Unmanned vehicle cluster leader following formation control method based on hybrid visual servo

Abstract

The invention discloses an unmanned vehicle cluster leader following formation control method based on hybrid visual servo, and belongs to the technical field of intelligent control and unmanned systems. The method comprises the following steps: constructing image coordinates; designing mnemonic symbols according to the image coordinates; constructing an image space relative kinematics equation for deriving the image coordinates according to the mnemonic symbols; establishing an image coordinate error mathematical model according to the image coordinates; presetting a performance function; establishing a transformation error expression according to the image coordinate error mathematical model and a preset performance function; deriving the transformation error, and establishing a relational expression between the transformation error first-order derivative and the simplified image space relative kinematics equation; constructing a visual servo error model based on the position; constructing an image-based visual servo controller according to the transformation error; constructing a position-based visual servo controller according to the transformation error and a position-based visual servo error model; a hybrid visual servo controller is constructed according to an image-based visual servo controller and a position-based visual servo controller. According to the invention, stable formation control satisfying the view field constraint condition can be realized without depending on depth information.

Description

Technical Field

[0001] This invention relates to a swarm leader-following formation control method for unmanned vehicle clusters based on hybrid vision servoing, belonging to the field of intelligent control and unmanned systems technology. Background Technology

[0002] With the development of intelligent robots and unmanned swarm systems, visual feedback-based formation control has significant application value in scenarios such as military reconnaissance, disaster relief, urban patrol, and complex environment detection. Unlike traditional single-vehicle autonomous control, unmanned swarm formation control requires the coordination and constraint among multiple individuals to ensure that the leader and followers can move stably according to a preset pattern.

[0003] Among various control methods, visual servoing has gradually become an important means of unmanned swarm formation due to its ability to directly utilize environmental and target information captured by cameras, avoiding reliance on complex external positioning systems. However, practical applications still face the following technical challenges: First, cameras have a limited field of view, which can lead to the risk of the leader leaving the camera's field of view during dynamic formation, resulting in control failure—the so-called "field of view constraint" problem. Second, the spatial depth information of target feature points is often difficult to obtain directly or has significant measurement errors, making vision-based 3D position estimation uncertain, which increases the complexity of formation control.

[0004] Existing methods mostly rely on single image-based visual servoing (IBVS) or position-based visual servoing (PBVS) control. However, the former is difficult to guarantee global geometric stability, while the latter is subject to field-of-view constraints. In other words, a single control mode often cannot meet the dual requirements of stability and real-time performance.

[0005] In summary, a hybrid visual servo formation control method that integrates the advantages of IBVS and PBVS is needed. This method can ensure the convergence of target image coordinates while taking into account 3D pose constraints, thereby effectively solving the problems of field of view constraints and unknown depth in complex environments and achieving stable formation control of unmanned swarm leader-follower. Summary of the Invention

[0006] This invention provides a swarm leader-follower formation control method for unmanned vehicle clusters based on hybrid visual servoing. On the one hand, it establishes an image space relative kinematic equation that does not require depth information, and further introduces a performance function and transformation error to transform image coordinate error into bounded systematic error. On the other hand, it constructs a hybrid visual servoing controller by fusing image-based and position-based visual servoing controllers in a weighted manner, and proves the stability of the controller using a Lyapunov function.

[0007] The technical solution of this invention is:

[0008] According to a first aspect of the present invention, a method for swarm leader-follower formation control of unmanned vehicles based on hybrid visual servoing is provided, comprising:

[0009] S1. For a swarm of wheeled mobile unmanned vehicles, establish a kinematic model of the wheeled mobile unmanned vehicles in the world coordinate system; based on the kinematic model of the wheeled mobile unmanned vehicles in the world coordinate system, establish the relationship between relative position and velocity, and expand the expression after differentiating the relative position; wherein, the swarm of wheeled mobile unmanned vehicles includes one leader and multiple followers;

[0010] S2. Construct image coordinates based on the expanded expression; design mnemonics based on image coordinates; construct image space relative kinematics equations with respect to image coordinates based on mnemonics; simplify the image space relative kinematics equations to obtain the simplified image space relative kinematics equations.

