Unmanned aerial vehicle distributed cluster motion control method in communication rejection environment

Abstract

The invention relates to an unmanned aerial vehicle distributed cluster motion control method in a communication rejection environment. The method comprises the steps of generating local environment information concentration about a task target and a threat region in a current environment based on a gene regulation and control network, determining a group speed action vector for the unmanned aerial vehicle to maintain a group form, an avoidance speed action vector for the unmanned aerial vehicle to avoid a threat area, a tracking speed action vector for the unmanned aerial vehicle to track the task target, a neighbor unmanned aerial vehicle observation cooperation speed action vector and motion constraint of the unmanned aerial vehicle according to the local environment information concentration; and designing a controller of the unmanned aerial vehicle, and carrying out distributed cluster motion control on the unmanned aerial vehicle by using the designed controller. By adopting the method, unmanned aerial vehicle distributed cluster control can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of drone cluster motion control, and in particular to a method for distributed drone cluster motion control in a communication denial environment. Background Art

[0002] With the development of drone technology, drones are being used in various scenarios, such as emergency network security, interference suppression, navigation deception, etc. Take drone emergency security as an example. When an area needs emergency security, a drone cluster is required to go to the target area to perform emergency security tasks. Since the emergency event may not be resolved, each target area has potential threats. The current conventional technology is for the control center to communicate and interact with the drone cluster, and to perform emergency security by issuing task execution instructions to the drone cluster. This method cannot be implemented after the communication interaction is lost. In addition, for a single drone, if the group form cannot be maintained, the advantages of group emergency security cannot be brought into play. There are also drone cluster designs based on intelligent agents, but they cannot cope with complex scene requirements. Summary of the invention

[0003] Based on this, it is necessary to provide a distributed cluster motion control method for drones in a communication denial environment to address the above technical problems.

[0004] A method for controlling the motion of a distributed cluster of unmanned aerial vehicles in a communication denial environment, the method comprising:

[0005] Generate local environmental information concentration about mission targets and threat areas in the current environment based on gene regulatory networks;

[0006] Determine, according to the local environment information concentration, a group speed action vector for the drones to maintain the group form, an evasion speed action vector for the drones to avoid the threat area, a tracking speed action vector for the drones to track the mission target, and a friendly drone observation coordination speed action vector;

[0007] Design a controller of the drone based on the group speed action vector, the avoidance speed action vector of the drone avoiding the threat area, the tracking speed action vector of the drone tracking the mission target, and the vector sum of the friendly drone observation coordination speed action vector, as well as the motion constraints of the drone;

[0008] Use the designed controller to control the distributed cluster motion of UAVs.

[0009] In one embodiment, the method further includes: setting the group velocity action vector of the drones to maintain the group shape to:

[0010] V e =k e ·(Cd -C m )·P e

[0011] Among them, V e represents the group velocity action vector, k e represents an adjustable parameter, C d Indicates the concentration value of the drone position in the grid with the preset range centered on the drone’s location, C m Indicates the minimum concentration in the grid, P e The direction vector indicating the grid where the drone is located pointing to the grid with the minimum concentration.

[0012] In one embodiment, the method further includes: obtaining a speed attenuation curve of the drone avoiding the threat area:

[0013]

[0014] Where r is the distance between the drone and the expected stopping point, a is the acceleration parameter, and p is the linear gain;

[0015] According to the speed attenuation curve, the evasion speed action vector of the UAV to evade the threat area is obtained as:

[0016]

[0017] Among them, r ds is the distance from the drone to its nearest obstacle, r shill is the distance from the drone to its desired obstacle avoidance stopping point, p shill and a shill is the linear gain and acceleration parameter, V s represents the velocity vector of the virtual agent, V ds Represents the components of the tracking velocity action vector.

[0018] In one embodiment, the method further includes: setting the speed action vector of the drone tracking corresponding to the mission target to be:

[0019]

[0020] P t Represents the direction vector pointing from the UAV to the corresponding mission target;

[0021] Set the speed action vector of the drone to avoid collision with other mission targets:

[0022]

[0023] Among them, P o Represents the direction vector from the drone to other drones in the grid;

[0024] According to the speed action vector of the drone tracking the corresponding mission target and the speed action vector of the drone avoiding collision with other mission targets, the tracking speed action vector of the drone tracking the mission target is calculated as:

[0025] V d =V ds +V do

[0026] In one of the embodiments, it further includes: setting a repulsive speed effect of a friendly UAV based on a position threat according to the position of the friendly UAV in the grid;

[0027] According to the speed of the friendly UAV in the grid, set the repulsion speed effect of the friendly UAV based on speed threat;

[0028] According to the average speed vector of the friendly UAVs in the grid, the speed alignment of the friendly UAVs is set;

[0029] According to the repulsive speed effect of friendly UAVs based on position threat, the repulsive speed effect of friendly UAVs based on speed threat and the speed alignment effect of friendly UAVs, the observation cooperative speed effect vector of friendly UAVs is obtained.

