Unmanned aerial vehicle formation control method and device, unmanned aerial vehicle and storage medium

Abstract

The application discloses a kind of unmanned plane formation control method, device, unmanned plane and storage medium, the method includes: with each unmanned plane in unmanned plane formation as node, establish virtual rigid structure, calculate the virtual mass point of the rigid structure, establish reference coordinate system with the virtual mass point as origin;Each described unmanned plane is projected onto the reference coordinate system, and the relative position between each unmanned plane and the virtual mass point is calculated;Based on the current state of the virtual mass point, the current state of each described unmanned plane and the relative position, determine actual formation configuration;Difference between expected formation configuration and actual formation configuration is calculated, and the relative position of each unmanned plane and virtual mass point in next time is determined based on difference, each described unmanned plane is controlled based on the relative position of next time. Above-mentioned scheme realizes the control of formation by the expected trajectory of virtual mass point and expected configuration, and has good rapidity and robustness.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) formation control technology, specifically to a UAV formation control method, device, UAV, and storage medium. Background Technology

[0002] With the rapid development of drone technology, research on drone swarm technology has gradually attracted widespread attention. Currently, researchers are exploring various drone swarm formations and application scenarios, covering military, civilian, and commercial fields. This has significant practical implications and broad application prospects, and will bring tremendous development opportunities and economic benefits to the military, civilian, and commercial sectors.

[0003] However, much of the current research on drone formation is still in the theoretical stage. Generally, a lead drone is set up when forming a formation. The position of each other drone is determined by the lead drone to maintain the formation. If the lead drone malfunctions or fails to obtain its information, the drone formation cannot operate normally. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method, apparatus, drone and storage medium for drone formation control that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for controlling unmanned aerial vehicle (UAV) formations is provided, the method comprising:

[0006] Using each drone in the drone formation as a node, a virtual rigid structure is established, the virtual mass point of the rigid structure is calculated, and a reference coordinate system with the virtual mass point as the origin is established.

[0007] Project each of the UAVs onto the reference coordinate system and calculate the relative position between each UAV and the virtual mass point;

[0008] Based on the current state of the virtual mass point, the current state of each UAV, and the relative position, the actual formation configuration is determined;

[0009] The difference between the desired formation configuration and the actual formation configuration is calculated, and the relative position of each UAV and the virtual mass point at the next moment is determined based on the difference. The UAVs are then controlled based on the relative positions at the next moment.

[0010] In some implementations, projecting each of the UAVs onto the reference coordinate system and calculating the relative position between each of the UAVs and the virtual mass specifically includes:

[0011] A first coordinate system is constructed to determine the kinematic models of the virtual mass and each UAV in the altitude channel and the horizontal channel. The kinematic model of the UAV in the horizontal channel includes external disturbances.

[0012] The relative distance between the UAV and the virtual mass in the first coordinate system and the angle between the relative distance and the X-axis of the first coordinate system are determined. Based on the relative distance, the angle, and the kinematic model, the relative position formula of each UAV relative to the virtual mass is obtained.

[0013] In some implementations, calculating the difference between the target formation configuration and the initial formation configuration, and determining the relative position of each UAV and the virtual particle at the next moment based on the difference, and controlling each UAV based on the relative position at the next moment includes:

[0014] The vertical force exerted by each UAV is determined based on the height difference in their relative positions at the next moment.

[0015] The attitude control parameters of each UAV are determined based on the difference in the horizontal direction and the difference in the yaw angle in the relative position at the next moment.

[0016] In some implementations, calculating the difference between the target formation configuration and the initial formation configuration, and determining the relative position of each UAV and the virtual particle at the next moment based on the difference, and controlling each UAV based on the relative position at the next moment further includes:

[0017] The pre-built model compensation controller determines the force parameters and / or attitude control parameters of each UAV based on the relative position at the next moment.

[0018] The model compensation controller is a controller based on the active disturbance rejection control principle, which includes a higher-order differentiator, a compensation function observer, and a model compensation control law.

[0019] In some embodiments, the method further includes:

[0020] The dynamic equations of the UAV are determined based on the reference coordinate system and the body coordinate system;

[0021] Based on the external interference factors experienced by the UAV, the formulas for the interference force and the interference torque experienced by the UAV are determined.

