Robust adaptive variable admittance control method based on different intentions in feature space

Abstract

The invention relates to a robust adaptive variable admittance control method based on different intentions in a feature space. The method comprises the following steps: determining a mapping relation between an actual contact force and a virtual contact force by adopting a mechanical arm kinematics and dynamics model; determining an adaptive stiffness coefficient based on the actual contact force change, and determining a robust adaptive virtual damping coefficient based on direct intention based on the virtual motion speed of the feature point; adjusting the robust adaptive virtual damping coefficient based on the expected contact force of the adjustment robot, and determining an admittance control virtual damping coefficient based on direct and indirect intentions; based on the mapping relation, the adaptive stiffness coefficient and the admittance control virtual damping coefficient, determining robust adaptive variable admittance control based on human intention in the feature space; and a robot image visual servo control law is determined based on the real-time image feature points and the expected feature points, the real-time joint speed of the mechanical arm is obtained, and the mechanical arm is controlled to move.

Description

Technical Field

[0001] The present application relates to the technical field of robot multi-sensor perception information fusion, and in particular to a robust adaptive variable admittance control method based on different intentions in a feature space. Background Art

[0002] Physical human-machine interaction technologies in industrial and medical scenarios such as precision assembly, parts inspection and repair, and robotic surgery usually rely on contact information obtained by force sensors. However, most of these systems lack in-depth perception of specific application scenarios, and in complex operating environments, external impact forces often have irreversible effects on robot control systems, causing oscillations, instability, and even loss of control, thereby reducing the dynamic performance and operational reliability of the system. Therefore, designing a robust control system with rich perception information and the ability to effectively respond to external impact interference has become the key to improving the stability and performance of human-machine collaboration.

[0003] Compliance control is the core technology for achieving safe and natural interaction between robots and the environment or humans, and has been widely used in robot contact tasks. As a collaborative control method, admittance control is implemented through force sensors and has a certain degree of robustness. It has been widely used in physical human-machine interaction. However, existing control systems generally find it difficult to balance compliance and robustness. In practical applications, many studies have failed to fully consider the impact of external impact forces, resulting in the inability to effectively achieve a balance between compliance and robustness.

[0004] To solve this problem, some studies have proposed variable admittance control methods based on human intention prediction and have achieved certain results, but the controller design of these methods is relatively complex and the amount of calculation is large. In addition, dampers based on shear thickening fluids have been used in the field of protection. Their velocity dependence makes them excellent in compliance and robustness, but these methods usually rely only on contact force information and lack in-depth perception of the environment. Therefore, robots need to be equipped with visual sensors to achieve more efficient task execution in complex and unstructured environments.

[0005] Visual servo systems have shown great advantages in unstructured environments due to their excellent accuracy, adaptability and autonomous decision-making capabilities. By combining image feedback, visual servo systems can adjust robot posture and path planning in real time, and have been widely used in various robot tasks. However, relying solely on force or visual sensor signals is often difficult to meet the needs of complex tasks. Therefore, combining vision with force sensors is an effective way to solve diverse tasks. Through the fusion of visual and force information, robots can perform human-machine interaction more flexibly, accurately and safely in dynamic environments. However, since force and visual information are usually at different execution levels in control system design, the system often converges to a local minimum, resulting in inconsistent convergence of force and visual references, which in turn affects system performance.

[0006] Therefore, in the related technology, there is an urgent need for a method that can effectively couple visual and force information in the feature space. Summary of the invention

[0007] Based on this, it is necessary to provide a robust adaptive variable admittance control method based on different intentions in the feature space that can effectively couple visual and force information in the feature space to address the above technical problems.

[0008] In a first aspect, the present application provides a robust adaptive variable admittance control method based on different intentions in a feature space. The method comprises:

[0009] The mapping relationship between actual contact force and virtual contact force is determined by using the kinematic and dynamic models of the robot arm;

[0010] determining an adaptive stiffness coefficient based on the actual contact force change, and determining a robust adaptive virtual damping coefficient based on direct intention based on the virtual motion speed of the feature point;

[0011] adjusting the robust adaptive virtual damping coefficient based on the contact force expected by the adjusted robot, and determining the virtual damping coefficient of the admittance control based on direct and indirect intentions;

[0012] Determine the robust adaptive variable admittance control based on human intention in the feature space based on the mapping relationship, the adaptive stiffness coefficient and the virtual damping coefficient of the admittance control;

[0013] Based on the real-time image feature points and expected feature points, the robot image visual servo control law is determined, the real-time joint speed of the robot arm is obtained, and the movement of the robot arm is controlled.

