A robot arm control method, device, electronic device and storage medium

Abstract

The present invention relates to the field of robot control technology. The present invention discloses a robot arm control method, device, electronic device and storage medium. The method includes: determining the singular state data of the end of the slave end robot arm; when the singular state data meets the preset singular condition, determining the impedance control force of each joint of the master end robot arm based on the real-time posture matrix of the end of the slave end robot arm, the singular state data and the joint parameter information of the master end robot arm; determining the target control force of each joint of the master end robot arm based on the kinematic torque data of the master end robot arm and the impedance control force of each joint of the master end robot arm, which is used to control the movement behavior of the corresponding joint in the master end robot arm. The impedance control force of each joint of the master end robot arm is dynamically adjusted according to the singular state data of the slave end robot arm, which is used to control the master end robot arm, avoid the master end aggravating the singularity problem of the slave end robot arm when it does not perceive the existence of the singular point of the slave end robot arm, and improve the safety of the robot arm.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a robot arm control method, device, electronic equipment and storage medium. Background Art

[0002] With the development of intelligent medical technology, surgical robots are being used more and more widely. It is crucial to ensure that the surgical robot arm can stably move to the target position during the operation.

[0003] During the operation of the robot arm, the robot arm may encounter singular position deformation, which may cause the movement speed to become unstable. Such singular positions may also cause some joints of the robot arm to lose control or the overall performance of the robot arm to decline. Therefore, a series of measures need to be taken to avoid singular positions. The method for avoiding singular positions can be to limit the robot's operating range, position posture and robot function, or to determine the orientation of the singular position and set the repulsion speed to avoid the singular position. There is a problem that the corresponding avoidance strategy cannot be determined according to the degree of deformation of the singular position of the robot arm, and the avoidance strategy cannot be determined according to the interaction between the robot arms at different ends, making it impossible to avoid the singular position accurately and in real time, resulting in unstable operation of the robot arm. Summary of the invention

[0004] The present invention provides a robot arm control method, device, electronic device and storage medium to solve the problem in the prior art that the corresponding singularity avoidance operation cannot be determined according to the singularity degree, and the problem that the singularity avoidance operation is untimely and unstable.

[0005] According to one aspect of the present invention, there is provided a robot arm control method, comprising:

[0006] Determine the singular state data of the end of the slave robot arm;

[0007] When the singular state data satisfies the preset singular condition, the real-time pose matrix of the end of the slave manipulator is obtained, and the impedance control force of each joint of the master manipulator is determined based on the real-time pose matrix of the end of the slave manipulator, the singular state data and the joint parameter information of the master manipulator;

[0008] The kinematic torque data of the master-end robot arm is obtained, the target control force of each joint of the master-end robot arm is determined based on the kinematic torque data and the impedance control force of each joint of the master-end robot arm, and the movement behavior of the corresponding joint in the master-end robot arm is controlled based on the target control force.

[0009] Optionally, the joint parameter information includes real-time motion speed data of each joint of the master-end manipulator, real-time angle data of each joint of the master-end manipulator and the posture matrix of the end of the master-end manipulator; based on the real-time posture matrix of the end of the slave-end manipulator, the singular state data and the joint parameter information of the master-end manipulator, the impedance control force of each joint of the master-end manipulator is determined, including: based on the mapping relationship between the posture matrix of the end of the slave-end manipulator and the posture matrix of the end of the master-end manipulator and the real-time posture matrix of the end of the slave-end manipulator, the target angle data of each joint of the master-end manipulator is calculated; and each joint of the master-end manipulator is obtained. The real-time motion speed data of each joint of the master-end robot arm is obtained, and the damping matrix data is determined based on the real-time motion speed data and singular state data of each joint of the master-end robot arm; the real-time angle data of each joint of the master-end robot arm is obtained, and the angle difference data corresponding to each joint is determined based on the target angle data and the real-time angle data of each joint, and the expected stiffness matrix data is determined based on the angle difference data and the singular state data; the impedance control force of each joint of the master-end robot arm is determined based on the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master-end robot arm and the corresponding angle difference data of each joint of the master-end robot arm.

[0010] Optionally, the target angle data of each joint of the master-end robotic arm are calculated based on the mapping relationship between the pose matrix of the slave-end robotic arm end and the pose matrix of the master-end robotic arm end and the real-time pose matrix of the slave-end robotic arm end, including: setting the real-time pose matrix of the slave-end robotic arm end to the matrix data corresponding to the pose matrix of the slave-end robotic arm end in the mapping relationship, performing inverse kinematics solution of the master-end robotic arm, and determining the target angle data of each joint of the master-end robotic arm.

[0011] Optionally, the damping matrix data is determined based on the real-time motion speed data and singular state data of each joint of the master-end robotic arm, including: determining the absolute value of the real-time motion speed data of each joint of the master-end robotic arm; determining the damping matrix data based on the product of the absolute value of the real-time motion speed data of each joint of the master-end robotic arm and the singular state data.

[0012] Optionally, determining the expected stiffness matrix data based on the angle difference data and the singular state data includes: determining the expected stiffness matrix data based on the product of the angle difference data and the singular state data.

[0013] Optionally, the method also includes: when the singular state data satisfies a preset singular condition, obtaining the motion velocity vector data of each joint of the main-end robot arm at the corresponding moment; for any joint of the main-end robot arm, determining the data range of the product value of the motion velocity vector data and the real-time motion velocity data, and determining the speed adjustment data based on the data range of the product value; comparing the singular state data with a preset singular upper limit threshold and a preset singular lower limit threshold to determine the data range of the singular state data, and determining the singular state adjustment data based on the data range of the singular state data; adjusting the damping matrix data and the expected stiffness matrix data based on the speed adjustment data to obtain adjusted damping matrix data and expected stiffness matrix data; and / or updating the singular state data based on the singular state adjustment data.