[0030] In one embodiment, the method further includes: according to the position of the friendly UAV in the grid, setting the repulsion speed of the friendly UAV based on the position threat to be:

[0031]

[0032] Among them, V np represents the repulsive velocity effect, r ij Represents drone i and r rep The distance between other drones j in the range, k np The velocity influence coefficient of the drone considering the positions of other friendly drones, P n represents the direction vector of UAV i towards its neighbor UAV j.

[0033] In one embodiment, the method further includes: setting the repulsion speed of the friendly UAV based on the speed threat to be:

[0034] According to the repulsive speed effect of the friendly UAV based on speed threat and the speed alignment effect of the friendly UAV, the UAV is considered based on the above behavior. rep The combined speed effect V generated by the speed of other drones within the range nv for:

[0035]

[0036]

[0037] Among them, k nf is the velocity alignment coefficient of the neighboring UAV, k nv V is the speed influence coefficient of the UAV considering the speed of other friendly UAVs. j is the speed of UAV j, r ij Represents drone i and r rep The distance between other drones j in the range, N d Represents drone rep The number of drones in range;

[0038] According to the repulsive speed effect of the friendly UAV based on the position threat and based on the above behavior considerations rep The comprehensive speed effect produced by the speed of other UAVs within the range, the coordinated speed effect vector of friendly UAV observation is obtained as:

[0039] V n =V np +V nv

[0040] Among them, V n Represents the coordinated velocity action vector of friendly UAV observation.

[0041] In one embodiment, the controller of the drone is further designed according to the group speed action vector, the avoidance speed action vector of the drone avoiding the threat area, the tracking speed action vector of the drone tracking the mission target, and the vector sum of the friendly drone observation coordination speed action vector, and the motion constraint of the drone:

[0042] V=V l ·R+(1-R)·(V e +V t +V d +V n )

[0043]

[0044] Among them, R is the inertial motion coefficient of the UAV, V max V is the maximum speed of the drone under current conditions. l The speed of the drone at the last moment calculated by the controller.

[0045] A distributed cluster motion control device for unmanned aerial vehicles in a communication denial environment, the device comprising:

[0046] A concentration calculation module, used to generate local environmental information concentration about mission targets and threat areas in the current environment based on the gene regulatory network;

[0047] A speed action vector calculation module is used to determine the group speed action vector of the drones maintaining the group form, the avoidance speed action vector of the drones avoiding the threat area, the tracking speed action vector of the drones tracking the mission target, and the friendly drone observation and coordination speed action vector according to the local environment information concentration;

[0048] A controller design module is used to design a controller of the UAV according to the vector sum of the group speed action vector, the avoidance speed action vector of the UAV avoiding the threat area, the tracking speed action vector of the UAV tracking the mission target, and the observation coordination speed action vector of the friendly UAV, as well as the motion constraints of the UAV;

[0049] The control module is used to control the distributed cluster motion of UAVs with a designed controller.

[0050] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0051] Generate local environmental information concentration about mission targets and threat areas in the current environment based on gene regulatory networks;

[0052] Determine, according to the local environment information concentration, a group speed action vector for the drones to maintain the group form, an evasion speed action vector for the drones to avoid the threat area, a tracking speed action vector for the drones to track the mission target, and a friendly drone observation coordination speed action vector;

[0053] Design a controller of the drone based on the group speed action vector, the avoidance speed action vector of the drone avoiding the threat area, the tracking speed action vector of the drone tracking the mission target, and the vector sum of the friendly drone observation coordination speed action vector, as well as the motion constraints of the drone;

[0054] Use the designed controller to control the distributed cluster motion of UAVs.

[0055] The above-mentioned method for controlling the motion of the distributed cluster of unmanned aerial vehicles in the communication denial environment generates the local environmental information concentration about the mission target and the threat zone in the current environment based on the gene regulatory network, and then determines the group speed action vector of the unmanned aerial vehicle to maintain the group morphology, the avoidance speed action vector of the unmanned aerial vehicle to avoid the threat zone, the tracking speed action vector of the unmanned aerial vehicle to track the mission target, and the friendly unmanned aerial vehicle observation coordination speed action vector according to the local environmental information concentration. According to the group speed action vector, the avoidance speed action vector of the unmanned aerial vehicle to avoid the threat zone, the tracking speed action vector of the unmanned aerial vehicle to track the mission target, and the vector sum of the friendly unmanned aerial vehicle observation coordination speed action vector, and the motion constraint of the unmanned aerial vehicle, the controller of the unmanned aerial vehicle is designed, and the designed controller is used to perform the distributed cluster motion control of the unmanned aerial vehicle. The present invention can complete the cluster roundup task when controlling the distributed cluster of unmanned aerial vehicles, and for the controller design of a single unmanned aerial vehicle, it can also maintain the cluster morphology when performing the task. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A gene regulatory network concentration mapping curve diagram in one embodiment;

