Active compliance control system of robot based on impedance control

Abstract

This invention discloses an active compliance control method for a robot based on impedance control. The method includes the following steps: obtaining a physical model of the robot and constructing a dynamic model of the robot; sampling, classifying, and processing the robot's historical path points on the surface of an object being manipulated; adjusting the position and posture of future path points relative to the surface of the object being manipulated by fitting the trajectories of the historical path points; and controlling the robot's movement along the optimized future motion path of the robot's end-point through optimized impedance control, while also controlling the force exerted on the end-point. This control method is more flexible and can adapt to the robot's end-point force control requirements under different working conditions, while also enabling more precise control of the robot's end-point displacement.

Description

Technical Field

[0001] The present invention belongs to the field of robot motion control, and in particular relates to an active compliance control method of a robot based on impedance control. Background Art

[0002] Conventional robot control methods often struggle when robots manipulate objects with complex surfaces. This is because these methods typically rely on preprogrammed motion paths and fixed control strategies, lacking the flexibility to adapt to complex and unpredictable surface characteristics. Traditional control methods, relying on preprogrammed fixed paths, often fail to accurately perform manipulation tasks on complex surfaces. For example, the irregularities, curves, and angles of complex surfaces can cause the pre-set paths to deviate from the desired operation. Due to this variability in surface characteristics, conventional control methods require frequent adjustments and recalibration during manipulation, resulting in low operational efficiency. When robots navigate complex surfaces, their lack of flexible adjustment capabilities may lead to multiple attempts to complete the task, increasing operation time. Traditional control methods lack real-time feedback and adjustment mechanisms, resulting in poor stability when manipulating complex surfaces. Unable to adapt to surface variations, the robot may slip or fall. Inadequate understanding of the mechanical properties of complex surfaces can lead to excessive or insufficient force, resulting in damage to the object or the robot itself. Traditional control methods cannot guarantee safety, especially when performing precision manipulation or handling delicate materials.

[0003] Specifically, complex surfaces include various shapes such as bumps, curves, and slopes, characteristics that cannot be addressed through simple path planning and fixed control strategies. Surface materials vary greatly, and parameters such as the coefficient of friction and hardness vary greatly. Traditional control methods cannot dynamically adjust to these changes. Conventional robotic systems are limited in the types and number of sensors, unable to provide sufficient real-time data to reflect the detailed characteristics of complex surfaces. The lack of high-precision, real-time force, visual, and tactile feedback prevents the control system from making timely adjustments. Traditional control algorithms are mostly fixed-parameter designs and lack adaptability. They are unable to adjust control strategies based on real-time feedback, lack self-learning and adaptive mechanisms, and are unable to optimize the operation process through accumulated experience. Traditional robotic systems have limited capabilities in environmental cognition and modeling, and are unable to accurately construct three-dimensional models of complex surfaces. They lack a deep understanding of the surface characteristics of the operated object and environmental changes, resulting in large errors in the planning and execution processes. Summary of the Invention

[0004] The purpose of the present invention is to provide a robot active compliance control system based on impedance control, which is used to solve the technical problem in existing solutions that robots cannot adaptively operate objects with complex surfaces.

[0005] The object of the present invention can be achieved through the following technical solution: The present invention provides a robot active compliance control method based on impedance control, characterized in that it includes the steps of:

[0006] Modeling: Obtain the physical model of the robot and build the robot's dynamic model;

[0007] Data processing: Sample a series of historical path points within the contact range between the robot end and the surface of the object being operated, calculate the normal vectors of the historical path points, and classify them into smooth points and non-smooth points;

[0008] Path optimization: Fit the trajectory of the path points and adjust the path of the robot end according to the final fitted trajectory;

[0009] Implementation control: Through optimized impedance control, the robot is controlled to move along the optimized future motion path of the robot end, while the force at the robot end is controlled. Specifically:

[0010]

[0011] Where ΔF is the difference between the desired contact force and the actual contact force at the end of the robot, m is the quality coefficient of the impedance control, E is the state error of the end of the robot, b is the damping coefficient, k is the stiffness coefficient, T is the control period, F is the desired contact force at the end of the robot, and F r is the actual contact force at the end of the robot, l r is the actual normal vector of the path point, and α is the control coefficient.