[0014] Optionally, determining the singular state data of the end of the slave robotic arm includes: constructing a DH parameter coordinate system of the slave robotic arm, and determining the Jacobian matrix of the slave robotic arm based on the DH parameter coordinate system of the slave robotic arm; performing singular value decomposition processing on the Jacobian matrix of the slave robotic arm, determining the condition number of the Jacobian matrix, and determining the singular state data of the end of the slave robotic arm based on the condition number.

[0015] According to another aspect of the present invention, there is provided a robot arm control device, comprising:

[0016] A singular state data determination module, used to determine the singular state data of the end of the slave end robot arm;

[0017] The impedance control force determination module is used to obtain the real-time pose matrix of the end of the slave manipulator when the singular state data meets the preset singular condition, and determine the impedance control force of each joint of the master manipulator based on the real-time pose matrix of the end of the slave manipulator, the singular state data and the joint parameter information of the master manipulator;

[0018] The joint control module is used to obtain the kinematic torque data of the master-end robot arm, determine the target control force of each joint of the master-end robot arm based on the kinematic torque data and the impedance control force of each joint of the master-end robot arm, and control the movement behavior of the corresponding joints in the master-end robot arm based on the target control force.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the robot arm control method of any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the robot arm control method of any embodiment of the present invention when executed.

[0024] The technical solution of the embodiment of the present invention can determine the singularity degree of the slave robotic arm by determining the singularity state data of the end of the slave robotic arm, which is helpful for the subsequent determination of the control force required to avoid the singularity; when the singularity state data meets the preset singularity condition, the real-time posture matrix of the end of the slave robotic arm is obtained, and the impedance control force of each joint of the master robotic arm is determined based on the real-time posture matrix of the end of the slave robotic arm, the singularity state data and the joint parameter information of the master robotic arm, thereby realizing the determination of the master robotic arm according to the real-time posture matrix of the end of the slave robotic arm, the singularity state data and other relevant parameter information. The impedance control force of each joint of the master-end robot is determined, and the impedance control force is dynamically adjusted through singular state data to ensure the timeliness and accuracy of determining the control force of the master-end robot arm; the kinematic torque data of the master-end robot arm is obtained, and the target control force of each joint of the master-end robot arm is determined based on the kinematic torque data and the impedance control force of each joint of the master-end robot arm, and the movement behavior of the corresponding joints in the master-end robot arm is controlled based on the target control force, and the target control force is obtained by merging the kinematic torque data and the impedance control force, so as to achieve precise control of the robot arm movement, which can better adapt to different working environments and task requirements. This scheme uses the singular state data at the end of the slave-end robot arm to determine the impedance control force of the master-end robot arm, thereby realizing a method of dynamically adjusting the impedance control force through the singular state data, ensuring the timeliness and accuracy of determining the control force of the master-end robot arm, solving the problem of not being able to determine the corresponding singularity avoidance operation according to the degree of singularity, as well as the problem of untimely and unstable singularity avoidance operation, improving the timeliness, stability and accuracy of singularity avoidance control, and avoiding the problem of the master end applying control force when the slave-end abnormality is not sensed, thereby increasing the singularity of the slave-end robot arm, improving the safety of the robot arm's singularity avoidance control, and thereby improving the overall performance of the robot arm.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a robot arm control method provided in Embodiment 1 of the present invention;

[0028] Figure 2 is a flow chart of a robot arm control method provided by Embodiment 2 of the present invention;

[0029] Figure 3 is a schematic diagram of the structure of a robot arm control device provided in Embodiment 3 of the present invention;

[0030] Figure 4 It is a structural schematic diagram of an electronic device for implementing the robot arm control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1This is a flow chart of a robot arm control method provided in the first embodiment of the present invention. This embodiment is applicable to the control of a robot arm, especially the control of a surgical robot arm used in a surgical scene. The method can be executed by a robot arm control device, which can be implemented in the form of hardware and / or software. The robot arm control device can be configured in electronic devices such as computers, controllers and servers. Figure 1 As shown, the method includes:

[0035] S110, determining the singular state data of the end of the slave robot arm.

[0036] Among them, the singular state data can be specifically understood as data used to characterize the degree to which the end of the robot is close to a singular position. The singular values ​​of the Jacobian matrix can be determined by analyzing the Jacobian matrix of the robot, and the singular state data can be determined based on the singular values. The Jacobian matrix is ​​a matrix that describes the relationship between the speed of the end of the robot and the speed of each joint. Mathematically, whether it is in a singular position is determined by the Jacobian matrix. When the determinant of the Jacobian matrix is ​​0, the Jacobian matrix cannot be inverted. Therefore, the movement speed of the robot in Cartesian space cannot be mapped to the joint space, resulting in the robot's speed being uncontrollable. This indicates that the robot is in a singular position, that is, the movement of the end of the robot is restricted.

[0037] Optionally, determining the singular state data of the end of the slave robotic arm includes: constructing a DH parameter coordinate system of the slave robotic arm, and determining the Jacobian matrix of the slave robotic arm based on the DH parameter coordinate system of the slave robotic arm; performing singular value decomposition processing on the Jacobian matrix of the slave robotic arm, determining the condition number of the Jacobian matrix, and determining the singular state data of the end of the slave robotic arm based on the condition number.

[0038] Specifically, the kinematic model of the slave robot arm is established through the construction method of the standard DH parameter coordinate system, and the DH parameters of each joint of the robot arm are obtained. The DH parameters include link offset, link length, link torsion angle and joint angle. The Jacobian matrix corresponding to the slave robot arm is calculated according to the DH parameter coordinate system of the robot arm, and then the Jacobian matrix is ​​decomposed into singular values, the condition number of the Jacobian matrix is ​​calculated, and it is calculated in real time whether the current position is close to the singular position, and the singular state data of the end of the slave robot arm is determined.

