A robust adaptive variable admittance control method for shear thickening fluid in feature space

Abstract

The present application relates to a method for robust adaptive shear thickening fluid variable admittance control in feature space. The method comprises: first, establishing a coordinate system of visual servo feature space and Cartesian space; then, determining the mapping relationship between actual contact force and virtual contact force using the kinematic and dynamic model of the manipulator based on the coordinate system; then, determining the robust adaptive damping coefficient based on the shear thickening fluid based on the maximum velocity feature point, and determining the adaptive stiffness coefficient based on the actual contact force change; then, determining the robust adaptive shear thickening fluid admittance control in feature space based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient; then, determining the visual servo control law based on the robust adaptive shear thickening fluid admittance control in feature space; finally, determining the real-time joint velocity of the manipulator based on the real-time image feature points and the visual servo control law, and controlling the movement of the manipulator.

Description

Technical Field

[0001] The present application relates to the technical field of human-machine collaborative control in a robot image visual servoing feature space, and in particular to a robust adaptive shear thickening fluid variable admittance control method in a feature space. Background Art

[0002] For robot control systems, external impact forces have a serious impact on robot control systems. For example, in robot application systems such as visual servoing and physical human-machine collaboration, the instantaneous disturbance caused by impact forces will challenge the robustness of the control system, causing the system to oscillate, become unstable, or even lose control, affecting the dynamic performance of the system. In addition, the system instability caused by impact forces can lead to misoperation and delays during human-machine interaction, reducing the user experience comfort. Operators need to frequently adjust and correct systems affected by impact forces, increasing operational difficulty and workload. Therefore, it is crucial to design a robust robot control system to cope with uncertain external impact forces.

[0003] In the prior art, control systems based on robustness are usually designed by using methods such as admittance control, impedance control, or equipping robots with visual sensors to perceive unstructured task environments. Admittance control only requires the contact force obtained by force sensors to achieve precise control of robot motion, and has certain stability and flexibility. Image visual servoing based on visual sensors uses cameras to obtain image information in the environment, and realizes robot motion control and positioning based on this information. However, for linear or nonlinear admittance control, it is difficult to distinguish whether the applied force is traction or impact, resulting in difficulty in balancing compliance and robustness. For the method of applying visual sensors, when the parameters of visual servoing are uncertain, the model will be inaccurate, thereby affecting the control performance. Therefore, in tasks in unstructured environments, it is often difficult to achieve a satisfactory balance between compliance and robustness by relying solely on the information provided by force sensors or visual sensors to realize robot control.

[0004] Therefore, in the related technology, there is an urgent need for a method that can improve the flexibility and robustness of the robot control system. Summary of the invention

[0005] Based on this, it is necessary to provide a robust adaptive shear thickening fluid variable admittance control method in feature space that can improve the flexibility and robustness of the robot control system in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for controlling variable admittance of a robust adaptive shear thickening fluid in a feature space. The method comprises:

[0007] Establish the coordinate system of visual servoing feature space and Cartesian space;

[0008] Based on the coordinate system, a kinematic and dynamic model of the robot arm is used to determine a mapping relationship between the actual contact force and the virtual contact force;

[0009] Determining a robust adaptive damping coefficient based on a shear thickening fluid based on the maximum velocity characteristic point, and determining an adaptive stiffness coefficient based on the actual contact force change;

[0010] Determining the robust adaptive shear thickening fluid admittance control in the characteristic space based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient;

[0011] Determining a visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space;

[0012] The real-time joint speed of the robot arm is determined based on the real-time image feature points and the visual servo control law to control the movement of the robot arm.

[0013] Optionally, in one embodiment of the present application, determining the mapping relationship between the actual contact force and the virtual contact force based on the coordinate system using the robot arm kinematics and dynamics model includes:

[0014] Determine the second-order kinematic model of visual servoing based on the kinematic model of the robot arm and the first-order kinematic model of visual servoing;

[0015] The mapping relationship between the actual contact force and the virtual contact force is determined based on the visual servo second-order kinematics model and the robot arm dynamics model.

[0016] Optionally, in one embodiment of the present application, the robust adaptive damping coefficient based on shear thickening fluid is expressed as:

[0017]

[0018] Among them, μ and n are adjustable parameters. It is the characteristic point with maximum velocity.