[0012] Preferably, the control step can be replaced by: adjusting the motion of the robot by a hybrid control method combining model predictive control and impedance control, and the input u of the hybrid control bind =γu m +(1-γ)u z , where γ is the balance coefficient, preferably sin 2 θ, θ is the angle between the actual contact force at the end of the robot and the normal vector of the path point, u m is the input of model predictive control, u z Impedance controlled input

[0013] Preferably, in the data processing step, when constructing the local neighborhood covariance matrix of the historical path points, the size of the neighborhood is adaptively determined.

[0014] Preferably, in the data processing step, the smoothing property of the path point is determined based on the product of the minimum eigenvalue ratio of the historical path point and the standard deviation of the normal vector angle.

[0015] Preferably, weight coefficients are introduced in the construction process of the local neighborhood covariance matrix.

[0016] Preferably, in the path optimization step, preliminary fitting is performed by randomly extracting some historical path points, and preliminary screening of the fitting trajectory is performed based on the angle between the neighborhood normal vectors of some randomly extracted smooth points and the trajectory normal vectors of the corresponding projection points on the preliminary fitting trajectory.

[0017] Preferably, in the path optimization step, the fitting trajectory is further screened and updated based on the normal vector angle of the smoothing point and the distance from the path point to the fitting trajectory.

[0018] The present invention also provides a robot active compliance control system based on impedance control, which is characterized by comprising:

[0019] A modeling module is used to obtain the physical model of the robot and build the dynamic model of the robot;

[0020] a data processing module for sampling a series of historical path points within the contact range between the robot end and the surface of the operation object based on the surface information of the robot's operation object, calculating the normal vectors of the historical path points, and classifying them into smooth points and non-smooth points;

[0021] The path optimization module is used to fit the trajectory of the path points and adjust the path of the robot end according to the final fitting trajectory;

[0022] The controller module controls the robot along the optimized future motion path of the robot end through optimized impedance control, while also controlling the force at the robot end. Specifically:

[0023]

[0024] Where ΔF is the difference between the desired contact force and the actual contact force at the end of the robot, m is the quality coefficient of the impedance control, E is the state error of the end of the robot, b is the damping coefficient, k is the stiffness coefficient, T is the control period, F is the desired contact force at the end of the robot, and F r is the actual contact force at the end of the robot, l r is the actual normal vector of the path point, and α is the control coefficient.

[0025] Preferably, the controller module can alternatively adjust the movement of the robot by a hybrid control method combining model predictive control and impedance control, and the input u of the hybrid control is bind =γu m +(1-γ)u z , where γ is the balance coefficient, preferably sin 2 θ, θ is the angle between the actual contact force at the end of the robot and the normal vector of the path point, u m is the input of model predictive control, u z Impedance controlled input.

[0026] The present invention also provides a storage medium, comprising at least one processor; and a memory communicatively connected to the at least one processor;

[0027] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute any of the aforementioned methods for active compliance control of robots.

[0028] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0029] This active compliant control method enables precise motion and force control of the robot. It exhibits robustness and anti-interference capabilities, optimizes path planning, improves fitting accuracy and efficiency, and achieves precise motion and force control. By employing optimized impedance control and dynamically adjusting the damping and stiffness coefficients, precise control of the robot's end contact force is achieved. This is particularly true in applications requiring precise manipulation and force control, such as medical robots and precision assembly, where external disturbances can be effectively suppressed, improving system stability and reliability. A hybrid control approach can also be employed, combining model predictive control and impedance control. By leveraging the trajectory tracking optimization capabilities of model predictive control and the force control capabilities of impedance control, the balance coefficient can be dynamically adjusted to achieve more precise and smooth robot motion path tracking and force control.

[0030] The hybrid control strategy incorporates dynamically adjustable parameters, enabling the control system to balance the influence of model predictive control and impedance control under varying operating conditions, enhancing the system's adaptability and flexibility. In actual operation, the system rapidly responds to environmental changes, maintains stable trajectory tracking and force control performance, and adapts to complex operating environments and mission requirements. By rationally allocating control inputs and optimizing energy consumption, it is particularly suitable for applications requiring long-term operation and high energy efficiency. Model predictive control reduces unnecessary motion, while impedance control balances energy consumption with mission execution requirements, improving overall system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below with reference to the accompanying drawings.

[0032] Figure 1 Schematic diagram of the working scenario for active compliant control of the robot.

[0033] Figure 2 This is a flow chart of the active compliance control method of a robot based on impedance control according to the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary operation and maintenance personnel in this field without making any creative efforts are within the scope of protection of the present invention.

