CN117656059A - Adaptive variable impedance control method and device, electronic equipment and storage medium - Google Patents

Abstract

The embodiment of the invention provides a self-adaptive variable impedance control method and device, electronic equipment and a storage medium. In the process of cooperatively grabbing the target object by the double mechanical arms, decoupling the internal force and the external force borne by the target object, and analyzing the internal force and the external force step by step to obtain a stress analysis result of the target object; the reference model generates an expected motion track of the mechanical arm according to a stress analysis result, and the self-adaptive controller adjusts impedance parameters by comparing error signals between the actual motion track and the expected motion track of the mechanical arm; in the operation process of the double mechanical arms, updating impedance parameters according to error signals output by the self-adaptive impedance control model; and adjusting the self-adaptive impedance control strategy according to the updated impedance parameters so that the double mechanical arms grasp the target object according to the self-adaptive impedance control strategy. Based on the above, the embodiment of the invention can enable the impedance parameters of the double mechanical arms to be flexibly adjusted when the double mechanical arms interact with the environment so as to better complete the grabbing task.

Description

Adaptive variable impedance control method and device, electronic equipment and storage medium

Technical field

Embodiments of the present invention relate to the field of robot control technology, and in particular, to an adaptive variable impedance control method and device, electronic equipment and storage media.

Background technique

Faced with the continuous complexity of modern production tasks and the flexibility of the production process, modern production has increasingly higher requirements for intelligence, functionality and diversification. The traditional working mode of a single robot arm with a fixed station is no longer suitable. In the environment of modern intelligent manufacturing. In specific process tasks such as collaborative assembly, welding, and handling of large or heavy payloads, dual robotic arms have stronger load capacity, wider work space, and diverse working methods. Compared with single robotic arms, multiple robotic arms The arm has a high degree of completion and flexible operation in the manufacturing process, and has wider application prospects. In the research on dual-arm control, the main control level involved needs to solve key issues, such as position and force control under the coordination of dual-arm robots. When the dual robotic arms work, the dual robotic arms need to meet certain constraints to maintain a high degree of coordination and consistency, while controlling the movement trajectory of the end of the robotic arm and the stress and external interference force of the robotic arm acting on the object. Otherwise, due to the presence of errors, the acted target object will generate large internal stress and destroy the target object itself. When the stiffness of the target object is too large, the robotic arm will be damaged. Therefore, how to complete the task of grabbing target objects with dual robotic arms while taking into account external forces and environmental factors has become an urgent technical problem to be solved.

Contents of the invention

Embodiments of the present invention provide an adaptive variable impedance control method and device, electronic equipment and storage media, which can enable dual robotic arms to flexibly adjust their impedance parameters when interacting with the environment to better complete target object control. Fetch tasks.

In a first aspect, embodiments of the present invention provide an adaptive variable impedance control method, including:

In the process of the dual robot arms cooperatively grabbing the target object, the internal force and the external force on the target object are decoupled, and the internal force and the external force are analyzed step by step to obtain the force analysis result of the target object;

Load an adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used to generate the desired motion trajectory of the mechanical arm based on the force analysis results. The adaptive controller is used Adjusting the impedance parameter by comparing the error signal between the actual movement trajectory of the robotic arm and the desired movement trajectory;

During the operation of the dual robotic arms, update the impedance parameters according to the error signal output by the adaptive impedance control model;

The adaptive impedance control strategy is adjusted according to the updated impedance parameter, so that the dual robotic arms grasp the target object according to the adaptive impedance control strategy.

In some embodiments, the method further includes:

Establish the coordinate system of the dual manipulator collaborative system. The position and attitude of the target object with respect to the reference coordinate system are solved using the following formula:

In the formula, is the transformation matrix of the target object relative to the center of mass coordinate system;/> It is the 3X3 rotation matrix of the object relative to the coordinate system at the center of mass;/> is the 3X1 position matrix of the target object relative to the coordinate system at the center of mass;

The target object is converted into a constraint between the target object and the robotic arm through the transformation between the coordinate system at the center of mass and the world coordinate system, which is expressed by the following formula:

In the formula, It is the homogeneous coordinate transformation of the coordinate system 0 at the center of mass relative to the world coordinate system W;/> Represents the homogeneous coordinate transformation of the base coordinate system of the dual robot arms relative to the world coordinate system;/> Represents the secondary transformation of the end coordinate system of the dual robotic arms relative to the base coordinates of the dual robotic arms;/> Represents the homogeneous transformation of the coordinate system of the center of mass of the target object relative to the end of the robotic arm;

The speed constraint relationship is analyzed through the following formula, so that the position and speed of the arms are consistent during the movement;

In the formula, Represents the speed of the end of the robotic arm relative to the world coordinate system;/> Represents the object’s velocity relative to the world coordinate system, angular velocity;/> Represents the position transformation matrix of the end of the manipulator arm relative to the world; P _i ^O represents the position transformation matrix of the end of the manipulator arm relative to the center of mass of the target object;/> Represents the direction rotation matrix of the center of mass of the target object relative to the world.

In some embodiments, decoupling the internal and external forces on the target object includes:

According to Newton's second law and Euler's equation, the state of the dual manipulator grabbing the target object is established, and the following dynamic equation of the target object is established:

in the formula I _O represents the inertia matrix at the center of mass of the target object; F _O ∈R ⁶ represents the vector force of the dual robotic arms acting on the target object; M _O ∈R ⁶ represents the mass inertia matrix of the target object;/> represents the linear acceleration and angular acceleration during the movement of the target object; C _O ∈R ⁶ represents the resultant force vector of the Coriolis force, gravity and centrifugal force of the target object; F _ext ∈R ⁶ represents the appropriate amount of external interference force acting on the target object Force; convert the above formula into the following formula:

In the formula, k=l, r represents the left arm and right arm of the double robotic arm, S _k ^T ∈ R6 represents the grasping matrix; F _k represents the force of the robotic arm acting on the target object; decompose the grasping matrix to obtain the external force formula F _I and get the internal force formula F _E :

in the formula Yes/> The generalized inverse of a matrix.

