An Adaptive Force-Position Hybrid Control Method for a Segment-Pasting Nine-Axis Linkage Robot

Abstract

The present application provides a self-adaptive force-position hybrid control method for a segment-pasting nine-axis linkage robot, which is applied to a segment-pasting nine-axis linkage robot. The robot includes multiple motion joints and multiple moving axes. In the segment-pasting task that can be used in shield tunnels, according to the expected pose to be pasted, the variable impedance coefficient existing in the movement of the robot is determined by combining the actual control parameters at the current moment. Then, by introducing the variable impedance coefficient, when the final pasted pose of the segment-pasting nine-axis linkage robot is at the expected pose, the joint angles of the respective motion joints and the expected moving positions of the moving axes are deduced backwards. And according to the result, a force control signal is generated to control the joint angle movement and the moving axis movement of the robot, so that finally the robot can adaptively and accurately execute segment pasting according to the expected pose to be achieved, and finally achieve the high-precision, high-efficiency and high-reliability effects of the segment-pasting task in the shield tunnel construction process.

Description

Technical Field

[0001] The present application relates to the technical field of automatic control and service robots, and particularly to an adaptive force-position hybrid control method for a nine-axis linkage robot for segment pasting. Background Art

[0002] With the continuous development of automation technology and the extensive promotion of robot applications, the application of robots in the fields of industry, manufacturing, logistics, etc. has become more and more common. Especially in recent years, with the rapid development of technologies such as artificial intelligence, visual recognition, and control algorithms, robot technology has been more widely applied.

[0003] The shield tunneling method is widely used in subway, railway and other underground projects. The application of sealant at the segment joint is a key link to ensure the sealing performance after assembly. However, the existing manual glue application method has low efficiency, unstable quality, and is prone to problems such as missed coating or uneven thickness. Moreover, it has a large labor intensity, many safety hazards, and inaccurate glue volume control leads to material waste. Although some automated glue application equipment has been put into use, traditional robot control systems often face the following challenges when dealing with complex and dynamically changing environments. There are a large number of uncertain factors in the construction environment, such as uneven ground, material deformation, the action of external forces, etc.

[0004] Traditional control methods are difficult to cope with these uncertainties, resulting in a decline in control performance. In tasks such as segment pasting, the robot needs to accurately control force and position simultaneously. For example, during the pasting process, the robot needs to maintain an appropriate contact force while ensuring the accuracy of the pasting position. When traditional force control or position control is used alone, it cannot meet this requirement. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide an adaptive force-position hybrid control method, device and electronic device for a nine-axis linkage robot for segment pasting, so as to achieve high-precision, high-efficiency and high-reliability effects in the segment pasting task during the shield tunnel construction process.

[0006] In a first aspect, the embodiments of the present application provide an adaptive force-position hybrid control method for a nine-axis linkage robot for segment pasting. The method includes:

[0007] The method is applied to a nine-axis linkage robot for segment pasting, and the nine-axis linkage robot for segment pasting includes a plurality of motion joints and a plurality of moving axes. The method includes:

[0008] Obtain the desired trajectory , the actual control parameters at the current moment, and the actual control parameters include: the actual pose at the current moment , the actual speed , the actual environmental acting force , the desired trajectory is the set movement trajectory of the segment pasting nine-axis linkage robot;

[0009] According to the preset intelligent impedance calculation model, determine the variable impedance coefficient when the segment pasting nine-axis linkage robot moves along the desired trajectory 0, where the preset intelligent impedance calculation model is a neural network model constructed based on the actual control parameters; f 0

[0010] Based on the variable impedance coefficient f 0, determine the expected trajectory of the segment pasting nine-axis linkage robot under the influence of the variable impedance coefficient f 0; ;

[0011] According to the expected trajectory Perform inverse kinematics solution of the robotic arm to determine the expected pose obtained after the segment pasting nine-axis linkage robot moves along the expected trajectory 0, and based on the expected pose 0, determine the expected joint angle at the next moment corresponding to each of the motion joints and the expected movement position at the next moment of each of the moving axes; 0

[0012] According to the expected pose and the actual pose , generate a force control signal 0, and based on the force control signal 0, control each of the motion joints to move to the expected joint angle, and control each of the moving axes to move to the expected movement position.