[0035] like Figure 1 As shown in the figure, in operation scenarios such as assembly, grinding, polishing, and clamping, the robot end-point needs to continuously track and contact the object being operated. Different areas of the object may or are likely to have different surface features. Based on this work scenario, active compliant control of the robot is required.

[0036] Example 1: This example is a robot active compliance control method based on model predictive control, which includes the following steps:

[0037] 1. Modeling:

[0038] 1.1 Obtaining the Robot's Physical Model: Obtain a rigid-body dynamics model of the robot by modeling its geometry, mass, inertia, and other physical parameters. Specifically, establish the robot's geometric model, including parameters such as joint lengths and link lengths; determine the robot's mass distribution, including the mass of each link and joint; and estimate the robot's inertial parameters, such as the moment of inertia and center of mass.

[0039] 1.2 Constructing the robot's dynamic model: Using the Lagrangian dynamics method, the robot's motion equations are derived, including the relationship between joint angles, joint angular velocities, and joint torques. Considering the incompleteness of the model and the presence of external interference, uncertainty compensation terms can be introduced to optimize the robot's dynamic model. The compensation terms can be estimated based on experimental data, observers, or machine learning methods. The optimized robot dynamic model is as follows:

[0040]

[0041] Where τ is the joint torque, q d is the desired joint position, is the desired joint velocity, is the expected joint acceleration, M is the mass matrix, which describes the mass distribution of the robot joints, C is the Coriolis force matrix, which describes the centrifugal force and Coriolis force generated by the joint motion, and G is the gravity matrix, which describes the gravity effect caused by the robot mass. is the adaptive term, is the reference acceleration of the adaptive term, e is the joint position error, K p , K dare proportional gain and differential gain respectively. By introducing uncertainty compensation terms in the modeling step, the robustness and anti-interference ability of the model can be improved.

[0042] 2. Data processing:

[0043] 2.1 Sampling: Sample a series of historical path points within the contact range between the robot end and the surface of the operation object.

[0044] 2.2 Historical path point processing: Calculate the normal vectors of historical path points and classify them into smooth points and non-smooth points. Specifically, construct the local neighborhood covariance matrix of the historical path points and calculate the eigenvalues and eigenvectors of the covariance matrix. The eigenvector corresponding to the minimum eigenvalue is the normal vector of the historical path point. If the product of the minimum eigenvalue ratio of the historical path point and the standard deviation of the normal vector angle is greater than the feature threshold, the path point is a non-smooth point, otherwise it is a smooth point. Based on the classified path points, downsample the smooth points and / or upsample the non-smooth points to ensure a moderate density of path points.

[0045] Construct the local neighborhood covariance matrix of the historical path point. The following will take a historical path point as an example to explain it in detail. i As the center, R is the radius of the sphere encompassing all historical path points, constructing the historical path point p i The local neighborhood point set of . A fixed neighborhood size can be selected, or it is more optimal to dynamically and adaptively set the neighborhood based on the distance. Specifically, R∈[R min ,R max ], R min is the minimum neighborhood radius, R max is the maximum neighborhood radius. The determination of R includes:

[0046] Get the robot's historical path point p i The velocity v of the robot end at the time, and the historical path point p i With centered on the path, traverse the neighboring historical path points in a spherical manner from the center outward according to the radius from small to large, until the robot passes through the two closest historical path points with the velocities of the robot end being v-∆v and v+∆v respectively.

[0047] According to the distance between the two closest historical path points and [R min ,R max ], determine R, specifically: if the distance between the two closest historical path points is greater than R max , then R=R max , in the process of constructing the covariance matrix, the weights of the points in the local neighborhood point set are the same; if the distance between the two closest historical path points ∈ [R min,R max ], then R is the distance between the two closest historical path points. In the process of constructing the covariance matrix, the weights of the points in the local neighborhood point set are the same; if the distance between the two closest historical path points is less than R min , then R=R min , and introduce weight coefficients in the process of covariance matrix construction, such as for historical path point p i The j-th point p in the local neighborhood point set j The weight coefficient is , where k,c∈R.

[0048] For historical path points in a fixed neighborhood, in the local neighborhood covariance matrix process, the weights of each point in the local neighborhood point set can be the same, and weight coefficients can be introduced as needed. The specific settings of the weight coefficients can be as shown above.