[0039] In a specific embodiment, the steps of determining the singular state data of the end of the slave robot arm are as follows:

[0040] Step 1: Establish the DH coordinate system of the slave robot;

[0041] The standard DH method is used to establish the kinematic model of the slave robot arm, and the DH parameters of each joint of the robot are obtained: The meanings of the DH parameters are as follows: , , , , where i=(1,2...7), represents the joint number;

[0042] : Connecting rod offset, indicating that Along Axis translation to distance;

[0043] : connecting rod length, indicating that Along Axis translation to distance;

[0044] : Connecting rod torsion angle, indicating that Along Axis rotation to Angle

[0045] : represents the joint angle of the i-th joint;

[0046] Exemplarily, the constructed DH parameter table of the slave end robot arm is as follows:

[0047]

[0048] The root DH parameters are processed through the simulation model to obtain the pose matrix of the end of the slave robot arm. , as shown below:

[0049] ;

[0050] Optionally, the following formula is processed by simulation software to determine the matrix of each joint of the slave robot arm: , i is the joint number of the slave robot arm, and the obtained Multiply and output the corresponding pose matrix .in, = , = , a= , d = , the corresponding pose matrix is ​​calculated by the following algorithm:

[0051] ;

[0052] Step 2: Calculate the Jacobian matrix of the slave robot according to the DH coordinate system ; Among them, the i-th column is as follows:

[0053] ;

[0054] For revolute joints have:

[0055] ;

[0056] For moving joints have:

[0057] ;

[0058] Step 3: Perform singular value decomposition on the Jacobian matrix, calculate the condition number of the Jacobian matrix, and calculate in real time whether the current position is close to the singular position;

[0059]

[0060] in, , They are all orthogonal matrices; is a diagonal matrix as follows:

[0061] ;

[0062] In the formula, yes The singular values ​​of is the maximum singular value, is the minimum singular value; when When , the Jacobian matrix is ​​full rank, the manipulator is in a non-singular position, and when When , the Jacobian matrix is ​​not full rank, and the manipulator is in a singular position; the relationship between the condition number and the singular value is: , ; When k is infinite, the slave robot is in a singular position; the k value is used as the singular state data of the slave robot.

[0063] S120. When the singular state data satisfies a preset singular condition, the real-time pose matrix of the end of the slave robot arm is obtained, and the impedance control force of each joint of the master robot arm is determined based on the real-time pose matrix of the end of the slave robot arm, the singular state data and the joint parameter information of the master robot arm.

[0064] Among them, the preset singular condition is specifically a judgment condition set for judging whether the end of the robot arm is in a singular position according to the singular state data, and the data approaching infinity can be set as the preset singular condition. The pose matrix of the end of the robot arm is a matrix used to describe the position and posture of the end effector of the robot arm. The real-time DH parameters of the robot arm are processed to obtain the corresponding real-time pose matrix. The joint parameter information of the master-end robot arm can be specifically understood as information used to determine the impedance force, including but not limited to the real-time motion speed data of each joint of the master-end robot arm, the real-time angle data of each joint of the master-end robot arm and the pose matrix of the end of the master-end robot arm.

[0065] Specifically, the preset singular conditions corresponding to the robot arm are obtained, and the singular state data is compared with the preset singular conditions to determine whether the singular state data meets the preset singular conditions. When the singular state data meets the preset singular conditions, the real-time pose matrix of the end of the slave robot arm is obtained, and the joint parameter information of the master robot arm is obtained at the same time, and then the dynamic adjustment impedance control parameters of the impedance control force equation are determined according to the real-time pose matrix of the end of the slave robot arm, the singular state data and the joint parameter information of the master robot arm, and the dynamically adjusted impedance control parameters are substituted into the impedance control force equation to obtain the impedance control force of each joint of the master robot arm; the dynamically adjusted impedance control parameters include damping matrix data and expected stiffness matrix data, and the damping matrix data and the expected stiffness matrix data are diagonal matrices, thereby avoiding the coupling effect between joints. The impedance control force equation is constructed based on the spring-damper-mass model, and the impedance control force equation is as follows:

[0066] , ;

[0067] in, is the impedance control force, is the speed of the joint, is the target angle of the joint, is the current angle of the joint, is the damping matrix data, is the expected stiffness matrix data.

[0068] In this embodiment, the singular state data of the slave-end robotic arm is applied to the impedance control force equation for determining the impedance control force of each joint of the master-end robotic arm, so as to realize dynamic adjustment of the impedance control force of each joint of the master-end robotic arm according to the singular state data of the slave-end robotic arm, so as to control the master-end robotic arm and the slave-end robotic arm to avoid singular positions, avoid the problem of the master end applying control force when it does not perceive the existence of a singular point in the slave-end robotic arm, thereby increasing the singularity of the slave-end robotic arm, and improve the safety of the robotic arm.

[0069] S130, obtaining kinematic torque data of the master-end robotic arm, determining the target control force of each joint of the master-end robotic arm based on the kinematic torque data and the impedance control force of each joint of the master-end robotic arm, and controlling the movement behavior of the corresponding joint in the master-end robotic arm based on the target control force.

[0070] It should be noted that during the movement of the robot arm, a force is generated due to the interaction between the rotation of the robot arm itself or the linear motion of the end effector of the robot arm and the rotational motion of the base of the robot arm. This phenomenon is called the Coriolis effect. In addition, gravity is a factor that cannot be ignored in joint dynamics, especially when the joint or object moves in the vertical direction. Gravity will cause the object to produce a downward acceleration, affecting the object's motion trajectory and speed. In the dynamic model, the influence of gravity needs to be considered through an appropriate mathematical model to ensure the accuracy of the calculation. Therefore, it is necessary to obtain the kinematic torque data of the master-end robot arm to compensate for the nonlinear torque acting on the robot arm. The kinematic torque data of the master-end robot arm includes the torque data corresponding to the Coriolis effect and the gravity effect respectively. The kinematic torque data is usually expressed by the formula Calculate and get, where C is the Coriolis force effect matrix, g is the gravity effect matrix, is the speed of the joint, is the current joint angle.