[0057] Figure 2 An environmental information concentration map for mapping a gene regulatory network in one embodiment;

[0058] Figure 3 An environmental information concentration map for mapping a gene regulatory network under local information in one embodiment;

[0059] Figure 4 It is a flowchart of a method for controlling the motion of a distributed cluster of drones in a communication denial environment in one embodiment;

[0060] Figure 5 A curve showing the change in speed force of a drone on a captured target and a non-captured target as a function of distance in an embodiment;

[0061] Figure 6 It is a schematic diagram of 12 UAVs in a distributed cluster movement in a static threat zone scenario to counter one enemy target in one embodiment;

[0062] Figure 7 It is a schematic diagram of 16 UAVs in a distributed cluster movement in a static threat zone scenario to counter one enemy target in one embodiment;

[0063] Figure 8 It is a schematic diagram of 20 drones in a distributed cluster movement in a static threat zone scenario to counter one enemy target in an embodiment;

[0064] Fig. 9A schematic diagram of using the GRN method to conduct distributed cluster movement of 20 drones in a dynamic threat zone scenario to counter two targets in an embodiment;

[0065] Fig.10 A schematic diagram of using the AGENT method to conduct distributed cluster movement of 20 drones in a dynamic threat zone scenario to counter two targets in an embodiment;

[0066] Fig.11 A schematic diagram of an embodiment of using the method of the present invention to conduct distributed cluster movement of 20 drones in a dynamic threat zone scenario to counter 2 targets;

[0067] Fig.12 It is a structural block diagram of a distributed cluster motion control device for unmanned aerial vehicles in one embodiment;

[0068] Fig.13 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0070] In the scenario of drone distributed clusters encircling drone targets, drones in the group are equipped with visual sensors and laser radar and other sensing equipment. Communication equipment is used when communication is not interfered with. Drones can obtain the speed and position information of neighboring drones within a certain distance by relying on their onboard equipment.

[0071] In the case of undisturbed communication, it is assumed that the drone can obtain the target's position, speed information, and threat area location in real time. After performing a countermeasure mission for a period of time, it is assumed that the communication is cut off. At this time, it is assumed that the drone can rely on its onboard sensors such as visual sensors and lidar to obtain the target's position, speed information, and the location information of the threat area within a certain range (within the sensor detection range).

[0072] Population morphological planning is achieved through gene regulatory networks. Gene regulation is the mechanism by which organisms control gene expression. Some microorganisms show high adaptability to environmental conditions during development and can quickly adjust the expression levels of different genes according to environmental changes. This study focuses on how microorganisms change their metabolism through gene regulation to adapt to the environment. Adaptability is the ability of a biological system to return to its original state after responding to external disturbances. It is also one of the important functions of gene regulatory networks and reflects the ability of organisms to survive in a changing environment. In biological systems, there are many examples of adaptability, such as bacterial chemotaxis, bacterial anabolism, and yeast transmembrane reactions.

[0073] In recent years, it has been proposed to use gene regulatory networks and morphological gradients to achieve morphological planning of swarm robot systems. The gene regulatory network mechanism of microorganisms regulates the production of their own proteins according to external environmental information. Changes in protein concentration affect their reception of environmental information, which further affects the behavior of microorganisms. This study established a mapping between microorganisms and drones, and applied the gene expression mechanism in biological morphogenesis to the morphogenesis of swarm drones. Drones use gene regulatory network mechanisms to establish a concentration field of comprehensive information on counter targets and threat areas in the current environment.

[0074] Commonly used mathematical models of gene regulatory networks include directed graph models, Boolean network models, Bayesian network models, differential equations, difference equation models, etc. For example, formula (1) indicates that the concentration p(t+1) at time step t+1 is the concentration p at time step t. j The result of the Laplace operator (or second-order spatial derivative) of (t). This means that the change in concentration over time is related to the "curvature" of its spatial distribution. Formula (2) is the definition of the Laplace operator, which represents the sum of the second-order spatial derivatives in the x and y directions, reflecting the distribution of concentration in space. These two formulas can also be regarded as a simulation of the diffusion of protein concentration in biological systems.

[0075] p j (t+1)=▽ 2 p j (t), (1)

[0076]

[0077] However, in a robot system, the robot may establish a continuously updated decision-making mechanism to gradually iterate its movement. Therefore, a differential equation is used to describe this characteristic, as shown in equations (3) and (4).

