Position visual servo control method based on tan type obstacle function

Abstract

The invention discloses a position visual servo control method based on a tan type obstacle function, and the method comprises the steps: constructing a position-based visual servo experiment platform of a mechanical arm, and the visual servo platform comprises the mechanical arm and a depth camera; performing target tracking on the selected target by using a depth camera, and estimating the 6D pose of the selected target; performing difference calculation on the 6D pose estimation result and a set expected pose to obtain pose error information of the selected target; according to a tan type Lyapunov barrier function, a PBVS controller based on the position is obtained; the PBVS controller based on the position calculates the movement speed of the depth camera according to the pose error information; and the joint speed of the mechanical arm is obtained according to the movement speed of the depth camera, the speed of the joints of the mechanical arm is adjusted till the selected target completely coincides with the expected pose, and visual servo control is completed. According to the method, the tan type obstacle function is introduced to restrain the pose error, so that the convergence time is shortened, and the steady-state and transient performance is improved.

Description

Technical Field

[0001] This application relates to the technical field of position-based visual servo control of robotic arms, and more particularly, to a position visual servo control method based on a tan-type barrier function. Background Technique

[0002] The visual servo control system involves knowledge and technologies in multiple fields such as machine vision technology, robotics principles, and control principles. This method drives the robot control system through visual information to adjust the robot's posture, trajectory, or position to achieve tasks such as visual positioning or trajectory tracking. In the past two decades, numerous researchers at home and abroad have conducted extensive research on this. Compared with sensorless systems, visual servo systems exhibit higher precision and flexibility when performing unstructured tasks.

[0003] Image-based visual servo uses the difference between the image features observed by the camera in real time and the desired features to control the robotic arm, moving the features in the camera image to the desired position. The advantage of this method is that it is insensitive to changes in the camera's internal parameters and image noise, but it also has the problem of singularity of the image Jacobian matrix, and due to the lack of control of the robotic arm in the Cartesian space, it may lead to the inability to achieve the trajectory in the Cartesian space. Position-based visual servo uses the known target geometric model and camera model, combined with the extracted image features, to estimate the position and posture of the target in the three-dimensional Cartesian coordinate system. The control error is the difference between the desired pose and the current pose of the target in the Cartesian space. This method realizes pose control in the Cartesian space, can directly specify control tasks in the Cartesian space, and is insensitive to changes in light intensity. In practical tasks that require quick response and execution, it is necessary to improve the dynamic response performance of the robot, that is, to increase the speed during the visual servo process of the robotic arm and reduce the time during the visual servo process.

[0004] To address the problem of slow convergence speed in the visual servo process, some scholars have proposed a visual servo controller with adaptive control gain parameters. This controller performs excellently in three-degree-of-freedom positioning and path tracking tasks and can provide high positioning accuracy. However, the system convergence time of this method is relatively long, resulting in low efficiency. Some scholars have proposed a new method of position-based visual servo, which is based on gradient descent and sliding mode control methods, and finally realizes the estimation of the transformation from the camera to the mobile robot while controlling the mobile robot. Some scholars have proposed a gain adaptive adjustment method, which uses reinforcement learning to dynamically adjust the gain parameters in the visual servo control rate to optimize the system performance, thereby improving the system convergence speed. Some scholars have designed a controller based on the Barrier Lyapunov Function (BLF). By introducing a logarithmic BLF to constrain the controller and improve the system performance, but this method cannot achieve arbitrary-precision tracking. The controllers studied above can only ensure the stability or asymptotic stability of the system, and the convergence speed is limited. Therefore, when performing high-precision visual servo tasks, the convergence speed still faces challenges. Summary of the Invention

[0005] To solve the above problems, the present application provides a position visual servo control method based on a tan-type barrier function, aiming to constrain the error in the Cartesian space through the tan-type Lyapunov barrier function to improve its steady-state and transient performance.

[0006] The first aspect of the embodiment of the present invention provides a position visual servo control method based on a tan-type barrier function, including: constructing a position-based visual servo experimental platform for the robotic arm, the visual servo platform including a robotic arm and a depth camera; using the depth camera to perform target tracking on a selected target and estimating the 6D pose of the selected target; calculating the difference between the 6D pose estimation result and the set desired pose to obtain the pose error information of the selected target; obtaining a position-based PBVS controller according to the tan-type Lyapunov barrier function; the position-based PBVS controller calculates the motion speed of the depth camera according to the pose error information; obtaining the joint speed of the robotic arm according to the motion speed of the depth camera, and adjusting the speed of the robotic arm joints until the selected target completely coincides with the desired pose, completing the visual servo control.

