Robot active compliance control system based on impedance control

Abstract

The invention discloses a robot active compliance control method based on impedance control. The robot active compliance control method comprises the following steps that a physical model of a robot is obtained, and a kinetic model of the robot is constructed; historical path points of the robot on the surface of the operation object are sampled, and the historical path points are classified and processed; through track fitting of the historical path points, the positions and poses of the future path points relative to the surface of the operation object are adjusted; and through optimized impedance control, the robot is controlled to move along the optimized future motion path of the tail end of the robot, and meanwhile, the force of the tail end of the robot is controlled. The control method is more flexible, can adapt to the control requirements of the tail end force of the robot under different working conditions, and can more accurately control the tail end displacement of the robot.

Description

Technical Field

[0001] The 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 fail to work when robots operate objects with complex surfaces. This is because these methods usually rely on pre-programmed motion paths and fixed control strategies, which cannot flexibly cope with complex and unpredictable surface characteristics. Conventional control methods rely on pre-programmed fixed paths, and often cannot accurately perform operation tasks when faced with complex surfaces. For example, the irregularities, curves, and multiple angles of complex surfaces cause the preset paths to deviate from actual needs. Due to the variable surface characteristics, conventional control methods require frequent adjustments and recalibration during the processing process, resulting in low operation efficiency. When the robot moves on a complex surface, it may take multiple attempts to complete the task due to the lack of flexible adjustment capabilities, which increases the operation time. Conventional control methods lack real-time feedback and adjustment mechanisms, resulting in poor stability when operating on complex surfaces. The robot may slip, tip over, etc. due to its inability to adapt to surface changes. Insufficient understanding of the mechanical properties of complex surfaces may lead to excessive or insufficient force, causing damage to the object or the robot itself, especially when precision operations or handling fragile materials, traditional control methods cannot guarantee safety.

[0003] Specifically, complex surfaces include various shapes such as unevenness, curves, and slopes, and these characteristics cannot be dealt with by simple path planning and fixed control strategies. The surface materials are diverse, and parameters such as friction coefficient and hardness vary greatly. Traditional control methods cannot dynamically adjust to adapt to these changes. The types and number of sensors in conventional robot systems are limited, and they cannot 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 makes it impossible for the control system to adjust in time. Traditional control algorithms are mostly fixed parameter designs, lack adaptability, and cannot adjust control strategies based on real-time feedback. They lack self-learning and adaptive mechanisms and cannot optimize the operation process through experience accumulation. Traditional robot systems have limited capabilities in environmental cognition and modeling, and cannot accurately construct three-dimensional models of complex surfaces. They lack a deep understanding of the surface characteristics of the operating object and environmental changes, resulting in large errors in the planning and execution process. 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 that the robot in the existing solution cannot adaptively operate an operating object with a complex surface.

[0005] The object of the present invention can be achieved by 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: Modeling: Obtain the physical model of the robot and build the dynamic model of the robot; Data processing: 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; Path optimization: Fit the path points to the trajectory and adjust the path of the robot end according to the final fitting trajectory; Implementation control: Through optimized impedance control, the robot is controlled to move along the optimized future motion path of the robot end, and the force at the robot end is controlled at the same time, specifically:

[0006] Where ΔF is the difference between the expected 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 expected 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.

[0007] 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 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.

[0008] 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.

[0009] Preferably, weight coefficients are introduced in the construction process of the local neighborhood covariance matrix.

[0010] Preferably, in the path optimization step, preliminary fitting is performed by randomly selecting 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 selected smooth points and the trajectory normal vectors of the corresponding projection points on the preliminary fitting trajectory.

[0011] 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.

[0012] The present invention also provides a robot active compliance control system based on impedance control, which is characterized by comprising: A modeling module, which is used to obtain the physical model of the robot and build a dynamic model of the robot; A data processing module, which 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 according to the surface information of the operation object of the robot, 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 to move along the optimized future motion path of the robot end through optimized impedance control, and controls the force at the robot end at the same time, specifically:

[0013] Where ΔF is the difference between the expected 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 expected 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.