[0013] Combined with the first aspect, in a second possible embodiment, the method further includes:

[0014] For the actual pose and the actual speed perform forward kinematics solution of the robotic arm to determine the actual trajectory of the segment pasting nine-axis linkage robot at the current moment ;

[0015] According to the actual trajectory and the desired trajectory , determine the movement tracking error of the segment pasting nine-axis linkage robot;

[0016] Input the movement tracking error into the preset intelligent impedance calculation model, and determine that the output result of the preset intelligent impedance calculation model is the variable impedance coefficient f 0.

[0017] Combined with the second possible embodiment of the first aspect, in the third possible embodiment, the movement tracking error includes: position tracking error , speed tracking error , acceleration tracking error , force error , and the preset intelligent impedance calculation model satisfies the variable impedance coefficient formula constraint;

[0018] The variable impedance coefficient formula is:

[0019]

[0020] wherein, is the variable impedance coefficient, is the actual inertia matrix varying with time, is the nominal parameter of the actual inertia matrix, is the actual damping matrix varying with time, is the nominal parameter of the actual damping matrix, is the actual stiffness matrix varying with the actual situation, is the nominal parameter of the actual stiffness matrix, is the stability coefficient.

[0021] Combined with the third possible embodiment of the first aspect, in the fourth possible embodiment, the method further includes:

[0022] Based on the variable impedance coefficient formula, construct an RFWNN neural network model to obtain the preset intelligent impedance calculation model;

[0023] Input the movement tracking error in the form of an input vector into the preset intelligent impedance calculation model, and the preset intelligent impedance calculation model adaptively outputs the variable impedance coefficient based on the input vector f 0.

[0024] Combined with the fourth possible embodiment of the first aspect, in the fifth possible embodiment, the preset intelligent impedance calculation model includes: an input layer, a fuzzification and wavelet layer, a rule layer, a waveform layer, and an output layer; the inputting the movement tracking error in the form of an input vector into the preset intelligent impedance calculation model, and the preset intelligent impedance calculation model adaptively outputs the variable impedance coefficient based on the input vector f 0, includes:

[0025] Input the input vector into the input layer;

[0026] Through the fuzzification and wavelet layer, by means of the Gaussian membership function, determine the output result of the wavelet nodes of the fuzzification and wavelet layer;

[0027] The rule layer calculates the activation degrees of the fuzzy rules of the rule layer according to the output results of the wavelet nodes;

[0028] The waveform layer and the output layer are used to perform defuzzification processing based on the activation degrees of the fuzzy rules, and output the variable impedance coefficient f 0.

[0029] Combined with the first aspect, in the sixth possible embodiment, the generating the force control signal according to the expected pose and the actual pose , includes: :

[0030] Taking the difference between the expected pose and the actual pose to generate a first error surface ;

[0031] Performing virtual control on the first error surface to obtain a virtual control vector , and based on the actual velocity and the virtual control vector , generating a second error surface ;

[0032] Based on the virtual control vector , the second error surface , combined with the actual dynamic model of the segment pasting nine-axis linkage robot, to generate the force control signal .

[0033] Combined with the sixth possible embodiment of the first aspect, in the seventh possible embodiment, the method further includes:

[0034] Substituting the virtual control vector , the second error surface into a preset force control signal generation formula, and outputting the force control signal ;

[0035] The preset force control signal generation formula satisfies:

[0036]

[0037] Wherein, is the actual Coriolis force and centrifugal force matrix of the segment pasting nine-axis linkage robot, is the actual gravity matrix of the segment pasting nine-axis linkage robot, The transposed matrix of the actual Jacobian matrix of the nine-axis linkage robot for segment pasting The actual environmental force of the nine-axis linkage robot for segment pasting The actual inertia matrix of the nine-axis linkage robot for segment pasting For the second error surface The gain parameter Is the virtual control law, and t is the filtering time constant;

[0038] Wherein, the virtual control law Satisfies:

[0039]