[0007] In an optional embodiment, the tan-type Lyapunov barrier function is as follows:

[0008]

[0009] where V m is the Lyapunov function, Γ m is the specified performance function, e mis the error of the system.

[0010] In an alternative embodiment, the position-based PBVS controller is:

[0011]

[0012] where V c is the motion speed of the depth camera, α is a positive control gain, is the pseudo-inverse of the interaction matrix K e and Ψ m is a parameter, and E m is a parameter.

[0013] In an alternative embodiment, it further includes evaluating the position-based PBVS controller based on a classical PBVS controller:

[0014]

[0015] where is the reciprocal of the Lyapunov function of V m l ∈ {p a}, p is the position, a is the attitude, is the error, i = 1, 2, 3, is a function that decays exponentially with time;

[0016] When there exists a minimum value ε s,0 > 0, V m ≤ ε s,0 holds for all cases, such that 0 ≤ V m (t) ≤ ε s,0 and hold, and the position-based PBVS controller represented by formula (2) is asymptotically stable.

[0017] In an alternative embodiment, the robotic arm is a 6-degree-of-freedom ur16e robotic arm, and the depth camera is an Intel RealSense D435i depth camera installed in the "eye-in-hand" manner

[0018] In an alternative embodiment, in the field of view of the depth camera, when the projections of the selected target in the x, y, and z-axis directions on the image coordinate system coincide with the desired pose in the image, the selected target completely coincides with the desired pose, and visual servo control is completed.

[0019] The second aspect of the embodiments of the present invention provides a position visual servo control device based on a tan-type barrier function, and the device includes:

[0020] Platform construction module: used to construct a position-based visual servo experimental platform for a robotic arm, and the visual servo platform includes a robotic arm and a depth camera;

[0021] Pose estimation module: used to perform target tracking on a selected target using a depth camera, and estimate the 6D pose of the selected target; calculate the difference between the 6D pose estimation result and the set desired pose to obtain the pose error information of the selected target;

[0022] Controller construction module: used to obtain a position-based PBVS controller according to the tan-type Lyapunov barrier function; the position-based PBVS controller calculates the motion speed of the depth camera according to the pose error information;

[0023] Servo control module: used to obtain the joint speed of the robotic arm according to the motion speed of the depth camera, and adjust the speed of the robotic arm joints until the selected target completely coincides with the desired pose, completing the visual servo control.

[0024] In the third aspect of the embodiments of the present invention, an electronic device is provided, which is characterized by including: a processor, a memory, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements a position visual servo control method based on the tan-type barrier function.

[0025] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which is characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it is a position visual servo control method based on the tan-type barrier function.

[0026] In the embodiments of the present disclosure, the present method designs a position-based PBVS controller based on the tan-type barrier function. The classical PBVS controller shows the disadvantages of poor transient performance and long convergence time during the visual servo process. By introducing the tan-type barrier function to constrain the pose error, the convergence time is shortened, and the stability of the system is proved by the second method of Lyapunov. The controller designed in this article is compared with the classical PBVS controller, and the convergence time of the controller designed in this article is reduced by about 40% compared with the classical PBVS controller. Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a flowchart of a position visual servo control method based on a tan-type barrier function proposed in an embodiment of the present application;

[0029] Figure 2 It is a diagram of a robotic arm visual servo experimental platform proposed in an embodiment of the present application;

[0030] Figure 3 It is a field of view diagram of a depth camera proposed in an embodiment of the present application;

[0031] Figure 4 (a) is the total error curve of a classical PBVS controller proposed in an embodiment of the present application;

[0032] Figure 4 (b) is the total attitude error curve of a classical PBVS controller proposed in an embodiment of the present application;

[0033] Figure 5 (a) is the total error curve of a controller based on a tan-type barrier function proposed in an embodiment of the present application;

[0034] Figure 5 (b) is the total attitude error curve of a controller based on a tan-type barrier function proposed in an embodiment of the present application;

[0035] Figure 6 It is a schematic diagram of an electronic device of the present application. Detailed implementation manners

[0036] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0037] Please refer to Figure 1 , Figure 1 It is a flowchart of a position visual servo control method based on a tan-type barrier function proposed in an embodiment of the present application. As Figure 1As shown in the figure, the three-phase line sequence detection method based on multi-dimensional visual analysis includes: constructing a vision servo experimental platform based on the position of the robotic arm, where the vision servo platform includes a robotic arm and a depth camera; using the depth camera to perform target tracking on a selected target and estimating the 6D pose of the selected target; calculating the difference between the 6D pose estimation result and the set desired pose to obtain the pose error information of the selected target; obtaining a position-based PBVS controller according to the tan-type Lyapunov barrier function; the position-based PBVS controller calculates the motion speed of the depth camera according to the pose error information; obtaining the joint speed of the robotic arm according to the motion speed of the depth camera, and adjusting the joint speed of the robotic arm until the selected target completely coincides with the desired pose, completing the vision servo control.