[0071] Specifically, the operation data of the master-end robotic arm is processed by calling the kinematic torque calculation formula to calculate the kinematic torque data of the master-end robotic arm, wherein the operation data of the master-end robotic arm includes the movement speed of each joint and the current joint angle data. The kinematic torque data and the impedance control force of each joint of the master-end robotic arm are combined to determine the target control force of each joint of the master-end robotic arm. Exemplarily, the kinematic torque data and the impedance control force of each joint of the master-end robotic arm can be summed, or corresponding weight information can be set and the kinematic torque data and the impedance control force of each joint of the master-end robotic arm can be weighted and summed based on the weight information to obtain the target control force of each joint of the master-end robotic arm, and then the movement behavior of the corresponding joint in the master-end robotic arm is controlled according to the target control force. Preferably, the formula for determining the target control force is as follows:

[0072] ;

[0073] in, is the magnitude of the impedance control force finally applied to the joint, It represents the kinematic part. It is the impedance control part.

[0074] In this embodiment, the target control force of each joint of the main-end robot arm is obtained by combining the kinematic torque data and the impedance control force of each joint of the main-end robot arm. This not only achieves avoiding singular positions through the impedance control force of each joint of the main-end robot arm, but also compensates for the nonlinear torque of the robot arm through the kinematic torque data, so as to improve the movement accuracy and performance of the robot arm.

[0075] The technical solution of this embodiment is to determine the singular state data of the end of the slave-end robotic arm; when the singular state data meets the preset singular condition, obtain the real-time pose matrix of the end of the slave-end robotic arm, and determine the impedance control force of each joint of the master-end robotic arm based on the real-time pose matrix of the end of the slave-end robotic arm, the singular state data and the joint parameter information of the master-end robotic arm; obtain the kinematic torque data of the master-end robotic arm, determine the target control force of each joint of the master-end robotic arm based on the kinematic torque data and the impedance control force of each joint of the master-end robotic arm, and control the movement behavior of the corresponding joints in the master-end robotic arm based on the target control force. This scheme uses the singular state data at the end of the slave-end robot arm to determine the impedance control force of the master-end robot arm, thereby realizing a method of dynamically adjusting the impedance control force through the singular state data, ensuring the timeliness and accuracy of determining the control force of the master-end robot arm, solving the problem of not being able to determine the corresponding singularity avoidance operation according to the degree of singularity, as well as the problem of untimely and unstable singularity avoidance operation, improving the timeliness, stability and accuracy of singularity avoidance control, and avoiding the problem of the master end applying control force when the slave-end abnormality is not sensed, thereby increasing the singularity of the slave-end robot arm, improving the safety of the robot arm's singularity avoidance control, and thereby improving the overall performance of the robot arm.

[0076] Embodiment 2

[0077] Figure 2 It is a flow chart of a robot arm control method provided in the second embodiment of the present invention. This embodiment is a further optimization of the method of the above embodiment. Optionally, the target angle data of each joint of the master-end robot arm is calculated based on the mapping relationship between the posture matrix of the slave-end robot arm end and the posture matrix of the master-end robot arm end and the real-time posture matrix of the slave-end robot arm end; the real-time motion speed data of each joint of the master-end robot arm is obtained, and the damping matrix data is determined based on the real-time motion speed data and singular state data of each joint of the master-end robot arm; the real-time angle data of each joint of the master-end robot arm is obtained, and the angle difference data corresponding to each joint is determined based on the target angle data and real-time angle data of each joint, and the expected stiffness matrix data is determined based on the angle difference data and singular state data; the impedance control force of each joint of the master-end robot arm is determined based on the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master-end robot arm and the corresponding angle difference data of each joint of the master-end robot arm. Figure 2 As shown, the method includes:

[0078] S210, determining the singular state data of the end of the slave robot arm.

[0079] S220. When the singular state data satisfies a preset singular condition, obtain a real-time pose matrix of the end of the slave robot arm.

[0080] S230, calculating the target angle data of each joint of the master end robot arm based on the mapping relationship between the posture matrix of the slave end robot arm end and the posture matrix of the master end robot arm end and the real-time posture matrix of the slave end robot arm end.

[0081] Among them, the pose matrix of the end of the master-end manipulator is a matrix used to describe the position and posture of the end effector of the master-end manipulator. Accordingly, the pose matrix of the end of the master-end manipulator can be determined by the method of determining the pose matrix of the end of the slave-end manipulator. The steps of determining the pose matrix of the end of the master-end manipulator can be performed sequentially by the same thread, or the pose matrix of the end of the master-end manipulator can be determined by a parallel thread processing method. Or, when determining the target angle data of each joint of the master-end manipulator, the pose matrix of the end of the master-end manipulator can be directly called from the preset storage space to improve the speed of determining the impedance control force of the master-end manipulator. The specific processing method is set according to actual needs.

[0082] For example, to determine the pose matrix of the end of the master end robot arm , by establishing the master-end DH parameter coordinate system, the pose matrix of the end of the master-end manipulator is calculated based on the master-end DH parameter coordinate system The master-side DH parameter coordinate system is as follows:

[0083]

[0084] Specifically, the mapping relationship between the pose matrix of the end of the slave manipulator and the pose matrix of the end of the master manipulator is called, and the target angle data of each joint of the master manipulator is calculated based on the mapping relationship between the pose matrix of the end of the slave manipulator and the pose matrix of the end of the master manipulator and the real-time pose matrix of the end of the slave manipulator. Optionally, the real-time pose matrix of the end of the slave manipulator is set to the matrix data corresponding to the pose matrix of the end of the slave manipulator in the mapping relationship, and the inverse kinematics of the master manipulator is solved to determine the target angle data of each joint of the master manipulator.