[0011] S3. Based on the image coordinates, establish a mathematical model of image coordinate error; preset the performance function; based on the mathematical model of image coordinate error and the preset performance function, establish a transformation error expression; differentiate the transformation error and establish the relationship between the first derivative of the transformation error and the simplified image space relative kinematics equation;

[0012] S4. Construct a position-based visual servoing error model;

[0013] S5. Based on the transformation error, construct an image-based visual servo controller; based on the transformation error and the position-based visual servo error model, construct a position-based visual servo controller; based on the image-based visual servo controller and the position-based visual servo controller, fuse them in a weighted manner to construct a hybrid visual servo controller.

[0014] Further, S2 includes:

[0015] S2.1. Based on the expanded expression, construct the image coordinates, wherein the image coordinates are constructed as follows:

[0016] 1) In the view of the follower camera, the coordinates of the feature point P on the leader are represented as follows:

[0017] [X, Y, Z] T =[-y lf ,h,x lf ] T ;

[0018] Where h represents the relative height between the camera's optical center and the feature point P; x lf y lf These represent the relative positions of the leader and followers in the x and y directions, respectively.

[0019] 2) Based on the coordinates of feature point P, construct the image coordinates s = (m, n) of the feature point mapped to the camera as follows:

[0020]

[0021] In the formula, m and n represent the horizontal and vertical coordinates of the feature points mapped to the camera image, respectively; k m and k n Let m0 and n0 represent the scaling factors in the similar triangle mapping of the camera, respectively, and (m0, n0) represent the position of the principal point of the camera in the camera plane.

[0022] S2.2 Design mnemonics based on image coordinates; construct the image space relative kinematics equations based on the mnemonics and differentiating them with respect to the image coordinates;

[0023] The mnemonic is:

[0024]

[0025] Therefore, the relative kinematic equation of image space, differentiated with respect to image coordinates s, is:

[0026]

[0027] The simplified equations of relative kinematics in image space yield:

[0028]

[0029] Where, θ lf Represents the relative angle between leaders and followers, v l v f These represent the linear velocities of the leader and followers, respectively; w f Indicates the leader's angular velocity; F and f represent intermediate variables.

[0030] Furthermore, the preset performance function ρ k for:

[0031]

[0032] In the formula, L>0 is the convergence rate, ρ ∞ >0 represents the maximum steady-state error, t represents time; k∈{1,2}; when k takes the value 1, ... C k Right now C 1, Right now When k is 2 C k Right now C 2, Right now C 1 = m * - m min , C 2 = n * – n min , m*, n* represent the desired horizontal and vertical coordinates of the image; m max m min Let n represent the maximum and minimum values ​​of the x-coordinate of the camera plane, respectively. max n min These represent the maximum and minimum values ​​of the camera plane's ordinate, respectively.

[0033] Furthermore, the transformation error expression is as follows:

[0034]

[0035] Where, when k is 1, ε k That is, ε1 represents the lateral transformation error; when k is 2, ε k That is, ε2 represents the longitudinal transformation error; when k takes 1, e k That is, e1, where e1 represents the horizontal coordinate error of the image; when k is 2, e k That is, e2, where e2 represents the vertical coordinate error of the image.

[0036] Furthermore, the image-based visual servo controller u I Designed as follows:

[0037]

[0038] Where K1 and K2 are positive gain matrices; ε = [ε1, ε2] T ε1 and ε2 represent the transformation errors in the horizontal and vertical directions; F represents the intermediate variables; D represents the fusion weights; and J is a diagonal matrix.

[0039] Furthermore, the position-based visual servo controller u p Designed as follows:

[0040] u p =-(2H(ε) T e p )-1)F -1 J -1 K3e p ;

[0041] in, K3 is a positive gain matrix; e p1 e p2 These represent the relative positional errors of the leader and followers in the x and y directions, respectively.

[0042] Furthermore, the hybrid vision servo controller u is designed as follows:

[0043] u = [v f ,w f ] T =Du I +(1-D)u p

[0044] Among them, v f w represents the linear velocity of the follower. f This represents the leader's angular velocity.