[0022] Based on the aforementioned dynamic equations, disturbance force formulas, and disturbance torques, the formulas relating the control torque and force of the UAV to the propeller speed are determined.

[0023] Based on the aforementioned relational formulas, the position system formula and attitude system formula for the UAV are determined.

[0024] In some embodiments, the method further includes:

[0025] The position system formula and attitude system formula are simplified to obtain the simplified formula;

[0026] The force parameters and / or attitude control parameters are determined based on the simplified formula and the model compensation controller.

[0027] In some implementations, calculating the difference between the target formation configuration and the initial formation configuration, and determining the relative position of each UAV and the virtual particle at the next moment based on the difference, and controlling each UAV based on the relative position at the next moment further includes:

[0028] The forces and attitude control parameters are converted and allocated to obtain the desired attitude signals required for the inner loop of each UAV, thereby determining the motor speed control values ​​of each UAV rotor.

[0029] According to another aspect of the present invention, a drone formation control device is provided, the device comprising:

[0030] The mass point calculation module is suitable for establishing a virtual rigid structure with each UAV in the UAV formation as a node, calculating the virtual mass point of the rigid structure, and establishing a reference coordinate system with the virtual mass point as the origin.

[0031] The position determination module is adapted to project each of the UAVs onto the reference coordinate system and calculate the relative position between each of the UAVs and the virtual mass point;

[0032] The formation determination module is adapted to determine the actual formation configuration based on the current state of the virtual mass point, the current state of each UAV, and the relative position.

[0033] The position control module is adapted to calculate the difference between the desired formation configuration and the actual formation configuration, and to determine the relative position of each UAV and the virtual mass point at the next moment based on the difference, and to control each UAV based on the relative position at the next moment.

[0034] According to another aspect of the present invention, a drone is provided, comprising: a processor and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform a drone formation control method according to any one of the above embodiments.

[0035] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores one or more programs, which, when executed by a processor, implement the unmanned aerial vehicle (UAV) formation control method according to any one of the above.

[0036] As can be seen from the above, the UAV formation control method disclosed in this invention uses a virtual structure method to control the formation. Its core idea is to make each UAV follow a mass point on a moving rigid structure. This scheme effectively avoids the problem of the entire formation being paralyzed when the navigator is damaged or unable to work properly, which is a problem in other formation control methods. After the formation and virtual mass point are determined, other UAVs will follow the moving virtual mass point. Each UAV in the formation can maintain the formation structure by tracking the ideal trajectory of the virtual mass point.

[0037] Furthermore, while maintaining formation, each UAV, in addition to tracking the trajectory of a given virtual mass, also needs to resist disturbances from other UAVs in the formation. This places certain demands on the speed and robustness of UAV control. This embodiment uses model compensated control (MCC) as the UAV controller to improve the speed and disturbance resistance of the system trajectory tracking.

[0038] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0040] Figure 1 A flowchart illustrating a drone formation control method according to an embodiment of the present invention is shown;

[0041] Figure 2 A schematic diagram of a drone formation control framework according to an embodiment of the present invention is shown;

[0042] Figure 3 A schematic diagram illustrating the positional relationship between the virtual centroid and other unmanned aerial vehicles in a virtual structure according to an embodiment of the present invention is shown.

[0043] Figure 4 A structural schematic diagram of a quadcopter unmanned aerial vehicle subjected to forces according to an embodiment of the present invention is shown;

[0044] Figure 5 A schematic diagram of the structure of a model compensation controller according to an embodiment of the present invention is shown;

[0045] Figure 6 A schematic diagram of a drone formation control device according to an embodiment of the present invention is shown;

[0046] Figure 7 A schematic diagram of the structure of an unmanned aerial vehicle (flight control system) according to an embodiment of the present invention is shown. Detailed Implementation

[0047] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0048] Figure 1 A flowchart illustrating a drone formation control method according to an embodiment of the present invention is shown. For a detailed implementation of this method, please refer to... Figure 2 The framework shown. The method includes the following steps:

[0049] Step S110: Using each drone in the drone formation as a node, establish a virtual rigid structure, calculate the virtual mass point of the rigid structure, and establish a reference coordinate system with the virtual mass point as the origin; see the schematic diagram of the virtual mass point. Figure 3 As shown;

[0050] Step S120: Project each of the UAVs onto the reference coordinate system and calculate the relative position between each of the UAVs and the virtual mass point;

[0051] Step S130: Determine the actual formation configuration based on the current state of the virtual mass point, the current state of each UAV, and the relative position.