In some embodiments, the force analysis results of the target object include:

In the formula, x _e and x _a represent the expected trajectory and the actual trajectory respectively; F _e and F _a represent the expected force and the actual force respectively, where F _k =F _a ; M _d represents the inertia matrix; B _d represents the damping matrix; K _d represents the stiffness matrix; ΔF represents the error value of the force;

Substituting the external force formula F _I and the internal force formula F _E into the above formula, we get the following formula:

Among them, F _a is expressed as:

F _k =F _a =K _r (x _e -x _a )

In the formula, K _r represents the stiffness of the target object.

In some embodiments, the solution process of the error signal is as follows:

e＝x _f +Δx-x _a

In the formula, x _f is the position error generated by traditional impedance, x _a is the actual trajectory, and Δx is the trajectory adjustment value;

In the formula, p(t) and v(t) are the proportional parameters and differential feedback gain parameters of the force error in adaptive control respectively; a(t) is the adjustment value in adaptive control; according to the following formula:

x _e =K _r ^-1 (F _e -ΔF)+x _a

In the formula,

Among them, the second-order system of the ideal reference model is as follows:

The error signal between the actual model and the ideal reference model is obtained:

The expression in state space is:

in,

In some embodiments, updating impedance parameters based on the error signal output by the adaptive impedance control model includes:

Based on the stability theorem of Lyapuno equation, the following equation is established:

V＝ΔF _e ^T PΔF _e +μ ₁ (B _m -B _l ) ² +μ ₂ (A _m -A _l ) ² + _μ3 (Y _l ) ₂

In the formula, According to Lyapunov's second theorem, Q=α ^T Pa; μ ₁ , μ ₂ and μ ₃ are positive numbers; P is a non-singular matrix; by differentiating the above formula, we get:

In the formula, According to Lyapunov’s second theorem, ensure/> is always less than 0, then except -ΔF _e ^T QΔF _e is not 0, all other terms are 0, and we get:

Therefore, the control law of the adaptive impedance control strategy is obtained:

in,

In the formula, K ₀ , K ₁ , K ₂ , β _p and β _v are all positive integral adaptive gain coefficients; a ₀ , p ₀ , v ₀ are the initial values selected by the adaptive control system.

In some embodiments, adjusting the adaptive impedance control strategy according to the updated impedance parameter includes:

By updating a(t), p(t), v(t) and eta(t), the adaptive impedance control strategy is updated.

In a second aspect, embodiments of the present invention also provide an adaptive variable impedance control device, which includes:

The analysis module is used to decouple the internal and external forces on the target object during the collaborative grabbing of the target object by the dual manipulators, and conduct a step-by-step analysis of the internal force and the external force to obtain the target object. Force analysis results;

A loading module is used to load an adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used to generate the desired motion trajectory of the mechanical arm based on the force analysis results, so The adaptive controller is used to adjust the impedance parameters by comparing the error signal between the actual movement trajectory of the robotic arm and the desired movement trajectory;

An update module, configured to update impedance parameters according to the error signal output by the adaptive impedance control model during the operation of the dual manipulator;

An execution module, configured to adjust an adaptive impedance control strategy according to the updated impedance parameter, so that the dual robotic arms grasp the target object according to the adaptive impedance control strategy.

In a third aspect, embodiments of the present invention also provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program The program implements the adaptive variable impedance control method as described in the first aspect.

In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to execute the adaptive variable impedance control method as described in the first aspect.

According to the adaptive variable impedance control method and device, electronic equipment and storage medium provided by embodiments of the present invention, the adaptive variable impedance control method includes: during the process of dual robot arms cooperatively grabbing the target object, the target object is subjected to Decouple the internal force and the external force, and conduct a step-by-step analysis of the internal force and the external force to obtain the force analysis results of the target object; load the adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used In order to generate the desired motion trajectory of the robotic arm based on the force analysis results, the adaptive controller is used to adjust the impedance parameters by comparing the error signal between the actual motion trajectory of the robotic arm and the desired operating trajectory; during the operation of the dual robotic arms, the adaptive controller is used to adjust the impedance parameters according to the automatic movement trajectory of the robotic arm. The error signal output by the adaptive impedance control model is updated to update the impedance parameters; the adaptive impedance control strategy is adjusted according to the updated impedance parameters, so that the dual robotic arms can grasp the target object according to the adaptive impedance control strategy. Based on this, through the force analysis and decoupling of the target object during the process of grasping the target object with both arms, the force is decoupled into internal force and external force. Through step-by-step analysis of internal force and external force, adaptive impedance control is used. The algorithm optimizes the dual-arm cooperative grasping target object system, making the system more stable and precise in movement. The adaptive impedance control model established by the present invention includes a reference model and an adaptive controller. The reference model is responsible for generating the desired movement trajectory of the robotic arm while taking into account the characteristics of the target object. The adaptive controller adjusts the impedance parameters in real time to adapt to changes in the environment by comparing the error signals of the actual movement and the desired trajectory. The adoption of the adaptive impedance control model enables the dual manipulator to show stronger adaptability in dynamic and uncertain environments. During operation, the adaptive controller uses an adaptive algorithm to update the impedance parameters by using the error signal, which is the difference between the actual motion and the desired trajectory. Continuous iteration of this process allows the robotic arm to continuously optimize its performance. In the real-time control and execution phase, the manipulator executes the adjusted control strategy based on the updated impedance parameters. Based on this, embodiments of the present invention can enable the dual robotic arms to flexibly adjust their impedance parameters when interacting with the environment to better complete the task of grasping the target object, and improve the robustness and performance of the dual robotic arms. .