[0085] The real-time pose matrix of the end of the slave robot arm is a known matrix, and the pose matrix of the end of the master robot arm is a matrix to be solved. The pose matrix of the end of the master robot arm includes the angle variable q of each joint, that is, q is used as the pose matrix In Variables, substitute the real-time pose matrix of the end of the slave robot into the mapping relationship between the pose matrix of the end of the slave robot and the pose matrix of the end of the master robot. The angle variables of each joint are unknown. According to the equal relationship between the elements of the matrices, the value of the angle variable q in the pose matrix of the end of the master robot is obtained to obtain the target angle data of each joint. The mapping relationship between the pose matrix of the end of the slave robot and the pose matrix of the end of the master robot is as follows:

[0086] ;

[0087] in, is the pose matrix of the end of the master robot arm; is the real-time pose matrix of the end of the slave manipulator. r is the proportional coefficient between the master and slave ends, which can be calibrated in advance through multiple tests. In actual application, it can also be adjusted according to actual needs to ensure the accuracy of the determined target angle data.

[0088] In this embodiment, the pose matrix of the master-end robotic arm end corresponding to the real-time pose matrix of the slave-end robotic arm end is calculated according to the mapping relationship between the pose matrix of the slave-end robotic arm end and the pose matrix of the master-end robotic arm end, so as to ensure the consistency of the pose matrices of the master and slave-end robotic arms, thereby ensuring the stability of the operation of the master and slave-end robotic arms.

[0089] S240, acquiring real-time motion speed data of each joint of the master-end robot arm, and determining damping matrix data based on the real-time motion speed data and singular state data of each joint of the master-end robot arm.

[0090] Specifically, the real-time motion speed of each joint of the master-end manipulator can be collected by a sensor device to obtain the real-time motion speed data of each joint of the master-end manipulator, and the real-time motion speed data of each joint of the master-end manipulator can be converted into a corresponding data matrix, and then substituted into the function of determining the damping matrix data with the singular state data, so as to obtain the damping matrix data. The formula is as follows:

[0091] ;

[0092] in, is the damping matrix data, Characterizes the real-time speed of each joint of the master robot arm, and k is the singular state data.

[0093] Optionally, determine the absolute value of the real-time motion speed data of each joint of the master-end robot arm; and determine the damping matrix data based on the product of the absolute value of the real-time motion speed data of each joint of the master-end robot arm and the singular state data.

[0094] Specifically, the real-time motion speed data of each joint of the master-end robot arm is operated with absolute values ​​to obtain the absolute values ​​of the real-time motion speed data of each joint of the master-end robot arm; based on the product of the absolute values ​​of the real-time motion speed data of each joint of the master-end robot arm and the singular state data, the damping matrix data is determined. The formula for determining the damping matrix data is as follows:

[0095] ;

[0096] In a specific embodiment, a corresponding adjustment coefficient is set for the damping matrix data to adjust the damping matrix data so that the obtained damping matrix data is more consistent with the master end robot arm, and the formula of the damping matrix data is updated as follows:

[0097] ;

[0098] in, It is the damping matrix data adjustment coefficient, which can be set according to the actual situation.

[0099] S250, obtaining the real-time angle data of each joint of the master end robot arm, determining the angle difference data corresponding to each joint based on the target angle data and the real-time angle data of each joint, and determining the expected stiffness matrix data based on the angle difference data and the singular state data.

[0100] Specifically, the real-time angle data of each joint of the master-end robot arm can be collected by the sensor device to obtain the real-time angle data of each joint of the master-end robot arm, the real-time angle data of each joint of the master-end robot arm can be converted into a corresponding data matrix, the target angle data and the real-time angle data of each joint can be calculated to determine the angle difference data corresponding to each joint, the angle difference data corresponding to each joint can be converted into a corresponding matrix data format, and the matrix data of the corresponding angle difference of the master-end robot arm can be obtained. , ; Then, it is substituted together with the singular state data into the function of determining the expected stiffness matrix data, thereby obtaining the expected stiffness matrix data. The function of determining the expected stiffness matrix data is as follows:

[0101] ;

[0102] in, is the expected stiffness matrix data, q represents the real-time angle data of each joint of the master end manipulator, and k is the singular state data. Exemplarily, the function of determining the expected stiffness matrix data can be obtained by multiplying the angle difference data and the singular state data, or the function of determining the expected stiffness matrix data can be obtained by performing other mathematical operations on the angle difference data and the singular state data.

[0103] Optionally, the expected stiffness matrix data is determined based on the product of the angle difference data and the singular state data.

[0104] Specifically, the formula for determining the expected stiffness matrix data is as follows:

[0105] ;

[0106] In a specific embodiment, an adjustment coefficient may also be set in the formula to adjust the expected stiffness matrix data to improve the accuracy of the expected stiffness matrix data. The formula for determining the expected stiffness matrix data is updated as follows:

[0107] ;

[0108] in, Adjust the coefficients for the desired stiffness matrix data.

[0109] On the basis of the above embodiments, the method also includes: when the singular state data satisfies a preset singular condition, obtaining the motion velocity vector data of each joint of the main-end robot arm at the corresponding moment; for any joint of the main-end robot arm, determining the data range of the product value of the motion velocity vector data and the real-time motion velocity data, and determining the speed adjustment data based on the data range of the product value; comparing the singular state data with a preset singular upper limit threshold and a preset singular lower limit threshold to determine the data range of the singular state data, and determining the singular state adjustment data based on the data range of the singular state data; adjusting the damping matrix data and the expected stiffness matrix data based on the speed adjustment data to obtain adjusted damping matrix data and expected stiffness matrix data; and / or updating the singular state data based on the singular state adjustment data.

[0110] It should be noted that in order to more accurately determine the impedance control force of each joint of the master-end manipulator according to the degree to which the end of the slave-end manipulator enters the singular position, a corresponding preset singular upper limit threshold and a preset singular lower limit threshold can be set for the singular state data to determine the data range to which the singular state data belongs, thereby adjusting the singular state data according to the singular state data being in different data ranges. In addition, the joint motion speed data of the master-end manipulator is also obtained at the moment when the slave-end manipulator is detected to be close to the singular position. The joint motion speed data at this moment has a direction. By judging whether the joint motion speed data at this moment and the real-time motion speed data of the joint of the master-end manipulator are in opposite motion directions, the speed adjustment data can be determined by determining the data range of the product value of the motion speed vector data and the real-time motion speed data.