[0052] Step S140: Calculate the difference between the desired formation configuration and the actual formation configuration, determine the relative position of each UAV and the virtual mass point at the next moment based on the difference, and control each UAV based on the relative position at the next moment. It should be noted that the ideal trajectory and desired position of the virtual mass point can be preset in advance as needed, and the ideal formation configuration of the UAVs is also preset in advance and can be changed as needed.

[0053] Specifically, in combination Figure 2As shown, by inputting the desired formation configuration, the virtual mass state, and the current state into the formation cooperative controller, the virtual mass state and the UAV's real-time state are used to solve for the actual formation configuration through relative position calculation. The difference between the virtual mass state and the desired formation configuration is then used to obtain the formation error, which is input into the XY direction motion control to calculate the UAV's acceleration information and thus control the UAV's inner loop.

[0054] Unmanned aerial vehicles (UAVs) are typical underactuated systems, thus requiring a hierarchical control strategy. Specifically, the outer loop controls the UAV's position within the formation, while the inner loop controls its attitude. During control, the outer loop controller needs to provide effective information to the inner loop controller.

[0055] In summary, this embodiment employs a virtual structure method for formation control. The core idea of ​​this method is to have each UAV follow a moving, rigid point on a fixed structure. This method effectively avoids the problem of the entire formation becoming paralyzed when the navigator is damaged or malfunctions, as is common in other formation control methods. After determining the formation and the virtual point, the other UAVs follow the moving point. By tracking the ideal trajectory of the point, the UAVs maintain the formation's structure. Furthermore, this method provides better speed and robustness for UAV formations.

[0056] In some embodiments, the step S120 of projecting each of the drones onto the reference coordinate system and calculating the relative position between each of the drones and the virtual mass point specifically includes:

[0057] A first coordinate system is constructed to determine the kinematic models of the virtual mass and each UAV in the altitude channel and the horizontal channel. The kinematic model of the UAV in the horizontal channel includes external disturbances.

[0058] The relative distance between the UAV and the virtual mass in the first coordinate system and the angle between the relative distance and the X-axis of the first coordinate system are determined. Based on the relative distance, the angle, and the kinematic model, the relative position formula of each UAV relative to the virtual mass is obtained.

[0059] Specifically, the virtual structure method described in this invention forms a rigid structure among multiple drones, with the drones acting as nodes in the rigid structure. The coordinate system on the rigid structure is used as the home coordinate system (the first coordinate system). When the rigid structure moves, the coordinates of the drones in the reference coordinate system remain unchanged, and the relative positions of each drone also remain constant, thus maintaining formation. The virtual mass points and drone positions in the home coordinate system are as follows: Figure 3 As shown. Where o e x e y e z eThe coordinate system is the Home coordinate system, o b x b y b z b The coordinate system is the Local coordinate system. (x) l y l Let ψ be the position of the virtual particle in the Home coordinate system. Its yaw angle is ψ. l (x) b y b The position of the drone in the Home coordinate system is shown below. The goal of this invention is to enable the drone and the virtual mass to fly in a corresponding formation, and to ensure that the yaw angle of the drone is consistent with the yaw angle of the virtual mass. Furthermore, it can achieve formation maintenance, transformation, and dispersal.

[0060] In formation control, the complete dynamic model of the quadrotor UAV will no longer be considered; only its kinematic model will be taken into account. The kinematic model of the quadrotor UAV's altitude channel is as follows:

[0061]

[0062] Where i = l, f represents the virtual mass and the drone respectively, z represents the displacement of the quadcopter drone in the Home coordinate system, and v represents the velocity of the quadcopter drone in the Earth coordinate system.

[0063] The kinematic model of the horizontal position channel of the virtual mass and the UAV can be established as

[0064]

[0065] Where, d i (i = x, y, z) is the disturbance term. Since UAVs are inevitably affected by external disturbances (wind disturbance, airflow between UAVs, etc.) when flying in formation, and the formation controller design does not consider a complete mathematical model of quadcopter UAVs, the disturbance term d is included in the kinematic model of the follower to approximate the influence of these external disturbances on the state of the quadcopter UAVs.