[0014] Optionally, in one embodiment of the present application, the determining the mapping relationship between the actual contact force and the virtual contact force using the robot arm kinematics and dynamics model includes:

[0015] Determine the second-order kinematic model of visual servoing based on the robot kinematic model and the transformation matrix from the robot end effector to the camera;

[0016] 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.

[0017] Optionally, in one embodiment of the present application, adjusting the robust adaptive virtual damping coefficient based on the contact force expected by the adjustment robot and determining the virtual damping coefficient of the admittance control based on direct and indirect intentions includes:

[0018] Predict the traction required when the robot collaborates with humans in real time, and determine the desired contact force for adjusting the robot;

[0019] Determining a desired virtual admittance parameter based on the desired contact force and robust adaptive virtual damping coefficient of the adjustment robot;

[0020] A direct and indirect intention based admittance control virtual damping coefficient is determined based on the desired virtual admittance parameter and the robust adaptive virtual damping coefficient.

[0021] Optionally, in one embodiment of the present application, determining and adjusting the contact force expected by the robot includes:

[0022] Compare the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point and calculate the trajectory curvature information;

[0023] The contact force expected by the robot is calculated and adjusted based on the curvature information.

[0024] Optionally, in one embodiment of the present application, the virtual damping coefficient of the admittance control based on direct and indirect intentions is:

[0025]

[0026] Among them, D vs is the virtual damping coefficient of admittance control based on direct and indirect intentions, D1 is the robust adaptive virtual damping coefficient based on direct intention, and D expected is the expected virtual admittance parameter.

[0027] Optionally, in one embodiment of the present application, the robust adaptive variable admittance control based on human intention in the feature space is defined as:

[0028]

[0029] Among them, M vs is the virtual quality coefficient in the feature space, D vs is the virtual damping coefficient of admittance control based on direct and indirect intention, Kvs is the adaptive stiffness coefficient in the feature space; 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 ω is the position of the feature point moved by the external contact force, f sext is the virtual contact force in the feature space.

[0030] Optionally, in one embodiment of the present application, the robot image visual servo control law is defined as:

[0031]

[0032] Among them, v c is the camera's speed, λ is the controller gain, is the pseudo-inverse matrix of the image interaction matrix, e ad is the error between the real-time image feature point and the expected feature point, is the velocity characteristic point, s ω is the position of the feature point moved by the external contact force, s d is the expected feature point position set initially.

[0033] In a second aspect, the present application also provides a robust adaptive variable admittance control device based on different intentions in a feature space. The device comprises:

[0034] A mapping relationship determination module is used to determine the mapping relationship between the actual contact force and the virtual contact force using the kinematics and dynamics model of the robot arm;

[0035] A robust adaptive damping coefficient and adaptive stiffness coefficient determination module, used to determine the adaptive stiffness coefficient based on the actual contact force change, and determine the robust adaptive virtual damping coefficient based on direct intention based on the virtual motion speed of the feature point;

[0036] A virtual damping coefficient determination module for admittance control based on direct and indirect intentions, used for adjusting the robust adaptive virtual damping coefficient based on adjusting the desired contact force of the robot, and determining the virtual damping coefficient for admittance control based on direct and indirect intentions;

[0037] An admittance control determination module, used to determine a robust adaptive variable admittance control based on human intention in a feature space based on the mapping relationship, the adaptive stiffness coefficient and the admittance control virtual damping coefficient;

[0038] The robot arm motion control module is used to determine the robot image visual servo control law based on real-time image feature points and expected feature points, obtain the real-time joint speed of the robot arm, and control the movement of the robot arm.

[0039] 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.

[0040] 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.

[0041] The robust adaptive variable admittance control method based on different intentions in the above feature space firstly adopts the kinematic and dynamic models of the manipulator to determine the mapping relationship between the actual contact force and the virtual contact force; then, the adaptive stiffness coefficient is determined based on the actual contact force change, and the robust adaptive virtual damping coefficient based on the direct intention is determined based on the virtual motion speed of the feature point; then, the robust adaptive virtual damping coefficient is adjusted based on the contact force expected by the robot, and the virtual damping coefficient of the admittance control based on the direct and indirect intention is determined; then, the robust adaptive variable admittance control based on human intention in the feature space is determined based on the mapping relationship, the adaptive stiffness coefficient and the virtual damping coefficient of the admittance control; finally, the robot image visual servoing control law is determined based on the real-time image feature points and the expected feature points, the real-time joint speed of the manipulator is obtained, and the movement of the manipulator is controlled. At the level of direct human intention, an adaptive virtual damping coefficient is designed in the feature space by analyzing the virtual speed of the feature points and using the characteristics of STF. At the same time, in order to ensure the stable convergence of the manipulator in the visual servoing process, a virtual stiffness coefficient that is adaptively adjusted according to the change of the contact force is designed. These strategies ensure compliance, robustness and stability during human-robot collaborative interaction. At the level of indirect human intention, a curvature-based force guidance method is designed based on the STF virtual damping coefficient. This method compares the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point, and uses the curvature information to calculate the contact force required by the robot. Through this strategy, the robot can accurately follow and guide the human force, thereby achieving smoother and more stable movement, and ensuring robustness and compliance in complex environments. By comparing the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point, the present invention successfully maps the indirect intention curvature model in Cartesian space to the feature space, realizing more accurate force guidance in the visual human-robot collaborative process, and can effectively coordinate the interaction between robots and humans. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is an application environment diagram of a robust adaptive variable admittance control method based on different intentions in a feature space in an embodiment;