[0049] The eigenvalue represents the variance of the covariance matrix in the direction of each eigenvector, while the eigenvector represents the principal direction of the covariance matrix, i.e., the normal vector of that point. For each path point, the eigenvector corresponding to the minimum eigenvalue is selected as its neighborhood normal vector. The adaptive neighborhood size selection method ensures that more appropriate neighborhood points are included when calculating the path point normal vector, ensuring a smaller neighborhood in smooth areas and a larger neighborhood in complex areas (such as edges and corners). This improves the accuracy, robustness, and timeliness of the normal vector estimation. A weighting coefficient is introduced to ensure that points closer to the center point in the neighborhood contribute more to the normal vector estimation, while points farther away contribute less, improving the accuracy of the normal vector estimation, especially when processing complex surfaces. Historical path points are classified into smooth and non-smooth points. Smooth points are used to fit overall trends, while non-smooth points are used to capture local sharp changes, such as edges and corners. This results in a fitting trajectory that better matches the actual surface, improving fitting accuracy and efficiency. The minimum eigenvalue ratio is the ratio of the minimum eigenvalue of the covariance matrix to all eigenvalues, and the standard deviation of the normal vector angle is the historical path point p i The standard deviation of the normal vector angle of each point in its local neighborhood point set is used, and the product of the minimum eigenvalue ratio and the standard deviation of the normal vector angle is used as the judgment indicator. This can comprehensively reflect the geometric characteristics of the local surface of the path point, such as the smoothness of the local surface and the stability of the normal vector change, and improve the classification accuracy, robustness and stability.

[0050] 3. Path optimization: The specific steps are as follows: (1) Divide the smooth points into several regions to form several sets, and divide the non-smooth points into several regions to form several sets. Select a small number of path points from each smooth point set and more path points from the non-smooth point set to perform preliminary trajectory fitting; (2) Randomly extract a part of the smooth points and calculate the angle between their neighborhood normal vector and the trajectory normal vector of the corresponding projection point on the preliminary fitting trajectory; (3) Set the angle threshold and filter value. If the number of smooth points with an angle less than the angle threshold is greater than the filter value, proceed to the next step, otherwise return to step (1); (4) Randomly extract several smooth points and non-smooth points, calculate the distance from each extracted point to the current fitting trajectory, set the distance threshold and filter value. If the number of path points with a distance less than the distance threshold is greater than the filter value, determine the current fitting trajectory as the final fitting trajectory; otherwise return to step (1). Use the final fitting trajectory to correct the robot's future path and adjust the position and posture information of the path points relative to the surface of the object being operated.

[0051] By randomly selecting some path points for preliminary fitting, the amount of calculation can be effectively reduced. The preliminary screening of the normal vector angles of smooth points with higher precision can quickly eliminate fitting results that do not conform to the actual surface characteristics. Further distance screening ensures that the fitting trajectory not only matches well in normal vectors, but also conforms to the actual surface characteristics in spatial distance, thereby improving fitting accuracy.

[0052] 4. Implement control: Control the robot along the optimized future motion path of the robot end while controlling the force at the end. By introducing model predictive control, based on the robot's kinematic model, the control input u of the model predictive control is calculated by minimizing the objective function of the model predictive control and setting constraints. The specific robot dynamics model is:

[0053]

[0054] Where τ is the joint torque, F is the contact force, J is the Jacobian matrix, and q d is the desired joint position, is the desired joint velocity, is the expected joint acceleration, M is the mass matrix, which describes the mass distribution of the robot joints, C is the Coriolis force matrix, which describes the centrifugal force and Coriolis force generated by the joint motion, and G is the gravity matrix, which describes the gravity effect caused by the robot mass. is the adaptive term, is the reference acceleration of the adaptive term, e is the joint position error, K p , K d are the proportional gain and the differential gain respectively.

[0055] The constraints of model predictive control include:

[0056] x i+1 =f(x i ,u i ), x i is the system state at time i;

[0057] x i ∈X, i=0,……,N-1, N is the control time domain, X is the system state constraint set;

[0058] u i ∈U, i=0,……,N-1, U is the system input constraint set.

[0059]

[0060]

[0061] Among them, F max is the maximum contact force applied by the robot end, A is the adjustment amount, S is the distance from the robot end to the operation object, S0 is the distance threshold, F b F is the upper limit of contact force. s is the lower limit of contact force. The objective function of model control is J(x,u).