[0066] x and y represent the horizontal positions in the Home coordinate system, respectively. x v y Let represent the horizontal velocity in the Local coordinate system. ψ and ω represent the yaw angle in the Home coordinate system and the yaw angular velocity in the Local coordinate system, respectively, where i = l and f represent the virtual particle and the UAV, respectively.

[0067] The relative distance between the virtual point mass and the drone in the Home coordinate system is l, and the angle between the virtual point mass and the X-axis of the Home coordinate system is θ. To establish a formation around a virtual mass, the relative positions between followers and the virtual leader need to be established in the virtual mass's local coordinate system and projected onto the virtual mass's coordinate system (reference coordinate system).

[0068]

[0069] In some embodiments, combined with Figure 2 As shown, the difference between the target formation configuration and the initial formation configuration is calculated, and the relative position of each UAV and the virtual mass point at the next moment is determined based on the difference. Controlling each UAV based on the relative position at the next moment includes:

[0070] The vertical force exerted by each UAV is determined based on the height difference in their relative positions at the next moment.

[0071] The attitude control parameters of each UAV are determined based on the difference in the horizontal direction and the difference in the yaw angle in the relative position at the next moment.

[0072] Specifically, in combination Figure 2 As shown, the vertical force mentioned above is F, and the attitude control parameters of each UAV include values ​​such as σ1, σ2, and σ3.

[0073] In some embodiments, calculating the difference between the target formation configuration and the initial formation configuration, and determining the relative position of each UAV and the virtual particle at the next moment based on the difference, and controlling each UAV based on the relative position at the next moment further includes:

[0074] The pre-built model compensation controller determines the force parameters and / or attitude control parameters of each UAV based on the relative position at the next moment.

[0075] The model compensation controller is a controller based on the active disturbance rejection control principle, which includes a higher-order differentiator, a compensation function observer, and a model compensation control law.

[0076] The expected relative positions of the virtual particle along the X and Y axes, i.e., the expected formation configuration, are as follows:

[0077]

[0078] Where l(t) is the offset of the UAV from the virtual mass point, and its orthogonal decomposition yields the formation configuration λ. xd , λ yd Therefore, it is only necessary to design a controller that makes λ x →λ xd , λ y →λ ydThis allows the formation to be maintained.

[0079] The above formula is then introduced into the expression formulas for each UAV in the formation (which can be constructed by combining the transformation formulas in the specific embodiments below). Each subsystem in the UAV expression formula is then described using a second-order differential equation in a nonlinear affine system.

[0080]

[0081] Where u is the input of the affine system, and b is the system parameter. For the total system disturbance, This represents the output of the second-order differential equation. A model compensation controller is designed based on the above formula to estimate the parameters.

[0082] It should be noted that model-compensated controllers (MCCs) are a control strategy designed to improve the performance of a control system by utilizing partial mathematical model information of the controlled object. This controller design method fully leverages information from the known model to improve the system's dynamic response, anti-interference characteristics, and robustness. Specifically, the application of MCCs involves the following aspects: Higher-order differentiators: Higher-order differentiators are used in control systems to estimate the differential information of the system output or reference input. In some cases, such as attitude control of quadcopters, higher-order differentiators are used to improve control algorithms to better handle the system's nonlinearity and unknown disturbances. Compensated function observers (CFOs): Compensated function observers are tools used to estimate unknown functions or disturbances in a system. Compared to extended state observers (ESOs), CFOs have higher type, higher accuracy, and stronger convergence. By modifying the structure of ESOs and employing the ideas of pure integral, compensation, and transfer function type, CFOs improve the estimation capability of unknown parts of the system. In some cases, CFOs can also be combined with advanced technologies such as neural networks to further improve estimation accuracy. Model-compensated control laws: Model-compensated control laws are control strategies designed based on a deep understanding of the system model. It utilizes estimation information provided by higher-order differentiators and compensation function observers to actively compensate the system, thereby eliminating or reducing the impact of disturbances on system performance. The design of the model-compensated control law requires comprehensive consideration of factors such as the system's dynamic characteristics, control objectives, and constraints.

[0083] In some embodiments, the method further includes:

[0084] The dynamic equations of the UAV are determined based on the reference coordinate system and the body coordinate system;

[0085] Based on the external interference factors experienced by the UAV, the formulas for the interference force and the interference torque experienced by the UAV are determined.