[0043] Figure 2 1 is a flow chart of a robust adaptive variable admittance control method based on different intentions in a feature space in one embodiment;

[0044] Figure 3 It is an overall control block diagram in one embodiment;

[0045] Figure 4 Schematic diagram of the composition of a control system in one embodiment;

[0046] Figure 5 A comparison diagram of the motion trajectory of the end effector and the feature point in one embodiment;

[0047] Figure 6 A schematic diagram of a specific experimental verification in an embodiment;

[0048] Figure 7 A schematic diagram of the results of the robustness and convergence verification of the design method when an impact force is applied during the visual servoing process in one embodiment;

[0049] Figure 8 Schematic diagram of experimental results of compliance, robustness and efficiency of human-machine collaborative motion on a straight trajectory in one embodiment;

[0050] Fig. 9 Schematic diagram of experimental results of compliance, robustness and efficiency of human-machine collaborative motion on a curved trajectory in one embodiment;

[0051] Fig.10 A schematic diagram of the advantages of the controller designed by the method in one embodiment compared with a pure visual servo controller in a hole experiment;

[0052] Fig.11 is a structural block diagram of a robust adaptive variable admittance control device based on different intentions in a feature space in an embodiment;

[0053] Fig.12 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] 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.

[0055] The robust adaptive variable admittance control method based on different intentions in the feature space provided by the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or it can be placed on the cloud or other network servers. Among them, the terminal can be but 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 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0056] In one embodiment, Figure 2 As shown in the figure, a robust adaptive variable admittance control method based on different intentions in feature space is provided. Figure 1 The server in the example is used to illustrate the following steps:

[0057] S201: The mapping relationship between the actual contact force and the virtual contact force is determined using the robot arm kinematics and dynamics model.

[0058] In the embodiment of the present application, firstly, the mapping relationship between the actual contact force of the environment and the virtual contact force in the visual servoing feature space is derived through the kinematics and dynamics model of the robot arm.

[0059] Specifically, in one embodiment of the present application, the use of the robot arm kinematics and dynamics model to determine the mapping relationship between the actual contact force and the virtual contact force includes:

[0060] S301: Determine a second-order kinematic model of visual servoing based on a robot arm kinematic model and a transformation matrix from the robot arm end effector to a camera.

[0061] 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.

[0062] In one embodiment of the present application, the relationship between the image feature points in the camera coordinate system projected onto the 2D image plane of the pinhole camera is:

[0063]

[0064] Among them, γ represents the focal length of the camera, z i Indicates the depth of the image feature point relative to the camera coordinate system.

[0065] The second-order time derivative of the image feature point on the 2D plane is:

[0066]

[0067] Among them, L s is the characteristic Jacobian matrix, which describes the mapping relationship between image feature changes and camera motion. is the derivative of the characteristic Jacobian matrix with respect to time, which represents the dynamic change of the relationship between the characteristic and the camera motion. c is the instantaneous speed of the camera, is the time derivative of the camera velocity, that is, the acceleration of the camera.

[0068] The kinematic equation of the robot arm is:

[0069]

[0070] Among them, x, are the position vector, velocity vector and acceleration vector under the operation room respectively, P(q) is the coordinate system mapping function, q, and are the joint position vector, velocity vector and acceleration vector in the robot joint space, e J e is the Jacobian matrix of the robot arm.

[0071] The second-order kinematic model of visual servoing is determined by the transformation matrix from the end effector of the robot to the camera and the kinematic equation of the robot:

[0072]

[0073] Among them, L s is the image interaction matrix, c T e is the transformation matrix from the robot end effector to the camera.

[0074] Through the dynamics of the robot arm:

[0075]

[0076] Where M(q) is the inertia matrix of the robot, τ is the control input, is the Coriolis matrix and the centripetal force matrix, G(q) is the gravity vector, is the Coulomb force, τ e is the external torque.