Description of drawings

Figure 1A is a flow chart of an adaptive variable impedance control method provided by an embodiment of the present invention;

Figure 1B is a schematic diagram of the coordinate system of the two-arm collaboration system provided by an embodiment of the present invention;

Figure 2 is a force analysis diagram of a target object provided by an embodiment of the present invention;

Figure 3 is a structural diagram of a reference model adaptive impedance control strategy provided by an embodiment of the present invention;

Figure 4 is a control block diagram of dual robotic arms provided by an embodiment of the present invention;

Figure 5 is a flow chart of a reference model adaptive impedance control strategy provided by an embodiment of the present invention;

Figure 6 is a hardware connection structure diagram of the experimental platform provided by one embodiment of the present invention;

Figure 7A is a constant force tracking diagram under adaptive impedance control of the reference model provided by an embodiment of the present invention;

Figure 7B is a constant trajectory tracking diagram under adaptive impedance control of the reference model provided by an embodiment of the present invention;

Figure 8A is a constant force tracking diagram under adaptive impedance control of the reference model provided by an embodiment of the present invention;

Figure 8B is an oblique trajectory tracking diagram under adaptive impedance control of the reference model provided by an embodiment of the present invention;

Figure 9A is a constant force tracking diagram under adaptive impedance control of the reference model provided by an embodiment of the present invention;

Figure 9B is a dynamic trajectory tracking diagram under adaptive impedance control of the reference model provided by an embodiment of the present invention;

Figure 10 is a schematic diagram of an adaptive variable impedance control device provided by an embodiment of the present invention;

Figure 11 is a schematic diagram of an electronic device provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

It should be noted that although the functional modules are divided in the device schematic diagram and the logical sequence is shown in the flow chart, in some cases, the modules can be divided into different modules in the device or the order in the flow chart can be executed. The steps shown or described. The terms "first", "second", etc. in the description, claims, and following drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

In the embodiments of the present invention, words such as "further", "exemplarily" or "optionally" are used as examples, illustrations or illustrations, and should not be interpreted as being more preferable or better than other embodiments or designs. Advantages. The use of the words "further," "exemplarily," or "optionally" is intended to present the relevant concepts in a specific manner.

First, some terms involved in this invention are analyzed:

Dual robot arm collaborative control: refers to the process in which two or more robot arm systems jointly perform tasks through collaborative control algorithms and environmental perception. This involves trajectory planning, end-effector operation, communication and collaborative control to ensure safety and coordination between robotic arms to improve production efficiency and accuracy while reducing potential risks.

Reference model adaptive impedance control: It is a control method for robots and automation systems. It is based on the mathematical model of the system and adaptively adjusts the impedance parameters to match the actual behavior of the system with the model behavior in response to external forces and The environment changes to achieve the desired performance. This allows the system to adapt flexibly in different work environments and tasks, improving performance and stability.

In order to more conveniently describe the working principles of the embodiments of the present invention later, an introduction to relevant technical scenarios will be given below.

Faced with the continuous complexity of modern production tasks and the flexibility of the production process, modern production has increasingly higher requirements for intelligence, functionality and diversification. The traditional working mode of a single robot arm with a fixed station is no longer suitable. In the environment of modern intelligent manufacturing. In specific process tasks such as collaborative assembly, welding, and handling of large or heavy payloads, dual robotic arms have stronger load capacity, wider work space, and diverse working methods. Compared with single robotic arms, multiple robotic arms The arm has a high degree of completion and flexible operation in the manufacturing process, and has wider application prospects. In the research on dual-arm control, the main control level involved needs to solve key issues, such as position and force control under the coordination of dual-arm manipulators. When the dual robotic arms work, the dual robotic arms need to meet certain constraints to maintain a high degree of coordination and consistency, while controlling the movement trajectory of the end of the robotic arm and the stress and external interference force of the robotic arm acting on the object. Otherwise, due to the presence of errors, the acted target object will generate large internal stress and destroy the target object itself. When the stiffness of the target object is too large, the robotic arm will be damaged. Therefore, how to complete the task of grabbing target objects with dual robotic arms while taking into account external forces and environmental factors has become an urgent technical problem to be solved.