[0078]

[0079] Where x(t) and y(t) are the functions of the two distance variables from the UAV to the target or threat area changing with time t. v is the concentration diffusion factor, which can adjust the mapping relationship between the distance interval and the concentration value. j (t) represents the protein concentration generated by the jth target or threat zone location information, N t is the total number of detected target or threat area grids. Formula (4) calculates the t , the comprehensive protein concentration produced by all detected targets or threat areas at the current location of the drone.

[0080] After the drone processes the threat zone and target location information, it constructs concentration information domains C1, C2, and C3 on the map. The concentration information mapping mechanism of the gene regulatory network can take many forms. The calculation methods of C1, C2, and C3 of different gene regulatory networks are not exactly the same. C1 and C2 process the input of the target location information and the input of the threat zone location information respectively. C3 is the coupled comprehensive concentration information field, which can also be formalized as a concentration information map of the drone. Among them, β1, β2, and β3 are the thresholds of the sigmoid function. Adjusting β can adjust the interval range generated by the entire concentration field. k is the concentration difference adjustment coefficient. The larger the k value, the more obvious the concentration difference. Figure 1 The mapping curve between concentration value and distance under different k and β parameters of the sigmoid function is shown.

[0081] C1(t+1)=sig(p,β1,k), (5)

[0082] C2(t+1)=1-sig(p,β2,k), (6)

[0083] C3(t+1)=sig(C1+C2,β3,k), (7)

[0084]

[0085] Through the above mechanism, the UAV maps the threat area and target location information into a comprehensive concentration information field such as Figure 2 In order to verify the robustness and actual deployment capability of the algorithm, this study uses a local information concentration field. That is, the drone only has a concentration information map within a certain range, such as Figure 3 shown.

[0086] After the UAV generates a local concentration information map based on the location of the threat zone and the target location, the UAV samples the concentration information map at a certain concentration value, and the closed figure (convex) formed is the target group shape.

[0087] In one embodiment, Figure 4 As shown, a method for controlling the motion of a distributed cluster of drones in a communication denial environment is provided, comprising the following steps:

[0088] Step 402 , generating local environmental information concentration about mission targets and threat areas in the current environment based on the gene regulatory network.

[0089] Step 404, based on the local environmental information concentration, determines the group speed action vector for the drones to maintain the group form, the avoidance speed action vector for the drones to avoid the threat area, the tracking speed action vector for the drones to track the mission target, and the friendly drone observation coordination speed action vector.

[0090] Step 406, design a controller for the drone based on the group velocity action vector, the avoidance velocity action vector for the drone to avoid the threat area, the tracking velocity action vector for the drone to track the mission target, and the vector sum of the friendly drone observation coordination velocity action vector, as well as the drone's motion constraints.

[0091] Step 408, using the designed controller to perform distributed cluster motion control of UAVs.

[0092] In the above-mentioned method for controlling the motion of a distributed cluster of drones in a communication-denied environment, a distributed finite state machine is designed according to different target decision-making scenarios that the drone may face, and the state and jump conditions of the finite state machine are set. In addition, a distributed game decision neural network is constructed to achieve the optimal target selection when considering different decision variables under local information of the drone. The network realizes the adaptive adjustment of the weight coefficients of each decision variable under local information through the historical data of the drone's environmental information and real-time observations. That is, the drone adjusts parameters while flying. The present invention realizes the autonomous decision-making and selection of mission targets by drones based on the surrounding local information in a communication-denied environment, and can be flexibly applied to a variety of drone decision-making mission target selection mission scenarios, and has the practicability of real-machine deployment.

[0093] In one embodiment, the group velocity action vector for the drones to maintain the group shape is set to:

[0094] V e =k e ·(C d -C m )·P e

[0095] Among them, V e represents the group velocity action vector, k e represents an adjustable parameter, C d It represents the concentration value of the drone position in the grid with the preset range centered on the drone position, C m Indicates the minimum concentration in the grid, P e Indicates the direction vector of the grid where the drone is located pointing to the grid with the minimum concentration.

[0096] Specifically, in the present invention, the drone calculates the concentration value of a 5*5 grid around the location where the drone is located. The concentration value of the grid where the drone is located is C d The minimum concentration of the 5*5 grid is C m , P e k is the direction vector of the grid where the drone is located pointing to the grid with the minimum concentration around the drone, e is an adjustable parameter, and the velocity effect V is calculated. e The drone uses the speed V eIt is possible to achieve grid motion with fewer friendly drones in a group formation.