[0077] Through further calculation, we get:

[0078]

[0079] External torque τ e It can be expressed as:

[0080]

[0081] Among them, f e is the force of the end effector of the robot arm, f c is the force in the camera coordinate system, is the transformation matrix from the robot end effector coordinate system to the camera coordinate system.

[0082] Then the virtual contact force in the characteristic space is:

[0083]

[0084] S203: Determine an adaptive stiffness coefficient based on the actual contact force change, and determine a robust adaptive virtual damping coefficient based on direct intention based on the virtual motion speed of the feature point.

[0085] In an embodiment of the present application, in order to ensure different functions in visual servoing and physical human-machine collaboration tasks, a virtual stiffness coefficient is designed to adaptively adjust according to different types of forces (traction or impact force). During the visual servoing process, the virtual stiffness coefficient can ensure the stable completion of the task even under impact force or without external contact. In human-machine collaboration tasks, it can enhance compliance under traction. Experimental analysis of the motion of the UR5 robot shows that the traction force is typically less than 15N, while the incompletely compensated force (set to less than 2N for safety) is managed by the virtual stiffness coefficient. According to this requirement, an adaptive virtual stiffness parameter is designed, which is expressed as follows:

[0086]

[0087] in, f e is the force of the end effector of the robot arm, s, kg, N, and m are the basic units in the International System of Units, s is the unit of time, second, kg is the unit of mass, kilogram, N is the unit of force, Newton, and m is the unit of length, meter. It means that when the force of the end effector of the robot arm is less than 2N or greater than 15N, the adaptive stiffness coefficient is assigned to an 8×8 diagonal matrix with a value of 200; It means that when the force of the end effector of the robot arm is greater than 2N and less than 15N, the adaptive stiffness coefficient is assigned an 8×8 diagonal matrix with a value of 0.

[0088] The modeling of direct intention plays a vital role in the human-machine collaboration process, enabling the robot to capture and infer human behavioral intentions in real time and dynamically adjust the control strategy based on this information. In combination with the unique properties of shear-thickening fluids, an adaptive damping coefficient is designed to cope with changes in external impact force and human-machine collaborative traction. When the robot arm comes into contact with the environment, the virtual force caused by the contact force will cause the offset of the desired feature point in the feature space, and as the contact force increases, the offset speed of the desired feature point will also increase. Based on this design, the system can dynamically adjust the damping coefficient according to real-time feedback, accurately control the system response, effectively adapt to external disturbances, and ensure stability, compliance and high-precision control in complex dynamic environments. When there is no contact force on the end effector, the system remains stable and performs normal servo tasks; when there is contact force on the end effector, the control system maps the force in the Cartesian space to the feature space, forming a virtual mass-damping-stiffness system, thereby affecting the offset of the desired feature point, and as the contact force increases, the offset speed and acceleration of the desired feature point will increase accordingly. The robust adaptive virtual damping coefficient based on direct intention is determined based on the virtual motion speed of the feature point as follows:

[0089]

[0090] in, μ>0, n>0 are adjustable parameters, is the virtual motion speed of the feature point.

[0091] S205: Adjusting the robust adaptive virtual damping coefficient based on the contact force expected by the robot, and determining the virtual damping coefficient of admittance control based on direct and indirect intentions.

[0092] In the embodiment of the present application, by predicting the traction required for the robot to collaborate with humans in real time and dynamically adjusting the admittance parameters, the robot can respond and adjust the control strategy in time according to the upcoming action, that is, based on the estimation of indirect intention, the robot can respond to environmental changes and human action intentions more flexibly, thereby accurately guiding the robot arm to move along a predetermined path. The original robust adaptive virtual damping coefficient based on direct intention is improved by combining indirect intention, and the virtual damping coefficient of admittance control based on direct and indirect intention is determined.

[0093] Specifically, in one embodiment of the present application, adjusting the robust adaptive virtual damping coefficient based on the contact force expected by the adjustment robot and determining the virtual damping coefficient of the admittance control based on direct and indirect intentions includes:

[0094] S401: Predict the traction force required for the robot to collaborate with humans in real time, and determine and adjust the desired contact force of the robot.

[0095] S403: Determine desired virtual admittance parameters based on the contact force desired by the adjustment robot and the robust adaptive virtual damping coefficient.

[0096] S405: Determine a virtual damping coefficient for admittance control based on direct and indirect intentions based on the desired virtual admittance parameter and the robust adaptive virtual damping coefficient.

[0097] In one embodiment of the present application, when a human interacts with the robot and moves it in a specific direction, the goal is to limit the robot's movement in the vertical direction, thereby reducing the interference of the error force. To achieve this, a lower admittance coefficient is selected along the direction of the contact force desired by the robot, while a higher admittance coefficient is applied in the vertical direction. A higher admittance coefficient results in a smaller movement amplitude and slower response in the vertical direction, improving the stability of the system.