Based on this, the present invention provides an adaptive variable impedance control method and device, electronic equipment and storage media. Among them, the adaptive variable impedance control method includes: during the process of dual robot arms cooperatively grabbing the target object, decoupling the internal and external forces on the target object, and performing step-by-step analysis of the internal and external forces to obtain the stress on the target object. Force analysis results; load the adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used to generate the desired motion trajectory of the robotic arm based on the force analysis results. The adaptive controller is used to compare mechanical The impedance parameters are adjusted based on the error signal between the actual movement trajectory of the arm and the expected movement trajectory; during the operation of the dual manipulators, the impedance parameters are updated based on the error signal output by the adaptive impedance control model; the adaptive impedance control is adjusted based on the updated impedance parameters. strategy to enable the dual manipulator arms to grasp the target object according to the adaptive impedance control strategy. Based on this, through the force analysis and decoupling of the target object during the process of grasping the target object with both arms, the force is decoupled into internal force and external force. Through step-by-step analysis of internal force and external force, adaptive impedance control is used. The algorithm optimizes the dual-arm cooperative grasping target object system, making the system more stable and precise in movement. The adaptive impedance control model established by the present invention includes a reference model and an adaptive controller. The reference model is responsible for generating the desired movement trajectory of the robotic arm while taking into account the characteristics of the target object. The adaptive controller adjusts the impedance parameters in real time to adapt to changes in the environment by comparing the error signals of the actual movement and the desired trajectory. The adoption of the adaptive impedance control model enables the dual manipulator to show stronger adaptability in dynamic and uncertain environments. During operation, the adaptive controller uses an adaptive algorithm to update the impedance parameters by using the error signal, which is the difference between the actual motion and the desired trajectory. Continuous iteration of this process allows the robotic arm to continuously optimize its performance. In the real-time control and execution phase, the manipulator executes the adjusted control strategy based on the updated impedance parameters. Based on this, embodiments of the present invention can enable the dual robotic arms to flexibly adjust their impedance parameters when interacting with the environment to better complete the task of grasping the target object, and improve the robustness and performance of the dual robotic arms. .

The embodiments of the present invention will be further described below with reference to the accompanying drawings.

As shown in FIG. 1A , FIG. 1A is a flow chart of an adaptive variable impedance control method provided by an embodiment of the present invention. The adaptive variable impedance control method may include but is not limited to steps S101 to S104.

Step S101: During the process of the dual manipulators cooperatively grabbing the target object, decouple the internal force and the external force on the target object, conduct a step-by-step analysis of the internal force and the external force, and obtain the force analysis results of the target object;

Step S102: Load the adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used to generate the desired motion trajectory of the mechanical arm based on the force analysis results. The adaptive controller is used to compare the mechanical arm with The error signal between the actual movement trajectory and the desired movement trajectory is used to adjust the impedance parameters;

Step S103: During the operation of the dual robotic arms, update the impedance parameters according to the error signal output by the adaptive impedance control model;

Step S104: Adjust the adaptive impedance control strategy according to the updated impedance parameters, so that the dual robotic arms grasp the target object according to the adaptive impedance control strategy.

In one embodiment, the motion trajectory planning of a single manipulator is based on the estimation of the operated target through kinematic transformation of the coordinate system. Therefore, the coordinate system of the dual-arm collaborative system established is shown in _Figure 1B. In Figure 1B, {W _X , W _Y , W _Z }, _{{ O system} ; _{ OR _{X ,} OR _Y , OR _Z }, _{{ OL} , R _Z } _{, { L} Conversion formula 1.

For the target object being operated, the position and attitude of the target object relative to the reference coordinate system are solved using Equation 1

In the formula, is the transformation matrix of the target object relative to the center of mass coordinate system;/> It is the 3X3 rotation matrix of the target object relative to the coordinate system at the center of mass;/> is the 3X1 position matrix of the target object relative to the coordinate system at the center of mass.

The target object is converted into a constraint between the target object and the robotic arm through the transformation between the coordinate system at the center of mass and the world coordinate system, which is expressed by the following equation 2:

In the formula, It is the homogeneous coordinate transformation of the coordinate system 0 at the center of mass relative to the world coordinate system W;/> Represents the homogeneous coordinate transformation of the base coordinate system of the dual robot arms relative to the world coordinate system;/> Represents the secondary transformation of the end coordinate system of the dual robotic arms relative to the base coordinates of the dual robotic arms;/> Represents the homogeneous transformation of the coordinate system of the center of mass of the target object relative to the end of the manipulator arm.

After passing the above position constraints of the arms, in order to achieve real-time collaborative operation in the true sense, in addition to the position constraints, it is also necessary to ensure the speed consistency of the arms during the movement. Therefore, it is necessary to pair the speed with Equation 3. The constraint relationship is analyzed to maintain the consistency of position and speed of the arms during movement and achieve coordinated operation.

In the formula, Represents the speed of the end of the robotic arm relative to the world coordinate system;/> Indicates the speed and angular velocity of the target object relative to the world coordinate system;/> Represents the position transformation matrix of the end of the manipulator arm relative to the world; P _i ^O represents the position transformation matrix of the end of the manipulator arm relative to the center of mass of the target object;/> Represents the direction rotation matrix of the center of mass of the target object relative to the world.

The force analysis of the target object is shown in Figure 2. In Figure 2, F _L , F _R , F _ext , τ _L , τ _R , and τ _ext are respectively expressed as the dual mechanical arms and external forces acting on the surface of the target object. According to Newton's second law and Euler's equation, the state of the dual manipulator grasping the object is established to establish the following dynamic equation of the target object:

Simplified to equation (5):

in the formula IO represents the inertia matrix at the center of mass of the target object; F _O ∈R ⁶ represents the vector force of the dual robotic arms acting on the object; M _O ∈R ⁶ represents the mass inertia matrix of the target object;/> represents the linear acceleration and angular acceleration during the movement of the target object; C _O ∈R ⁶ represents the resultant force vector of the Coriolis force, gravity and centrifugal force of the target object; F _ext ∈R ⁶ represents the appropriate amount of external interference force acting on the target object Force transforms equation (5) into equation (6).

In the formula, k=l, r represents the left arm and right arm of the double robotic arm, S _k ^T ∈ R ⁶ represents the grasping matrix; F _k represents the force of the robotic arm acting on the target object. The six-dimensional force sensor can directly Data collection. The grasping matrix can be decomposed to obtain the external force formula (7) and the internal force formula (8):

in the formula Yes/> The generalized inverse of a matrix.