[0062] In the control step, the robot's kinematic and dynamic models are utilized, and model predictive control is employed to predict future states and behaviors, thereby adjusting the control input in real time. This allows for more accurate predictions of the robot's end-of-line motion trajectory and force trends, allowing the robot to more accurately follow the desired path while responding quickly in dynamic environments. This not only considers the optimized motion of the robot's end-of-line path, but also adjusts the end-of-line contact force in real time. By precisely controlling the contact force, the robot can ensure safety and precision when contacting the task object. For example, in assembly operations, the controller can adjust the contact force to avoid damaging parts or maintain appropriate friction to stabilize the workpiece. Dynamic contact force limits based on distance and motion state are introduced to adapt to different operating scenarios and working conditions. By dynamically adjusting the contact force limits, the application of contact force can be optimized under different distances and environmental conditions, thereby maximizing the safety and efficiency of the operation. For example, in collaborative robots, this capability can ensure a safe distance and contact force between the robot and the human operator.

[0063] The maximum contact force constraint includes force control strategies for different situations, corresponding to different force control strategies when the robot end approaches or moves away from the work object. This enables the system to flexibly respond to different working environments and task requirements, preventing the robot from applying excessive force, thereby avoiding damage to the object being operated or the robot itself. By adjusting the contact force, the interaction between the robot end and the surface can be more precisely controlled, thereby improving the accuracy of tracking unknown surfaces, increasing the robustness of the system, and reducing deviations caused by external disturbances or uncertainties. This precise control allows the robot to flexibly respond to different work scenarios, ensuring that the appropriate contact force is maintained at different distances, thereby improving the accuracy and reliability of operations.

[0064] Example 2: Figure 2 As shown, this embodiment is a robot active compliance control method based on impedance control, which includes the steps of modeling, data processing, path optimization, and implementation control, wherein the modeling, data processing, and path optimization steps can adopt the same or similar design as Example 1.

[0065] Specifically, in the control step, the robot is controlled to move along the optimized future motion path of the robot end through optimized impedance control, while the force at the robot end is controlled, specifically:

[0066]

[0067] Where ΔF is the difference between the desired contact force and the actual contact force at the end of the robot, m is the quality coefficient of the impedance control, E is the state error of the end of the robot, b is the damping coefficient, k is the stiffness coefficient, T is the control period, F is the desired contact force at the end of the robot, and F r is the actual contact force at the end of the robot, l r is the actual normal vector of the path point, and α is the control coefficient.

[0068] In the optimized impedance control, the damping part and the stiffness part are optimized, which can make the control system more flexible and adapt to the control requirements of the robot end force under different working conditions. At the same time, the force of the robot end can be controlled more accurately, which is especially important for applications that require fine operation and force control, such as medical robots, precision assembly and other fields. It can also enhance the system's ability to suppress external disturbances and improve the system's stability and reliability, especially in complex working environments. This ability is particularly prominent.

[0069] Example 3: This example is an active compliance control method for a robot based on impedance control, which includes the steps of modeling, data processing, path optimization, and implementation control, wherein the modeling, data processing, and path optimization steps can adopt the same or similar design as Example 1.

[0070] Specifically, in the control implementation step, the robot's motion is adjusted to fit the optimized path by integrating a hybrid control method of model predictive control and impedance control.

[0071] The integrated control input u bind =γu m +(1-γ)u z , where γ is the balance coefficient, preferably sin 2 θ, θ is the angle between the contact force and the normal vector of the path point, u m is the input of the model predictive control, which can be equal to the input in Example 1, u z is the impedance-controlled input, which can be equal to the input in Example 2.

[0072] In the control implementation, model predictive control (MPC) is often used to optimize the robot's trajectory tracking, generating optimal control inputs by predicting future states. Impedance control, on the other hand, primarily controls contact forces and the forces at the robot's end-point. Combining these two control strategies, leveraging their respective strengths, can both optimize the robot's motion path and precisely control its forces and torques. The introduction of the parameter γ allows it to dynamically adjust with the robot's motion state, thereby balancing the influence of MPC and impedance control under varying motion paths and operating conditions. For example, when the robot needs to more precisely follow the optimized path (when θ is small), γ is larger, relying more on MPC inputs. However, when responding to large changes in external forces (when θ is large), γ is smaller, relying more on impedance control inputs. By integrating MPC and impedance control, more accurate and smoother robot path tracking can be achieved. MPC optimizes the prediction of future motion states, enabling the robot to more accurately follow the optimized path; simultaneously, impedance control effectively adjusts forces and torques to adapt to changes in the external environment and task requirements. The dynamic adjustment of parameter γ makes the control system more adaptable and flexible. In practice, this means the robot can quickly respond to environmental changes while maintaining stable trajectory tracking and force control performance. By properly distributing control inputs, energy consumption can be effectively optimized, especially in applications with long operating times and high energy conservation requirements. Model predictive control helps reduce unnecessary motion, while impedance control effectively balances energy consumption with task execution requirements.