[0014] 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 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.

[0015] The present invention also provides a storage medium, comprising at least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program executable 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 active compliance control methods for robots.

[0016] Compared with the existing solutions, the present invention achieves the following beneficial effects: This active compliance control method can achieve precise motion and force control of the robot. This method has good robustness and anti-interference ability, optimizes path planning, improves fitting accuracy and efficiency, and achieves precise motion control and force control. By adopting optimized impedance control, the damping coefficient and stiffness coefficient are dynamically adjusted to achieve precise control of the contact force at the end of the robot. Especially in applications that require fine operation and force control, such as medical robots and precision assembly, it can effectively suppress external disturbances and improve the stability and reliability of the system. It is also possible to combine model predictive control and impedance control through a hybrid control method, utilize the trajectory tracking optimization capability of model predictive control and the force control capability of impedance control, dynamically adjust the balance coefficient, and achieve more accurate and smooth robot motion path tracking and force control.

[0017] In the hybrid control strategy, dynamic adjustment parameters are introduced to enable the control system to balance the influence of model predictive control and impedance control under different working conditions, enhancing the adaptability and flexibility of the system. In actual operation, the system can quickly respond to environmental changes, maintain stable trajectory tracking and force control performance, and adapt to complex working environments and task requirements. By reasonably allocating control inputs and optimizing energy consumption, it is particularly suitable for application scenarios with long-term operation and high energy-saving requirements. Model predictive control reduces unnecessary movement, and impedance control balances energy consumption with task execution requirements, improving the overall efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings.

[0019] Figure 1 Schematic diagram of the working scenario for active compliant control of the robot.

[0020] Figure 2 The figure is a flow chart of the active compliance control method of a robot based on impedance control according to the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the 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 described embodiments 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 creative work are within the scope of protection of the present invention.

[0022] like Figure 1 As shown in the figure, in the operation scenarios such as assembly, grinding, polishing, clamping, etc., the robot end needs to continuously track and contact the operation object, and different areas of the operation object may or are likely to have different surface features. Based on this work scenario, it is necessary to implement active compliance control on the robot.

[0023] Embodiment 1: This embodiment is a robot active compliance control method based on model predictive control, which includes the following steps: 1. Modeling: 1.1 Obtain the physical model of the robot: Obtain the rigid body dynamics model of the robot by modeling the robot's geometry, mass, inertia and other physical parameters. Specifically, establish the robot's geometric model, including parameters such as joint length and connecting rod length; determine the robot's mass distribution, including the mass of each connecting rod, the mass of the joint, etc.; estimate the robot's inertial parameters, such as moment of inertia, center of mass position, etc.

[0024] 1.2 Construct the dynamic model of the robot: Use the Lagrangian dynamics method to derive the robot's motion equations, which include the relationship between joint angles, joint angular velocities, and joint torques. Considering the incompleteness of the model and the existence 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 or observers or machine learning-based methods. The optimized robot dynamic model is as follows:

[0025] Where τ is the joint torque, q d is the desired joint position, is the expected joint velocity, is the expected joint acceleration, M is the mass matrix, describing the mass distribution of the robot joints, C is the Coriolis force matrix, describing the centrifugal force and Coriolis force generated by joint motion, G is the gravity matrix, describing the gravity effect caused by the robot mass, is an adaptive term, is the reference acceleration of the adaptive term, e is the joint position error, K p , K d are 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.

[0026] 2. Data processing: 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.

[0027] 2.2 Historical path point processing: Calculate the normal vectors of historical path points and classify historical path points into smooth points and non-smooth points. Specifically: construct the local neighborhood covariance matrix of 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 characteristic threshold, the path point is a non-smooth point, otherwise it is a smooth point; according to the classified path points, downsample the smooth points and / or upsample the non-smooth points to ensure a moderate density of path points.