[0045] According to a second aspect of the present invention, a terminal device is provided, including a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0046] The beneficial effects of this invention are:

[0047] (1) This invention first derives the kinematic model of the Mecanum wheeled mobile unmanned vehicle in the world coordinate system, and establishes the relative position and velocity relationship between the leader and the follower in the coordinate system, providing a rigorous mathematical basis for subsequent visual servo control. Then, it constructs image coordinates (i.e., camera model) and derives the relative kinematic equations of image space in combination with kinematics, reducing the impact of depth measurement error on control accuracy. Subsequently, this invention introduces a performance function and transformation error method to transform image coordinate error into bounded systematic error, ensuring the convergence of target image coordinates under finite field of view constraints, and solving the problem that traditional visual formation is prone to failure when the leader leaves the camera's field of view.

[0048] (2) Based on the relative pose between the leader and the follower, this invention constructs a three-dimensional PBVS error model and proposes a hybrid visual servo control law that integrates the advantages of IBVS and PBVS, avoiding the shortcomings of a single control mode. Furthermore, the final uniform boundedness and stability of the system under weighted fusion conditions are rigorously proved by using Lyapunov functions, providing theoretical guarantees for the safe and reliable operation of unmanned swarm formations.

[0049] (3) This invention designs a multi-vehicle platooning simulation platform to comprehensively verify the platooning trajectory, wheel speed changes, and error convergence characteristics of the leader and followers. Simulation results show that the method proposed in this invention can effectively prevent the leader from leaving the followers' field of vision, ensuring the stability of multi-vehicle visual platooning control, which is of great significance for promoting the research on intelligent collaborative control of unmanned systems. Attached Figure Description

[0050] Figure 1 This is a flowchart of the method of the present invention.

[0051] Figure 2 A diagram showing the relative positions of leaders and followers.

[0052] Figure 3 This is a leader-following formation diagram provided according to an embodiment of the present invention.

[0053] Figure 4 This is a leader following formation trajectory curve provided according to an embodiment of the present invention.

[0054] Figure 5 This is a leader following formation error curve provided according to an embodiment of the present invention.

[0055] Figure 6 This is an image coordinate error curve provided according to an embodiment of the present invention.

[0056] Figure 7 The diagram shows the Mecanum wheel speed curve of follower 1 according to an embodiment of the present invention.

[0057] Figure 8 The following is a Mecanum wheel speed curve diagram of follower 2 provided according to an embodiment of the present invention.

[0058] Figure 9 The diagram shows the Mecanum wheel speed curve of follower 3 according to an embodiment of the present invention.

[0059] Figure 10 The diagram shows the Mecanum wheel speed curve of follower 4 according to an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0061] Example 1: As Figures 1-10 As shown, according to a first aspect of the present invention, a method for leader-follower formation control of unmanned vehicle swarms based on hybrid visual servoing is provided, comprising the following steps:

[0062] S1. Based on the kinematic model of the Mecanum wheeled mobile unmanned vehicle in the world coordinate system, establish the relationship between relative position and velocity, and expand the expression after differentiating the relative position to obtain the expanded expression.

[0063] Furthermore, the kinematic model of the Mecanum wheeled mobile unmanned vehicle in the world coordinate system is as follows:

[0064]

[0065] Where, x i y i θ i Let i represent the x-axis position, y-axis position, and turning angle of autonomous vehicle i in the world coordinate system; i∈{l,f}, where l represents the leader in the Mecanum wheeled autonomous vehicle swarm, and f represents the follower in the Mecanum wheeled autonomous vehicle swarm (a Mecanum wheeled autonomous vehicle swarm includes one leader and multiple followers); x represents i y i θ i The first derivative of represents the linear velocity and angular velocity in the x and y directions, respectively. v ix v iy Let w represent the linear velocities of the autonomous vehicle i in the x and y directions, respectively. i Represents the angular velocity of driverless vehicle i; rotation matrix R represents the radius of the Mecanum wheel; γ x γ y These represent the front and rear wheelbase and the left and right wheelbase of the autonomous vehicle, respectively; w i1 ,w i2 ,w i3 ,w i4 These represent the rotational speeds of Mecanum wheels 1, 2, 3, and 4 of driverless vehicle i, respectively.

[0066] Based on the kinematic model of the Mecanum wheeled mobile unmanned vehicle in the world coordinate system, the relationship between relative position and velocity is established as follows:

[0067]

[0068] Where, r lf =[x lf y lf ] T x lf y lf θ represents the relative positions of the leader and follower in the x and y directions, respectively; f Indicates the following corner; r l r f These represent the positions of the leader and the follower, respectively; r l =[x l y l ] T r f =[x f y f ] T ;x l y lThis represents the x-axis and y-axis positions of the autonomous vehicle acting as the leader in the world coordinate system; x f y f This indicates the x-axis and y-axis positions of the autonomous vehicle acting as a follower in the world coordinate system.