[0019] Optionally, in one embodiment of the present application, the adaptive stiffness coefficient is expressed as:

[0020]

[0021] in, e f e is the force of the end effector of the robot arm, and diag represents a diagonal matrix.

[0022] Optionally, in one embodiment of the present application, the robust adaptive shear thickening fluid admittance control in the feature space is defined as:

[0023]

[0024] Among them, M vs , D vs , K vs are the virtual mass, damping and stiffness in the feature space respectively; e vs is the feature point error; is the acceleration of the feature point error; is the speed of feature point error; s d is the expected feature point position set initially, s w is the position of the feature point moved by the external contact force.

[0025] Optionally, in one embodiment of the present application, the determining of the visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space comprises:

[0026] Determining a visual servo controller error based on the desired feature point and the current feature point;

[0027] A visual servo control law is determined based on the visual servo controller error and a visual servo first-order kinematics model.

[0028] In a second aspect, the present application also provides a robust adaptive shear thickening fluid variable admittance control device in a characteristic space. The device comprises:

[0029] A coordinate system establishment module is used to establish the coordinate system of the visual servo feature space and the Cartesian space;

[0030] A mapping relationship determination module, used to determine the mapping relationship between the actual contact force and the virtual contact force based on the coordinate system using the robot arm kinematics and dynamics model;

[0031] A robust adaptive damping coefficient and adaptive stiffness coefficient determination module, used for determining a robust adaptive damping coefficient and an adaptive stiffness coefficient based on a shear thickening fluid based on a maximum velocity characteristic point;

[0032] An admittance control determination module, used for determining the robust adaptive shear thickening fluid admittance control in the characteristic space based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient;

[0033] A visual servo control law determination module, used for determining the visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space;

[0034] The robot arm motion control module is used to determine the real-time joint speed of the robot arm based on the real-time image feature points and the visual servo control law, and control the movement of the robot arm.

[0035] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the methods described in the above embodiments.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in each of the above embodiments are implemented.

[0037] The above-mentioned robust adaptive shear thickening fluid variable admittance control method in a feature space first establishes a coordinate system of a visual servo feature space and a Cartesian space; then, based on the coordinate system, a mapping relationship between actual contact force and virtual contact force is determined using a robot arm kinematic and dynamic model; then, a robust adaptive damping coefficient based on the shear thickening fluid is determined based on the maximum velocity feature point, and an adaptive stiffness coefficient is determined based on the actual contact force change; then, based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient, a robust adaptive shear thickening fluid admittance control in the feature space is determined; then, a visual servo control law is determined based on the robust adaptive shear thickening fluid admittance control in the feature space; finally, the real-time joint velocity of the robot arm is determined based on the real-time image feature points and the visual servo control law to control the movement of the robot arm. That is to say, in the process of variable admittance control, the admittance control and visual servoing are coupled in the feature space to avoid the convergence of the visual servoing to the local minimum. The robust adaptive damping coefficient designed based on the phase change characteristics of the shear thickening fluid ensures that the traction robot can be comfortable and stable in the physical human-machine collaboration process. At the same time, the robust adaptive stiffness coefficient designed based on the analysis of the contact force changes with the size of the contact force, so that the robot can converge quickly during the servo process and complete the servo task even if an unexpected external impact force occurs. The flexibility and robustness of the robot control system are improved, and the safety and stability of the robot in unstructured and complex environments are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is an application environment diagram of a robust adaptive shear thickening fluid variable admittance control method in a feature space in one embodiment;

[0039] Figure 2 It is a schematic flow chart of a robust adaptive shear thickening fluid variable admittance control method in a feature space in one embodiment;

[0040] Figure 3 A schematic diagram of establishing a coordinate system for a robot arm visual servoing experimental platform in one embodiment;

[0041] Figure 4A schematic diagram of a robust adaptive shear thickening fluid variable admittance control experiment in a feature space in one embodiment;

[0042] Figure 5 A schematic diagram of a comparison of the robustness of impact force in a visual servoing process in one embodiment;

[0043] Figure 6 A schematic diagram of the comparison of traction compliance in a human-machine collaborative process in one embodiment;

[0044] Figure 7 Schematic diagram of the comparison between the robustness of traction and impact force and the dissipation of impact energy in the human-machine collaborative process in one embodiment

[0045] Figure 8 It is a flowchart of specific steps of a method for robust adaptive shear thickening fluid variable admittance control in a feature space in one embodiment;

[0046] Fig. 9 It is a structural block diagram of a robust adaptive shear thickening fluid variable admittance control device in a characteristic space in one embodiment;

[0047] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The embodiment of the present application provides a robust adaptive shear thickening fluid variable admittance control method in a feature space, which can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0050] In one embodiment, Figure 2 As shown in the figure, a robust adaptive shear thickening fluid variable admittance control method in feature space is provided. Figure 1 The server in the example is used to illustrate the following steps:

[0051] S201: Establishing the coordinate system of the visual servoing feature space and the Cartesian space.