[0040] Wherein, Is the gain parameter of the first error surface Is the expected pose The corresponding expected velocity

[0041] In a second aspect, the present application provides an adaptive force-position hybrid control device for a nine-axis linkage robot for segment pasting. The device is applied to a nine-axis linkage robot for segment pasting. The nine-axis linkage robot for segment pasting includes a plurality of moving joints and a plurality of moving axes. The device includes:

[0042] An input module for obtaining a desired trajectory And the actual control parameters at the current moment. The actual control parameters include: the actual pose at the current moment The actual velocity The actual environmental force , and the desired trajectory is the set moving trajectory of the nine-axis linkage robot for segment pasting;

[0043] A variable impedance coefficient determination module for determining the variable impedance coefficient Existing when the nine-axis linkage robot for segment pasting moves according to the desired trajectory f 0. Wherein, the preset intelligent impedance calculation model is a neural network model constructed based on the actual control parameters;

[0044] An expected trajectory determination module for determining, based on the variable impedance coefficient f 0, the expected trajectory of the nine-axis linkage robot for segment pasting under the influence of the variable impedance coefficient f 0 ;

[0045] An inverse kinematics solution module for, according to the expected trajectory Perform inverse kinematics solution of the robotic arm to determine the expected pose of the segment pasting nine-axis linkage robot after moving according to the expected trajectory and the obtained expected pose , and based on the expected pose determine the expected joint angles at the next moment corresponding to each of the motion joints and the expected moving positions at the next moment of each of the moving axes;

[0046] A position control module, configured to generate a force control signal based on the expected pose and the actual pose , and based on the force control signal , control each of the motion joints to move to the expected joint angle, and control each of the moving axes to move to the expected moving position.

[0047] Combined with the second aspect, in the second possible embodiment, the input module is further configured to:

[0048] Perform forward kinematics solution of the robotic arm for the actual pose and the actual speed to determine the actual trajectory of the segment pasting nine-axis linkage robot at the current moment ;

[0049] Determine the moving tracking error of the segment pasting nine-axis linkage robot according to the actual trajectory and the expected trajectory ;

[0050] Input the moving tracking error into the preset intelligent impedance calculation model to determine that the output result of the preset intelligent impedance calculation model is the variable impedance coefficient f 0.

[0051] Combined with the second possible embodiment of the second aspect, in the third possible embodiment, the moving tracking error includes: position tracking error , speed tracking error , acceleration tracking error , force error , and the preset intelligent impedance calculation model satisfies the constraint of the variable impedance coefficient formula;

[0052] The variable impedance coefficient formula is:

[0053]

[0054] wherein, is the variable impedance coefficient, is the actual inertia matrix varying with time, is the nominal parameter of the actual inertia matrix, is the actual damping matrix that varies with time, is the nominal parameter of the actual damping matrix, is the actual stiffness matrix that varies with actual changes, is the nominal parameter of the actual stiffness matrix, is the stability coefficient.

[0055] Combined with the third possible embodiment of the second aspect, in the fourth possible embodiment, the variable impedance coefficient determination module is further configured to:

[0056] Based on the variable impedance coefficient formula, construct an RFWNN neural network model to obtain the preset intelligent impedance calculation model;

[0057] Input the mobile tracking error in the form of an input vector into the preset intelligent impedance calculation model, and the preset intelligent impedance calculation model adaptively outputs the variable impedance coefficient based on the input vector f 0.

[0058] Combined with the fourth possible embodiment of the second aspect, in the fifth possible embodiment, the preset intelligent impedance calculation model includes: an input layer, a fuzzification and wavelet layer, a rule layer, a waveform layer, and an output layer; the variable impedance coefficient determination module is further configured to:

[0059] Input the input vector into the input layer;

[0060] Through the fuzzification and wavelet layer, with the help of the Gaussian membership function, determine the output result of the wavelet nodes of the fuzzification and wavelet layer;

[0061] The rule layer calculates the activation degree of each fuzzy rule of the rule layer according to the output result of the wavelet nodes;

[0062] Adopt the waveform layer and the output layer to perform defuzzification processing based on the activation degree of each fuzzy rule, and output the variable impedance coefficient f 0.