[0069] For relative position r lf Differentiating, we get:

[0070]

[0071] The expression for the derivative above can be expanded as follows:

[0072]

[0073] Where, θ lf Represents the relative angle between leaders and followers, w f This represents the angular velocity of the autonomous vehicle acting as the leader; v l v f These represent the linear velocity of the leader and the follower, respectively.

[0074] S2. Construct image coordinates based on the expanded expression; design mnemonics based on image coordinates; construct image space relative kinematics equations with respect to image coordinates based on mnemonics; simplify the image space relative kinematics equations to obtain the simplified image space relative kinematics equations.

[0075] Further, S2 includes:

[0076] S2.1. Based on the expanded expression, construct the image coordinates, wherein the image coordinates are constructed as follows:

[0077] 1) In the view of the follower camera, the coordinates of the feature point P on the leader are represented as follows:

[0078] [X,Y,Z] T =[-y lf ,h,x lf ] T ;;

[0079] Where h represents the relative height between the camera's optical center and the feature point P; x lf y lf These represent the relative positions of the leader and followers in the x and y directions, respectively. The relative positions of the leader and followers are as follows: Figure 2 As shown in the figure, the coordinates of the feature points on the leader in the view of the follower camera are as shown in the above formula.

[0080] 2) Based on the coordinates of feature point P, construct the image coordinates s = (m, n) of the feature point mapped to the camera as follows:

[0081]

[0082] In the formula, m and n represent the horizontal and vertical coordinates of the feature points mapped to the camera image, respectively; k m and k n Let m0 and n0 represent the scaling factors in the similar triangle mapping of the camera, respectively, and (m0, n0) represent the position of the principal point of the camera in the camera plane.

[0083] S2.2 Design mnemonics based on image coordinates; construct the image space relative kinematics equations based on the mnemonics and differentiating them with respect to the image coordinates;

[0084] Let the mnemonics p and q be:

[0085]

[0086] Therefore, the relative kinematic equation of image space, differentiated with respect to image coordinates s, is:

[0087]

[0088] From the relative kinematics equations, we know that depth information is not needed. The image space relative kinematics equations can be simplified to:

[0089]

[0090] in, and

[0091] S3. Based on the image coordinates, establish a mathematical model of image coordinate error; preset the performance function; based on the mathematical model of image coordinate error and the preset performance function, establish a transformation error expression; differentiate the transformation error and establish the relationship between the first derivative of the transformation error and the simplified image space relative kinematic equation; through S3, transform the image coordinate error into a bounded error, thereby achieving convergence control of the target image coordinates under the premise of satisfying the field of view constraint;

[0092] Further, S3 includes:

[0093] Based on the image coordinates, a mathematical model for image coordinate error is established. The expression for the mathematical model for image coordinate error is as follows:

[0094] e1 = mm*, e2 = nn*;

[0095] In the formula, m* and n* represent the desired horizontal and vertical coordinates of the image; e1 and e2 represent the horizontal and vertical coordinate errors of the image, respectively.

[0096] Preset performance function ρ k for:

[0097]

[0098] In the formula, L > 0 represents the convergence rate, and ρ ∞ >0 represents the maximum steady-state error, t represents time; k∈{1,2}; when k takes 1, ρ k That is, ρ1, C k Right now C 1, Right now When k is 2, ρ k That is, ρ2, C k Right now C 2, Right now C 1 = m * - m min , C 2 = n * – n min , m max m min Let n represent the maximum and minimum values ​​of the x-coordinate of the camera plane, respectively. max n min These represent the maximum and minimum values ​​of the ordinate of the camera plane, respectively; e represents the natural constant.