In one embodiment, traditional impedance control implements a control strategy of adjusting force based on position error, and admittance control implements a control strategy of adjusting position based on force error. Therefore, position-based impedance control is also called admittance control. The impedance/admittance control strategy relies on the second-order system of "mass-impedance-spring", and the dynamics of the target object is established using the second-order system of "mass-impedance-spring". The present invention decomposes the dual-manipulator cooperation target object task into an inner ring and an outer ring. The inner ring mainly controls the internal force F _I to prevent the robot arm from damaging the target object, and the outer ring controls the external force F _E to ensure that the target object completes the cooperation task. From equations (7) and (8), we can know that the force acting on the target object by the double manipulator can be divided into internal force and external force, so equation (9) can be transformed into equation (10).

In the formula, x _{e and} x _a represent the expected trajectory and the actual trajectory respectively; F _e and F _a represent the expected force and the actual force respectively, where F _k = F _a ; M _d represents the inertia matrix; B _d represents the damping matrix; K _d represents the stiffness matrix; ΔF represents the error value of the force.

Substitute equations (7) and (8) into equation (9):

According to formula (10), F _a can be expressed:

F _k =F _a =K _r (x _e -x _a ) (11)

Among them, in the process of mathematical modeling, there is no real real-time data collected by the force sensor in the simulation stage for the actual force F _a in the function, so the actual force F a is modeled through the function through Equation (11) Instead of the force measured by the actual six-dimensional sensor, the modeled function is applied in the simulation to achieve the effect of the control strategy. In the formula, K _r represents the stiffness of the target object.

Because of the uncertainty about the stiffness of the target object and the uncertainty about the grasping environment. As a result, traditional impedance control and reference model adaptive admittance control do not have good expectations in terms of force tracking and position tracking performance respectively. This article combines traditional impedance control with improved adaptive impedance control, and gives the position control algorithm , making the reference model adaptive impedance control perform well in terms of force and position tracking performance.

In the above control strategy, by adding the trajectory adjustment value Δx to the actual trajectory x _a to change its form, equation (12) is obtained. Δx is formula (13), which satisfies the adaptive adjustment strategy for unknown environments, so the overall error is given an adaptive adjustment function. The reference model adaptive impedance control is combined with impedance control, and a position controller is added to fit the actual position and position error of the robotic arm. Achieve good effects on force and position tracking of the control system.

e＝x _f +Δx-x _a (12)

In the formula, x _f is the position error generated by traditional impedance; x _a is the actual trajectory; Δx is the trajectory adjustment value.

In the formula, p(t) and v(t) are the proportional parameters and differential feedback gain parameters of the force error in adaptive control respectively; a(t) is the adjustment value in adaptive control.

According to formula (9) and formula (11), formula (13) can be obtained:

x _e =K _r ^-1 (F _e -ΔF)+x _a (14)

Putting formula (14) and formula (13) into (9), we get formula (15):

In the formula,

The second-order system of the ideal reference model is equation (16):

Subtracting equation (16) from equation (15), we can get the error between the actual model and the ideal reference model:

The expression in state space is:

in, In order to prove the stability of the reference model adaptive admittance control, the present invention establishes the following equation based on the stability theorem of the Lyapuno equation:

V＝ΔF _e ^T PΔF _e +μ ₁ (B _m -B _l ) ² +μ ₂ (A _m -A _l ) ² +μ ₃ (Y _l ) ² (19)

in the formula According to Lyapunov's second theorem, Q=α ^T Pα; μ ₁ , μ ₂ and μ ₃ are positive numbers; P is a non-singular matrix; by differentiating equation (19), we get:

in the formula Through Lyapunov’s second theorem, ensure/> is always less than 0, then except -ΔF _e ^T QΔF _e is not 0, all other terms are 0, so:

Therefore, the control law of the adaptive impedance control strategy is obtained:

in,

In the formula, K ₀ , K ₁ , K ₂ , β _p and β _v are all positive integral adaptive gain coefficients; a ₀ , p ₀ , v ₀ are the initial values selected by the adaptive control system; by applying appropriate coefficient values, This can meet the requirements of adaptive control of actual contact force. The reference model adaptive impedance control strategy is shown in Figure 3.

In one embodiment, when the two arms cooperate to hold a common target object that interacts with the ring, the target object will be acted upon by internal and external forces. Based on the above reference model adaptive impedance control strategy, this paper designs a dual manipulator reference model adaptive impedance control strategy. The main control block diagram is shown in Figure 4. Figure 4 shows the symmetrical control strategy of dual manipulators and the external impedance control and internal impedance control strategies of the dual manipulator control system.

In Figure 4, F _Ea and F _Ee are represented as the actual external force and the expected external force respectively. F _Ia and F _Ie respectively represent the actual internal force and the expected internal force. σF _E and σF _I respectively represent the error of the actual external force and the expected external force and the error of the actual internal force and the expected external force. σX _E , σX _I represent the position compensation generated by the external reference model adaptive impedance controller and the internal force reference model adaptive impedance controller respectively. X _c and X _e respectively represent the actual trajectory of the target object and the expected trajectory of the target object. X _a , X _{al ,} and θ _l and θ _r respectively represent the actual motion angles of the left and right robot arm joints generated through inverse kinematics through the actual motion trajectories of the end effectors of the left and right robot arms.

