UAV swarm behavior control method, system and device based on navigator

Abstract

This application discloses a leader-based UAV swarm behavior control method, system, and device, relating to the field of UAV control technology. The method uses control force parameters and time-varying control parameters to control the behavior of a leader UAV, expanding the traditional fixed parameter model to a time-varying parameter model. By sending time-varying control parameters to control the behavior of follower UAVs, the behavior of the UAV swarm is controlled, enabling distributed control. This reduces communication resource usage and effectively enhances the scalability of the UAV swarm.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a method, system, and device for controlling the behavior of drone clusters based on a navigator. Background Art

[0002] In nature, groups composed of large numbers of autonomous individuals, such as bee swarms, fish schools, and bird flocks, are common. Through simple interactions between individuals, these groups often develop complex collective movements, such as migration and predation. These highly coordinated and orderly movements are known as swarming behavior. By combining research on swarming behavior with drone swarming technology, drones can accomplish complex tasks such as reconnaissance, search and rescue, and resource exploration through simple inter-machine communication. This provides drone swarms with enhanced situational awareness, mission execution efficiency, and greater fault tolerance. However, traditional rule-based approaches are rigid, and as the number of drones increases, communication resources also increase dramatically, leading to a dimensionality explosion. Current control methods for drone behavior, such as path planning, suffer from poor scalability and are unable to adapt to complex environments, hindering the large-scale application of drone swarms. Summary of the Invention

[0003] This application aims to solve at least one of the technical problems existing in the prior art. To this end, this application proposes a navigator-based drone swarm behavior control method, system, and device that can effectively enhance the scalability of drone swarms while reducing communication resource usage.

[0004] On the one hand, an embodiment of the present application provides a navigator-based drone swarm behavior control method, comprising the following steps:

[0005] Obtain motion mode instructions and shape mode instructions;

[0006] Determining control force parameters in a navigator dynamics model according to the motion modal instruction, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term;

[0007] Determining time-varying control parameters in a navigator dynamics model according to the shape modal instructions, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient;

[0008] Based on the navigator dynamics model, the navigator UAV is behaviorally controlled according to the time-varying control parameters and the control force parameters;

[0009] The time-varying control parameters are sent to a plurality of follower UAVs, so that the follower UAVs perform behavior control according to the time-varying control parameters based on a follower dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

[0010] According to some embodiments of the present application, the navigator-based drone swarm behavior control method further includes the following steps:

[0011] Obtaining a preset neighbor range of the navigator drone, where the preset neighbor range includes a separation force action range, an alignment force action range, and a cohesion force action range;

[0012] The neighboring drones of the navigator drone are determined according to the preset neighbor range, wherein the neighboring drones are other navigator drones within the preset neighbor range.

[0013] According to some embodiments of the present application, determining the neighboring drones of the navigator drone based on the preset neighbor range includes the following steps:

[0014] Determining a plurality of first neighbors within the separation force action range according to the separation force action range in the preset neighbor range, wherein the separation force action range is determined by a separation force radius;

[0015] Determining a plurality of second neighbors within the alignment force action range according to the alignment force action range in the preset neighbor range, wherein the alignment force action range is determined by an alignment force radius;

[0016] Determining a plurality of third neighbors within the cohesive force action range according to the cohesive force action range in the preset neighbor range, wherein the cohesive force action range is determined by the cohesive force radius;

[0017] A plurality of the first neighbors, a plurality of the second neighbors, and a plurality of the third neighbors are determined as neighboring drones of the leader drone.

[0018] According to some embodiments of the present application, determining the control force parameters in the navigator dynamics model according to the motion mode instruction includes the following steps:

[0019] Determine the motion mode of the leader according to the motion mode instruction;

[0020] Calculating the force applied to the leader UAV based on the number of adjacent UAVs;

[0021] The control force parameters in the navigator dynamics model are determined according to the navigator motion mode and the force condition.

[0022] According to some embodiments of the present application, the step of calculating the force applied to the leader drone based on the number of adjacent drones includes the following steps:

[0023] Obtaining navigator information, neighbor information, and an expected average distance of a cluster, wherein the navigator information includes first speed information and first position information of the navigator UAV, and the neighbor information includes second speed information and second position information of the neighbors;

[0024] Calculating the separation force on the leader drone according to the number of the first neighbors, the first position information, and the second position information of the first neighbors;

[0025] Calculating the alignment force on the leader drone according to the number of the second neighbors, the first speed information, and the second speed information of the second neighbors;

[0026] The cohesive force exerted on the leader UAV is calculated according to the number of the third neighbors, the first position information, the second position information of the third neighbors, and the expected average distance of the cluster.