[0097] In one embodiment, the speed attenuation curve of the drone avoiding the threat area is obtained as follows:

[0098]

[0099] Where r is the distance between the drone and the expected stopping point, a is the acceleration parameter, and p is the linear gain;

[0100] According to the speed attenuation curve, the evasion speed action vector of the UAV to evade the threat area is obtained as:

[0101]

[0102] Among them, r ds is the distance from the drone to its nearest obstacle, r shill is the distance from the drone to its desired obstacle avoidance stopping point, p shill and a shill is the linear gain and acceleration parameter, V s represents the velocity vector of the virtual agent, V ds Represents the components of the tracking velocity action vector.

[0103] In this embodiment, the drone needs to avoid threat areas or obstacles. In a strong confrontation environment, such threat areas may be dynamic. Moreover, the perception range of distributed autonomous drones may be limited when they rely only on recording sensors under communication conditions. Therefore, the speed effect V of the drone avoiding the threat area under local information is designed. t D(r,a,p) is the speed attenuation curve of the drone avoiding the threat area

[14] Where r is the distance between the drone and the expected stopping point, a is the acceleration parameter, and p is the linear gain, which also determines the intersection between the different deceleration phases of the D(r,a,p) function. ds is the distance from the drone to its nearest obstacle, r shill is the distance from the drone to its desired obstacle avoidance stopping point, p shill and a shill Linear gain and acceleration parameters. Assume that there is a virtual agent at the nearest obstacle point of the drone, V s is the velocity vector of the virtual agent, whose velocity direction is perpendicular to the obstacle boundary and points to the field, and its size is an adjustable parameter.

[0104] In one embodiment, the speed action vector of the drone tracking the corresponding mission target is set to:

[0105]

[0106] P t Indicates the direction vector pointing from the drone to the corresponding mission target; the speed action vector of the drone to avoid collision with other mission targets is set as:

[0107]

[0108] Among them, P o Represents the direction vector pointing from the drone to other drones in the grid; according to the speed action vector of the drone tracking the corresponding mission target and the speed action vector of the drone avoiding collision with other mission targets, the tracking speed action vector of the drone tracking mission target is calculated as:

[0109] V d =V ds +V do

[0110] In this embodiment, the drone needs to get close to the target in the process of capturing the drone target, but it cannot be too close to the target. In order to capture the target, the drone cannot collide with the target. Figure 5 The curve depicts three behaviors of the drone. When the distance to the target is too close, it accelerates (the rate of change / acceleration becomes larger and larger, and the farther away from the target, the faster it moves), moving away from the target to be countered. When the distance to the target is too far, it accelerates to track the target to be countered at an appropriate speed. When the distance to the enemy target is moderate, the speed action term approaches zero.

[0111] In addition, while the drone avoids collision with the counter-target of its choice, it must also avoid collision with other enemy drones.

[0112] In one of the embodiments, according to the position of the friendly UAV in the grid, a repulsive speed effect of the friendly UAV based on the position threat is set; according to the speed of the friendly UAV in the grid, a repulsive speed effect of the friendly UAV based on the speed threat is set; according to the average value of the speed vectors of the friendly UAVs in the grid, a speed alignment effect of the friendly UAVs is set; according to the repulsive speed effect of the friendly UAV based on the position threat, the repulsive speed effect of the friendly UAV based on the speed threat and the speed alignment effect of the friendly UAVs, a friendly UAV observation collaborative speed effect vector is obtained.

[0113] In one embodiment, according to the position of the friendly drone in the grid, the repulsion speed of the friendly drone based on the position threat is set as follows:

[0114]

[0115]

[0116] Among them, V np represents the repulsive velocity effect, r ij Represents drone i and r rep The distance between other drones j in the range, k np The velocity influence coefficient of the drone considering the positions of other friendly drones, P n represents the direction vector of UAV i towards its neighbor UAV j.

[0117] In addition, according to the speed of the friendly UAV in the grid, the repulsive speed effect of the friendly UAV based on speed threat is set as follows: According to the repulsive speed effect of the friendly UAV based on speed threat and the speed alignment effect of the friendly UAV, the UAV is considered based on the above behavior. rep The combined speed effect V generated by the speed of other drones within the range nv for:

[0118]

[0119] Among them, k nf is the velocity alignment coefficient of the neighboring UAV, k nv V is the speed influence coefficient of the UAV considering the speed of other friendly UAVs. j is the speed of UAV j, r ij Represents drone i and r rep The distance between other drones j in the range, N d Represents drone rep The number of drones in range;

[0120] According to the repulsive speed effect of the friendly UAV based on the position threat and based on the above behavior considerations rep The comprehensive speed effect produced by the speed of other UAVs within the range, the coordinated speed effect vector of friendly UAV observation is obtained as:

[0121] V n =V np +V nv

[0122] Among them, V n Represents the coordinated velocity action vector of friendly UAV observation.