[0098] In the feature space, to set higher admittance in the vertical direction and lower admittance in the horizontal direction, the parameters should be changed proportionally with the speed as follows:

[0099]

[0100] Among them, γ1 and γ2 are the admittance parameters in the vertical and horizontal directions respectively. is the estimated velocity of the feature point after being subjected to the force.

[0101] In order to guide the adjustment of the desired contact force of the robot, a higher admittance parameter is set in the direction perpendicular to the force. If the u direction is the direction of the desired contact force of the robot, the desired virtual admittance parameter is set to:

[0102]

[0103] in, It is the rotation matrix derived from the desired force in the uv direction, which represents the rotation relationship between the robot base coordinate system and the u-axis coordinate system aligned in the desired direction. By using the rotation matrix R, the admittance controller can adjust the parameters along the desired motion direction, so that the robot can accurately follow the operator's intention and improve the accuracy and smoothness of the trajectory.

[0104] The rotation matrix R is calculated based on the contact force expected by the adjusted robot. The specific formula is:

[0105]

[0106] Among them, F v,expected and F u,expected They are used to adjust the components of the robot's expected contact force in the uv direction respectively.

[0107] In the feature space, to ensure that the robot remains stable without external contact force during the visual servoing task, we set the minimum value of the damping coefficient to: In this case, the virtual damping matrix of the admittance control is in the eigenspace when

[0108] Afterwards, a virtual damping coefficient for admittance control based on direct and indirect intentions is determined based on the desired virtual admittance parameter and the robust adaptive virtual damping coefficient.

[0109] Specifically, in one embodiment of the present application, the virtual damping coefficient of the admittance control based on direct and indirect intentions is:

[0110]

[0111] Among them, D vs is the virtual damping coefficient of admittance control based on direct and indirect intentions, D1 is the robust adaptive virtual damping coefficient based on direct intention, and D expected is the expected virtual admittance parameter.

[0112] In one embodiment of the present application, determining and adjusting the desired contact force of the robot includes:

[0113] S501: Compare the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point, and calculate the trajectory curvature information.

[0114] S503: Calculate and adjust the desired contact force of the robot based on the curvature information.

[0115] In one embodiment of the present application, the curvature of the trajectory of the next cycle can be calculated by the velocity of the feature point in the current cycle:

[0116]

[0117] in, and is the estimated velocity and acceleration of the feature point in the u direction, and is the estimated velocity and acceleration of the feature point in the v direction.

[0118] The expected contact force of the robot is calculated and adjusted based on the curvature information of the velocity set of the feature points in the current cycle:

[0119]

[0120] in, represents the centripetal force direction vector, is the tangential driving force direction vector.

[0121] S207: Determine a robust adaptive variable admittance control based on human intention in a feature space based on the mapping relationship, the adaptive stiffness coefficient and the admittance control virtual damping coefficient.

[0122] In the embodiment of the present application, a robust adaptive variable admittance controller based on human intention in the feature space is obtained through the mapping relationship between different spaces and different human intentions.

[0123] Specifically, in one embodiment of the present application, the robust adaptive variable admittance control based on human intention in the feature space is defined as:

[0124]

[0125] Among them, M vs is the virtual quality coefficient in the feature space, D vs is the virtual damping coefficient of admittance control based on direct and indirect intention, K vs is the adaptive stiffness coefficient in the feature space; 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 ω is the position of the feature point moved by the external contact force, f sext is the virtual contact force in the feature space.

[0126] S209: Determine the robot image visual servo control law based on the real-time image feature points and the expected feature points, obtain the real-time joint speed of the robot arm, and control the movement of the robot arm.

[0127] In an embodiment of the present application, the robot image visual servo control law is determined based on the error between the expected feature point and the current feature point of the image of the QR code captured by the camera in real time, and the joint speed of the robotic arm is obtained in real time based on the robot image visual servo control law, thereby controlling the motion of the robotic arm and completing the visual servo process.

[0128] In one embodiment of the present application, the robot image visual servo control law is defined as:

[0129]

[0130] Among them, v c is the camera's speed, λ is the controller gain, is the pseudo-inverse matrix of the image interaction matrix, e ad is the error between the real-time image feature point and the expected feature point, is the velocity characteristic point, s ω is the position of the feature point moved by the external contact force, sd is the expected feature point position set initially.