[0052] In the embodiment of the present application, firstly, the coordinate system of the visual servo feature space and the Cartesian space is established, that is, the reference frame of the manipulator control system and the coordinate system of the admittance control based on the feature point in the feature space are set, such as Figure 3 As shown, this is a schematic diagram of establishing a coordinate system for the robotic arm visual servo experimental platform.

[0053] S203: Determine the mapping relationship between the actual contact force and the virtual contact force using the robot arm kinematics and dynamics model based on the coordinate system.

[0054] In the embodiment of the present application, under the established coordinate system, the kinematics and dynamics model of the robot arm is used to derive the mapping relationship between the actual contact force of the environment and the virtual contact force in the visual servoing feature space.

[0055] S205: Determine a robust adaptive damping coefficient based on the shear thickening fluid based on the maximum velocity characteristic point, and determine an adaptive stiffness coefficient based on the actual contact force change.

[0056] In the embodiment of the present application, the shear thickening fluid theory is applied to determine the robust adaptive damping coefficient based on the maximum velocity characteristic point to adapt to the external impact force and the human-machine collaborative traction force, and the adaptive stiffness coefficient is determined by analyzing the change of the actual contact force during the human-machine interaction process. The appropriate adaptive stiffness coefficient can accomplish the following tasks:

[0057] 1) During the visual servoing process, when the robot arm is subjected to impact force, it can continue to complete the servo;

[0058] 2) In the process of human-machine collaboration, the process of driving the robotic arm is smoother;

[0059] 3) After the human-machine collaboration process is completed, the robot arm can be reset through servo and the servo speed is faster.

[0060] S207: Determine the robust adaptive shear thickening fluid admittance control in the characteristic space based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient.

[0061] In an embodiment of the present application, in the feature space, the admittance control based on the robot arm is to add a mass-damping-stiffness system to the end effector, and the mapping relationship between the actual contact force in the Cartesian space and the virtual contact force in the feature space, as well as the robust adaptive damping coefficient and the adaptive stiffness coefficient, is combined to determine the robust adaptive shear thickening fluid admittance control in the feature space.

[0062] S209: Determine a visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space.

[0063] In an embodiment of the present application, the error between the expected feature point and the current feature point is determined based on the robust adaptive shear thickening fluid admittance control in the feature space, and the visual servoing control law is determined based on the error combined with the visual servoing first-order kinematic model.

[0064] S211: Determine the real-time joint speed of the robotic arm based on the real-time image feature points and the visual servo control law, and control the movement of the robotic arm.

[0065] In the embodiment of the present application, the camera collects the image feature points of the QR code in real time, combines the visual servo control law to determine the real-time joint speed of the robot arm, and controls the motion of the robot arm to complete the visual servo process. Figure 4 As shown in Figure 2, during the process of the robot arm moving from the initial position ps to the visual servo target position p.0, the movement of the robot arm is driven by the visual servo control. During this period, an impact force is applied to verify the robustness of the variable admittance control in the servo process, as shown in Figure 2. Figure 5 As shown in the figure, it is the result of robustness verification. It can be seen that when the impact force is the largest, the convergence speed is faster and the fluctuation of the convergence process is smaller, that is, it has better robustness. In the process of the robot arm moving from the target position p.0 to p.1 and from p.0 to p.2, the end effector is dragged to verify the compliance of the variable admittance control in the human-machine collaboration process, as shown in the figure. Figure 6 As shown in the figure, the compliance verification result is obtained. It can be seen that the average traction force required is the smallest when the distance moved in the X, Y and Z directions of the traction end effector is the largest. In the process of the robot arm moving from the target position p.0 to p.1 and p.0 to p.2, the impact force is applied while dragging the end effector to verify the robustness of the variable admittance control and the dissipation of impact energy in the human-machine collaboration process, as shown in the figure. Figure 7 As shown, the impact forces in the same and opposite directions as the traction force are applied during the traction stage, and the robustness of the variable admittance control and the dissipation of the impact energy are verified.