[0027] According to some embodiments of the present application, determining the time-varying control parameters in the navigator dynamics model according to the shape modal instruction includes the following steps:

[0028] Adjust the radius of action of the preset neighbor range of the Navigator UAV according to the shape mode command;

[0029] The time-varying control parameters in the navigator dynamics model are adjusted according to the change result of the action radius.

[0030] According to some embodiments of the present application, the navigator dynamics model further includes a friction term, which is used to determine the friction force based on the current speed and the expected speed, and the time-varying control parameter further includes a friction weight coefficient.

[0031] On the other hand, an embodiment of the present application further provides a navigator-based drone swarm behavior control system, which is applied to a navigator drone in a drone swarm, wherein the drone swarm includes a navigator drone and several follower drones. The navigator-based drone swarm behavior control system includes:

[0032] The first module is used to obtain motion mode instructions and shape mode instructions;

[0033] A second module is configured to determine control force parameters in a navigator dynamics model according to the motion modal instruction, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term;

[0034] A third module is configured to determine time-varying control parameters in the navigator dynamics model according to the shape modal instruction, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient;

[0035] A fourth module is configured to control the behavior of the navigator UAV based on the navigator dynamics model and the time-varying control parameters and the control force parameters;

[0036] The fifth module is configured to send the time-varying control parameters to a plurality of follower UAVs, so that the follower UAVs perform behavior control according to the time-varying control parameters based on a follower dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

[0037] On the other hand, an embodiment of the present application further provides a navigator-based drone swarm behavior control system, which includes a navigator drone and several follower drones:

[0038] The navigator UAV is used to obtain motion mode instructions and shape mode instructions; determine the control force parameters in the navigator dynamics model according to the motion mode instructions, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term; determine the time-varying control parameters in the navigator dynamics model according to the shape mode instructions, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient; based on the navigator dynamics model, the behavior of the navigator UAV is controlled according to the time-varying control parameters and the control force parameters; and send the time-varying control parameters to a plurality of follower UAVs;

[0039] The follower UAV is used to receive the time-varying control parameters and perform behavior control on the leader UAV according to the time-varying control parameters and the control force parameters based on a leader dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

[0040] On the other hand, an embodiment of the present application further provides an electronic device, including 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:

[0041] Obtain motion mode instructions and shape mode instructions;

[0042] Determining control force parameters in a navigator dynamics model according to the motion modal instruction, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term;

[0043] Determining time-varying control parameters in a navigator dynamics model according to the shape modal instructions, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient;

[0044] Based on the navigator dynamics model, the navigator UAV is behaviorally controlled according to the time-varying control parameters and the control force parameters;

[0045] The time-varying control parameters are sent to a plurality of follower UAVs, so that the follower UAVs perform behavior control according to the time-varying control parameters based on a follower dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

[0046] The above-mentioned technical solution of the present application has at least one of the following advantages or beneficial effects: the behavior of the leader UAV is controlled by control force parameters and time-varying control parameters, the traditional fixed parameter model is expanded to a time-varying parameter model, and the behavior of the follower UAV is controlled by sending time-varying control parameters, thereby controlling the behavior of the UAV cluster and realizing distributed control. On the basis of reducing the occupation of communication resources, the scalability of the UAV cluster is effectively enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the navigator-based drone cluster behavior control method provided in an embodiment of the present application;

[0048] Figure 2 This is a schematic diagram of the drone neighbor determination range provided by an embodiment of the present application;

[0049] Figure 3 Schematic diagram of time-varying control parameter values ​​under shape modal instructions provided in an embodiment of the present application;

[0050] Figure 4 This is a framework diagram of the drone cluster behavior control system provided by an embodiment of the present application;

[0051] Figure 5 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar components or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present application and are not to be construed as limiting the present application.

[0053] In the description of this application, it should be understood that descriptions involving orientation, such as up, down, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0054] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or suggesting relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0055] The control model for drone swarms determines the motion rules of drones at every moment during a mission. Research in this area can generally be categorized into the following control methods: local rule-based control, virtual structure control, leader-wingman control, and artificial potential field control.

[0056] Local rule-based methods consist of a series of indivisible rules, each corresponding to a specific purpose or task. The effects of these rules are weighted and normalized, and then fed to a controller, which in turn controls the behavior of the individual birds. This method originates from the Boids model developed by Reynolds in 1987 for the flight behavior of bird flocks. This model adheres to the three principles of aggregation, alignment, and separation: movement toward the center of a neighborhood, matching the average speed of other individuals in the area, and avoiding close proximity to other individuals. This model is highly scalable. While local rule-based control methods offer simple control and implementation, they are difficult to mathematically analyze and cannot guarantee the stability of flocking operations.