[0131] In the above-mentioned robust adaptive variable admittance control method based on different intentions in feature space, first, the kinematic and dynamic models of the manipulator are used to determine the mapping relationship between the actual contact force and the virtual contact force; then, the adaptive stiffness coefficient is determined based on the actual contact force change, and the robust adaptive virtual damping coefficient based on the direct intention is determined based on the virtual motion speed of the feature point; then, the robust adaptive virtual damping coefficient is adjusted based on the contact force expected by the robot, and the virtual damping coefficient of the admittance control based on the direct and indirect intentions is determined; then, the robust adaptive variable admittance control based on human intention in feature space is determined based on the mapping relationship, the adaptive stiffness coefficient and the virtual damping coefficient of the admittance control; finally, the robot image visual servoing control law is determined based on the real-time image feature points and the expected feature points, the real-time joint speed of the manipulator is obtained, and the movement of the manipulator is controlled. At the level of direct human intention, an adaptive virtual damping coefficient is designed in the feature space by analyzing the virtual speed of the feature points and using the characteristics of STF. At the same time, in order to ensure the stable convergence of the manipulator in the visual servoing process, a virtual stiffness coefficient that is adaptively adjusted according to the change of contact force is designed. These strategies ensure compliance, robustness and stability during human-robot collaborative interaction. At the level of indirect human intention, a curvature-based force guidance method is designed based on the STF virtual damping coefficient. This method compares the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point, and uses the curvature information to calculate the contact force required by the robot. Through this strategy, the robot can accurately follow and guide the human force, thereby achieving smoother and more stable movement, and ensuring robustness and compliance in complex environments. By comparing the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point, the present invention successfully maps the indirect intention curvature model in Cartesian space to the feature space, realizing more accurate force guidance in the visual human-robot collaborative process, and can effectively coordinate the interaction between robots and humans.

[0132] like Figure 3 As shown in the figure, it is the overall control block diagram. Figure 4 The figure shows the composition of a control system in a practical application. The control algorithm and visual processing are completed on the Ubuntu 18.04LTS operating system. The software framework is based on the VisualServo Platform (ViSP) and the Robot Operating System (ROS).

[0133] like Figure 5As shown in the figure, it is a comparison diagram of the motion trajectory of the end effector and the feature point. F.1 to F.4 represent feature points 1 to 4 respectively. It can be observed that the motion trajectory of the robot end effector in the xy direction is consistent with the motion trajectory of the feature point in the uv direction, indicating that the motion trajectory of the end effector can be accurately mapped to the motion of the feature point, ensuring that the robot moves along the desired path. It also shows that the indirect intention method from Cartesian space to feature space has been effectively applied, further improving the performance and stability of the admittance control system.

[0134] In one embodiment of the present application, the effect of the present method is verified by a specific experiment, such as Figure 6 As shown in the figure, the experiment consists of four parts: 1) Visual servo experiment process: the robot moves from the initial position PO to PA. During this period, the impact force is applied to verify the robustness and convergence of the designed method in the servo process; 2) Straight path human-machine collaboration experiment process: the end effector is dragged back and forth from position PA to position PB, and the flexibility and robustness in the human-machine collaboration process are verified during this period; 3) Curved path human-machine collaboration experiment process: the end effector is dragged back and forth from position PA to position PB, and the flexibility and robustness in the human-machine collaboration process are verified during this period; 4) Hole experiment process: by comparing with the pure visual servo controller, the superiority of the coupled admittance control visual servo controller is demonstrated. In the experiment, the diameter of the shaft is 7 mm and the diameter of the hole is 8 mm. The workpiece used is a square wooden board with a side length of 20 cm and a hole depth of 2 cm. A 36h11 series AprilTag with a size of 5 cm is pasted on the surface of the wooden board.

[0135] like Figure 7 As shown in the figure, the robustness and convergence of the designed method when the impact force is applied during the visual servoing process are verified. When the impact force applied is the largest, the convergence speed of this method is faster and the fluctuation of the convergence process is smaller, that is, it has better robustness and convergence.

[0136] like Figure 8 and Fig. 9 The following are the experimental results of compliance, robustness and efficiency of human-machine collaborative motion in straight and curved trajectories. In the experiment, the robot end effector applied instantaneous impact force in two human-machine collaborative processes and two visual servo processes. The results show that the controller has the best stability performance in both straight and curved motion, with the smallest fluctuation in the task, can effectively cope with external shocks, maintain low traction, and complete the task in the shortest time. In contrast, other controllers have large fluctuations and poor stability. Overall, the proposed controller performed well in the compliance and robustness verification, showing higher stability, lower traction and strong impact resistance.