[0063] Combined with the second aspect, in the sixth possible embodiment, the position control module is further configured to:

[0064] The generated force control signal according to the expected pose and the actual pose , includes: , including:

[0065] For the expected pose and the actual pose Take the difference to generate the first error surface ;

[0066] Perform virtual control on the first error surface to obtain a virtual control vector , and based on the actual velocity and the virtual control vector , generate a second error surface ;

[0067] Based on the virtual control vector , the second error surface , combined with the actual dynamic model of the segment pasting nine-axis linkage robot, generate the force control signal .

[0068] Combined with the sixth possible embodiment of the second aspect, in the seventh possible embodiment, the position control module is further configured to:

[0069] Substitute the virtual control vector , the second error surface into a preset force control signal generation formula, and output the force control signal ;

[0070] The preset force control signal generation formula satisfies:

[0071]

[0072] wherein, is the actual Coriolis force and centrifugal force matrix of the segment pasting nine-axis linkage robot, is the actual gravity matrix of the segment pasting nine-axis linkage robot, is the transposed matrix of the actual Jacobian matrix of the segment pasting nine-axis linkage robot, is the actual environmental acting force of the segment pasting nine-axis linkage robot, is the actual inertia matrix of the segment pasting nine-axis linkage robot, is the gain parameter of the second error surface , is the virtual control law, and t is the filtering time constant;

[0073] wherein, the virtual control law satisfies:

[0074]

[0075] wherein, is the gain parameter of the first error surface, is the expected pose corresponding expected velocity.

[0076] In a third aspect, an embodiment of the present application provides an electronic device, where the electronic device includes: a processor; and a memory for storing a program; wherein the program includes instructions that, when executed by the processor, cause the processor to execute the adaptive force-position hybrid control method for the segment pasting nine-axis linkage robot described in the first aspect.

[0077] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the adaptive force-position hybrid control method for the segment pasting nine-axis linkage robot described in the first aspect.

[0078] Advantages of the present application:

[0079] The present application provides an adaptive force-position hybrid control method for a segment pasting nine-axis linkage robot. The method obtains a desired trajectory, the actual pose, actual speed, and actual environmental force at the current moment. Then, based on a preset intelligent impedance calculation algorithm, it determines the variable impedance coefficient existing when the segment pasting nine-axis linkage robot moves along the desired trajectory. And based on this variable impedance coefficient, it determines the expected trajectory that the segment pasting nine-axis linkage robot can be in under the influence of this variable impedance coefficient. Then, it performs inverse kinematics solution of the robotic arm according to this expected trajectory to determine the expected pose obtained after the segment pasting nine-axis linkage robot moves along this expected trajectory. Then, based on this expected pose, it back-calculates the expected joint angles of the segment pasting nine-axis linkage robot at the next moment and the expected moving positions of the moving axes. Finally, according to this expected pose and the actual pose, it generates a force control signal, and based on this force control signal, it controls the motion joints of the robot to move to the expected joint angles and controls the moving axes of the robot to move to the expected moving positions.

[0080] In this way, by selecting the embodiment of the present application, in the segment pasting task used in shield tunnels, according to the expected pose to be pasted, combined with the current actual pose, the variable impedance coefficient existing during the movement of the segment pasting nine-axis linkage robot can be determined. Then, by introducing this variable impedance coefficient, it is back-calculated the poses corresponding to each motion joint and moving axis when the final pasting pose of the segment pasting nine-axis linkage robot is at the target pose, as well as the joint angles of each motion joint corresponding to this pose and the expected moving positions of the moving axes. And according to this pose, it controls the joint angle movement and moving axis movement of the segment pasting nine-axis linkage robot, so that finally the segment pasting nine-axis linkage robot can adaptively and accurately execute segment pasting according to the expected pose to be achieved, and finally achieve the effects of high precision, high efficiency, and high reliability in the segment pasting task during the construction of shield tunnels. Description of the Drawings

[0081] In the following description of exemplary embodiments with reference to the accompanying drawings, more details, features, and advantages of the present application are disclosed. In the drawings:

[0082] Figure 1 A schematic structural diagram of a segment-pasting nine-axis linkage robot provided by an embodiment of the present application is shown;

[0083] Figure 2 A schematic flowchart of an adaptive force-position hybrid control method for a segment-pasting nine-axis linkage robot provided by an embodiment of the present application is shown;

[0084] Figure 3 Another schematic flowchart of an adaptive force-position hybrid control method for a segment-pasting nine-axis linkage robot provided by an embodiment of the present application is shown;

[0085] Figure 4 Another schematic flowchart of an adaptive force-position hybrid control method for a segment-pasting nine-axis linkage robot provided by an embodiment of the present application is shown;

[0086] Figure 5 Another schematic flowchart of an adaptive force-position hybrid control method for a segment-pasting nine-axis linkage robot provided by an embodiment of the present application is shown;

[0087] Figure 6 Another schematic flowchart of an adaptive force-position hybrid control method for a segment-pasting nine-axis linkage robot provided by an embodiment of the present application is shown;

[0088] Figure 7 A schematic logical structure diagram of an adaptive force-position hybrid control device for a segment-pasting nine-axis linkage robot provided by an embodiment of the present application is shown;

[0089] Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present application is shown. Detailed implementation manners

[0090] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Instead, these embodiments are provided to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not used to limit the protection scope of the present application.

[0091] It should be understood that the various steps recorded in the method embodiments of the present application can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this regard.

[0092] As used herein, the term "including" and its variations are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first", "second", etc. mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0093] It should be noted that the modification of "one" and "multiple" mentioned in this application is illustrative rather than restrictive. Those skilled in the art should understand that, unless clearly specified otherwise in the context, it should be understood as "one or more".

[0094] In a first aspect, the present application provides a method for adaptive force-position hybrid control of a segment-pasting nine-axis linkage robot. This method is applied to any electronic device with the function of adaptive force-position hybrid control of a segment-pasting nine-axis linkage robot, and this electronic device can be a segment-pasting robot.

[0095] As an example, this method can be applied to a segment-pasting nine-axis linkage robot, and the structure of the segment-pasting nine-axis linkage robot can be as Figure 1 shown, consisting of an XYZ pasting platform and a six-axis robotic arm, Figure 1 and the numbers (number 1 to number 6) shown in

[0096] Therefore, Figure 1 identify the joints of the six-axis robotic arm. X, Y, and Z represent the three moving axes of the pasting platform of the segment-pasting robot in space. Therefore, the specific position and angle of segment pasting are affected by the positions and angles of the joints of the six-axis robotic arm, as well as the moving position of the pasting platform. That is, the specific pose of segment pasting depends on the poses of each joint and each moving axis. In the embodiments of the present application, the pose can be understood as the general term of position + angle, and the pose change depends on the change of position, and / or, the change of angle. Figure 1 There are a total of 9 factors affecting the position and angle of segment pasting in

[0097] In some possible embodiments, the method is applied to a segment-pasting nine-axis linkage robot, which includes multiple motion joints and multiple moving axes, as can be Figure 2 shown. The method includes the following steps:

[0098] S21. Obtain the desired trajectory and the actual control parameters at the current moment.

[0099] Among them, the desired trajectory is represented by the symbol . The actual control parameters include: the actual pose , the actual speed , and the actual environmental acting force . The desired trajectory is the set moving trajectory of the segment-pasting nine-axis linkage robot.

[0100] S22. According to the preset intelligent impedance calculation model, determine the variable impedance coefficient when the segment-pasting nine-axis linkage robot moves along the desired trajectory.

[0101] Among them, the variable impedance coefficient can be represented by the symbol f 0. The preset intelligent impedance calculation model is a neural network model constructed based on the actual control parameters.

[0102] S23. Based on the variable impedance coefficient, determine the expected trajectory of the segment-pasting nine-axis linkage robot under the influence of the variable impedance coefficient; among them, the expected trajectory can be represented by the symbol .