[0111] Specifically, when the singular state data meets the preset singular condition, the motion speed vector data of each joint of the master-end manipulator at the corresponding moment is obtained; for any joint of the master-end manipulator, the product value of the motion speed vector data of each joint of the master-end manipulator and the real-time motion speed data is calculated, the magnitude relationship between the product value and the value zero is judged, whether the product value is greater than 0 or less than 0 is determined, and the speed adjustment data is determined based on the data range of the product value; the speed adjustment data The assignment function is as follows:

[0112] ;

[0113] in, Indicates the real-time joint running speed of the master end robot arm. Indicates the motion velocity vector data (with direction) recorded when the singularity is triggered.

[0114] Obtain the preset singular upper limit threshold and the preset singular lower limit threshold corresponding to the robot arm, compare the singular state data with the preset singular upper limit threshold and the preset singular lower limit threshold, determine the data range of the singular state data, and determine the singular state adjustment data according to the data range of the singular state data; determine the singular state adjustment data The assignment function is as follows:

[0115] ;

[0116] in, is the singular lower limit threshold, is the singular upper limit threshold; the singular state adjustment data is determined according to the above assignment function.

[0117] In the case of determining the speed adjustment data and / or the singular state adjustment data, the damping matrix data and the expected stiffness matrix data are adjusted based on the speed adjustment data to obtain the adjusted damping matrix data and the expected stiffness matrix data; and / or, the singular state data is updated based on the singular state adjustment data. The calculation formulas for the adjusted damping matrix data and the expected stiffness matrix data are as follows:

[0118] ;

[0119] ;

[0120] In this embodiment, the damping matrix data and the expected stiffness matrix data are adjusted by determining the speed adjustment data and updating the singular state data, so as to determine the damping matrix data and the expected stiffness matrix data according to the actual operation conditions of the master-end robotic arm, improve the accuracy of the damping matrix data and the expected stiffness matrix data, and thus determine the accuracy of the impedance control force of the master-end robotic arm.

[0121] S260, determining the impedance control force of each joint of the master-end robotic arm based on the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master-end robotic arm, and the corresponding angle difference data of each joint of the master-end robotic arm.

[0122] Specifically, the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master-end robotic arm, and the corresponding angle difference data of each joint of the master-end robotic arm are substituted into the impedance control force equation for calculation to obtain the impedance control force of each joint of the master-end robotic arm.

[0123] S270, obtaining kinematic torque data of the master-end robotic arm, determining the target control force of each joint of the master-end robotic arm based on the kinematic torque data and the impedance control force of each joint of the master-end robotic arm, and controlling the movement behavior of the corresponding joint in the master-end robotic arm based on the target control force.

[0124] The technical solution of this embodiment is to determine the singular state data of the end of the slave-end robot arm; obtain the real-time posture matrix of the end of the slave-end robot arm when the singular state data meets the preset singular condition; based on the mapping relationship between the posture matrix of the end of the slave-end robot arm and the posture matrix of the end of the master-end robot arm and the slave end; obtain the real-time motion speed data of each joint of the master-end robot arm, determine the damping matrix data based on the real-time motion speed data and singular state data of each joint of the master-end robot arm; obtain the real-time angle data of each joint of the master-end robot arm, and determine the damping matrix data based on the target angle data and real-time angle data of each joint. Determine the angle difference data corresponding to each joint, and determine the expected stiffness matrix data based on the angle difference data and the singular state data; determine the impedance control force of each joint of the master-end robotic arm based on the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master-end robotic arm and the corresponding angle difference data of each joint of the master-end robotic arm; obtain the kinematic torque data of the master-end robotic arm, determine the target control force of each joint of the master-end robotic arm based on the kinematic torque data and the impedance control force of each joint of the master-end robotic arm, and control the motion behavior of the corresponding joints in the master-end robotic arm based on the target control force. This scheme uses the singular state data at the end of the slave-end robot arm to determine the impedance control force of the master-end robot arm, thereby realizing a method of dynamically adjusting the impedance control force through the singular state data, ensuring the timeliness and accuracy of determining the control force of the master-end robot arm, solving the problem of not being able to determine the corresponding singularity avoidance operation according to the degree of singularity, as well as the problem of untimely and unstable singularity avoidance operation, improving the timeliness, stability and accuracy of singularity avoidance control, and avoiding the problem of the master end applying control force when the slave-end abnormality is not sensed, thereby increasing the singularity of the slave-end robot arm, improving the safety of the robot arm's singularity avoidance control, and thereby improving the overall performance of the robot arm.

[0125] Embodiment 3

[0126] Figure 3 Schematic diagram of the structure of a robot arm control device provided by the third embodiment of the present invention. Figure 3 As shown, the device comprises:

[0127] A singular state data determination module 310, used to determine the singular state data of the end of the slave end robot arm;

[0128] The impedance control force determination module 320 is used to obtain the real-time pose matrix of the end of the slave manipulator when the singular state data meets the preset singular condition, and determine the impedance control force of each joint of the master manipulator based on the real-time pose matrix of the end of the slave manipulator, the singular state data and the joint parameter information of the master manipulator;

[0129] The joint control module 330 is used to obtain the kinematic torque data of the master-end robot arm, determine the target control force of each joint of the master-end robot arm based on the kinematic torque data and the impedance control force of each joint of the master-end robot arm, and control the movement behavior of the corresponding joint in the master-end robot arm based on the target control force.