[0099] Based on the mathematical model of image coordinate error and the preset performance function, a transformation error expression is established; the transformation error ε k The expression is:

[0100]

[0101] Taking the derivative of the transformation error, we establish the relationship between the first derivative of the transformation error and the simplified equations of relative kinematics of the image space:

[0102]

[0103] Where ε = [ε1, ε2] T ε1 and ε2 represent the transformation errors in the horizontal and vertical directions; J = diag(J1, J2); Compensation term δ = [δ1, δ2] T ,

[0104] S4. Construct a position-based visual servoing PBVS error model;

[0105] The location-based visual servoing error model is as follows:

[0106]

[0107] In the formula, e p1e p2 These represent the relative positional errors of the leader and followers in the x and y directions, respectively; x lf y lf These represent the relative positions of the leader and followers in the x and y directions, respectively. and This represents the expected relative positions of the leader and followers in the x and y directions.

[0108] S5. Based on the transformation error, construct an image-based visual servoing IBVS controller; based on the transformation error and the position-based visual servoing error model, construct a position-based visual servoing PBVS controller; based on the image-based visual servoing controller and the position-based visual servoing controller, fuse them in a weighted manner to construct a hybrid visual servoing controller.

[0109] Further, S5 includes:

[0110] Based on the transformation error, an image-based visual servo controller is constructed. I Designed as follows:

[0111]

[0112] Where K1 and K2 are positive gain matrices;

[0113] Based on the transformation error and the position-based visual servoing error model, a position-based visual servoing controller is constructed. p Designed as follows:

[0114] u p =-(2H(ε) T e p )-1)F -1 J -1 K3e p ;

[0115] in, K3 is a positive gain matrix;

[0116] Based on image-based and position-based visual servo controllers, a hybrid visual servo controller is constructed by weighted fusion. The hybrid visual servo controller u is designed as follows:

[0117] u = [v f ,w f ] T =Du I +(1-D)u p

[0118] Among them, the fusion weight

[0119] The system's uniform eventual boundedness and stability under weighted fusion are proven using the Lyapunov function; the Lyapunov function V is defined as follows:

[0120]

[0121] Assume we have λ min (K2)>σ1, for ε T e p For Lyapunov functions ≥ 0, the derivatives are:

[0122]

[0123] In the formula, c1=2λ min (K1) and Similarly, when ε T e p When <0, there is Therefore, according to Lyapunov's theory, the transformation error is ultimately uniformly bounded, thus completing the proof. The boundedness of the transformation error satisfies the field-of-view constraints within the following range:

[0124] S6. Based on the control method of the present invention, construct as follows: Figure 3 The multi-vehicle platooning simulation platform shown is used, where l represents the leader, and f1, f2, f3, and f4 represent follower 1, follower 2, follower 3, and follower 4, respectively. The initial state of the autonomous vehicles is set, where h = -0.27 and the scaling factor k is... m =k n =616, the position of the principal point of the camera in the camera plane is m0=320, n0=240, the convergence rate is L=0.6, and the maximum steady-state error is ρ. ∞ =10, positive gain matrix K1=diag(4,3), K2=diag(0.005,0.002), K3=diag(0.8,0.4), expected relative position between leader and follower 1. and [1, -0.3] T The expected relative position between leader and follower 2 and The range is [1, -0.2]. T The expected relative position between the leader and the follower 3 and [1, 0.2] T The expected relative position between the leader and the followers 4 and [1, 0.3] TThe leader's initial position in the x and y directions is (2, 4), and the initial positions of followers 1, 2, 3, and 4 in the x and y directions are (0, 8), (0, 6), (0, 2), and (0, 0), respectively; Figure 4 As shown, the horizontal and vertical axes represent the positions in the x and y directions, respectively. The Leader trajectory represents the movement trajectory of the leader, the Trajectory of Follower1 represents the movement trajectory of follower 1, the Trajectory of Follower2 represents the movement trajectory of follower 2, the Trajectory of Follower3 represents the movement trajectory of follower 3, and the Trajectory of Follower4 represents the movement trajectory of follower 4. Figure 5 As shown, the horizontal axis represents formation time, and the vertical axis represents formation error. Formation error 1 represents the formation error curve for follower 1, Formation error 2 represents the formation error curve for follower 2, Formation error 3 represents the formation error curve for follower 3, and Formation error 4 represents the formation error curve for follower 4. From these formation error curves, it can be seen that the formation errors of follower 1, follower 2, follower 3, and follower 4 are all close to 0. Figure 6 As shown, the horizontal axis represents formation time, and the vertical axis represents image coordinate error (i.e., e1 or e2). Specifically, Them-error of follower 1 represents the horizontal coordinate error curve of follower 1's image, The n-error of follower 1 represents the vertical coordinate error curve of follower 1's image, The m-error of follower 2 represents the horizontal coordinate error curve of follower 2's image, The n-error of follower 3 represents the horizontal coordinate error curve of follower 3's image, The n-error of follower 4 represents the horizontal coordinate error curve of follower 4's image, and The n-error of follower... 4 represents the image coordinate error curve of follower 4. From the above image coordinate error curve, it can be seen that the image coordinate error curves of follower 1, follower 2, follower 3, and follower 4 are all close to 0, ensuring that the leader's feature points are always within the camera's field of view of followers 1, 2, 3, and 4, thus satisfying the field of view constraint; as shown... Figure 7As shown, the horizontal axis represents formation time, and the vertical axis represents the Mecanum wheel speeds. Specifically, the rotational speed of wheel 1 on follower 1 represents the speed of wheel 1 of follower 1, the rotational speed of wheel 2 on follower 1 represents the speed of wheel 2 of follower 1, the rotational speed of wheel 3 on follower 1 represents the speed of wheel 3 of follower 1, and the rotational speed of wheel 4 on follower 1 represents the speed of wheel 4 of follower 1. Figure 8 As shown, the horizontal axis represents the formation time, and the vertical axis represents the rotational speed of the wheels. Specifically, the rotational speed of wheel 1 on follower 2 represents the rotational speed of wheel 1 of follower 2, the rotational speed of wheel 2 on follower 2 represents the rotational speed of wheel 2 of follower 2, the rotational speed of wheel 3 on follower 2 represents the rotational speed of wheel 3 of follower 2, and the rotational speed of wheel 4 on follower 2 represents the rotational speed of wheel 4 of follower 2. Figure 9 As shown, the horizontal axis represents the formation time, and the vertical axis represents the rotational speed of the wheels. Specifically, "The rotational speed of wheel 1 on follower 3" represents the rotational speed of wheel 1 of follower 3, "The rotational speed of wheel 2 on follower 3" represents the rotational speed of wheel 2 of follower 3, "The rotational speed of wheel 3 on follower 3" represents the rotational speed of wheel 3 of follower 3, and "The rotational speed of wheel 4 on follower 3" represents the rotational speed of wheel 4 of follower 3. Figure 10As shown, the horizontal axis represents the formation time, and the vertical axis represents the rotational speed of the wheels. Specifically, "The rotational speed of wheel 1 on follower 4" represents the rotational speed of wheel 1 of follower 4, "The rotational speed of wheel 2 on follower 4" represents the rotational speed of wheel 2 of follower 4, "The rotational speed of wheel 3 on follower 4" represents the rotational speed of wheel 3 of follower 4, and "The rotational speed of wheel 4 on follower 4" represents the rotational speed of wheel 4 of follower 4. Figures 7-10 It is understood that the hybrid vision servo controller designed in this invention can be used on Mecanum vehicles.

[0125] As can be seen from the above technical solution, the present invention can achieve stable formation control that meets the field of view constraints without relying on depth information, effectively improving the collaborative operation capability of unmanned swarms in complex environments, and has important theoretical value and application significance.

[0126] According to a second aspect of the present invention, a terminal device is provided, including a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0127] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for controlling a leader-follower formation of a swarm of unmanned vehicles based on hybrid visual servoing, the method comprising: The application relates to a method for constructing a mixed visual servo controller for a wheeled mobile unmanned vehicle cluster. S1, for a wheeled mobile unmanned vehicle cluster, a kinematic model of a wheeled mobile unmanned vehicle in a world coordinate system is established; according to the kinematic model of the wheeled mobile unmanned vehicle in the world coordinate system, a relative position and velocity relationship is established, and the relative position is derived to obtain an expanded expression; wherein the wheeled mobile unmanned vehicle cluster comprises a leader and a plurality of followers; S2, according to the expanded expression, an image coordinate is constructed; a mnemonic symbol is designed according to the image coordinate; an image space relative kinematic equation for deriving the image coordinate is constructed according to the mnemonic symbol; the image space relative kinematic equation is simplified to obtain a simplified image space relative kinematic equation; S3, according to the image coordinate, an image coordinate error mathematical model is established; a performance function is preset; a transformation error expression is established according to the image coordinate error mathematical model and the preset performance function; the transformation error is derived to establish a relationship between the first-order derivative of the transformation error and the simplified image space relative kinematic equation; S4, a visual servo error model based on position is constructed; S5, according to the transformation error, an image-based visual servo controller is constructed; according to the transformation error and the visual servo error model based on position, a visual servo controller based on position is constructed; the image-based visual servo controller and the visual servo controller based on position are fused in a weighted manner to construct a mixed visual servo controller.