[0103] S24. Perform inverse kinematics solution for the robotic arm according to the expected trajectory to determine the expected pose obtained after the segment-pasting nine-axis linkage robot moves along the expected trajectory, and based on the expected pose, determine the expected joint angles of each motion joint at the next moment and the expected moving positions of each moving axis at the next moment.

[0104] Among them, the expected pose can be represented by the symbol . The expected joint angle of the motion joint at the next moment refers to the joint angle of the motion joint when the segment-pasting nine-axis linkage robot is in the target expected pose , where the target expected pose is the expected pose at the next moment corresponding to the expected pose at the current moment K. Similarly, the expected moving position of the moving axis at the next moment refers to: the position of the moving axis when the segment-pasting nine-axis linkage robot is in the target expected pose .

[0105] S25. Generate a force control signal based on the expected pose and the actual pose, and based on the force control signal, control each of the motion joints to move to the expected joint angle and control each of the moving axes to move to the expected moving position.

[0106] This method obtains the desired trajectory, the actual pose, the actual speed, and the actual environmental force at the current moment. Then, based on a preset intelligent impedance calculation algorithm, it determines the variable impedance coefficient existing when the segment pasting nine-axis linkage robot moves along the desired trajectory. And based on this variable impedance coefficient, it determines the expected trajectory that the segment pasting nine-axis linkage robot can be in under the influence of this variable impedance coefficient. Then, it performs inverse kinematics solution for the robotic arm according to this expected trajectory to determine the expected pose obtained after the segment pasting nine-axis linkage robot moves along this expected trajectory. Then, based on this expected pose, it back-calculates the expected joint angle of the segment pasting nine-axis linkage robot at the next moment and the expected moving position of the moving axis. Finally, based on this expected pose and the actual pose, it generates a force control signal, and based on this force control signal, controls the motion joints of the robot to move to the expected joint angle and controls the moving axes of the robot to move to the expected moving position.

[0107] In this way, by selecting the embodiment of the present application, in the segment pasting task used in the shield tunnel, according to the expected pose to be pasted, combined with the current actual pose, the variable impedance coefficient existing during the movement of the segment pasting nine-axis linkage robot can be determined. Then, by introducing this variable impedance coefficient, the poses corresponding to each motion joint and moving axis when the final pasting pose of the segment pasting nine-axis linkage robot is in this target pose can be determined. And according to this pose, it controls the movement of the joint angle of the segment pasting nine-axis linkage robot and the movement of the moving axis, so as to calibrate and compensate the movement amount of the motion joint and the moving axis according to this variable impedance coefficient, so that finally the segment pasting nine-axis linkage robot can adaptively and accurately perform segment pasting according to the expected pose to be achieved, and finally achieve the high-precision, high-efficiency, and high-reliability effects of the segment pasting task during the shield tunnel construction process.

[0108] The following will elaborate on the above steps S21 to S25 in combination with specific implementation examples:

[0109] In the embodiment of the present application, the segment-pasting nine-axis linkage robot has the capabilities of data acquisition, data analysis, and data storage. During the operation of the segment-pasting nine-axis linkage robot, log records can be generated according to the specific operation conditions. In these log records, various motion parameters, program instructions, segment-pasting results, and other information of the segment-pasting robot during the execution of segment pasting are clearly stored. In addition, the segment-pasting nine-axis linkage robot also has a program running function, and can execute the segment-pasting task according to the program settings by loading, compiling, debugging, and running the set segment-pasting program. During the operation of the segment-pasting nine-axis linkage robot, the specific pose of the current segment-pasting nine-axis linkage robot can be obtained in real time through the set position sensor and angle sensor, and the specific angles and positions of the pose are recorded in the log record in the form of motion parameters.

[0110] Based on this, as an implementation method, during the execution of step S21, the real-time data of the position sensor and angle sensor at the current moment can be obtained, and then the real-time control parameters at the current moment can be calculated based on this real-time data to obtain the actual pose at the current moment. Calculate the actual running speed of the segment-pasting nine-axis linkage robot , and the actual environmental acting force existing during the operation . As another implementation method, during the execution of step S21, the real-time data at the current moment recorded in the log record can be read, and using the same calculation method, the actual pose at the current moment can be calculated , actual speed , actual environmental acting force .