In one embodiment, an adaptive impedance model is constructed for dual manipulators grasping target objects;

The impedance algorithm based on model reference adaptive control is an advanced control method designed to enable mechanical systems to flexibly adjust their impedance to adapt to uncertainties and changes when interacting with the environment. First, the algorithm focuses on accurately modeling the mechanical system, including modeling the structural and dynamic characteristics of the dual robotic arms, and designing an impedance model of parameters such as stiffness and damping to describe the mechanical arm when it interacts with the environment. dynamic response.

In actual operation, in order to obtain comprehensive status information, the algorithm collects status data such as the position, speed, and acceleration of the robotic arm through sensors, and fuses data from force and torque sensors to obtain more comprehensive information about the interaction with the environment. This step provides real-time and rich feedback for subsequent control decisions.

Next, the algorithm establishes a model-referenced adaptive control framework. The framework includes a reference model and an adaptive controller. The reference model is responsible for generating the desired robot arm motion trajectory while taking into account the characteristics of the target object. The adaptive controller adjusts the impedance parameters in real time to adapt to changes in the environment by comparing the error signals of the actual movement and the desired trajectory. This framework enables mechanical systems to exhibit greater adaptability in dynamic and uncertain environments.

During operation, the adaptive controller uses an adaptive algorithm to update the impedance parameters by using the error signal, which is the difference between the actual motion and the desired trajectory. Continuous iteration of this process enables the system to continuously optimize its performance.

In the real-time control and execution phase, the manipulator executes the adjusted control strategy based on the updated impedance parameters. This allows the robotic arm to flexibly adjust its impedance when interacting with the environment to better complete the grasping task.

In order to ensure the stability and superior performance of the system, the algorithm regularly evaluates the system, including the success rate of grabbing, movement speed, stability, etc. By adjusting and optimizing the parameters of the impedance model and adaptive controller based on performance in actual operations, the algorithm can work well in specific application areas such as industrial automation and service robotics. This impedance algorithm based on model reference adaptive control provides a flexible, intelligent and adaptable control method for mechanical systems, improving the robustness and performance of the system.

In one embodiment, the present invention uses a model reference algorithm to solve the adaptive impedance control of a two-arm robot grasping a target object in a continuous action space. The entire algorithm process is shown in Figure 5. First, by initializing the parameters and status of the two-arm cooperative grasping target object, the target object's force analysis and force decoupling are performed during the two-arm cooperative grasping process. For internal and external forces, through step-by-step analysis of internal and external forces, an adaptive impedance control algorithm is used to optimize the dual-arm cooperative grasping target object system, making the system more stable and accurate in action. Define the parameters Δ trajectory adjustment value, K ₀ , K ₁ , K ₂ , β _p and β _v positive integral adaptive gain coefficients; the initial values of a ₀ , p ₀ , v ₀ adaptive control system selection are proved through the Lyapunov equation. The stability of the traditional impedance controller is analyzed and referenced. In the process of proving the stability, an adaptive controller is obtained. The system parameters are adaptively adjusted through the adaptive controller. The adaptive controller compares the actual motion with the expected The error signal of the trajectory adjusts the impedance parameters in real time to adapt to changes in the environment. This framework enables mechanical systems to show stronger adaptability in dynamic and uncertain environments by updating a(t), p(t), v(t) and eta(t), making the adaptive impedance The control strategy is updated to determine whether the inverse solution q value of Effectiveness and Robustness.

In one embodiment, in order to verify the practicability of the adaptive control algorithm, the present invention will conduct experiments in the experimental platform shown in Figure 6. The experimental platform includes two UR5 robotic arms, a UR5 robotic arm control cabinet, a UR5 robotic arm teaching pendant, a PC, a six-dimensional force sensor, and a switch. Two of the UR5 robotic arms communicate through their own control cabinets. In the present invention, the UR5 robotic arms communicate through the Ethernet communication protocol, and the six-dimensional force sensor communicates through the TCP communication protocol. Two UR5 robotic arms and six-dimensional force sensors are connected to the switch, and the switch communicates and transmits data with the PC through the TCP/IP communication protocol. The PC can directly control the movement of the robotic arm and read the data of the six-dimensional force sensor in real time.

In the simulation experiment, the standard force signal is set as the manipulator end force tracking target, the tracking force is set in the z-axis direction of the manipulator end operating space, and no tracking target force values are set in other axis directions. Set the target force value F _e =8 in the z-axis direction, set the simulation time to 3 s, and observe the force displacement tracking effect. As shown in Figure 7A and Figure 7B, for the reference model adaptive impedance control, adding the adaptive adjustment error module can see that during the entire process, there is no overshoot in tracking the desired force and desired trajectory, and the desired force and trajectory are tracked without overshoot. The expected error is reduced during force tracking. Through the above experimental results, it can be concluded that the reference model adaptive impedance control avoids shortcomings such as overshoot and excessive expected error through error adjustment.

In the simulation experiment, the standard force signal is set as the manipulator end force tracking target, the tracking force is set in the z-axis direction of the manipulator end operating space, and no tracking target force values are set in other axis directions. Set the target force value F _e =8 in the z-axis direction, set the simulation time to 3 s, and observe the force displacement tracking effect. As shown in Figure 8A and Figure 8B, it can be seen that the simulation results of the expected force and expected trajectory tracking of the reference model adaptive impedance control under the inclined trajectory are known. It can be seen from Figure 8A and Figure 8B that for the desired force Fe ₌ 8N, in the reference model adaptive impedance control, after error adjustment control, the parameters are the same as the above simulation. The simulation results are shown in Figure 8A and Figure 8B. For Under the tracking of the expected force, the expected error is small and there is no overshoot. The effect of tracking the desired inclined trajectory in the reference model adaptive impedance control is good. It can be seen from the above simulation results that the reference model adaptive impedance control can achieve the desired force and desired trajectory.