2. The hybrid visual servo based swarm leadership formation control method of unmanned vehicles according to claim 1, wherein, The S2 comprises: S2.1, according to the expanded expression, an image coordinate is constructed, and the construction of the image coordinate is as follows: 1) in the camera field of view of the follower, the coordinates of the feature point P on the leader are expressed as: [X, Y, Z] T = [-y lf , h, x lf ] T ; where h represents the relative height between the optical center of the camera and the feature point P; x lf , y lf respectively represent the relative positions of the leader and the follower in the x direction and the y direction; 2) according to the coordinates of the feature point P, the image coordinates s=(m, n) of the feature point mapping to the camera are constructed as: In the formula, m, n respectively represent the horizontal and vertical coordinates of the feature point mapping to the image of the camera; k m and k n respectively represent the scaling factors in the camera similar triangle mapping, and (m0, n0) represents the position of the principal point of the camera in the camera plane. S2.2, a mnemonic symbol is designed according to the image coordinate; an image space relative kinematic equation for deriving the image coordinate is constructed according to the mnemonic symbol; Let the mnemonic symbol be: Therefore, the image space relative kinematic equation for deriving the image coordinate s is: The simplified image space relative kinematic equation is: where θ lf represents the relative angle between the leader and the follower, v l , v f represent the linear velocities of the leader and the follower, respectively; w f represents the angular velocity of the leader; F, f represent intermediate variables.

3. The hybrid visual servo based swarm leadership formation control method of unmanned vehicles according to claim 1, wherein, The preset performance function p k is: where L > 0 is the rate of convergence, p > 0 is the maximum steady state error, and t represents time. ∞ where L > 0 is the rate of convergence, p > 0 is the maximum steady state error, and t represents time. k e {1,2}; when k takes 1, C k i.e. C 1, i.e. when k takes 2, C k i.e. C 2, i.e. C 1 = m* - m min , C 2 = n* - n min , m*, n* denote the desired image horizontal and vertical coordinates; m max , m min denote the maximum and minimum values of the horizontal coordinates of the camera plane, n max , n min denote the maximum and minimum values of the vertical coordinates of the camera plane.

4. The hybrid visual servo based swarm leadership formation control method of unmanned vehicles according to claim 3, wherein, The transformation error expression is: where εk, k = 1, 2, is the transformation error in the k direction, and e k i.e., ε1, represents the transformation error in the lateral direction; when k takes 2, ε k i.e., ε2, represents the transformation error in the longitudinal direction; when k takes 1, e k i.e., e1, e1 represents the image lateral coordinate error; when k takes 2, e k i.e., e2, e2 represents the image longitudinal coordinate error.

5. The hybrid visual servo based swarm leadership formation control method of unmanned vehicles according to claim 1, wherein, The image-based visual servoing controller u I is designed to: where K1 and K2 are positive gain matrices; ε = [ε1, ε2] T , ε1, ε2 represent the transformation errors in the horizontal and vertical directions; F represents an intermediate variable; D represents a fusion weight; and J is a diagonal matrix.

6. The hybrid visual servoing based swarm leadership formation control method of unmanned vehicles according to claim 5, wherein, The position-based visual servoing controller u p is designed to: u p = - (2H(ε T e p ) - 1)F -1 J -1 K3e p ; wherein, K3 is a positive gain matrix; e p1 , e p2 respectively represent the relative position error of the leader and the follower in the x direction, y direction.

7. The hybrid visual servoing based swarm leadership formation control method of unmanned vehicles according to claim 6, wherein, The mixed visual servo controller u is designed as: u = [v f ,w f ] T = Du I +(1-D)u p where v f represents the linear velocity of the follower; w f represents the angular velocity of the leader.

8. A terminal device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program is configured to cause the processor to perform the method of any one of claims 1 to 7. The processor executes the computer program to realize the steps of the method in any one of claims 1-7.

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