[0086] Based on the aforementioned dynamic equations, disturbance force formulas, and disturbance torques, the formulas relating the control torque and force of the UAV to the propeller speed are determined.

[0087] Based on the aforementioned relational formulas, the position system formula and attitude system formula for the UAV are determined.

[0088] In this design, the UAV is assumed to be a rigid body, with its geometric center and center of mass remaining constant. An inertial coordinate system O is defined. e =[x e y e , z e ] T (i.e., reference coordinate system) and body coordinate system O b =[x b ,y b ,z b ] T The positive directions of both coordinate systems follow the right-hand rule. Let... and These are the position vector and velocity vector in the inertial coordinate system, respectively. and These represent the angular velocity vector in the body coordinate system and the three attitude angles (roll, pitch, and yaw). The derivation of the formulas can be found in the specific implementation examples below.

[0089] In some embodiments, the method further includes:

[0090] The position system formula and attitude system formula are simplified to obtain the simplified formula;

[0091] The force parameters and / or attitude control parameters are determined based on the simplified formula and the model compensation controller.

[0092] In some specific embodiments, step S140, calculating the difference between the target formation configuration and the initial formation configuration, and determining the relative position of each UAV and the virtual mass point at the next moment based on the difference, and controlling each UAV based on the relative position at the next moment further includes:

[0093] Combination Figure 3 As shown, the force and attitude control parameters are converted and allocated to obtain the desired attitude signal required by the inner ring of each UAV, and then the motor speed control value of each UAV rotor is determined.

[0094] According to this embodiment, the conversion of the control information of the UAV inner ring based on the position information of the outer ring is realized. Specific Implementation

[0096] The specific implementation process of this embodiment includes: obtaining the state information of each aircraft, the trajectory information of the virtual centroid, and the formation configuration information; calculating the formation configuration error of each UAV; in the control section, the aforementioned Model Compensation Controller (MCC) is used, an acceleration signal is obtained, and the acceleration signal is converted into the desired attitude signal required by the UAV's inner loop and transmitted to the UAV via UDP communication. The UAV performs inner loop control based on the received desired attitude signal.

[0097] Specifically, establishing a mathematical model of the drone is the foundation for its analysis and research. Modeling drones, such as quadcopter drones... Figure 4 As shown.

[0098] Assume the UAV is a rigid body, with its geometric center and center of mass remaining constant. Define an inertial coordinate system O. e =[x e ,y e ,z e ] T With body coordinate system O b =[x b ,y b ,z b ] T The positive directions of both coordinate systems follow the right-hand rule. Let... and These are the position vector and velocity vector in the inertial coordinate system, respectively. and These represent the angular velocity vector and three attitude angles (roll, pitch, and yaw) in the body coordinate system. From the body coordinate system O... b Transform to inertial coordinate system O e rotation matrix for

[0099]

[0100] The dynamic equations of the quadcopter UAV are as follows:

[0101]

[0102] in,

[0103]

[0104] In the formula, m is the mass of the UAV, and J = [J x J y J z ] T For quadcopter drones in O b Along x b ,y b ,z bMoments of inertia along the three coordinate axes. F = [0, 0, u1] T For the total thrust, G = [0, 0, -mg] T Let g be the force of gravity, g be the acceleration due to gravity, and τ = [u2, u3, u4]. T To control the torque. D and d represent the external disturbance force and disturbance torque, respectively. The external disturbance is divided into two parts: the quadcopter at O... e The drag force and drag torque experienced along the three coordinate axes in Ob. Random disturbance forces and torques caused by gusts, unmodeled errors, and other factors. Assuming the magnitudes of drag and drag torque are proportional to their corresponding velocities and angular velocities, respectively, and that random disturbance forces and torques satisfy the norm boundedness condition, i.e.

[0105]

[0106] K i (i = 1, ..., 6) represents the corresponding drag and drag moment coefficients, σ D =[σ1, σ2, σ3] T For random disturbance force, σ d =[σ4, σ5, σ6] T For random disturbance torque, ε D With ε d This represents the upper bound of random disturbance forces and torques.

[0107] The UAV has an "X" shaped structure, and the relationship between total thrust and control torque and propeller speed is as follows:

[0108]

[0109] in This indicates the rotational speed of the propeller. It is the tensile coefficient. It is the torque coefficient. This indicates the distance from the center of the machine body to the motor.