[0123] In one embodiment, according to the group speed action vector, the avoidance speed action vector of the UAV avoiding the threat area, the tracking speed action vector of the UAV tracking mission target, and the vector sum of the friendly UAV observation coordination speed action vector, and the UAV motion constraint, the UAV controller is designed as follows:

[0124] V=V l ·R+(1-R)·(Ve +V t +V d +V n )

[0125]

[0126] Among them, R is the inertial motion coefficient of the UAV, V max V is the maximum speed of the drone under current conditions. l The speed of the drone at the last moment calculated by the controller.

[0127] In summary, the present invention designs a simulation experiment of UAV distributed cluster in countering illegal flight. In order to verify the robustness of the method proposed in this study, this study designs a single target counterattack experiment with different group sizes in a static threat zone environment ( Figure 6 , Figure 7 and Figure 8 ). In order to be closer to the actual environment, the present invention designs a dynamic threat zone multi-target countermeasure experiment. In order to verify the superiority of the algorithm, the present invention applies the control method of the present invention ( Fig. 9 ) and the GRN method ( Fig.10 ), and the AGENT method ( Fig.11 ) were subjected to comparative tests.

[0128] from Figure 6 , Figure 7 , Figure 8 It can be seen that when the group sizes are 12, 16, and 20 respectively, the distributed countermeasures against a single black-flying drone scenario using the method of the present invention can achieve good behavior of tracking the countermeasure target through a complex static threat area.

[0129] from Fig. 9 , Fig.10 , Fig.11 It can be seen that when the group size is 20, the distributed countermeasures of two target scenarios by the method of the present invention deployed by drones can achieve good tracking and countermeasures of two targets through complex dynamic threat areas, and can also produce a distributed and uniform distribution effect.

[0130] It should be understood that although Figure 4 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 4At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0131] In one embodiment, Fig.12 As shown, a distributed decision-making device for multi-targets of a drone cluster in a communication denial environment is provided, including: a concentration calculation module 1202, a velocity action vector calculation module 1204, a controller design module 1206 and a decision output module 1208, wherein:

[0132] The framework construction module 1202 is used to construct a distributed decision-making finite state machine and a game decision neural network for drones in the drone cluster; the states of the drones in the distributed decision-making finite state machine include: decision holder, normal decision maker, entangled person, and lost person;

[0133] The parameter adjustment module 1204 is used to adjust the parameters of the game decision neural network in real time in the current decision round according to the decision data stored in the previous decision round stored by the drone and the real-time data currently acquired;

[0134] The state jump logic setting module 1206 is used to set the state jump rules in the distributed decision-making finite state machine; the state jump rules include: if the state of the distributed decision-making finite state machine of the drone is a normal decision maker, and it is judged that the distance between the drone and any task target is greater than the drone decision distance threshold, then the state of the drone is judged to jump to a decision holder; if it is judged that the short-term decision jump of the drone is greater than the short-term decision memory capacity of the drone multiplied by the short-term decision tolerance jump rate of the drone, then the state of the drone is judged to jump to a lost person; if the drone that jumps to a decision holder is less than the decision distance from any task target Threshold, the state of the decision holder's drone jumps to a normal decision maker. If the current lost state bit of the decision holder's drone is 1, the state of the decision holder's drone jumps to a lost one. If the distance of the drone that jumps to a tangled one from any mission target is greater than the decision distance threshold, the state of the tangled one's drone jumps to a decision holder. If the distance of the drone that jumps to a tangled one from all mission targets increases or decreases, the state of the tangled one's drone jumps to a lost one. If the number of steps counted by the drone that jumps to a lost one reaches the threshold, the state of the lost one's drone jumps to a normal decision maker.

[0135] The decision output module 1208 is used for the drones in the normal decision maker and the entangled state to output decisions through the game decision neural network after real-time parameter adjustment; the drones in the lost state determine the optimal mission target of the lost state as the decision output through the comparison relationship between the number of mission targets and the number of drones within a preset distance; and the drones in the decision holder state maintain the decision of the previous decision round.

[0136] In one embodiment, the group velocity action vector for the drones to maintain the group shape is set to:

[0137] V e =k e ·(C d -C m )·P e

[0138] Among them, V e represents the group velocity action vector, k e represents an adjustable parameter, C d It represents the concentration value of the drone position in the grid with the preset range centered on the drone position, C m Indicates the minimum concentration in the grid, P e Indicates the direction vector of the grid where the drone is located pointing to the grid with the minimum concentration.

[0139] In one embodiment, the speed attenuation curve of the drone avoiding the threat area is obtained as follows:

[0140]

[0141] Where r is the distance between the drone and the expected stopping point, a is the acceleration parameter, and p is the linear gain;

[0142] According to the speed attenuation curve, the evasion speed action vector of the UAV to evade the threat area is obtained as:

[0143]

[0144] Among them, r ds is the distance from the drone to its nearest obstacle, r shill is the distance from the drone to its desired obstacle avoidance stopping point, p shill and a shill is the linear gain and acceleration parameter, V s represents the velocity vector of the virtual agent, V ds Represents the components of the tracking velocity action vector.