[0038] In this embodiment, the vision servo experimental platform is the basic hardware support of the entire control system, mainly composed of a robotic arm and a depth camera. The robotic arm is responsible for performing actual actions, while the depth camera is used as a key visual perception device to obtain environmental information. Through the checkerboard calibration method, the internal and external parameter matrices of the depth camera are obtained. The external parameters of the depth camera are used for the specific parameters of the "eye-in-hand" installation of the camera, and the internal parameters of the depth camera are used for pose estimation.

[0039] By using the Jacobian matrix to describe the mapping relationship between the joint space and the end effector, and obtaining the joint speed of the robotic arm according to the motion speed of the depth camera.

[0040] Furthermore, the tan-type Lyapunov barrier function is as follows:

[0041]

[0042] In the formula, V m is the Lyapunov function, Γ m is the specified performance function, and e m is the error of the system.

[0043] In this embodiment, when the error is large, the derivative of the tan function is large and the control input is strong, which helps to quickly reduce the position error; when the error is small, the tan function becomes smooth, thus avoiding over-adjustment and ensuring the accuracy of the system.

[0044] When there are no limiting conditions, formula (1) can be calculated using L'Hopital's rule:

[0045]

[0046] When the system has infinite constraints, the tan-type Lyapunov barrier function can be replaced by a quadratic form, which is equivalent to the system having no constraints. Therefore, the tan-type Lyapunov barrier function is applicable in both constrained and unconstrained cases.

[0047] Furthermore, the position-based PBVS controller is as follows:

[0048]

[0049] wherein, V c is the motion speed of the depth camera, α is a positive control gain, is the pseudo-inverse of the interaction matrix K e , Ψ m is a parameter, and E m is a parameter.

[0050] In this embodiment, the parameter Ψ m and the parameter E m are as follows:

[0051]

[0052] wherein, is a function with time decaying exponentially:

[0053]

[0054] wherein, η is a parameter used to adjust the error convergence speed, and is usually set between 0.1 and 0.2 to ensure the smoothness of the performance function, and are constants. When the visual servo is at a distance of 0.6 m from the target, the field of view of the camera is 0.82×0.46 m, needs to be greater than the maximum value of the position error, and is usually taken as 1. The attitude error range of the target is (-π, π), needs to be greater than the maximum value of the attitude error, and is usually taken as π. and are parameters for adjusting the magnitudes of the position error and the attitude error respectively, and are usually taken as 0.001 and 0.01. When t→∞, the value of the performance function approaches and The actual position and attitude errors are less than the preset error thresholds and

[0055] Furthermore, it further includes evaluating the position-based PBVS controller based on the classical PBVS controller:

[0056]

[0057] wherein, is the reciprocal of the Lyapunov function of V m , l∈{p a}, p represents the position, and a represents the attitude, is the error, i = 1, 2, 3, is a function of time decaying exponentially;

[0058] When there exists a minimum value ε s,0 > 0, V m ≤ ε s,0 holds for all cases, such that 0 ≤ V m (t) ≤ ε s,0 and holds, and the position-based PBVS controller represented by formula (2) is asymptotically stable.

[0059] In this embodiment, the existing classical PBVS controller is:

[0060] Let the current depth camera coordinate system be O cm , the desired depth camera coordinate system be O ce , the object coordinate system be O a , and the coordinates of the object coordinate system relative to the current depth camera coordinate system be The coordinates of the origin of the object coordinate system relative to the desired camera coordinate system are

[0061] The calculated minimized error is e m :

[0062]

[0063] where

[0064]

[0065] In the formula, s m is the current visual feature vector, θu is the rotation angle, s e is the desired visual feature vector;

[0066] The calculated relationship between the object motion and feature change in the depth camera coordinate system is:

[0067]

[0068] where

[0069] V c = (v c , ω c )

[0070] In the formula, K s is the interaction matrix associated with the image feature vector s, V c is the spatial velocity of the depth camera, where v c is the instantaneous linear velocity of the depth camera, ω c is the instantaneous angular velocity of the depth camera;

[0071] Calculate the relationship between the depth camera speed and the minimized error over time:

[0072]

[0073] Among them,

[0074]