[0057] The virtual structure method treats the entire swarm as a rigid virtual structure, with each drone acting as a relatively fixed point on this rigid structure. The drones' movements simply track the movement of these virtual points on the rigid structure. This method is commonly used for swarm formation control. It places low demands on the drone controllers and allows for easy specification of swarm behavior. However, due to its centralized implementation, this method places high demands on the communication and computing performance of the central control system. The formation movement is rigid, lacking flexibility and adaptability.

[0058] The concept behind the leader-wingman method is to designate one drone as the leader, with the remaining drones acting as wingmen. These drones maintain a fixed relative position to the leader and follow the leader's movements. This control strategy is based on a preset formation. By assigning different drones as leaders, the network topology can be altered, resulting in different formations. However, this method places high demands on the leader, requiring the processing and transmission of large amounts of data. A failure of the leader can affect the entire swarm, challenging its survivability and flight safety.

[0059] The basic idea of ​​the artificial potential field method is to treat the area surrounding an individual as an abstract, artificial force field. Obstacles and other nearby members of the swarm repel the individual, while targets and other distant members attract it. Under the combined force, the individual moves in the direction of minimum potential energy. Each individual's actions are controlled by a potential function. By designing a suitable potential function, the overall behavior of the swarm can be controlled. However, designing a potential function is often difficult and can easily lead to non-convergence.

[0060] The artificial potential field and virtual structure methods used in related technologies cannot achieve effective behavioral control, and local rule control methods cannot form stable behavioral patterns. The leader-wingman model suffers from drawbacks such as high data transmission pressure and poor security. Moreover, since the leader aircraft is often easily discovered and attacked during confrontations, a hit would have a significant impact on the entire swarm flight.

[0061] In order to solve the problems of fixed parameters, rigid rules, inability to adapt to complex environments, and poor scalability of the path planning method used for drone behavior control, Figure 1 The present application provides a method for controlling the behavior of a drone swarm based on a navigator, which may include but is not limited to the following steps:

[0062] S101: Obtaining motion mode instructions and shape mode instructions;

[0063] S102: determining control force parameters in a navigator dynamics model according to the motion modal instruction, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term;

[0064] S103: determining time-varying control parameters in the navigator dynamics model according to the shape modal command, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient;

[0065] S104: Based on the navigator dynamics model, the navigator UAV is controlled according to the time-varying control parameters and control force parameters;

[0066] S105: Sending the time-varying control parameters to the multiple follower UAVs, so that the follower UAVs perform behavior control on the follower UAVs according to the time-varying control parameters based on the follower dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

[0067] In step S101 of some embodiments, specifically, based on a traditional rule-based behavior control model, a drone swarm is divided into a number of leader drones and a number of follower drones. The selection and number of leader drones can be dynamically adjusted based on mission requirements and environmental changes. Multiple leader drones in the swarm receive behavior control signals sent by the user, extract motion modal instructions and shape modal instructions from the signals, and decompose behavior control into motion trajectory control and cluster shape control. The motion modal instructions are used to control the motion trajectory, and the shape modal instructions are used to control the cluster shape.

[0068] In step S102 of some embodiments, the motion state of a drone is affected by neighboring drones and follows basic motion rules, including separation, alignment, and cohesion. Separation is intended to avoid collisions and overlaps between drones and to maintain a certain distance between them. Alignment is intended to maintain a consistent drone cluster formation. Cohesion is intended to maintain the relative positions of drones. To achieve motion trajectory control of drone cluster behavior, control forces are added to the traditional drone dynamics model. A leader dynamics model is pre-established based on inter-drone motion rules. The leader dynamics model includes separation force terms, alignment force terms, cohesion force terms, and control force terms. The leader drone can generate its own control force Fctrl based on the motion modal command to control the magnitude and direction of the leader drone's resultant force, thereby affecting acceleration or velocity.

[0069] In step S103 of some embodiments, the leader UAV determines the weight coefficients of each component force in the leader dynamics model based on the shape modal instruction. Specifically, each force vector in the leader dynamics model is normalized and multiplied by the corresponding weight. This process can be regarded as unifying the desired motion state of the UAV with the physical constraints. The weight coefficients of each component force are time-varying parameters that change with the behavior of the UAV cluster. The normalization process is as shown in Equation (1):

[0070]

[0071] Among them, α=1, β=1.