[0137] like Fig.10 As shown in the figure, the controller designed for this method has advantages over the pure visual servo controller in the hole experiment. The experimental analysis shows that under the designed variable admittance control method, the robot arm can adaptively adjust through the virtual mass-spring-damper system regardless of the presence of disturbances. When the end effector failed to enter the hole for the first time, the system used vision and force feedback to try the insertion task again and finally completed it successfully. This shows that the system has good impact resistance and stability. In contrast, without admittance control, the task failed due to the impact force of the wooden board on the end effector, and the pure visual servo system entered an emergency shutdown state.

[0138] 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.

[0139] Based on the same inventive concept, the embodiment of the present application also provides a robust adaptive variable admittance control device based on different intentions in a feature space for implementing the robust adaptive variable admittance control method based on different intentions in the feature space involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the robust adaptive variable admittance control device based on different intentions in one or more feature spaces provided below can refer to the limitations of the robust adaptive variable admittance control method based on different intentions in the feature space above, and will not be repeated here.

[0140] In one embodiment, Fig.11 As shown, a robust adaptive variable admittance control device 1100 based on different intentions in a feature space is provided, comprising: a mapping relationship determination module 1101, a robust adaptive damping coefficient and an adaptive stiffness coefficient determination module 1103, an admittance control virtual damping coefficient determination module 1105 based on direct and indirect intentions, an admittance control determination module 1107 and a manipulator motion control module 1109, wherein:

[0141] The mapping relationship determination module 1101 is used to determine the mapping relationship between the actual contact force and the virtual contact force by using the kinematics and dynamics model of the robot arm.

[0142] The robust adaptive damping coefficient and adaptive stiffness coefficient determination module 1103 is used to determine the adaptive stiffness coefficient based on the actual contact force change, and determine the robust adaptive virtual damping coefficient based on direct intention based on the virtual motion speed of the feature point.

[0143] The virtual damping coefficient determination module 1105 for admittance control based on direct and indirect intentions is used to adjust the robust adaptive virtual damping coefficient based on the contact force expected by the robot and determine the virtual damping coefficient for admittance control based on direct and indirect intentions.

[0144] The admittance control determination module 1107 is used to determine the robust adaptive variable admittance control based on human intention in the feature space based on the mapping relationship, the adaptive stiffness coefficient and the admittance control virtual damping coefficient.

[0145] The robot arm motion control module 1109 is used to determine the robot image visual servo control law based on the real-time image feature points and the expected feature points, obtain the real-time joint speed of the robot arm, and control the movement of the robot arm.

[0146] In one embodiment of the present application, the mapping relationship determination module is further used to:

[0147] Determine the second-order kinematic model of visual servoing based on the robot kinematic model and the transformation matrix from the robot end effector to the camera;

[0148] 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.

[0149] In one embodiment of the present application, the virtual damping coefficient determination module for admittance control based on direct and indirect intentions is further used for:

[0150] Predict the traction required when the robot collaborates with humans in real time, and determine the desired contact force for adjusting the robot;

[0151] Determining a desired virtual admittance parameter based on the desired contact force and robust adaptive virtual damping coefficient of the adjustment robot;

[0152] A direct and indirect intention based admittance control virtual damping coefficient is determined based on the desired virtual admittance parameter and the robust adaptive virtual damping coefficient.

[0153] In one embodiment of the present application, determining and adjusting the desired contact force of the robot includes:

[0154] Compare the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point and calculate the trajectory curvature information;

[0155] The contact force expected by the robot is calculated and adjusted based on the curvature information.

[0156] In one embodiment of the present application, the virtual damping coefficient of the admittance control based on direct and indirect intentions is:

[0157]

[0158] Among them, D vs is the virtual damping coefficient of admittance control based on direct and indirect intentions, D1 is the robust adaptive virtual damping coefficient based on direct intention, and D expected is the expected virtual admittance parameter.

[0159] In one embodiment of the present application, the robust adaptive variable admittance control based on human intention in the feature space is defined as:

[0160]

[0161] Among them, M vs is the virtual quality coefficient in the feature space, D vs is the virtual damping coefficient of admittance control based on direct and indirect intention, K vs is the adaptive stiffness coefficient in the feature space; 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 ω is the position of the feature point moved by the external contact force, f sext is the virtual contact force in the feature space.

[0162] In one embodiment of the present application, the robot image visual servo control law is defined as:

[0163]

[0164] Among them, v c is the camera's speed, λ is the controller gain, is the pseudo-inverse matrix of the image interaction matrix, e ad is the error between the real-time image feature point and the expected feature point, is the velocity characteristic point, s ω is the position of the feature point moved by the external contact force, s d is the expected feature point position set initially.

[0165] Each module in the robust adaptive variable admittance control device based on different intentions under the above feature space can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0166] 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.11 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 variable admittance control method based on different intentions 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 shell, or an external keyboard, touchpad or mouse.

[0167] Those skilled in the art will understand that Fig.11 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.