[0110] For ease of controller design, the quadcopter UAV dynamics model can be divided into two subsystems: position system Π1 and attitude system Π2, as detailed below:

[0111]

[0112]

[0113] in

[0114]

[0115] Each subsystem in position system Π1 and attitude system Π2 can be described by a second-order differential equation in a nonlinear affine system.

[0116]

[0117] Where u is the input of the affine system, and b is the system parameter. For the total system disturbance, This represents the output of the second-order differential equation. Based on the above equation, a model compensation controller is designed. The model compensation controller consists of a higher-order differentiator, a compensation function observer, and a model compensation control law.

[0118] 1) Design a HOD (High-Order Differential) to quickly track the reference signal and extract the higher-order derivative of the reference signal.

[0119] The HOD design process is as follows:

[0120]

[0121] Where v i (i = 1, 2, 3) represents the internal state of the HOD system. For y r And the estimation of its higher-order differentials.

[0122]

[0123] a h This is an adjustable parameter that characterizes the bandwidth of the HOD. A larger bandwidth results in better performance for y. r The higher the accuracy of its higher-order differential estimation, the greater the impact on the system.

[0124] 2) Design a CFO to estimate the system state y. The total disturbance f. The CFO design process is as follows:

[0125]

[0126] Where z i (i = 1, 2, 3) represents the internal state of the CFO system. To The estimate. Among them...

[0127]

[0128] a c This is an adjustable parameter that characterizes the bandwidth of the CFO. A larger bandwidth results in higher accuracy in state estimation, but also greater noise in the estimation.

[0129] 3) Design the model compensation control law, based on the HOD method described above for y. r Its higher-order differential estimation, and the CFO's... Based on the estimation, the final design model compensation control law makes the system tracking error gradually converge to 0, that is: when t→∞, (y r -y)→0.

[0130]

[0131] in a m >0 indicates an adjustable parameter.

[0132] Similarly, the same approach is used to design the controller for each subsystem.

[0133] Combination Figure 6 As shown, the present invention also protects a drone formation control device 600, the drone formation control device 600 comprising:

[0134] The mass point calculation module 610 is suitable for establishing a virtual rigid structure with each UAV in the UAV formation as a node, calculating the virtual mass point of the rigid structure, and establishing a reference coordinate system with the virtual mass point as the origin.

[0135] The position determination module 620 is adapted to project each of the UAVs onto the reference coordinate system and calculate the relative position between each of the UAVs and the virtual mass point.

[0136] The formation determination module 630 is adapted to determine the actual formation configuration based on the current state of the virtual mass point, the current state of each UAV, and the relative position.

[0137] The position control module 640 is adapted to calculate the difference between the desired formation configuration and the actual formation configuration, and to determine the relative position of each UAV and the virtual mass point at the next moment based on the difference, and to control each UAV based on the relative position at the next moment.

[0138] According to this embodiment, a virtual structure method is used to control the formation. The core idea of ​​the virtual structure method is to have each drone follow a mass point on a moving rigid structure. This device effectively avoids the problem of the entire formation being paralyzed when the navigator is damaged or malfunctions, which is common in other formation control methods. After the formation and virtual mass point are determined, the other drones will follow the moving mass point. The drones can maintain the formation structure by tracking the ideal trajectory of the mass point. Furthermore, the above device can also bring better speed and robustness to the drone formation.

[0139] In some embodiments, the position determination module 620 projects each of the UAVs onto the reference coordinate system and calculates the relative position between each of the UAVs and the virtual mass point, specifically including:

[0140] A first coordinate system is constructed to determine the kinematic models of the virtual mass and each UAV in the altitude channel and the horizontal channel. The kinematic model of the UAV in the horizontal channel includes external disturbances.

[0141] The relative distance between the UAV and the virtual mass in the first coordinate system and the angle between the relative distance and the X-axis of the first coordinate system are determined. Based on the relative distance, the angle, and the kinematic model, the relative position formula of each UAV relative to the virtual mass is obtained.

[0142] In some embodiments, the position control module 640 calculates the difference between the target formation configuration and the initial formation configuration, and determines the relative position of each UAV and the virtual particle at the next moment based on the difference. Controlling each UAV based on the relative position at the next moment includes:

[0143] The vertical force exerted by each UAV is determined based on the height difference in their relative positions at the next moment.