[0072] The navigator dynamics model of this embodiment is as shown in formula (2):

[0073] F i (t) = k s F si +k a F ai +k c F ci +k ctrl +F ctrl (2)

[0074] Among them, F si 、Fai and F ci They represent the separation force, alignment force and cohesion force of the i-th UAV, respectively, and K p (p=s,a,c,ctrl) represents the weight coefficient of each component force acting on the UAV.

[0075] In step S104 of some embodiments, the leader drone substitutes the obtained control force parameters and time-varying control parameters into equation (2) for iterative calculation, updates the states of the velocity vector and position vector, and controls the behavior of the drone cluster by controlling the position and velocity of the drone. The state update equations are as follows:

[0076]

[0077]

[0078] in, represents the position vector of the i-th drone at time t, represents the velocity vector of the i-th UAV at time t, Δt represents the iteration time, and a i represents the acceleration of the i-th UAV.

[0079] In step S105 of some embodiments, the leader drone sends the time-varying control parameters to several nearby follower drones, so that the follower drones update the time-varying control parameters based on the follower dynamics model and perform behavioral control on the follower drones according to the updated time-varying control parameters. The follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term. The follower dynamics model is as shown in Equation (5):

[0080] F i (t) = k s F si +k a F ai +k c F ci (5)

[0081] Among them, K p (p=s,a,c) represents the weight coefficient of each component force acting on the UAV.

[0082] The embodiment of the present application transmits time-varying control parameters through inter-machine communication to achieve behavioral control of the follower drone by the leader drone. It can use simple communication to achieve behavior control of the drone cluster, and effectively enhances the scalability of the drone cluster on the basis of reducing communication resource usage.

[0083] In an embodiment of the present application, the navigator-based drone swarm behavior control method of the embodiment of the present application may include but is not limited to the following steps:

[0084] S201: Obtaining a preset neighbor range of the navigator UAV, where the preset neighbor range includes a separation force action range, an alignment force action range, and a cohesion force action range;

[0085] S202: Determine neighboring drones of the navigator drone according to a preset neighbor range, wherein the neighboring drones are other navigator drones within the preset neighbor range.

[0086] In some embodiments, as the number of drones increases, if each drone needs to perform calculations with all other drones, this will increase the computational burden on the system. Figure 2 The preset neighborhood range for a single drone includes the separation force range Qs, alignment force range Qa, and cohesion force range Qc. For a leader drone, other leader drones within its range are considered neighbors, while follower drones are not. For a follower drone, all neighboring drones within its range are considered neighbors. After obtaining the preset neighborhood range, the leader drone determines the neighboring other leader drones based on the neighborhood range.

[0087] In the embodiment of the present application, the leader drone is controlled to regard other leader drones as neighbors and does not regard follower drones as neighbors, so that when the number of drones increases, the computing burden can be reduced and the system scalability is enhanced.

[0088] In the embodiment of the present application, the step of determining the neighboring drones of the navigator drone according to the preset neighbor range in step S202 includes but is not limited to the following steps:

[0089] S301: determining a plurality of first neighbors within the separation force action range according to a separation force action range in a preset neighbor range, wherein the separation force action range is determined by a separation force radius;

[0090] S302: determining a plurality of second neighbors within the alignment force action range according to the alignment force action range in the preset neighbor range, wherein the alignment force action range is determined by the alignment force radius;

[0091] S303: determining a plurality of third neighbors within the cohesive force action range according to the cohesive force action range in the preset neighbor range, wherein the cohesive force action range is determined by the cohesive force radius;

[0092] S304: Determine a plurality of first neighbors, a plurality of second neighbors, and a plurality of third neighbors as neighboring drones of the navigator drone.

[0093] In some embodiments, reference Figure 2, with the navigator UAV as the center, the separation force range Qs is determined according to the separation force radius Rs. If the straight-line distance between the navigator UAV at the center and the adjacent navigator UAV in space is less than Rs, then the adjacent navigator UAV is located in the separation force range and is the first neighbor of the navigator UAV at the center.

[0094] The alignment force range Qa is determined based on the alignment force radius Ra. If the straight-line distance between the central navigator UAV and the adjacent navigator UAV in space is greater than Rs and less than Ra, then the adjacent navigator UAV is within the alignment force range and is the second neighbor of the central navigator UAV.

[0095] The cohesive force range Qc is determined based on the cohesive force radius Rc. If the straight-line distance between the central navigator UAV and the adjacent navigator UAV in space is greater than Ra and less than Rc, then the adjacent navigator UAV is located in the cohesive force range and is the third neighbor of the central navigator UAV.