[0066] In the above-mentioned robust adaptive shear thickening fluid variable admittance control method in a feature space, first, a coordinate system of a visual servo feature space and a Cartesian space is established; then, a mapping relationship between actual contact force and virtual contact force is determined based on the coordinate system using a robot arm kinematic and dynamic model; then, a robust adaptive damping coefficient based on the shear thickening fluid is determined based on the maximum velocity feature point, and an adaptive stiffness coefficient is determined based on the actual contact force change; then, a robust adaptive shear thickening fluid admittance control in a feature space is determined based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient; then, a visual servo control law is determined based on the robust adaptive shear thickening fluid admittance control in the feature space; finally, the real-time joint velocity of the robot arm is determined based on the real-time image feature points and the visual servo control law to control the movement of the robot arm. That is to say, in the process of variable admittance control, the admittance control and visual servoing are coupled in the feature space to avoid the convergence of the visual servoing to the local minimum. The robust adaptive damping coefficient designed based on the phase change characteristics of the shear thickening fluid ensures that the traction robot can be comfortable and stable in the physical human-machine collaboration process. At the same time, the robust adaptive stiffness coefficient designed based on the analysis of the contact force changes with the size of the contact force, so that the robot can converge quickly during the servo process and complete the servo task even if an unexpected external impact force occurs. The flexibility and robustness of the robot control system are improved, and the safety and stability of the robot in unstructured and complex environments are enhanced.

[0067] In one embodiment of the present application, determining the mapping relationship between the actual contact force and the virtual contact force based on the coordinate system using the robot arm kinematics and dynamics model includes:

[0068] S301: Determine a second-order kinematic model of visual servoing based on the kinematic model of the robot arm and the first-order kinematic model of visual servoing.

[0069] S303: Determine a mapping relationship between actual contact force and virtual contact force based on the visual servo second-order kinematics model and the robot arm dynamics model.

[0070] In one embodiment of the present application, the dynamics and kinematics model of the robot arm is expressed using the following formula.

[0071]

[0072] Among them, q, and are the joint position vector, velocity vector and acceleration vector in the joint space respectively; M(q) is the inertia matrix of the robot; is the Coriolis matrix and the centripetal force matrix; G(q) is the gravity vector; are the Coulomb, viscosity and static friction vectors; τ eis the external torque; τ is the control input; e J e is the Jacobian matrix of the robot; x, are the position vector, velocity vector and acceleration vector in the operation room respectively, and P(q) is the coordinate system mapping function.

[0073] The second-order kinematic model of visual servo is calculated through the robot kinematic model, the first-order kinematic model of visual servo and the transformation matrix from the robot end effector to the camera. Specifically, the formulas of the first-order kinematic model and the second-order kinematic model of visual servo are shown below.

[0074]

[0075] Among them, L s is the image interaction matrix, v c is the camera's movement speed, c T e is the transformation matrix from the robot end effector to the camera.

[0076] Through the calculation of the robot arm dynamics model and the secondary kinematics model of visual servo, it can be obtained:

[0077]

[0078] Among them, τ e is the external torque and can be expressed by the following formula.

[0079]

[0080] in, e f e is the force of the end effector of the robot arm, c f c is the force in the camera coordinate system, is the transpose of the transformation matrix from the robot end effector to the camera, is the transpose of the Jacobian matrix of the robot.

[0081] Then the mapping relationship between the actual contact force and the virtual contact force in the visual servoing feature space is:

[0082]

[0083] Among them, f sext is the virtual contact force in the feature space.

[0084] In this embodiment, by coupling the admittance control and the visual servoing in the feature space, the convergence of the visual servoing to the local minimum is avoided.

[0085] In one embodiment of the present application, the robust adaptive damping coefficient based on shear thickening fluid is expressed as:

[0086]

[0087] Among them, μ and n are adjustable parameters. It is the characteristic point with maximum velocity.

[0088] In one embodiment of the present application, when the robot arm has contact force, a virtual force is generated in the feature space, resulting in the desired feature point s d The displacement occurs, and the displacement speed of the feature point is expected to increase when the contact force acts. In order to obtain the displacement speed of the feature point more accurately, the feature point with the maximum speed is selected as the reference to design the adaptive damping coefficient based on the shear thickening fluid, which is specifically expressed as:

[0089]

[0090] Among them, μ>0 and n>0 are adjustable parameters.