[0111] In the embodiment of the present application, the desired trajectory is the movement trajectory preset by the segment-pasting program, which is used to indicate the trajectory that the segment-pasting nine-axis linkage robot needs to move to achieve the expected segment-pasting effect. During the execution of step S21, this desired trajectory can be obtained by reading the parameters set in the program. This desired trajectory is the expected trajectory. Since there are actual interference factors such as friction and external acting forces during the operation of the segment-pasting nine-axis linkage robot, the segment-pasting nine-axis linkage robot cannot accurately reach the target pose corresponding to this desired trajectory. Therefore, it is necessary to use the method provided in the embodiment of the present application to perform motion compensation on the motion joints and moving axes of the segment-pasting nine-axis linkage robot, so that the final motion joints and moving axes are finally at the joint angles and moving axis positions corresponding to the desired trajectory, so that the final pose of the segment-pasting nine-axis linkage robot is consistent with the target pose expected in the desired trajectory.

[0112] On this basis, how to accurately evaluate the interference of factors such as the external environmental force and friction force on the movement of the nine-axis linkage robot for segment pasting has become the key to achieving accurate segment pasting. In the embodiment of the present application, a variable impedance coefficient is introduced to determine the interference situation existing during the operation of the nine-axis linkage robot for segment pasting through actual control parameters.

[0113] Among them, the variable impedance coefficient specifically refers to the parameter of the impedance characteristic change when the nine-axis linkage robot for segment pasting interacts with the external environment, and parameters such as inertia, damping, and stiffness existing during the movement of the robot can be dynamically adjusted through this variable impedance coefficient. As an implementation manner, by continuously and dynamically adjusting this variable impedance coefficient, the nine-axis linkage robot for segment pasting can flexibly adjust its own mechanical characteristics according to different segment pasting task requirements and external environmental conditions, so as to achieve accurate, efficient, and flexible segment pasting operations.

[0114] In some possible embodiments, it can be as Figure 3 shown in the process schematic diagram. By inputting the desired trajectory and the current actual control parameters into a preset intelligent impedance calculation model, the preset intelligent impedance calculation model is based on the input desired trajectory , the current actual control parameters (including the actual pose , the actual speed , the actual environmental force ) to calculate the variable impedance coefficient f 0, that is, the variable impedance coefficient f 0 is determined by executing the above step S22. As an implementation manner, this step S22 can be implemented through the following steps S22-1 to step S22-3:

[0115] S22-1. Perform forward kinematics solution for the actual pose and the actual speed to determine the actual trajectory of the nine-axis linkage robot for segment pasting at the current moment.

[0116] Among them, forward kinematics solution refers to solving the pose (position and attitude) of the end of the nine-axis linkage robot for segment pasting in space according to the motion parameters of the joints and moving axes of the nine-axis linkage robot for segment pasting (such as joint angles, joint speeds, moving axis moving displacements, moving axis moving speeds, etc.). The end of the nine-axis linkage robot for segment pasting specifically refers to the operation part where the segment pasting robot performs segment pasting. Among them, the pose points at multiple consecutive moments are connected in chronological order to obtain the actual trajectory at the current moment.

[0117] S22-2. According to the actual trajectory and the expected trajectory , determine the movement tracking error of the segment pasting nine-axis linkage robot.

[0118] In some possible embodiments, the movement tracking error includes: position tracking error , speed tracking error , acceleration tracking error , force error .

[0119] In the embodiments of the present application, in order to enable the joint angles of the movement joints of the segment pasting nine-axis linkage robot at the current moment and the positions of the moving axes at the current moment to adjust their stiffness, damping, and inertia according to the requirements of the segment pasting task to adapt to different environmental interactions, the present application specifically conducts a variable impedance control design on the segment pasting nine-axis linkage robot, so that when the segment pasting nine-axis linkage robot interacts with an unknown changing environment, it can adapt to the impedance parameters corresponding to the unknown environment.