[0073] Embodiment 4: This embodiment is a robot active compliance control system, which includes a modeling module, a data processing module, a path optimization module and a controller module. Each module is used to implement the relevant steps of any of the above embodiments.

[0074] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A robot active compliance control method based on impedance control, characterized in that: Including steps: Modeling: Obtain the physical model of the robot and build the robot's dynamic model; Data processing: Sample a series of historical path points within the contact range between the robot end and the surface of the object being operated, calculate the normal vectors of the historical path points, and classify them into smooth points and non-smooth points; Path optimization: Fit the trajectory of the path points and adjust the path of the robot end according to the final fitted trajectory; Implementation control: Through optimized impedance control, the robot is controlled to move along the optimized future motion path of the robot end, while the force at the robot end is controlled. Specifically: , where ΔF is the difference between the desired contact force and the actual contact force at the end of the robot, m is the quality coefficient of impedance control, E is the state error of the end of the robot, b is the damping coefficient, k is the stiffness coefficient, T is the control period, F is the desired contact force at the end of the robot, and F r is the actual contact force at the end of the robot, l r is the actual normal vector of the path point, and α is the control coefficient.

2. The active compliance control method of a robot based on impedance control according to claim 1, characterized in that: The control steps can be replaced by: adjusting the robot's motion through a hybrid control method combining model predictive control and impedance control, and the input u of the hybrid control bind for u bind =γu m +(1-γ)u z , where γ is the balance coefficient, u m is the input of model predictive control, u z Impedance controlled input.

3. The active compliance control method of a robot based on impedance control according to claim 1, characterized in that: In the data processing step, when constructing the local neighborhood covariance matrix of the historical path points, the size of the neighborhood is adaptively determined.

4. The active compliance control method of a robot based on impedance control according to claim 1, characterized in that: In the data processing step, the smoothing property of the path point is determined based on the product of the minimum eigenvalue ratio of the covariance matrix of the historical path point and the standard deviation of the normal vector angle.

5. The active compliance control method of a robot based on impedance control according to claim 1, characterized in that: The weight coefficient is introduced in the construction process of the local neighborhood covariance matrix.

6. The active compliance control method of a robot based on impedance control according to claim 1, characterized in that: In the path optimization step, preliminary fitting is performed by randomly selecting some historical path points, and the fitting trajectory is preliminarily screened based on the angle between the neighborhood normal vectors of some randomly selected smooth points and the trajectory normal vectors of the corresponding projection points on the preliminary fitting trajectory.

7. The active compliance control method of a robot based on impedance control according to claim 1, characterized in that: In the path optimization step, the fitting trajectory is further screened and updated based on the normal vector angle of the smoothing point and the distance from the path point to the fitting trajectory. 8.An active compliance control system for robots based on impedance control, characterized in that: include: A modeling module is used to obtain the physical model of the robot and build the dynamic model of the robot; A data processing module is used to sample a series of historical path points within the contact range between the robot end and the surface of the operation object, calculate the normal vectors of the historical path points, and classify them into smooth points and non-smooth points; The path optimization module is used to fit the trajectory of the path points and adjust the path of the robot end according to the final fitting trajectory; The controller module controls the robot along the optimized future motion path of the robot end through optimized impedance control, while also controlling the force at the robot end. Specifically: , where ΔF is the difference between the desired contact force and the actual contact force at the end of the robot, m is the quality coefficient of impedance control, E is the state error of the end of the robot, b is the damping coefficient, k is the stiffness coefficient, T is the control period, F is the desired contact force at the end of the robot, and F r is the actual contact force at the end of the robot, l r is the actual normal vector of the path point, and α is the control coefficient.

9. The robot active compliance control system based on impedance control according to claim 8, characterized in that: The controller module can alternatively adjust the robot's motion through a hybrid control method that combines model predictive control and impedance control. The input u of the hybrid control bind for u bind =γu m +(1-γ)u z , where γ is the balance coefficient, and the value of γ is sin 2 θ, θ is the angle between the actual contact force at the end of the robot and the normal vector of the path point, u m is the input of model predictive control, u z Impedance controlled input.

10. A storage medium comprising at least one processor; and a memory communicatively connected to the at least one processor; in, The memory stores a computer program that can be executed by at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the robot active compliance control method according to any one of claims 1 to 7.

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