[0091] In this embodiment, by determining the robust adaptive damping coefficient based on the shear thickening fluid based on the maximum velocity characteristic point, the robust adaptive damping coefficient increases with the increase of velocity, thereby dissipating kinetic energy and enhancing robustness when the external force is a large impact force. On the contrary, when the external force is weak and acts as a traction force, the damping is low and the compliance of the system is enhanced.

[0092] In one embodiment of the present application, the adaptive stiffness coefficient is expressed as:

[0093]

[0094] in, e f e is the force of the end effector of the robot arm, and diag represents a diagonal matrix.

[0095] In one embodiment of the present application, based on the laboratory six-degree-of-freedom UR5 collaborative robot and the wrist-mounted six-axis FT300 force / torque sensor, through a large number of experimental statistical analysis, the traction force when dragging the end effector of the robot arm is less than 15N. Since force sensing cannot be fully compensated, even if the end effector has no direct contact force, there will be an incompletely compensated force of less than 2N. Therefore, the virtual stiffness coefficient determined by the adaptive adjustment based on the actual contact force change is expressed as follows:

[0096]

[0097] In this embodiment, the robust adaptive stiffness coefficient designed based on the analysis of the contact force varies with the magnitude of the contact force, so that the robot arm can converge quickly during the servo process and can complete the servo task even if an unexpected external impact force occurs.

[0098] In one embodiment of the present application, the robust adaptive shear thickening fluid admittance control in the feature space is defined as:

[0099]

[0100] Among them, M vs , D vs , K vs are the virtual mass, damping and stiffness in the feature space respectively; e vs is the feature point error; is the acceleration of the feature point error; is the speed of feature point error; s d is the expected feature point position set initially, s w is the position of the feature point moved by the external contact force.

[0101] In one embodiment of the present application, the mapping relationship between the actual contact force in Cartesian space and the virtual contact force in feature space, as well as the robust adaptive damping coefficient and the adaptive stiffness coefficient, is combined to determine the robust adaptive shear thickening fluid admittance control in feature space, which is specifically expressed as follows:

[0102]

[0103] Among them, M vs , D vs , K vs are the virtual mass, damping and stiffness in the feature space respectively; e vs is the feature point error; is the acceleration of the feature point error; is the speed of feature point error; s d is the expected feature point position set initially, s w is the position of the feature point moved by the external contact force.

[0104] When there is no contact force on the end effector of the robot arm, s ω =s d When there is a contact force on the end effector, the control system maps the actual contact force in Cartesian space to the feature space, in s d A virtual mass-damping-stiffness system is formed on the surface. Due to the virtual contact force, s ω ≠s d , leading to the expected feature point s d Offset to s w .

[0105] In one embodiment of the present application, the determining of the visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space comprises:

[0106] S401: Determine a visual servo controller error based on expected feature points and current feature points.

[0107] S403: Determine a visual servo control law based on the visual servo controller error and a visual servo first-order kinematics model.

[0108] In one embodiment of the present application, first, the visual servo controller error is determined based on the expected feature point and the current feature point, and is defined as:

[0109] e ad =s w -s

[0110] Among them, s w is the position of the feature point moved by the external contact force, and s is the actual position of the feature point.

[0111] Afterwards, the visual servo control law is determined through the first-order kinematic model of visual servoing and the error derivative of the visual servoing controller. It is expressed as:

[0112]

[0113] Among them, L s is the image interaction matrix, v c The camera's movement speed

[0114] The visual servo control law is shown as follows.

[0115]

[0116] in, is the pseudo-inverse of the image interaction matrix, v c is the camera's movement speed, is the visual servo controller error derivative, expressed as:

[0117]

[0118] To ensure that the visual servoing error decreases exponentially, Then the visual servo control law is expressed as:

[0119]

[0120] Where λ is the controller gain.

[0121] The following is a specific example to illustrate the specific implementation steps of a robust adaptive shear thickening fluid variable admittance control method in a feature space of the present application. Figure 8 As shown, first, S501, establish the coordinate system of the visual servo feature space and the Cartesian space. Then, S503, determine the mapping relationship between the actual contact force and the virtual contact force based on the coordinate system using the robot arm kinematics and dynamics model. Specifically, S505-S507, determine the visual servo second-order kinematics model based on the robot arm kinematics model and the visual servo first-order kinematics model; determine the mapping relationship between the actual contact force and the virtual contact force based on the visual servo second-order kinematics model and the robot arm dynamics model.