[0130] The technical solution of this embodiment is to determine the singular state data of the end of the slave robot arm through a singular state data determination module; the impedance control force determination module obtains the real-time pose matrix of the end of the slave robot arm when the singular state data meets the preset singular condition, and determines the impedance control force of each joint of the master robot arm based on the real-time pose matrix of the end of the slave robot arm, the singular state data and the joint parameter information of the master robot arm; the joint control module obtains the kinematic torque data of the master robot arm, determines the target control force of each joint of the master robot arm based on the kinematic torque data and the impedance control force of each joint of the master robot arm, and controls the movement behavior of the corresponding joints in the master robot arm based on the target control force. This scheme uses the singular state data at the end of the slave-end robot arm to determine the impedance control force of the master-end robot arm, thereby realizing a method of dynamically adjusting the impedance control force through the singular state data, ensuring the timeliness and accuracy of determining the control force of the master-end robot arm, solving the problem of not being able to determine the corresponding singularity avoidance operation according to the degree of singularity, as well as the problem of untimely and unstable singularity avoidance operation, improving the timeliness, stability and accuracy of singularity avoidance control, and avoiding the problem of the master end applying control force when the slave-end abnormality is not sensed, thereby increasing the singularity of the slave-end robot arm, improving the safety of the robot arm's singularity avoidance control, and thereby improving the overall performance of the robot arm.

[0131] Based on the above embodiment, optionally, the joint parameter information includes real-time motion speed data of each joint of the master-end robotic arm, real-time angle data of each joint of the master-end robotic arm and the posture matrix of the end of the master-end robotic arm; the impedance control force determination module 320 includes a target angle data determination unit, a damping matrix data determination unit, an expected stiffness matrix data determination unit and an impedance control force determination unit.

[0132] Optionally, a target angle data determination unit is used to calculate the target angle data of each joint of the master end manipulator arm based on a mapping relationship between the pose matrix of the slave end manipulator arm end and the pose matrix of the master end manipulator arm end and the real-time pose matrix of the slave end manipulator arm end;

[0133] A damping matrix data determination unit, used to obtain real-time motion speed data of each joint of the master end mechanical arm, and determine the damping matrix data based on the real-time motion speed data and singular state data of each joint of the master end mechanical arm;

[0134] The expected stiffness matrix data unit is used to obtain the real-time angle data of each joint of the master end robot arm, determine the angle difference data corresponding to each joint based on the target angle data and the real-time angle data of each joint, and determine the expected stiffness matrix data based on the angle difference data and the singular state data;

[0135] The impedance control force determination unit is used to determine the impedance control force of each joint of the master end robotic arm based on the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master end robotic arm and the corresponding angle difference data of each joint of the master end robotic arm.

[0136] Optionally, a target angle data determination unit is specifically used to set the real-time pose matrix of the end of the slave robot arm to the matrix data corresponding to the pose matrix of the end of the slave robot arm in the mapping relationship, perform inverse kinematics solution of the master robot arm, and determine the target angle data of each joint of the master robot arm.

[0137] The damping matrix data determination unit is specifically used to determine the absolute value of the real-time motion speed data of each joint of the master end robot arm; based on the product of the absolute value of the real-time motion speed data of each joint of the master end robot arm and the singular state data, the damping matrix data is determined.

[0138] The expected stiffness matrix data determining unit is specifically used to determine the expected stiffness matrix data based on the product of the angle difference data and the singular state data.

[0139] Optionally, the device is also specifically used to obtain the motion velocity vector data of each joint of the main-end robot arm at the corresponding moment when the singular state data satisfies a preset singular condition; for any joint of the main-end robot arm, determine the data range of the product value of the motion velocity vector data and the real-time motion velocity data, and determine the speed adjustment data based on the data range of the product value; compare the singular state data with a preset singular upper limit threshold and a preset singular lower limit threshold to determine the data range of the singular state data, and determine the singular state adjustment data based on the data range of the singular state data; adjust the damping matrix data and the expected stiffness matrix data based on the speed adjustment data to obtain adjusted damping matrix data and expected stiffness matrix data; and / or update the singular state data based on the singular state adjustment data.

[0140] Optionally, the singular state data determination module 310 is specifically used to construct the DH parameter coordinate system of the slave robotic arm, determine the Jacobian matrix of the slave robotic arm based on the DH parameter coordinate system of the slave robotic arm; perform singular value decomposition on the Jacobian matrix of the slave robotic arm, determine the condition number of the Jacobian matrix, and determine the singular state data at the end of the slave robotic arm based on the condition number.

[0141] The robot arm control device provided in the embodiment of the present invention can execute the robot arm control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0142] Embodiment 4

[0143] Figure 4 1 is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0144] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0145] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0146] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a robotic arm control method.

[0147] In some embodiments, the robot arm control method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the robot arm control method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the robot arm control method in any other appropriate manner (e.g., by means of firmware).

[0148] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] The computer program for implementing the robot arm control method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0150] Embodiment 5

[0151] Embodiment 5 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute a robot arm control method, the method comprising:

[0152] Determine the singular state data of the end of the slave robot arm;

[0153] When the singular state data satisfies the preset singular condition, the real-time pose matrix of the end of the slave manipulator is obtained, and the impedance control force of each joint of the master manipulator is determined based on the real-time pose matrix of the end of the slave manipulator, the singular state data and the joint parameter information of the master manipulator;

[0154] The kinematic torque data of the master-end robot arm is obtained, the target control force of each joint of the master-end robot arm is determined based on the kinematic torque data and the impedance control force of each joint of the master-end robot arm, and the movement behavior of the corresponding joint in the master-end robot arm is controlled based on the target control force.