[0075] In the formula, I3 is the 3-order identity matrix, and [A] × is the skew-symmetric matrix of A;

[0076] The error decreases exponentially:

[0077]

[0078] Among them,

[0079]

[0080] The classical PBVS controller is a traditional control method in the field of visual servo. By comparing it with the position-based PBVS controller, please refer to Figure Figure 4 (a), Figure 4 (b), Figure 5 (a) and Figure 5 (b), where Figure 4 is the total error curve of the classical PBVS controller, Figure 4 (b) is the total attitude error curve of the classical PBVS controller, Figure 5 (a) is the total error curve of the controller based on the tan-type barrier function, Figure 5 (b) is the total attitude error curve of the controller based on the tan-type barrier function. It can be seen that in the initial stage of visual servo, the control method designed in this application shows better dynamic response, and the visual feature error converges faster. The experimental results verify the effectiveness of the method designed in this paper and confirm that the visual servo control method based on the tan-type barrier Lyapunov has better steady-state and transient performance.

[0081] Furthermore, the robotic arm is a 6-degree-of-freedom ur16e robotic arm, and the depth camera is an Intel RealSense D435i depth camera installed in the "eye-in-hand" manner.

[0082] In this embodiment, please refer to Figure 2 , Figure 2 is the robotic arm visual servo experimental platform proposed in an embodiment of this application, as shown in Figure 2As shown, the 6 - degree - of - freedom design enables the robotic arm to achieve flexible movement in three - dimensional space. The "eye - in - hand" installation method allows the camera to move with the robotic arm, obtaining visual information near the end of the robotic arm in real time. The Intel RealSense D435i depth camera has depth perception ability and image acquisition performance, and can quickly and accurately obtain the depth information of objects in the surrounding environment and high - quality color images.

[0083] Furthermore, in the field of view of the depth camera, when the projections of the x, y, and z axes of the selected target in the image coordinate system coincide with the desired pose in the image, the selected target completely coincides with the desired pose, and visual servo control is completed.

[0084] In this embodiment, please refer to Figure 3 , Figure 3 is the field - of - view diagram of the depth camera proposed in an embodiment of the present application. As shown in Figure 3 , in the image coordinate system, the desired pose is presented in a specific projection form. The yellow arrow represents the projection of the desired pose in the image coordinate system, while the x, y, and z axes of the selected target are respectively marked by red, green, and blue arrows. When these arrows completely coincide in the image, it means that the selected target is highly consistent with the desired pose in terms of spatial position and attitude angle. At this time, it can be determined that the selected target completely coincides with the desired pose. The achievement of this coincidence state marks the successful completion of visual servo control, and the robotic arm also accurately reaches the desired position, completing the predetermined visual servo task.

[0085] The embodiment of the present disclosure also provides a position - based visual servo control device based on the tan - type barrier function. The device includes:

[0086] Platform construction module: used to construct a position - based visual servo experimental platform for the robotic arm. The visual servo platform includes a robotic arm and a depth camera;

[0087] Pose estimation module: used to track the selected target with the depth camera and estimate the 6D pose of the selected target; calculate the difference between the 6D pose estimation result and the set desired pose to obtain the pose error information of the selected target;

[0088] Controller construction module: used to obtain a position - based PBVS controller according to the tan - type Lyapunov barrier function; the position - based PBVS controller calculates the movement speed of the depth camera according to the pose error information;

[0089] Servo control module: used to obtain the joint speed of the robotic arm according to the movement speed of the depth camera, adjust the speed of the robotic arm joints until the selected target completely coincides with the desired pose, and complete visual servo control.

[0090] Embodiments of the present disclosure also provide an electronic device. Please refer to Figure 6 , Figure 6 which is a schematic diagram of the electronic device shown in the embodiments of the present disclosure. As Figure 6 shown, the electronic device 100 includes: a memory 110 and a processor 120. The memory 110 and the processor 120 are communicatively connected via a bus. A computer program is stored in the memory 110, and the computer program can run on the processor 120, thereby implementing the steps in the position visual servo control method based on the tan-type barrier function disclosed in the embodiments of the present disclosure.

[0091] Embodiments of the present disclosure also provide a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of a computer device, the computer device can execute the steps in the position visual servo control method based on the tan-type barrier function as in the embodiments of the present disclosure.

[0092] In summary, in the field of position-based visual servo control of robotic arms, the classical PBVS controller has relatively obvious defects during actual operation. Its transient performance is poor, meaning that at the initial stage of visual servo, the system's response to errors is not fast enough to make effective adjustment actions quickly. At the same time, the convergence time is long, which significantly increases the time required for the robotic arm to complete the visual servo task and seriously affects work efficiency.