[0096] After the center leader drone determines all adjacent leader drones based on the separation force radius, alignment force radius, and cohesion force radius, the center leader drone regards several first neighbors, several second neighbors, and several third neighbors as its neighbor drones.

[0097] In the embodiment of the present application, different radiuses are used to determine the ranges of action, so as to divide other navigator drones according to the ranges of action, and to prepare for the subsequent steps of calculating the force conditions according to the neighboring drones of the navigator drone.

[0098] In the embodiment of the present application, the step of determining the control force parameters in the navigator dynamics model according to the motion mode instruction in step S102 may include but is not limited to the following steps:

[0099] S401: Determine the motion mode of the navigator according to the motion mode instruction;

[0100] S402: Calculating the force on the leader UAV based on the number of adjacent UAVs;

[0101] S403: Determine control force parameters in the navigator dynamics model according to the navigator motion mode and force conditions.

[0102] In some embodiments, the navigator UAV has two motion modes, namely uniform linear motion mode and constant speed turning motion mode. The navigator UAV receives motion mode instructions to determine the motion mode. If uniform linear motion is performed, the navigator UAV moves according to the speed and direction specified by the user. When performing the task of batch generating UAV cluster behavior data, the speed and direction can be randomly generated in batches within a specific range. The separation force, alignment force and separation force are calculated according to the number of adjacent navigator UAVs. The navigator UAV generates control force parameters for providing acceleration in the specified direction to reach the specified speed or to maintain the navigator UAV in a balanced state after reaching the specified speed.

[0103] If a constant-speed turn is performed, the Navigator UAV moves according to the turning radius and turning direction specified by the user. When performing the task of batch generating UAV cluster behavior data, the turning radius and turning direction can be randomly generated in batches within a specific range. The Navigator UAV generates control force parameters for maintaining balance and providing turning centripetal force.

[0104] The embodiment of the present application generates a control force to control the flight motion of the drone by controlling the leader drone, and can adjust the control force according to the motion mode of the drone to control the motion trajectory of the drone cluster.

[0105] In the embodiment of the present application, the step of calculating the force applied to the leader UAV based on the number of adjacent UAVs in step S402 may include but is not limited to the following steps:

[0106] S501: Acquire navigator information, neighbor information, and an expected average distance of a cluster, wherein the navigator information includes first speed information and first position information of the navigator UAV, and the neighbor information includes second speed information and second position information of the neighbors;

[0107] S502: Calculate the separation force on the leader UAV based on the number of first neighbors, the first position information, and the second position information of the first neighbors;

[0108] S503: Calculate the alignment force on the leader UAV based on the number of second neighbors, the first speed information, and the second speed information of the second neighbors;

[0109] S504: Calculate the cohesive force on the leader UAV according to the number of third neighbors, the first location information, the second location information of the third neighbors, and the expected average distance of the cluster.

[0110] In some embodiments, the navigator UAV information includes the number of the navigator's first neighbor, second neighbor, and third neighbor, and the navigator's own position and speed information; the neighbor information includes the position and speed information of the neighbor UAVs themselves. By analyzing the navigator's force, it is determined that the forces acting on the navigator UAV include separation force, alignment force, and cohesion force, and the calculation formulas are as follows:

[0111]

[0112]

[0113]

[0114] Where Ns, Na, and Nc represent the number of drones within the radius of separation force, alignment force, and cohesion force, respectively, ||·|| represents the Euclidean norm, and x i and v i Respectively represent the first position information and first speed information of the i-th UAV, x j and v j They represent the second position information and the second speed information of the j-th UAV respectively, and d represents the expected average distance of the cluster.

[0115] According to formulas (6) to (8), the separation force, alignment force and cohesive force acting on the navigator UAV are calculated to obtain the resultant force acting on the navigator UAV.

[0116] The embodiment of the present application determines the component forces acting on the navigator UAV based on the navigator UAV and neighboring UAVs in different ranges of action, thereby obtaining the resultant force acting on the navigator UAV, providing data for subsequent steps to generate control force parameters for precise control of the UAV.

[0117] In the embodiment of the present application, the step of determining the time-varying control parameters in the navigator dynamics model according to the shape modal instruction in step S103 may include but is not limited to the following steps:

[0118] S601: Adjusting the effective radius of the preset neighbor range of the navigator UAV according to the shape mode instruction;

[0119] S602: Adjusting the time-varying control parameters in the navigator dynamics model according to the change result of the action radius.

[0120] In some embodiments, the drone cluster has three shape modes, namely, aligned shape mode, separated shape mode and cohesive shape mode. The navigator drone receives shape mode instructions, adjusts the effective radius of the preset neighbor range of the navigator drone according to the shape mode instructions, and adjusts the time-varying control parameters in the navigator dynamic model according to the change in the effective radius.