[0028] Construct the local neighborhood covariance matrix of the historical path point. The following will take a historical path point as an example for specific explanation. i As the center, R is the radius of the sphere that encompasses all historical path points, constructing the historical path point p i The local neighborhood point set of R∈[R min ,R max ], R min is the minimum neighborhood radius, R max is the maximum neighborhood radius. The determination of R includes: Get the robot's historical path points p i The velocity v of the robot end at the time, and the historical path point p i With centered at 9, the robot traverses 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 whose velocities at the robot end are v-∆v and v+∆v respectively; 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 each point 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 mentioned above. In the process of constructing the covariance matrix, the weights of each point in the local neighborhood point set are the same; if the distance between the two closest historical path points mentioned above is less than R min , then R=R min , and introduce weight coefficients in the process of covariance matrix construction, such as for the historical path point p i The jth point p in the local neighborhood point set j The weight coefficient is , where k,c∈R.

[0029] 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.

[0030] The eigenvalue represents the variance of the covariance matrix in the direction of each eigenvector, while the eigenvector represents the main direction of the covariance matrix, that is, the normal vector of the point. For each path point, the eigenvector corresponding to the minimum eigenvalue is selected as its neighborhood normal vector. The method of adaptively selecting the neighborhood size allows more suitable neighborhood points to be included when calculating the path point normal vector, ensuring that a smaller neighborhood is selected in smooth areas and a larger neighborhood is selected in complex areas (such as edges and corners), thereby improving the accuracy, robustness and immediacy of normal vector estimation. The introduction of weight coefficients makes the points closer to the center point in the neighborhood contribute more to the normal vector estimation, while the points farther away contribute less, improving the accuracy of normal vector estimation, especially when dealing with complex surfaces. The historical path points are classified into smooth points and non-smooth points. Smooth points are used to fit the overall trend, and non-smooth points are used to capture local sharp changes, such as edges and corners. In this way, a fitting trajectory that is more in line with the actual surface can be obtained, improving the 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 and the product of the minimum eigenvalue ratio and the standard deviation of the normal vector angle are used as the judgment index. 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.

[0031] 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.

[0032] 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 the fitting results that do not conform to the actual surface characteristics. Further distance screening ensures that the fitting trajectory not only matches well in the normal vector, but also conforms to the actual surface characteristics in terms of spatial distance, thereby improving the fitting accuracy.

[0033] 4. Implement control: Control the robot to move along the optimized future motion path of the robot end, and control the force at the robot end. By introducing model predictive control, based on the robot's kinematic model, by minimizing the objective function of model predictive control and setting constraints, the control input u of model predictive control is calculated. The robot dynamics model is specifically:

[0034] Among them, τ is the joint torque, F is the contact force, J is the Jacobian matrix, q d is the desired joint position, is the expected joint velocity, is the expected joint acceleration, M is the mass matrix, describing the mass distribution of the robot joints, C is the Coriolis force matrix, describing the centrifugal force and Coriolis force generated by joint motion, G is the gravity matrix, describing the gravity effect caused by the robot mass, is an adaptive term, is the reference acceleration of the adaptive term, e is the joint position error, K p , K d are proportional gain and differential gain respectively.

[0035] The constraints of model predictive control include: xi+1 =f(x i ,u i ), x i is the system state at time i; x i ∈X, i=0,……,N-1, N is the control time domain, X is the system state constraint set; u i ∈U, i=0,…,N-1, U is the system input constraint set.

[0036]

[0037]

[0038] 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, and F b is the upper limit of contact force, F s is the lower limit of contact force. The objective function of model control is J(x,u).

[0039] In the implementation of the control step, the robot's kinematic and dynamic models are used to adopt model predictive control to predict future states and behaviors, so as to adjust the control input in real time. It can more accurately predict the motion trajectory and force change trend of the robot's end, so that the robot can move more accurately along the desired path and respond quickly in a dynamic environment. Not only the optimized motion of the robot's end path is considered, but also the contact force of the end is adjusted in real time. By accurately controlling the contact force, the robot can ensure safety and accuracy 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 limit, 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.