[0144] The attitude control parameters of each UAV are determined based on the difference in the horizontal direction and the difference in the yaw angle in the relative position at the next moment.

[0145] In some embodiments, the position control module 640 calculates the difference between the target formation configuration and the initial formation configuration, and determines the relative position of each UAV and the virtual particle at the next moment based on the difference. Controlling each UAV based on the relative position at the next moment further includes:

[0146] The pre-built model compensation controller determines the force parameters and / or attitude control parameters of each UAV based on the relative position at the next moment.

[0147] The model compensation controller is a controller based on the active disturbance rejection control principle, which includes a higher-order differentiator, a compensation function observer, and a model compensation control law.

[0148] In some embodiments, the device 600 is further adapted to:

[0149] The dynamic equations of the UAV are determined based on the first coordinate system and the body coordinate system;

[0150] Based on the external interference factors experienced by the UAV, the formulas for the interference force and the interference torque experienced by the UAV are determined.

[0151] Based on the aforementioned dynamic equations, disturbance force formulas, and disturbance torques, the formulas relating the control torque and force of the UAV to the propeller speed are determined.

[0152] Based on the aforementioned relational formulas, the position system formula and attitude system formula for the UAV are determined.

[0153] In some embodiments, the device 600 is further adapted to:

[0154] The position system formula and attitude system formula are simplified to obtain the simplified formula;

[0155] The force parameters and / or attitude control parameters are determined based on the simplified formula and the model compensation controller.

[0156] In some embodiments, the position control module 640 calculates the difference between the target formation configuration and the initial formation configuration, and determines the relative position of each UAV and the virtual particle at the next moment based on the difference. Controlling each UAV based on the relative position at the next moment further includes:

[0157] The forces and attitude control parameters are converted and allocated to obtain the desired attitude signals required for the inner loop of each UAV, thereby determining the motor speed control values ​​of each UAV rotor.

[0158] It should be noted that the specific implementation methods of the above-mentioned device embodiments can be referred to the specific implementation methods of the corresponding methods described above, and will not be repeated here.

[0159] It should be noted that:

[0160] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0161] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0162] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0163] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0164] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0165] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the UAV formation control device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0166] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the UAV formation control method described in any of the above method embodiments.

[0167] Figure 7 The diagram shows a structural schematic of an embodiment of the UAV of the present invention. The specific embodiments of the present invention do not limit the specific structure of the UAV.

[0168] like Figure 7 As shown, the UAV (including the flight control system) may include: a processor 702, a communications interface 704, a memory 706, and a communication bus 708.

[0169] The processor 702, communication interface 704, and memory 706 communicate with each other via communication bus 708. Communication interface 704 is used to communicate with other network elements such as clients or other servers. Processor 702 executes program 710, specifically performing the relevant steps in the above-described embodiment of the UAV formation control method for UAVs.

[0170] Specifically, program 710 may include program code that includes computer operation instructions.

[0171] The processor 702 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the drone may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0172] Memory 706 is used to store program 710. Memory 706 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0173] Specifically, program 710 can be used to cause processor 702 to perform the operations corresponding to the above-described UAV formation control method embodiments.