[0122] Then, S509, a robust adaptive damping coefficient based on the shear thickening fluid is determined based on the maximum velocity characteristic point, and an adaptive stiffness coefficient is determined based on the actual contact force change. Specifically, S511, the robust adaptive damping coefficient based on the shear thickening fluid is expressed as:

[0123]

[0124] Among them, μ and n are adjustable parameters. It is the characteristic point with maximum velocity.

[0125] S513, the adaptive stiffness coefficient is expressed as:

[0126]

[0127] in, e f e is the force of the end effector of the robot arm, and diag represents a diagonal matrix.

[0128] Then, S515, based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient, the robust adaptive shear thickening fluid admittance control in the feature space is determined. Specifically, S517, the robust adaptive shear thickening fluid admittance control in the feature space is defined as:

[0129]

[0130] Among them, M vs , D vs , K vs are the virtual mass, damping and stiffness in the feature space respectively; e vs is the feature point error; is the acceleration of the feature point error; is the speed of feature point error; s d is the expected feature point position set initially, s w is the position of the feature point moved by the external contact force.

[0131] Then, S519, a visual servo control law is determined based on the robust adaptive shear thickening fluid admittance control in the feature space. Specifically, S521-S523, a visual servo controller error is determined based on the expected feature point and the current feature point. The visual servo control law is determined based on the visual servo controller error and the visual servo first-order kinematics model.

[0132] Finally, S525, the real-time joint speed of the robotic arm is determined based on the real-time image feature points and the visual servo control law, and the movement of the robotic arm is controlled.

[0133] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0134] Based on the same inventive concept, the embodiment of the present application also provides a robust adaptive shear thickening fluid variable admittance control device under a feature space for realizing the above-mentioned robust adaptive shear thickening fluid variable admittance control method under a feature space. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the robust adaptive shear thickening fluid variable admittance control device under a feature space provided below can refer to the limitations of the robust adaptive shear thickening fluid variable admittance control method under a feature space above, and will not be repeated here.

[0135] In one embodiment, Fig. 9 As shown, a robust adaptive shear thickening fluid variable admittance control device 900 in a feature space is provided, comprising: a coordinate system establishment module 901, a mapping relationship determination module 903, a robust adaptive damping coefficient and an adaptive stiffness coefficient determination module 905, an admittance control determination module 907, a visual servoing control law determination module 909 and a robot arm motion control module 911, wherein:

[0136] The coordinate system establishing module 901 is used to establish the coordinate system of the visual servoing feature space and the Cartesian space.

[0137] The mapping relationship determination module 903 is used to determine the mapping relationship between the actual contact force and the virtual contact force based on the coordinate system using the robot arm kinematics and dynamics model.

[0138] The robust adaptive damping coefficient and adaptive stiffness coefficient determination module 905 is used to determine the robust adaptive damping coefficient and adaptive stiffness coefficient based on the shear thickening fluid based on the maximum velocity characteristic point.

[0139] The admittance control determination module 907 is used to determine the robust adaptive shear thickening fluid admittance control in the feature space based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient.

[0140] The visual servo control law determination module 909 is used to determine the visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space.

[0141] The robot arm motion control module 911 is used to determine the real-time joint speed of the robot arm based on the real-time image feature points and the visual servo control law, and control the motion of the robot arm.

[0142] In one embodiment of the present application, the mapping relationship determination module is further used to:

[0143] Determine the second-order kinematic model of visual servoing based on the kinematic model of the robot arm and the first-order kinematic model of visual servoing;

[0144] The mapping relationship between the actual contact force and the virtual contact force is determined based on the visual servo second-order kinematics model and the robot arm dynamics model.

[0145] In one embodiment of the present application, the robust adaptive damping coefficient based on shear thickening fluid is expressed as:

[0146]

[0147] Among them, μ and n are adjustable parameters. It is the characteristic point with maximum velocity.

[0148] In one embodiment of the present application, the adaptive stiffness coefficient is expressed as:

[0149]

[0150] in, e f e is the force of the end effector of the robot arm, and diag represents a diagonal matrix.