In the simulation experiment, the standard force signal is set as the manipulator end force tracking target, the tracking force is set in the z-axis direction of the manipulator end operating space, and no tracking target force values are set in other axis directions. Set the target force value in the z-axis direction F _e = 8, set the trajectory value to Xe = 0.1 + 0.1 sin (3π), set the simulation time to 3 s, and observe the force displacement tracking effect. Figure 9A and Figure 9B show the simulation results of the desired force and desired trajectory tracking of the reference model adaptive impedance control under the sinusoidal trajectory. In the reference model adaptive impedance control, after error adjustment control, the expected force tracking of the reference model adaptive impedance control under the sinusoidal dynamic trajectory can be seen from Figure 9A. The stable force stabilizes at around 8N after 0.7s, and there is no periodicity. Shock. It can be seen from the above simulation results that the force under the dynamic trajectory can be stabilized through the reference model adaptive impedance control, while the expected error of the force is small.

Compared with the existing technology, the present invention does not need to learn based on a large amount of robot arm parameter data. At the same time, the existing technology needs to use different optimization algorithms for comparison in the process of performing deep learning for parameter optimization. However, different The weights and loss functions of the optimization algorithms are different. During the experiment, the optimization function is prone to fall into local optimality or overfitting. This article avoids the process of learning with a large amount of data. The error value between the feedback data and the expected data changes in real time through the adaptive algorithm to adapt to the desired action, reducing the cumbersome process. At the same time, there will be no chance of falling into local optimality and over-fitting. Condition.

In addition, as shown in Figure 10, one embodiment of the present invention also discloses an adaptive variable impedance control device, which includes:

The analysis module 110 is used to decouple the internal and external forces on the target object during the cooperative grasping of the target object by the two manipulators, and conduct a step-by-step analysis of the internal and external forces to obtain the force analysis results of the target object;

The loading module 120 is used to load the adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used to generate the desired motion trajectory of the mechanical arm according to the force analysis results. The adaptive controller is used to pass Compare the error signal between the actual movement trajectory of the robot arm and the expected movement trajectory to adjust the impedance parameters;

The update module 130 is used to update the impedance parameters according to the error signal output by the adaptive impedance control model during the operation of the dual manipulator;

The execution module 140 is used to adjust the adaptive impedance control strategy according to the updated impedance parameters, so that the dual robotic arms grasp the target object according to the adaptive impedance control strategy.

The adaptive variable impedance control device in the embodiment of the present invention is used to execute the adaptive variable impedance control method in the above embodiment. The specific processing process is the same as the adaptive variable impedance control method in the above embodiment, and will not be repeated here. Repeat.

In addition, as shown in Figure 11, one embodiment of the present invention also discloses an electronic device, including: at least one processor 210; at least one memory 220, used to store at least one program; when at least one program is processed by at least one When the controller 210 is executed, the adaptive variable impedance control method as in any previous embodiment is implemented.

In addition, an embodiment of the present invention also discloses a computer-readable storage medium in which computer-executable instructions are stored, and the computer-executable instructions are used to execute the adaptive variable impedance control method as in any of the previous embodiments.

The system architecture and application scenarios described in the embodiments of the present invention are to more clearly explain the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art will know that with the system architecture With the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

Those of ordinary skill in the art can understand that all or some steps, systems, and functional modules/units in the devices disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof.

In hardware implementations, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may consist of several physical components. Components execute cooperatively. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit . Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As is known to those of ordinary skill in the art, the term computer storage media includes volatile and nonvolatile media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. removable, removable and non-removable media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, tapes, disk storage or other magnetic storage devices, or may Any other medium used to store the desired information and that can be accessed by a computer. Additionally, it is known to those of ordinary skill in the art that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

The terms "component", "module", "system", etc. used in this specification are used to refer to computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process, processor, object, executable file, thread of execution, program or computer running on a processor. Through the illustrations, both applications running on the computing device and the computing device may be components. One or more components can reside in a process or thread of execution, and the component can be localized on one computer or distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. A component may, for example, be based on a signal having one or more data packets (eg, data from two components interacting with another component, such as a local system, a distributed system, or a network, such as the Internet, which interacts with other systems via signals) Communicate through local or remote processes.

Claims (10)