[0121] For example, referring to Figure 3 , the separation force weight, alignment force weight and cohesion force weight values ​​of the UAV in the flight state when the shape alignment mode is in alignment are taken as the normal parameter values. Under the shape alignment mode, the various time-varying control parameters maintain the parameters in the normal flight state and remain unchanged.

[0122] In the separation shape mode, the separation radius Rs of each drone in the swarm is doubled, the separation force weight Ks is increased, and the alignment force weight Ka and cohesion force weight Kc are gradually reduced from their normal values ​​to zero. By increasing the separation force radius, the number of neighboring drones within the leader's separation force range increases, thereby increasing the leader's separation force, allowing it to better maintain distance from its neighbors and avoid collisions. When controlling drone behavior based on the separation shape mode command, the separation force weight is increased, while the alignment force weight and cohesion force weight are reduced, causing the leader to pay more attention to its first neighbor and less attention to other neighbors.

[0123] In the cohesive shape mode, the cohesive radius Rc of each drone in the swarm is increased fivefold, the separation weight Ks is reduced, and the alignment weights Ka and Kc are gradually increased to their normal values. By increasing the cohesive radius, the number of neighboring drones within the leader's cohesive range increases, thereby increasing the leader's cohesion and better maintaining the integrity of the swarm. When controlling drone behavior based on cohesive shape mode commands, the alignment and cohesion weights are increased, while the separation weight is reduced, causing the leader drone to pay more attention to its second and third neighbors and less attention to other neighbors.

[0124] The Navigator UAV updates its status by performing iterative calculations on the dynamics model according to the changes in the time-varying control parameters.

[0125] It is understandable that the effective radius of the preset neighbor range can be adjusted according to the actual needs of drone cluster behavior control. 2 times and 5 times are only exemplary and can also be other values. The embodiments of this application do not impose specific limitations.

[0126] The embodiment of the present application controls the changes in the neighborhood range of the leader UAV through shape modal instructions, adjusts the time-varying control parameters, achieves more targeted guidance and coordination, and effectively enhances the behavior control of UAV clusters in complex environments.

[0127] In an embodiment of the present application, the navigator dynamics model also includes a friction term, which is used to determine the friction force based on the current speed and the expected speed. The time-varying control parameters also include a friction weight coefficient.

[0128] In some embodiments, in order to ensure the boundedness and stability of the cluster motion, the navigator dynamics model also adds friction and friction weight coefficient to make the drone speed return to the desired speed. The calculation formula of friction is as shown in formula (9):

[0129]

[0130] Among them, F fi represents the friction force of the i-th UAV, v i represents the current speed of the i-th UAV, V e Indicates the expected speed.

[0131] The navigator dynamics model of this embodiment is as shown in formula (10):

[0132] F i (t) = k s F si +k a F ai +k c F ci +k f F fi +k ctrl +F ctrl (10)

[0133] Among them, K p (p=s,a,c,f,ctrl) represents the weight coefficient of each component force acting on the UAV.

[0134] The friction term is used as a constraint. When the speed specified by the user is inconsistent with the current speed of the drone, the friction force is used to make the drone cluster converge to the new desired speed.

[0135] The embodiment of the present application adds a friction term to the navigator dynamics model to control the drone cluster to converge to a new desired speed, thereby controlling the behavior of the drone cluster.

[0136] In some embodiments, the drone cluster behavior control method of the embodiment of the present application can be applied not only to the actual control process, but also to the simulation system. For example, referring to Figure 4 , the simulation system of the embodiment of the present application is as follows:

[0137] In the simulation system, users customize simulation control-related parameters such as simulation time, simulation step size, and simulation map size; set cluster initialization-related parameters such as cluster drone sorties, number of navigators, and expected speed; set expected shape modal instructions including dispersion, cohesion, and alignment, as well as motion modal instructions such as uniform speed straight flight, uniformly accelerated straight flight, and constant speed turning.

[0138] After the setup is completed, the cluster is initialized, including generating the initial drone cluster position, speed and movement direction, and calculating the distance between each drone in the cluster.