[0155] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0156] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0157] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0158] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0159] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0160] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A robot arm control method, characterized in that: include: Determine the singular state data of the end of the slave robot arm; When the singular state data satisfies a preset singular condition, a real-time pose matrix of the end of the slave manipulator arm is obtained, and the impedance control force of each joint of the master manipulator arm is determined based on the real-time pose matrix of the end of the slave manipulator arm, the singular state data and the joint parameter information of the master manipulator arm; Acquire kinematic torque data of the master-end manipulator, determine target control force of each joint of the master-end manipulator based on the kinematic torque data and impedance control force of each joint of the master-end manipulator, and control the motion behavior of the corresponding joint in the master-end manipulator based on the target control force; The joint parameter information includes the real-time motion speed data of each joint of the master-end manipulator, the real-time angle data of each joint of the master-end manipulator, and the position matrix of the end of the master-end manipulator; The step of determining the impedance control force of each joint of the master-end mechanical arm based on the real-time posture matrix of the end of the slave-end mechanical arm, the singular state data, and the joint parameter information of the master-end mechanical arm comprises: Calculate the target angle data of each joint of the master end manipulator arm based on the mapping relationship between the pose matrix of the slave end manipulator arm end and the pose matrix of the master end manipulator arm end and the real-time pose matrix of the slave end manipulator arm end; Acquire real-time motion speed data of each joint of the master-end mechanical arm, and determine damping matrix data based on the real-time motion speed data of each joint of the master-end mechanical arm and the singular state data; Acquire real-time angle data of each joint of the master end robot arm, determine angle difference data corresponding to each joint based on target angle data and real-time angle data of each joint, and determine expected stiffness matrix data based on the angle difference data and the singular state data; Determine the impedance control force of each joint of the master-end robotic arm based on the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master-end robotic arm, and the corresponding angle difference data of each joint of the master-end robotic arm; The method further comprises: When the singular state data satisfies a preset singular condition, obtaining motion velocity vector data of each joint of the master end manipulator at a corresponding moment; For any joint of the master-end manipulator, determine a data range of a product value of the motion speed vector data and the real-time motion speed data, and determine speed adjustment data based on the data range of the product value; Comparing the singular state data with a preset singular upper limit threshold and a preset singular lower limit threshold to determine a data range of the singular state data, and determining singular state adjustment data based on the data range of the singular state data; The damping matrix data and the expected stiffness matrix data are adjusted based on the speed adjustment data to obtain the adjusted damping matrix data and the expected stiffness matrix data; and / or the singular state data is updated based on the singular state adjustment data.

2. The method according to claim 1, characterized in that: The target angle data of each joint of the master end manipulator arm is calculated based on the mapping relationship between the pose matrix of the slave end manipulator arm end and the pose matrix of the master end manipulator arm end and the real-time pose matrix of the slave end manipulator arm end, including: The real-time pose matrix of the end of the slave robot arm is set to the matrix data corresponding to the pose matrix of the end of the slave robot arm in the mapping relationship, and the inverse kinematics of the master robot arm is solved to determine the target angle data of each joint of the master robot arm.

3. The method according to claim 1, characterized in that The determining of damping matrix data based on the real-time motion speed data of each joint of the master end mechanical arm and the singular state data comprises: Determine the absolute value of the real-time motion speed data of each joint of the master end robot arm; The damping matrix data is determined based on the absolute value of the real-time motion speed data of each joint of the master-end robot arm and the product of the singular state data.

4. The method according to claim 1, characterized in that The determining of expected stiffness matrix data based on the angle difference data and the singular state data comprises: The expected stiffness matrix data is determined based on the product of the angle difference data and the singular state data.

5. The method according to claim 1, characterized in that The determining of the singular state data of the end of the slave robot arm includes: Constructing a DH parameter coordinate system of the slave end mechanical arm, and determining a Jacobian matrix of the slave end mechanical arm based on the DH parameter coordinate system of the slave end mechanical arm; The Jacobian matrix of the slave manipulator is subjected to singular value decomposition processing to determine the condition number of the Jacobian matrix, and the singular state data of the end of the slave manipulator is determined based on the condition number.

6. A robot arm control device, characterized in that: include: A singular state data determination module, used to determine the singular state data of the end of the slave end robot arm; An impedance control force determination module is used to obtain the real-time posture matrix of the end of the slave manipulator arm when the singular state data meets the preset singular condition, and determine the impedance control force of each joint of the master manipulator arm based on the real-time posture matrix of the end of the slave manipulator arm, the singular state data and the joint parameter information of the master manipulator arm; a joint control module, configured to obtain kinematic torque data of the master-end manipulator, determine a target control force of each joint of the master-end manipulator based on the kinematic torque data and the impedance control force of each joint of the master-end manipulator, and control the motion behavior of the corresponding joint in the master-end manipulator based on the target control force; The joint parameter information includes the real-time motion speed data of each joint of the master-end manipulator, the real-time angle data of each joint of the master-end manipulator and the posture matrix of the end of the master-end manipulator; the impedance control force determination module is specifically used to calculate the target angle data of each joint of the master-end manipulator based on the mapping relationship between the posture matrix of the end of the slave-end manipulator and the posture matrix of the end of the master-end manipulator and the real-time posture matrix of the end of the slave-end manipulator; obtain the real-time motion speed data of each joint of the master-end manipulator, based on the real The method comprises the following steps: determining damping matrix data based on the real-time motion speed data and the singular state data of each joint of the master-end manipulator, obtaining real-time angle data of each joint of the master-end manipulator, determining angle difference data corresponding to each joint based on target angle data and real-time angle data of each joint, and determining expected stiffness matrix data based on the angle difference data and the singular state data; determining impedance control force of each joint of the master-end manipulator based on the damping matrix data, the expected stiffness matrix data, the real-time motion speed data of each joint of the master-end manipulator and the corresponding angle difference data of each joint of the master-end manipulator; The device is also specifically used for: when the singular state data satisfies a preset singular condition, obtaining the motion velocity vector data of each joint of the master-end manipulator at the corresponding moment; for any joint of the master-end manipulator, determining the data range of the product value of the motion velocity vector data and the real-time motion velocity data, and determining the speed adjustment data based on the data range of the product value; comparing the singular state data with a preset singular upper limit threshold and a preset singular lower limit threshold to determine the data range of the singular state data, and determining the singular state adjustment data based on the data range of the singular state data; adjusting the damping matrix data and the expected stiffness matrix data respectively based on the speed adjustment data to obtain the adjusted damping matrix data and the expected stiffness matrix data; and / or updating the singular state data based on the singular state adjustment data.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the robot arm control method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the robot arm control method according to any one of claims 1 to 5 when executed.

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