[0168] 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.

[0169] 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 method embodiments are implemented.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 variable admittance control method based on different intentions in feature space, characterized in that: The method comprises: The mapping relationship between actual contact force and virtual contact force is determined by using the kinematic and dynamic models of the robot arm; determining an adaptive stiffness coefficient based on the actual contact force change, and determining a robust adaptive virtual damping coefficient based on direct intention based on the virtual motion speed of the feature point; adjusting the robust adaptive virtual damping coefficient based on the contact force expected by the adjusted robot, and determining the virtual damping coefficient of the admittance control based on direct and indirect intentions; Determine the robust adaptive variable admittance control based on human intention in the feature space based on the mapping relationship, the adaptive stiffness coefficient and the virtual damping coefficient of the admittance control; Based on the real-time image feature points and expected feature points, the robot image visual servo control law is determined, the real-time joint speed of the robot arm is obtained, and the movement of the robot arm is controlled.

2. The robust adaptive variable admittance control method based on different intentions in feature space according to claim 1, characterized in that: The mapping relationship between the actual contact force and the virtual contact force determined by using the robot arm kinematics and dynamics model includes: Determine the second-order kinematic model of visual servoing based on the robot kinematic model and the transformation matrix from the robot end effector to the camera; 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 robust adaptive variable admittance control method based on different intentions in feature space according to claim 1, characterized in that: The method of adjusting the robust adaptive virtual damping coefficient based on the contact force expected by the robot and determining the virtual damping coefficient of the admittance control based on direct and indirect intentions includes: Predict the traction required when the robot collaborates with humans in real time, and determine the desired contact force for adjusting the robot; Determining a desired virtual admittance parameter based on the desired contact force and robust adaptive virtual damping coefficient of the adjustment robot; A direct and indirect intention based admittance control virtual damping coefficient is determined based on the desired virtual admittance parameter and the robust adaptive virtual damping coefficient.

4. The robust adaptive variable admittance control method based on different intentions in feature space according to claim 3 is characterized in that: The determining and adjusting the desired contact force of the robot comprises: Compare the actual motion trajectory of the robot end effector with the virtual motion trajectory of the feature point and calculate the trajectory curvature information; The contact force expected by the robot is calculated and adjusted based on the curvature information.

5. The robust adaptive variable admittance control method based on different intentions in feature space according to claim 3, characterized in that: The virtual damping coefficient of the admittance control based on direct and indirect intentions is: Among them, D vs is the virtual damping coefficient of admittance control based on direct and indirect intentions, D1 is the robust adaptive virtual damping coefficient based on direct intention, and D expected is the expected virtual admittance parameter.

6. The robust adaptive variable admittance control method based on different intentions in feature space according to claim 1, characterized in that: The robust adaptive variable admittance control based on human intention in the feature space is defined as: Among them, M vs is the virtual quality coefficient in the feature space, D vs is the virtual damping coefficient of admittance control based on direct and indirect intention, K vs is the adaptive stiffness coefficient in the feature space; 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 ω is the position of the feature point moved by the external contact force, f sext is the virtual contact force in the feature space.

7. The robust adaptive variable admittance control method based on different intentions in feature space according to claim 1, characterized in that: The robot image visual servo control law is defined as: Among them, v c is the camera's speed, λ is the controller gain, is the pseudo-inverse matrix of the image interaction matrix, e ad is the error between the real-time image feature point and the expected feature point, is the velocity characteristic point, s ω is the position of the feature point moved by the external contact force, s d is the expected feature point position set initially.

8. A robust adaptive variable admittance control device based on different intentions in feature space, characterized in that: The device comprises: A mapping relationship determination module is used to determine the mapping relationship between the actual contact force and the virtual contact force using the kinematics and dynamics model of the robot arm; A robust adaptive damping coefficient and adaptive stiffness coefficient determination module, used to determine the adaptive stiffness coefficient based on the actual contact force change, and determine the robust adaptive virtual damping coefficient based on direct intention based on the virtual motion speed of the feature point; A virtual damping coefficient determination module for admittance control based on direct and indirect intentions, used for adjusting the robust adaptive virtual damping coefficient based on adjusting the desired contact force of the robot, and determining the virtual damping coefficient for admittance control based on direct and indirect intentions; An admittance control determination module, used to determine a robust adaptive variable admittance control based on human intention in a feature space based on the mapping relationship, the adaptive stiffness coefficient and the admittance control virtual damping coefficient; The robot arm motion control module is used to determine the robot image visual servo control law based on real-time image feature points and expected feature points, obtain the real-time joint speed of the robot arm, and control the movement of the robot arm.

9. 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 7 are implemented.

10. 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 7 are implemented.

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