[0093] However, the PBVS controller based on the tan-type barrier function designed in this application effectively overcomes the above problems. This controller innovatively introduces the tan-type barrier function, which precisely constrains the pose error through its unique function characteristics. When the error is large, the tan-type barrier function can generate a strong control input to prompt the robotic arm to quickly adjust its posture, significantly accelerating the speed of error reduction. When the error gradually becomes small, the function characteristics can make the adjustment process smooth, avoiding the impact of over-adjustment on system accuracy. The Lyapunov second method is also used to strictly prove the stability of the system. In this way, the convergence time of the designed controller is reduced by about 40%, greatly improving the work efficiency of the visual servo system, enabling the robotic arm to complete tasks in a shorter time, and bringing higher benefits to related application fields.

[0094] Embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a device for implementing the functions specified in multiple blocks.

[0095] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or the functions specified in multiple blocks.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, such that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or the functions specified in multiple blocks.

[0097] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0098] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0099] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0100] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising said element.

[0101] The above provides a detailed introduction to a position visual servo control method based on a tan-type barrier function. In this article, specific examples are used to elaborate on the principle and implementation of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A position visual servo control method based on a tan type obstacle function, characterized in that: include: S1. Construct a position-based visual servo experimental platform for a robotic arm, wherein the visual servo platform includes a robotic arm and a depth camera; S2. Use a depth camera to track the selected target and estimate the 6D pose of the selected target; perform difference calculation between the 6D pose estimation result and the set expected pose to obtain the pose error information of the selected target; S3. According to the tan-type Lyapunov barrier function, a position-based PBVS controller is obtained; the position-based PBVS controller calculates the movement speed of the depth camera according to the posture error information; S4. The joint speed of the robot arm is obtained according to the movement speed of the depth camera, and the speed of the robot arm joint is adjusted until the selected target completely coincides with the expected posture, thus completing the visual servo control.

2. The position visual servo control method based on tan type obstacle function according to claim 1 is characterized in that: The tan-type Lyapunov barrier function is as follows: Where V m is the Lyapunov function, Γ m is the specified performance function, e m is the system error.

3. The position visual servo control method based on tan type obstacle function according to claim 2 is characterized in that: The location-based PBVS controller is: Where V c is the speed of the depth camera, α is the positive control gain, is the interaction matrix K e The pseudo-inverse of m is the parameter, E m as a parameter.

4. The position visual servo control method based on tan type obstacle function according to any one of claims 2-3, characterized in that: The step 3 also includes evaluating the position-based PBVS controller based on the classic PBVS controller: In the formula, V m The inverse of the Lyapunov function, l∈{pa}, p is the position, a is the posture, e i l is the error, i=1,2,3, is a function that decays exponentially with time; When there is a minimum value ε s,0 >0, V m ≤ε s,0 This holds true for all cases, such that 0≤V m (t)≤ε s,0 and It holds true that the position-based PBVS controller represented by formula (2) is asymptotically stable.

5. The position visual servo control method based on tan type obstacle function according to claim 1, characterized in that: The robotic arm is a 6-DOF ur16e robotic arm, and the depth camera is an Intel RealSense D435i depth camera installed in an "eye on hand" manner.

6. The position visual servo control method based on tan type obstacle function according to claim 1, characterized in that: In the depth camera's field of view, when the x, y, and z-axis directions of the selected target coincide with the projection of the desired pose in the image coordinate system in the image, the selected target completely coincides with the desired pose, completing visual servo control.

7. The position visual servo control device based on tan type obstacle function according to any one of claims 1 to 6, characterized in that: The device comprises: Platform construction module: used to construct a position-based visual servo experimental platform for a robotic arm, wherein the visual servo platform includes a robotic arm and a depth camera; Pose estimation module: used to track the selected target using a depth camera and estimate the 6D pose of the selected target; the difference between the 6D pose estimation result and the set expected pose is calculated to obtain the pose error information of the selected target; A controller building module: used to obtain a position-based PBVS controller according to a tan-type Lyapunov obstacle function; the position-based PBVS controller calculates the motion speed of the depth camera according to the posture error information; Servo control module: used to obtain the joint speed of the robotic arm according to the movement speed of the depth camera, and adjust the speed of the robotic arm joints until the selected target completely coincides with the desired posture, thus completing the visual servo control.

8. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the position visual servo control method based on a tan-type obstacle function as described in any one of claims 1 to 6 when executing the program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the position visual servo control method based on a tan-type obstacle function as described in any one of claims 1 to 6 is implemented.

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