[0145] In one embodiment, the speed action vector of the drone tracking corresponding to the mission target is set to:

[0146]

[0147] P t Represents the direction vector pointing from the UAV to the corresponding mission target;

[0148] Set the speed action vector of the drone to avoid collision with other mission targets:

[0149]

[0150] Among them, P o Represents the direction vector from the drone to other drones in the grid;

[0151] According to the speed action vector of the drone tracking the corresponding mission target and the speed action vector of the drone avoiding collision with other mission targets, the tracking speed action vector of the drone tracking the mission target is calculated as:

[0152] V d =V ds +V do

[0153] In one of the embodiments, a repulsive speed effect of a friendly UAV based on a position threat is set according to the position of the friendly UAV within the grid;

[0154] According to the speed of the friendly UAV in the grid, set the repulsion speed effect of the friendly UAV based on speed threat;

[0155] According to the average speed vector of the friendly UAVs in the grid, the speed alignment of the friendly UAVs is set;

[0156] According to the repulsive speed effect of friendly UAVs based on position threat, the repulsive speed effect of friendly UAVs based on speed threat and the speed alignment effect of friendly UAVs, the observation cooperative speed effect vector of friendly UAVs is obtained.

[0157] In one embodiment, according to the position of the friendly drone in the grid, the repulsion speed of the friendly drone based on the position threat is set as follows:

[0158]

[0159] Among them, V np represents the repulsive velocity effect, r ij Represents drone i and r rep The distance between other drones j in the range, k np The velocity influence coefficient of the drone considering the positions of other friendly drones, P n represents the direction vector of UAV i towards its neighbor UAV j.

[0160] In one embodiment, according to the repulsive speed effect of the friendly UAV based on the speed threat and the speed alignment effect of the friendly UAV, the UAV is considered based on the above behavior. rep The combined speed effect V generated by the speed of other drones within the range nv for:

[0161]

[0162] Among them, k nf is the velocity alignment coefficient of the neighboring UAV, k nv V is the speed influence coefficient of the UAV considering the speed of other friendly UAVs. j is the speed of UAV j, r ij Represents drone i and r rep The distance between other drones j in the range, N d Represents drone rep The number of drones in range;

[0163] According to the repulsive speed effect of the friendly UAV based on the position threat and based on the above behavior considerations rep The comprehensive speed effect produced by the speed of other UAVs within the range, the coordinated speed effect vector of friendly UAV observation is obtained as:

[0164] V n =V np +V nv

[0165] Among them, V n Represents the coordinated velocity action vector of friendly UAV observation.

[0166] In one embodiment, according to the group speed action vector, the avoidance speed action vector of the drone avoiding the threat area, the tracking speed action vector of the drone tracking the mission target, and the vector sum of the friendly drone observation coordination speed action vector, and the motion constraint of the drone, the controller of the drone is designed as follows:

[0167] V=V l ·R+(1-R)·(V e +V t +V d +V n )

[0168]

[0169] Among them, R is the inertial motion coefficient of the UAV, V max V is the maximum speed of the drone under current conditions. lThe speed of the drone at the last moment calculated by the controller.

[0170] For the specific definition of the distributed decision-making device for multi-objectives of drone swarms, please refer to the definition of the distributed decision-making method for multi-objectives of drone swarms above, which will not be repeated here. Each module in the above-mentioned distributed decision-making device for multi-objectives of drone swarms can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0171] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.13 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for controlling the motion of a distributed cluster of drones in a communication denial environment is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0172] Those skilled in the art will understand that Fig.13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0173] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0174] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0176] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for controlling the motion of a distributed cluster of drones in a communication denial environment, characterized in that: The method comprises: Generate local environmental information concentration about mission targets and threat areas in the current environment based on gene regulatory networks; Determine, according to the local environmental information concentration, a group speed action vector for the drones to maintain the group form, an evasion speed action vector for the drones to avoid the threat area, a tracking speed action vector for the drones to track the mission target, and a friendly drone observation coordination speed action vector; Design a controller of the drone based on the group speed action vector, the avoidance speed action vector of the drone avoiding the threat area, the tracking speed action vector of the drone tracking the mission target, and the vector sum of the friendly drone observation coordination speed action vector, as well as the motion constraints of the drone; Use the designed controller to control the distributed cluster motion of UAVs.

2. The method according to claim 1, characterized in that The steps to set the group velocity action vector for the drones to maintain the group shape include: Set the group velocity action vector of the drones to maintain the group shape as: V e =k e ·(C d -C m )·P e Among them, V e represents the group velocity action vector, k e represents an adjustable parameter, C d It represents the concentration value of the drone position in the grid with the preset range centered on the drone position, C m Indicates the minimum concentration in the grid, P e Indicates the direction vector of the grid where the drone is located pointing to the grid with the minimum concentration.

