drones in a small group and 9 drones in a large group) were simulated multiple times. Specifically, 3 drones were grouped together, and every 3 drones in the first layer were grouped together, so the 18 drones were divided into 6 groups; the second layer used the six groups in the first layer as members and divided them into three groups again, so the second layer had two large groups, and was divided into two layers when the hierarchical control method was adopted. The initial state of the drone was a Gaussian distribution randomly selected in the [0, 100] × [0, 100] × [0, 100]. In the simulation, the expected distance = 8, repulsion distance =4, attraction distance = 200. Add obstacles at (51,63,8.3) and (51,60,34) respectively, and set the fault occurrence time to 10s and the maximum fault tolerance time to 10s. Set parameters k s = 1, k o = 20, k c = 1, expected velocity v=(0.5, 0.6, 0).

[0058] The simulation results of the UAV with launch failure are as follows Figure 4 .exist Figure 4 The solid black line in the middle represents a faulty drone, while the remaining solid lines represent normal drones. Black spheres represent obstacles. The drones' flight paths, moving from left to right, initially cluster into groups. The faulty drone exceeded the threshold within the maximum tolerance time of 10.30 seconds and was discarded. The remaining drones followed their designated trajectories, avoiding obstacles along their flight paths. This demonstrates good obstacle avoidance performance.

[0059] For the UAV receiving failure simulation Figure 5 ,exist Figure 5 The black solid line in the middle represents a faulty drone, the remaining solid lines represent normal drones, and the black spheres represent obstacles. Figure 4 Almost identically, the faulty drone separated from the drone swarm at 10.32 seconds.

[0060] For the simulation of UAV transmission and reception failure, Figure 6 As shown, in Figure 6 The black solid line in the middle represents a faulty drone, the remaining solid lines represent normal drones, and the black spheres represent obstacles. Figure 4 Almost identically, the faulty drone separated from the drone swarm at 10.29 seconds.

[0061] When a UAV fails and no fault-tolerant control is introduced, the formation effect is as follows: Figure 7 As shown, in Figure 7 The black solid line in the middle represents a faulty drone, the remaining solid lines represent normal drones, and the black spheres represent obstacles. Figure 4 Almost, but the faulty drone and its neighbor stagnated after flying for a while, affecting the flight path of the entire group. Moreover, the faulty drone was out of control and there was a risk of hitting obstacles, which shows the importance of the proposed formation method.

[0062] The present invention explores a task-oriented large-scale UAV swarm formation reconstruction method based on event triggering. First, a UAV formation motion model under external disturbance is proposed. Then, based on event triggering, the controller model of the traditional artificial potential field in formation reconstruction is improved. The formation and obstacle avoidance effectiveness of this method are verified through simulation, proving the effectiveness of the formation reconstruction method through event triggering control proposed in the present invention. The present invention focuses on solving problems such as the existence of obstacles in complex environments, poor scalability of UAV formations, and communication failures. First, a UAV formation motion model under external disturbance is proposed. A multi-layer graph model is used to address the poor scalability of complex environments. In the face of UAV communication failures, formation control based on an event triggering mechanism is adopted. Finally, the obstacle avoidance method and the rationality of the method for detecting UAVs with communication failures are verified through simulation.

[0063] Compared to traditional formation aggregation technology, this invention can more precisely adjust the convergence speed and direction of drones, not only improving the efficiency of drones, but also flexibly adjusting the dynamic response of the formation according to mission requirements. By introducing a time-triggered mechanism, the present invention enables drone formations to flexibly adjust and quickly change formations in response to different fault types. Precise time control enables drones to make optimal adjustments in a timely manner, improving the formation's fault tolerance during mission execution and enhancing its adaptability to different environments. Regarding obstacle avoidance, this invention enables each drone to maintain a desired distance from other drones, ensuring that drones always maintain a safe distance from obstacles.

[0064] Another embodiment of the present invention provides a storage medium storing a computer program for executing the steps of the method for reconfiguring a large-scale drone swarm formation based on an event-triggered mechanism described in the above embodiments. Another aspect of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor invokes the computer program in the memory, the processor executes the steps of the method for reconfiguring a large-scale drone swarm formation based on an event-triggered mechanism described in the above embodiments. Specifically, the integrated module implemented in the form of a software functional module can be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes instructions for causing an electronic device (such as a personal computer, server, or network device) or a processor to execute some of the steps of the method described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0065] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A large-scale UAV swarm formation reconstruction method based on event triggering mechanism, characterized by: include: S1: Build a UAV formation model and obtain the time and fault type of the faulty UAVs in the UAV formation. The fault types include UAV signal transmitter failure, UAV signal receiver failure, and UAV signal transmitter and signal receiver failure. S2: Obtain the force between each UAV and its neighboring UAVs, and the force between each UAV and obstacles; S3: Obtain the predicted location of the faulty UAV or neighboring UAVs based on the fault type of the faulty UAV; S4: Calculating the distance error between the actual position and the predicted position of the faulty UAV, and discarding the faulty UAVs whose distance error exceeds a preset control threshold; S5: According to the fault type and predicted location of the retained faulty UAV, control expressions of the faulty UAV and the neighboring UAVs are obtained to control the faulty UAV and the neighboring UAVs.

2. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 1 is characterized in that: Build a UAV formation model, including: Construct the drone formation into an undirected graph model: ， in, Indicates drone, N represents the number of drones in the drone formation, Indicates drone i With drones j There is information interaction, drones j For drones i Neighbor drones; According to the drone i The dynamic equation of the UAV is obtained i The actual control input variable , and The expression of , where , and Respectively represent drones i The tilt angle, lift and thrust of the aircraft.

3. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 2 is characterized in that: According to the drone i The dynamic equation of the UAV is obtained i The actual control input variable , and Expressions include: According to the drone i The dynamic equation of the UAV is obtained i Dynamic expression: ， in, , and Respectively represent drones i Velocity scalar under no disturbance , drones i Heading angle and drones i Track angle Relative to time t The derivative of Indicates drone i quality, g represents the acceleration due to gravity, Indicates drone i The tilt angle, 、 and Respectively represent drones i thrust, drag, and lift; Through the feedback linearization process, the UAV is obtained i The linear time-invariant form of : ， in, x i , y i , z i Respectively represent drones i On the vertical axis xyz Position coordinate components in three directions, 、 and Respectively represent drones i On the vertical axis xyz Velocity components in three directions; , , Respectively 、 and In drones i The derivatives with respect to time in three directions, i.e., the drone i On the vertical axis xyz The acceleration in three directions, 、 and Indicates drone i The control variables correspond to the drones i acceleration in three directions; Obtain the drone i The actual control input variable , and The expression is: ， ， 。 4. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 2 is characterized in that: Said S1 further comprises: Get a drone i Non-neighbor drones in the exclusion zone and obstacles The expression: ， ， in, Indicates drone i A collection of neighbor drones, Indicates drone i The radius of the exclusion zone, represents the collection of drones in the drone formation, Indicates drone i Distance to another drone or obstacle.

5. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 4 is characterized in that: The S2 includes: The expression of the potential function is obtained according to the artificial potential field method: ， in, Indicates drone i With drones j The potential function between and represents a positive coefficient, Indicates drone j A collection of Obtain the UAV according to the potential function i With drones j The force between: ， in, Indicates drone i With drones j The force between Indicates drone i With drones j the distance between them; Obtain the UAV according to the potential function i Force between the object and the obstacle: ， in, Indicates drone i The force between the obstacle and Indicates drone i The distance to the obstacle, Indicates finding the norm.

6. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 5 is characterized in that: The S3 includes: When the fault type of the faulty drone is a signal transmitter failure, and the signal receiver is normal, the neighboring drone calculates the faulty drone i Over time t Predicted location of: ， in, Indicates drone i The time when the failure occurred, Indicates drone i The actual location when the fault occurred, Indicates drone i In time t The predicted position at time ; When the fault type of the faulty drone is a signal receiver failure and the signal transmitter is normal, the faulty drone calculates the neighboring drones j Over time t Predicted location of: ， in, Indicates drone i The time when the failure occurred, Indicates drone i When a malfunction occurs, its neighbor drone j The real location, Indicates drone j In time t The predicted position at time ; When the fault type of the faulty drone is the signal receiver and signal transmitter failure, the neighboring drone calculates the faulty drone i Over time t The predicted location of the faulty drone is calculated by the neighboring drones. j Over time t The predicted location.

7. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 6 is characterized in that: The S4 includes: Determine on the faulty UAV whether the distance error between the actual position and the predicted position of the faulty UAV is greater than a preset control threshold: ， in, Indicates drone i In time t The predicted position at time Indicates drone i In time t The real position at the time, a Indicates preset parameters; Faulty drones whose distance error between their actual position and predicted position is greater than a preset control threshold are discarded from the drone formation.

8. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 7 is characterized in that: The S5 includes: If the fault type of the faulty drone is a signal transmitter failure, the controller of the current faulty drone remains unchanged, and the neighboring drone j The control expression is: ， in, Indicates the use of faulty drones i The predicted location of the faulty drone i Its neighbor drones j The force between Indicates a faulty drone i Neighbor Drone j Among its neighboring drones, except for the faulty drone i The sum of the forces of all drones except Indicates the current drone j The sum of the forces of all its obstacles, Indicates the first layer, Indicates the total number of hierarchical control layers, Indicates the Layer Individual Neighbors, From the second layer to the The sum of the forces between individuals in each layer, Indicates the Individual center of the layer j Neighborhood Center k The sum of the forces, For the preset parameters, Indicates drone j speed, represents the desired speed of all drones.

9. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 8 is characterized in that: The S5 includes: If the fault type of the faulty drone is a signal receiver failure, the controller of the neighboring drone remains unchanged, and the current faulty drone i The control expression is: ， in, Indicates the use of neighbor drones j The predicted location of the faulty drone i Its neighbor drones j The force between Indicates a faulty drone i The sum of the forces of all its neighbor drones, Indicates the current faulty drone i The sum of the forces of all its obstacles, From the second layer to the The sum of the forces between individuals in each layer, Indicates the Individual center of the layer i Neighborhood Center j The sum of the forces, Indicates drone i speed.

10. The large-scale UAV swarm formation reconstruction method based on event triggering mechanism according to claim 9 is characterized in that: The S5 includes: If the fault type of the faulty drone is a signal transmitter failure or a signal receiver failure, the faulty drone i The control expression is: ， Neighbor Drone j The control expression is: 。

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