[0174] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A method for controlling unmanned aerial vehicle (UAV) formations, characterized in that, The method includes: Using each drone in the drone formation as a node, a virtual rigid structure is established, the virtual mass point of the rigid structure is calculated, and a reference coordinate system with the virtual mass point as the origin is established. Project each of the UAVs onto the reference coordinate system and calculate the relative position between each UAV and the virtual mass point; Based on the current state of the virtual mass point, the current state of each UAV, and the relative position, the actual formation configuration is determined; Calculate the difference between the desired formation configuration and the actual formation configuration, determine the relative position of each UAV and the virtual mass point at the next moment based on the difference, and control each UAV based on the relative position at the next moment. Specifically, projecting each of the drones onto the reference coordinate system and calculating the relative position between each drone and the virtual mass point includes: A first coordinate system is constructed, forming a rigid structure among multiple drones, with the drones serving as nodes of the rigid structure. The coordinate system on the rigid structure is used as the first coordinate system to determine the kinematic models of the virtual mass point and each drone in the altitude channel and the horizontal channel. The kinematic model of the drone in the horizontal channel includes external disturbances. Determine the relative distance between the UAV and the virtual mass in the first coordinate system, and the angle between the relative distance and the X-axis of the first coordinate system. Based on the relative distance, the angle, and the kinematic model, obtain the relative position formula of each UAV relative to the virtual mass. The process includes calculating the difference between the desired formation configuration and the actual formation configuration, determining the relative position of each UAV and the virtual mass point at the next moment based on the difference, and controlling each UAV based on the relative position at the next moment, which includes: The vertical force exerted by each UAV is determined based on the height difference in their relative positions at the next moment. The attitude control parameters of each UAV are determined based on the difference in the horizontal direction and the difference in the yaw angle in the relative position at the next moment. The calculation of the difference between the desired formation configuration and the actual formation configuration, and the determination of the relative position of each UAV and the virtual mass point at the next moment based on the difference, and the control of each UAV based on the relative position at the next moment further include: The pre-built model compensation controller determines the force parameters and / or attitude control parameters of each UAV based on the relative position at the next moment. The model compensation controller is a controller based on the active disturbance rejection control principle, which includes a higher-order differentiator, a compensation function observer, and a model compensation control law.

2. The method according to claim 1, characterized in that, The method further includes: The dynamic equations of the UAV are determined based on the reference coordinate system and the body coordinate system; Based on the external interference factors experienced by the UAV, the formulas for the interference force and the interference torque experienced by the UAV are determined. Based on the aforementioned dynamic equations, disturbance force formulas, and disturbance torque formulas, the formulas relating the control torque and force of the UAV to the propeller speed are determined. Based on the aforementioned relational formulas, the position system formula and attitude system formula for the UAV are determined.

3. The method according to claim 2, characterized in that, The method further includes: The position system formula and attitude system formula are simplified to obtain the simplified formula; The force parameters and / or attitude control parameters are determined based on the simplified formula and the model compensation controller.

4. The method according to any one of claims 1-3, characterized in that, Calculating the difference between the desired formation configuration and the actual formation configuration, and determining the relative position of each UAV and the virtual mass point at the next moment based on the difference, and controlling each UAV based on the relative position at the next moment further includes: The forces and attitude control parameters are converted and allocated to obtain the desired attitude signals required for the inner loop of each UAV, thereby determining the motor speed control values ​​of each UAV rotor.

5. A drone formation control device, characterized in that, The device includes: The mass point calculation module is suitable for establishing a virtual rigid structure with each UAV in the UAV formation as a node, calculating the virtual mass point of the rigid structure, and establishing a reference coordinate system with the virtual mass point as the origin. The position determination module is adapted to project each of the UAVs onto the reference coordinate system and calculate the relative position between each of the UAVs and the virtual mass point; The formation determination module is adapted to determine the actual formation configuration based on the current state of the virtual mass point, the current state of each UAV, and the relative position. The position control module is adapted to calculate the difference between the desired formation configuration and the actual formation configuration, and to determine the relative position of each UAV and the virtual mass point at the next moment based on the difference, and to control each UAV based on the relative position at the next moment. The location determination module is also suitable for: A first coordinate system is constructed, forming a rigid structure among multiple drones, with the drones serving as nodes of the rigid structure. The coordinate system on the rigid structure is used as the first coordinate system to determine the kinematic models of the virtual mass point and each drone in the altitude channel and the horizontal channel. The kinematic model of the drone in the horizontal channel includes external disturbances. Determine the relative distance between the UAV and the virtual mass in the first coordinate system, and the angle between the relative distance and the X-axis of the first coordinate system. Based on the relative distance, the angle, and the kinematic model, obtain the relative position formula of each UAV relative to the virtual mass. The position control module is also suitable for: The vertical force exerted by each UAV is determined based on the height difference in their relative positions at the next moment. The attitude control parameters of each UAV are determined based on the difference in the horizontal direction and the difference in the yaw angle in the relative position at the next moment. The pre-built model compensation controller determines the force parameters and / or attitude control parameters of each UAV based on the relative position at the next moment. The model compensation controller is a controller based on the active disturbance rejection control principle, which includes a higher-order differentiator, a compensation function observer, and a model compensation control law.

6. A drone, characterized in that, It includes a processor and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the unmanned aerial vehicle (UAV) formation control method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the UAV formation control method according to any one of claims 1-4.

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