1. An adaptive variable impedance control method, including: In the process of the dual robot arms cooperatively grabbing the target object, the internal force and the external force on the target object are decoupled, and the internal force and the external force are analyzed step by step to obtain the force analysis result of the target object; Load an adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used to generate the desired motion trajectory of the mechanical arm based on the force analysis results. The adaptive controller is used Adjusting the impedance parameter by comparing the error signal between the actual movement trajectory of the robotic arm and the desired movement trajectory; During the operation of the dual robotic arms, update the impedance parameters according to the error signal output by the adaptive impedance control model; The adaptive impedance control strategy is adjusted according to the updated impedance parameter, so that the dual robotic arms grasp the target object according to the adaptive impedance control strategy. 2. The method according to claim 1, characterized in that, the method further comprises: Establish the coordinate system of the dual manipulator collaborative system. The position and attitude of the target object with respect to the reference coordinate system are solved using the following formula: In the formula, is the transformation matrix of the target object relative to the center of mass coordinate system;/> It is the 3X3 rotation matrix of the object relative to the coordinate system at the center of mass;/> is the 3X1 position matrix of the target object relative to the coordinate system at the center of mass; The target object is converted into a constraint between the target object and the robotic arm through the transformation between the coordinate system at the center of mass and the world coordinate system, which is expressed by the following formula: In the formula, It is the homogeneous coordinate transformation of the coordinate system O at the center of mass relative to the world coordinate system W;/> Represents the homogeneous coordinate transformation of the base coordinate system of the dual robot arms relative to the world coordinate system;/> Represents the secondary transformation of the end coordinate system of the dual robotic arms relative to the base coordinates of the dual robotic arms;/> Represents the homogeneous transformation of the coordinate system of the center of mass of the target object relative to the end of the robotic arm; The speed constraint relationship is analyzed through the following formula, so that the position and speed of the arms are consistent during the movement; In the formula, Represents the speed of the end of the robotic arm relative to the world coordinate system;/> Represents the object’s velocity relative to the world coordinate system, angular velocity;/> Represents the transformation matrix of the position of the end of the robotic arm relative to the world;/> Represents the position transformation matrix of the end of the robotic arm relative to the center of mass of the target object;/> Represents the direction rotation matrix of the center of mass of the target object relative to the world. 3. The method according to claim 1, characterized in that decoupling the internal force and external force on the target object includes: According to Newton's second law and Euler's equation, the state of the dual manipulator grabbing the target object is established, and the following dynamic equation of the target object is established: in the formula I _O represents the inertia matrix at the center of mass of the target object; F _O ∈R ⁶ represents the vector force of the dual robotic arms acting on the target object; M _O ∈R ⁶ represents the mass inertia matrix of the target object;/> represents the linear acceleration and angular acceleration during the movement of the target object; C _O ∈R ⁶ represents the resultant force vector of the Coriolis force, gravity and centrifugal force of the target object; F _ext ∈R ⁶ represents the appropriate amount of external interference force acting on the target object Force; convert the above formula into the following formula: In the formula, k=l, r represents the left arm and right arm of the double robotic arm, S _k ^T ∈ R ⁶ represents the grasping matrix; F _k represents the force of the robotic arm acting on the target object; decompose the grasping matrix to obtain the external force Formula F _I and the internal force formula F _E are obtained: in the formula Yes/> The generalized inverse of a matrix. 4. The method according to claim 3, characterized in that the force analysis results of the target object include: In the formula, x _e and x _a represent the expected trajectory and the actual trajectory respectively; F _e and F _a represent the expected force and the actual force respectively, where F _k =F _a ; M _d represents the inertia matrix; B _d represents the damping matrix; K _d represents the stiffness matrix; ΔF represents the error value of the force; Substituting the external force formula F _I and the internal force formula F _E into the above formula, we get the following formula: Among them, F _a is expressed as: F _k =F _a =K _r (x _e -x _a ) In the formula, K _r represents the stiffness of the target object. 5. The method according to claim 1, characterized in that the solution process of the error signal is as follows: e＝x _f +Δx-x _a In the formula, x _f is the position error generated by traditional impedance, x _a is the actual trajectory, and Δx is the trajectory adjustment value; In the formula, p(t) and v(t) are the proportional parameters and differential feedback gain parameters of the force error in adaptive control respectively; a(t) is the adjustment value in adaptive control; according to the following formula: x _e =K _r ^-1 (F _e -ΔF)+x _a In the formula, Among them, the second-order system of the ideal reference model is as follows: The error signal between the actual model and the ideal reference model is obtained: The expression in state space is: in, 6. The method of claim 5, wherein updating impedance parameters according to the error signal output by the adaptive impedance control model includes: Based on the stability theorem of Lyapuno equation, the following equation is established: V＝ΔF _e ^T PΔF _e +μ ₁ (B _m -B _l ) ² +μ ₂ (A _m -A _l ) ² +μ ₃ (Y _l ) ² In the formula, According to Lyapunov's second theorem, Q=α ^T Pα; μ ₁ , μ ₂ and μ ₃ are positive numbers; P is a non-singular matrix; by differentiating the above formula, we get: In the formula, According to Lyapunov’s second theorem, ensure/> is always less than 0, then except -ΔF _e ^T QΔF _e is not 0, all other terms are 0, and we get: Therefore, the control law of the adaptive impedance control strategy is obtained: in, In the formula, κ ₀ , κ ₁ , κ ₂ , β _p and β _v are all positive integral adaptive gain coefficients; a ₀ , p ₀ , v ₀ are the initial values selected by the adaptive control system. 7. The method according to claim 6, wherein the adjusting an adaptive impedance control strategy according to the updated impedance parameters includes: By updating a(t), p(t), v(t) and eta(t), the adaptive impedance control strategy is updated. 8. An adaptive variable impedance control device, characterized in that the device includes: The analysis module is used to decouple the internal force and the external force on the target object during the process of the dual robot arms cooperating to grab the target object, and conduct a step-by-step analysis of the internal force and the external force to obtain the target object's Force analysis results; A loading module is used to load an adaptive impedance control model. The adaptive impedance control model includes a reference model and an adaptive controller. The reference model is used to generate the desired motion trajectory of the mechanical arm based on the force analysis results, so The adaptive controller is used to adjust the impedance parameters by comparing the error signal between the actual movement trajectory of the robotic arm and the desired movement trajectory; An update module, configured to update impedance parameters according to the error signal output by the adaptive impedance control model during the operation of the dual manipulator; An execution module, configured to adjust an adaptive impedance control strategy according to the updated impedance parameter, so that the dual robotic arms grasp the target object according to the adaptive impedance control strategy. 9. An electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, it implements claims 1 to 7 The adaptive variable impedance control method described in any one of the above. 10. A computer-readable storage medium storing computer-executable instructions, the computer-executable instructions being used to execute the adaptive variable impedance control method according to any one of claims 1 to 7.