[0139] In order to explore the different characteristics of different cluster behaviors, the dataset also calculates six eigenvectors of the cluster, namely, the cluster center of mass, the cluster average velocity, the cluster angular momentum, the cluster rotation, the radial variance, and the divergence. The cluster center of mass can characterize the trajectory characteristics of the cluster motion to a certain extent; the average velocity can roughly characterize the motion state of the cluster; the cluster angular momentum represents the true angular momentum of each drone relative to the cluster center of mass; the cluster rotation represents the angular momentum of the cluster after normalization under the condition of ignoring the length of the moment arm; the radial variance represents the variance of the distance from each drone to the center of mass; and the divergence represents the average square distance from each drone to the center of mass. The specific calculation method of each eigenvector is as shown in Equations (11) to (16):

[0140] Cluster centroids:

[0141] Average cluster speed:

[0142] Cluster angular momentum:

[0143] Cluster rotation amount:

[0144] Cluster radial variance:

[0145] Cluster Divergence:

[0146] Where N is the number of drones in the drone cluster.

[0147] By conducting flight simulation through the simulation system, the time series data set can be quickly generated, and the cluster behavior can be characterized through multiple indicators, thereby improving the reliability of the flight mission.

[0148] The present application also provides a navigator-based UAV swarm behavior control system, including:

[0149] The first module is used to obtain motion mode instructions and shape mode instructions;

[0150] The second module is used to determine the control force parameters in the navigator dynamics model according to the motion modal command, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term and a control force term;

[0151] A third module is used to determine time-varying control parameters in the navigator dynamics model according to the shape modal command, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient;

[0152] The fourth module is used to control the behavior of the navigator UAV based on the navigator dynamics model and time-varying control parameters and control force parameters;

[0153] The fifth module is used to send the time-varying control parameters to several follower drones so that the follower drones can perform behavioral control based on the time-varying control parameters based on the follower dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

[0154] The present application also provides a navigator-based UAV swarm behavior control system, which includes a navigator UAV and several follower UAVs:

[0155] The navigator UAV is used to obtain motion mode instructions and shape mode instructions; determine the control force parameters in the navigator dynamics model according to the motion mode instructions, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term; determine the time-varying control parameters in the navigator dynamics model according to the shape mode instructions, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient; based on the navigator dynamics model, the behavior of the navigator UAV is controlled according to the time-varying control parameters and the control force parameters; and send the time-varying control parameters to several follower UAVs;

[0156] The follower UAV is used to receive time-varying control parameters and perform behavior control on the leader UAV according to the time-varying control parameters and control force parameters based on the leader dynamics model. The follower dynamics model includes separation force terms, alignment force terms, and cohesion force terms.

[0157] It can be understood that the contents of the above-mentioned navigator-based UAV cluster behavior control method embodiment are applicable to the present system embodiment. The functions specifically implemented by the present system embodiment are the same as those in the above-mentioned navigator-based UAV cluster behavior control method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned navigator-based UAV cluster behavior control method embodiment.

[0158] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned office recommendation method. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0159] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0160] Please refer to Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0161] The processor 501 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0162] The memory 502 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program codes are stored in the memory 502 and are called by the processor 501 to execute the office recommendation method of the embodiments of this application.

[0163] Input / output interface 503, used to implement information input and output;

[0164] Communication interface 504, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0165] Bus 505 , which transmits information between various components of the device (e.g., processor 501 , memory 502 , input / output interface 503 , and communication interface 504 );

[0166] The processor 501 , the memory 502 , the input / output interface 503 and the communication interface 504 are connected to each other in communication within the device via a bus 505 .

[0167] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Certain physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. As is well known to those skilled in the art, the embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments, and various changes may be made within the scope of knowledge possessed by those skilled in the art without departing from the purpose of the present application.

Claims

1. A navigator-based UAV swarm behavior control method, characterized in that: The navigator-based UAV swarm behavior control method is applied to a navigator UAV in a UAV swarm, wherein the UAV swarm includes a navigator UAV and several follower UAVs. The navigator-based UAV swarm behavior control method includes the following steps: Obtain motion mode instructions and shape mode instructions; Determining control force parameters in a navigator dynamics model according to the motion modal instruction, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term; Determining time-varying control parameters in a navigator dynamics model according to the shape modal instructions, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient; Based on the navigator dynamics model, the navigator UAV is behaviorally controlled according to the time-varying control parameters and the control force parameters; The time-varying control parameters are sent to a plurality of follower UAVs, so that the follower UAVs perform behavior control according to the time-varying control parameters based on a follower dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

2. The method for controlling the behavior of a swarm of drones based on a navigator according to claim 1, characterized in that: The navigator-based UAV cluster behavior control method further includes the following steps: Obtaining a preset neighbor range of the navigator drone, where the preset neighbor range includes a separation force action range, an alignment force action range, and a cohesion force action range; The neighboring drones of the navigator drone are determined according to the preset neighbor range, wherein the neighboring drones are other navigator drones within the preset neighbor range.