[0151] In one embodiment of the present application, the robust adaptive shear thickening fluid admittance control in the feature space is defined as:

[0152]

[0153] Among them, M vs , D vs , K vs are the virtual mass, damping and stiffness in the feature space respectively; e vs is the feature point error; is the acceleration of the feature point error; is the speed of feature point error; s d is the expected feature point position set initially, s w is the position of the feature point moved by the external contact force.

[0154] In one embodiment of the present application, the visual servo control law determination module is further used to:

[0155] Determining a visual servo controller error based on the desired feature point and the current feature point;

[0156] A visual servo control law is determined based on the visual servo controller error and a visual servo first-order kinematics model.

[0157] Each module in the above-mentioned robust adaptive shear thickening fluid variable admittance control device under a feature space can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0158] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a robust adaptive shear thickening fluid variable admittance control method in a feature space is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0159] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0160] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0161] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0162] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0163] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0165] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A robust adaptive shear thickening fluid variable admittance control method in feature space, characterized in that: The method comprises: Establish the coordinate system of visual servoing feature space and Cartesian space; Based on the coordinate system, a kinematic and dynamic model of the robot arm is used to determine a mapping relationship between the actual contact force and the virtual contact force; Determining a robust adaptive damping coefficient based on a shear thickening fluid based on the maximum velocity characteristic point, and determining an adaptive stiffness coefficient based on the actual contact force change; Determining the robust adaptive shear thickening fluid admittance control in the characteristic space based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient; Determining a visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space; The real-time joint speed of the robot arm is determined based on the real-time image feature points and the visual servo control law to control the movement of the robot arm.

2. The method for controlling variable admittance of a robust adaptive shear thickening fluid in a feature space according to claim 1, characterized in that: The mapping relationship between the actual contact force and the virtual contact force is determined based on the coordinate system using the robot arm kinematics and dynamics model, including: Determine the second-order kinematic model of visual servoing based on the kinematic model of the robot arm and the first-order kinematic model of visual servoing; The mapping relationship between the actual contact force and the virtual contact force is determined based on the visual servo second-order kinematics model and the robot arm dynamics model.

3. The method for controlling variable admittance of a robust adaptive shear thickening fluid in a feature space according to claim 1, characterized in that: The robust adaptive damping coefficient based on shear thickening fluid is expressed as: Among them, μ and n are adjustable parameters. It is the characteristic point with the maximum velocity.

4. The method for controlling variable admittance of a robust adaptive shear thickening fluid in a feature space according to claim 1, characterized in that: The adaptive stiffness coefficient is expressed as: in, e f e is the force of the end effector of the robot arm, and diag represents a diagonal matrix.

5. The method for controlling variable admittance of a robust adaptive shear thickening fluid in a feature space according to claim 1, characterized in that: The robust adaptive shear-thickening fluid admittance control in the feature space is defined as: Among them, M vs , D vs , K vs are the virtual mass, damping and stiffness in the feature space respectively; e vs is the feature point error; is the acceleration of the feature point error; is the speed of feature point error; s d is the expected feature point position set initially, s w is the position of the feature point moved by the external contact force, f sext is the virtual contact force in the feature space.

6. The method for controlling variable admittance of a robust adaptive shear thickening fluid in a feature space according to claim 1, characterized in that: The method for determining the visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space comprises: Determining a visual servo controller error based on the desired feature point and the current feature point; A visual servo control law is determined based on the visual servo controller error and a visual servo first-order kinematics model.

7. A robust adaptive shear thickening fluid variable admittance control device in feature space, characterized in that: The device comprises: A coordinate system establishment module is used to establish the coordinate system of the visual servo feature space and the Cartesian space; A mapping relationship determination module, used to determine the mapping relationship between the actual contact force and the virtual contact force based on the coordinate system using the robot arm kinematics and dynamics model; A robust adaptive damping coefficient and adaptive stiffness coefficient determination module, used to determine a robust adaptive damping coefficient based on a shear thickening fluid based on a maximum velocity characteristic point, and to determine an adaptive stiffness coefficient based on the actual contact force change; An admittance control determination module, used for determining the robust adaptive shear thickening fluid admittance control in the characteristic space based on the mapping relationship, the robust adaptive damping coefficient and the adaptive stiffness coefficient; A visual servo control law determination module, used for determining the visual servo control law based on the robust adaptive shear thickening fluid admittance control in the feature space; The robot arm motion control module is used to determine the real-time joint speed of the robot arm based on the real-time image feature points and the visual servo control law, and control the movement of the robot arm.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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