3. The method according to claim 1, characterized in that: The evasion speed action vectors for setting the drone to evade the threat area include: The speed attenuation curve of the drone avoiding the threat area is obtained as follows: Where r is the distance between the drone and the expected stopping point, a is the acceleration parameter, and p is the linear gain; According to the speed attenuation curve, the evasion speed action vector of the UAV to evade the threat area is obtained as: Among them, r ds is the distance from the drone to its nearest obstacle, r shill is the distance from the drone to its desired obstacle avoidance stopping point, p shill and a shill is the linear gain and acceleration parameter, V s represents the velocity vector of the virtual agent, V ds Represents the components of the tracking velocity action vector.

4. The method according to claim 1, characterized in that The steps of setting the tracking speed action vector of the UAV to track the mission target include: Set the speed action vector of the drone tracking corresponding to the mission target to: V ds =(0.0482x 9 -2.0435x 8 +37.0859x 7 -375.8633x 6 +2327.6x 5 。 -9051.6x 4 +21861x 3 -31211x 2 +23737x-7208.9)·P t P t Represents the direction vector pointing from the UAV to the corresponding mission target; Set the speed action vector of the drone to avoid collision with other mission targets: Among them, P o Represents the direction vector from the drone to other drones in the grid; According to the speed action vector of the drone tracking the corresponding mission target and the speed action vector of the drone avoiding collision with other mission targets, the tracking speed action vector of the drone tracking the mission target is calculated as: V d =V ds +V do 5. The method according to claim 1, characterized in that The steps to set the friendly UAV observation cooperative velocity action vector include: According to the position of the friendly UAV in the grid, set the repulsion speed effect of the friendly UAV based on the position threat; According to the speed of the friendly UAV in the grid, set the repulsion speed effect of the friendly UAV based on speed threat; According to the average speed vector of the friendly UAVs in the grid, the speed alignment of the friendly UAVs is set; According to the repulsive speed effect of friendly UAV based on position threat, the repulsive speed effect of friendly UAV based on speed threat and the speed alignment effect of friendly UAV, the observation cooperative speed effect vector of friendly UAV is obtained.

6. The method according to claim 5, characterized in that According to the position of the friendly drone in the grid, set the repulsion speed effect of the friendly drone based on the position threat, including: According to the position of the friendly drone in the grid, the repulsion speed of the friendly drone based on the position threat is set as follows: Among them, V np represents the repulsive velocity effect, r ij Represents drone i and r rep The distance between other drones j in the range, k np The velocity influence coefficient of the drone considering the positions of other friendly drones, P n represents the direction vector of UAV i towards its neighbor UAV j.

7. The method according to claim 5, characterized in that According to the repulsive speed effect of friendly UAV based on position threat, the repulsive speed effect of friendly UAV based on speed threat and the speed alignment effect of friendly UAV, the friendly UAV observation cooperative speed effect vector is obtained, including: According to the repulsive speed effect of the friendly UAV based on speed threat and the speed alignment effect of the friendly UAV, the UAV is considered based on the above behavior. rep The combined speed effect V generated by the speed of other drones within the range nv for: Among them, k nf is the velocity alignment coefficient of the neighboring UAV, k nv V is the speed influence coefficient of the UAV considering the speed of other friendly UAVs. j is the speed of UAV j, r ij Represents drone i and r rep The distance between other drones j in the range, N d Represents drone rep The number of drones in range; According to the repulsive speed effect of the friendly UAV based on the position threat and based on the above behavior considerations rep The comprehensive speed effect produced by the speed of other UAVs within the range, the coordinated speed effect vector of friendly UAV observation is obtained as: V n =V np +V nv Among them, V n Represents the coordinated velocity action vector of friendly UAV observation.

8. The method according to any one of claims 1 to 5, characterized in that: According to the group speed action vector, the avoidance speed action vector of the UAV avoiding the threat area, the tracking speed action vector of the UAV tracking the mission target, and the vector sum of the friendly UAV observation coordination speed action vector, and the UAV motion constraint, the UAV controller is designed, including: According to the group speed action vector, the avoidance speed action vector of the UAV avoiding the threat area, the tracking speed action vector of the UAV tracking the mission target, and the vector sum of the friendly UAV observation coordination speed action vector, as well as the UAV motion constraints, the UAV controller is designed as: V=V l ·R+(1-R)·(V e +V t +V d +V n ) Among them, R is the inertial motion coefficient of the UAV, V max V is the maximum speed of the drone under current conditions. l The speed of the drone at the last moment calculated by the controller.

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