3. The method for controlling the behavior of a swarm of drones based on a navigator according to claim 2, characterized in that: The step of determining the neighboring UAVs of the navigator UAV according to the preset neighbor range includes the following steps: Determining a plurality of first neighbors within the separation force action range according to the separation force action range in the preset neighbor range, wherein the separation force action range is determined by a separation force radius; Determining a plurality of second neighbors within the alignment force action range according to the alignment force action range in the preset neighbor range, wherein the alignment force action range is determined by an alignment force radius; Determining a plurality of third neighbors within the cohesive force action range according to the cohesive force action range in the preset neighbor range, wherein the cohesive force action range is determined by the cohesive force radius; A plurality of the first neighbors, a plurality of the second neighbors, and a plurality of the third neighbors are determined as neighboring drones of the leader drone.

4. The method for controlling the behavior of a swarm of drones based on a navigator according to claim 3, characterized in that: Determining the control force parameters in the navigator dynamics model according to the motion mode instruction includes the following steps: Determine the motion mode of the leader according to the motion mode instruction; Calculating the force applied to the leader UAV based on the number of adjacent UAVs; The control force parameters in the navigator dynamics model are determined according to the navigator motion mode and the force condition.

5. The method for controlling the behavior of a swarm of drones based on a navigator according to claim 4, characterized in that: Calculating the force applied to the leader UAV according to the number of adjacent UAVs comprises the following steps: Obtaining navigator information, neighbor information, and an expected average distance of a cluster, wherein the navigator information includes first speed information and first position information of the navigator UAV, and the neighbor information includes second speed information and second position information of the neighbors; Calculating the separation force on the leader drone according to the number of the first neighbors, the first position information, and the second position information of the first neighbors; Calculating the alignment force on the leader drone according to the number of the second neighbors, the first speed information, and the second speed information of the second neighbors; The cohesive force exerted on the leader UAV is calculated according to the number of the third neighbors, the first position information, the second position information of the third neighbors, and the expected average distance of the cluster.

6. The method for controlling the behavior of a swarm of drones based on a navigator according to claim 2, characterized in that: Determining the time-varying control parameters in the navigator dynamics model according to the shape modal instruction comprises the following steps: Adjust the radius of action of the preset neighbor range of the Navigator UAV according to the shape mode command; The time-varying control parameters in the navigator dynamics model are adjusted according to the change result of the action radius.

7. The method for controlling the behavior of a swarm of drones based on a navigator according to claim 1, characterized in that: The navigator dynamics model further includes a friction term, which is used to determine the friction force according to the current speed and the expected speed. The time-varying control parameter further includes a friction weight coefficient.

8. A navigator-based UAV swarm behavior control system, characterized in that: The navigator-based UAV swarm behavior control system is applied to a navigator UAV in a UAV swarm, wherein the UAV swarm includes a navigator UAV and several follower UAVs. The navigator-based UAV swarm behavior control system includes: The first module is used to obtain motion mode instructions and shape mode instructions; A second module is configured to determine control force parameters in a navigator dynamics model according to the motion modal instruction, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term; A third module is configured to determine time-varying control parameters in the navigator dynamics model according to the shape modal instruction, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient; A fourth module is configured to control the behavior of the navigator UAV based on the navigator dynamics model and the time-varying control parameters and the control force parameters; The fifth module is configured to send the time-varying control parameters to a plurality of follower UAVs, so that the follower UAVs perform behavior control according to the time-varying control parameters based on a follower dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

9. A navigator-based UAV swarm behavior control system, characterized in that: The navigator-based UAV swarm behavior control system includes a navigator UAV and several follower UAVs: The navigator UAV is used to obtain motion mode instructions and shape mode instructions; determine the control force parameters in the navigator dynamics model according to the motion mode instructions, wherein the navigator dynamics model includes a separation force term, an alignment force term, a cohesion force term, and a control force term; determine the time-varying control parameters in the navigator dynamics model according to the shape mode instructions, wherein the time-varying control parameters include a separation force weight coefficient, an alignment force weight coefficient, a cohesion force weight coefficient, and a control force weight coefficient; based on the navigator dynamics model, the behavior of the navigator UAV is controlled according to the time-varying control parameters and the control force parameters; and send the time-varying control parameters to a plurality of follower UAVs; The follower UAV is used to receive the time-varying control parameters and perform behavior control on the leader UAV according to the time-varying control parameters and the control force parameters based on a leader dynamics model, wherein the follower dynamics model includes a separation force term, an alignment force term, and a cohesion force term.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for controlling the behavior of a swarm of drones based on a navigator according to any one of claims 1 to 7 is implemented.

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