[0040] The maximum contact force constraint includes force control strategies in different situations, corresponding to different force control strategies when the robot end approaches or moves away from the work object, so that the system can flexibly respond to different working environments and task requirements, and can prevent the robot from applying excessive force, thereby avoiding damage to the operated object or the robot itself. By adjusting the contact force, the interaction between the robot end and the surface can be more accurately controlled, thereby improving the accuracy of tracking unknown surfaces, increasing the robustness of the system, and reducing deviations caused by external disturbances or uncertain factors. This precise control allows the robot to respond flexibly in different working scenarios, ensuring that the appropriate contact force is maintained at different distances, thereby improving the accuracy and reliability of the operation.

[0041] 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.

[0042] Specifically, in the implementation control step, the robot is controlled to move along the optimized future motion path of the robot end through optimized impedance control, and the force of the robot end is controlled at the same time, specifically:

[0043] Where ΔF is the difference between the expected 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 expected 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.

[0044] 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's end force under different working conditions. At the same time, the force at the robot's 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.

[0045] 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 may adopt the same or similar design as that of Example 1.

[0046] Specifically, in the implementation control 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.

[0047] 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 may be equal to the input in Example 2.

[0048] In the implementation of the control step, model predictive control is usually used to optimize the robot's trajectory tracking and generate the optimal control input by predicting the future state. Impedance control is mainly used to control the contact force and the force at the end of the robot. Combining these two control strategies and taking advantage of their respective advantages can not only optimize the robot's motion path, but also accurately control the robot's force and torque. The introduction of parameter γ allows γ to be dynamically adjusted with the robot's motion state, thereby balancing the influence of model predictive control and impedance control under different motion paths and working conditions. For example, when the robot needs to follow the optimized path more accurately (θ is small), γ is larger and relies more on model predictive control input; while when it needs to respond to large changes in external forces (θ is large), γ is smaller and relies more on impedance control input. By integrating model predictive control and impedance control, more accurate and smooth robot motion path tracking can be achieved. Model predictive control can optimize the prediction of future motion states, allowing the robot to follow the optimized path more accurately; at the same time, impedance control can effectively adjust the force and torque 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 practical operation, this means that the robot can respond quickly to environmental changes while maintaining stable trajectory tracking and force control performance. Through reasonable distribution of control inputs, energy consumption can be effectively optimized, especially in application scenarios with long-term operation and high energy saving requirements. Model predictive control helps to reduce unnecessary movements, while impedance control can effectively control the balance between energy consumption and task execution requirements.

[0049] 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.

[0050] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. 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 solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A robot active compliance control method based on impedance control, characterized in that: Includes steps: Modeling: Obtain the physical model of the robot and build the dynamic model of the robot; Data processing: 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; Path optimization: Fit the path points to the trajectory and adjust the path of the robot end according to the final fitting trajectory; Implementation control: Through optimized impedance control, the robot is controlled to move along the optimized future motion path of the robot end, and the force at the robot end is controlled at the same time, 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 robot active compliance control method based on impedance control according to claim 1, characterized in that: The control steps can be replaced by: adjusting the robot 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, 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.

3. The robot active compliance control method 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 robot active compliance control method 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 robot active compliance control method 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 robot active compliance control method based on impedance control according to claim 1, characterized in that: In the path optimization step, a preliminary fitting is performed by randomly selecting some historical path points, and the fitting trajectory is initially 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 robot active compliance control method 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. The active compliance control system of the robot based on impedance control is characterized by: include: A modeling module, which is used to obtain the physical model of the robot and build a dynamic model of the robot; A data processing module, which 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 to move along the optimized future motion path of the robot end through optimized impedance control, and controls the force at the robot end at the same time, 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, 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.

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 executable 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 the robot active compliance control method as described in any one of claims 1-7.

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