[0120] When the external environmental change is known, the expected impedance of the segment pasting nine-axis linkage robot provided by the present application (that is, the impedance under ideal conditions should satisfy the mathematical model of the following formula (1)) can be described by the following formula (1):

[0121] Formula (1)

[0122] Where respectively represent: represents the expected inertia matrix that changes with time. represents the expected damping matrix that changes with time, represents the expected stiffness matrix that changes with time, and all three are matrices of. To ensure the response linearity and decoupling on each degree of freedom, these impedance parameters are selected as diagonal matrices.

[0123] Among them, in formula (1), respectively represent the position tracking error, speed tracking error, and acceleration tracking error existing in the segment pasting nine-axis linkage robot, represents the force error, and this position tracking error , speed tracking error , acceleration tracking error , force error can be calculated by the following formula (2), that is, when performing the above step S22-2, the movement tracking error of the segment pasting nine-axis linkage robot can be determined by the following formula (2):

[0124] Formula (2)

[0125] Among them, in the implementation of this application and respectively represent a(t) the first derivative and the second derivative. Corresponding to physical quantities, the first derivative is velocity and the second derivative is acceleration. For other similar variables, the same applies and will not be explained in detail later. is the expected environmental force. is the actual environmental force.

[0126] Based on the above formula (1) and formula (2), the expected impedance mathematical model of the segment-pasting nine-axis linkage robot can be obtained. Further, considering the actual unknown environment, it is necessary to ensure that the robot can adapt to its impedance parameters. By improving the above formula (1) and formula (2), the actual impedance mathematical model of the segment-pasting nine-axis linkage robot is obtained, and this actual impedance mathematical model satisfies the following formula (3):

[0127] Formula (3)

[0128] Among them, is the actual inertia matrix that changes with time. is the nominal parameter of the actual inertia matrix. is the actual damping matrix that changes with time. is the nominal parameter of the actual damping matrix. is the actual stiffness matrix that changes with the actual situation. is the nominal parameter of the actual stiffness matrix.

[0129] Among them and and are known. and and are dynamic parameters that change with time and are unknown.

[0130] By transforming formula (3), formula (3) can be rewritten as the following formula (4):

[0131] Formula (4)

[0132] Among them, formula (4) can be simplified to the following formula (5):

[0133] Formula (5)

[0134] By transforming formula (5), the following variable impedance coefficient formula can be obtained, that is, the corresponding variable impedance coefficient can be calculated through formula (6) f 0:

[0135] Formula (6)

[0136] Wherein, is the stability coefficient, which is used to represent the stability of the variable impedance characteristic and is affected by the variable impedance coefficient f 0, and can be specifically calculated by the following formula (7):

[0137] Formula (7)

[0138] Wherein, is a constant or a designed parameter, usually greater than 0,

[0139] Specifically, a preset intelligent impedance calculation model can be constructed based on this formula (6), and the following step S22-3 can be further executed to determine the variable impedance coefficient of the segment-pasting nine-axis linkage robot.

[0140] S22-3. Input the moving tracking error into the preset intelligent impedance calculation model, and determine that the output result of the preset intelligent impedance calculation model is the variable impedance coefficient f 0.

[0141] As an implementation manner, in the process of executing step S22-3, it can be realized through the following steps S22-31 and S22-32:

[0142] S22-31. Based on the variable impedance coefficient formula, construct an RFWNN neural network model to obtain the preset intelligent impedance calculation model;

[0143] S22-32. Input the moving tracking error into the preset intelligent impedance calculation model in the form of an input vector, and the preset intelligent impedance calculation model adaptively outputs the variable impedance coefficient based on the input vector f 0.

[0144] Selecting the embodiment of the present application to adaptively adjust the variable impedance coefficient f 0 of the RFWNN can enable the segment-pasting nine-axis linkage robot to interact with a changing and unknown environment.

[0145] Specifically, an RFWNN (Recurrent Fuzzy wavelet Neural Network) neural network model can be constructed based on the variable impedance coefficient formula corresponding to the above formula (6) to obtain a preset intelligent impedance calculation model, and then the above moving tracking error is input into the preset intelligent impedance calculation model in the form of an input vector, and the preset intelligent impedance calculation model constructed by the RFWNN neural network model adaptively outputs the variable impedance coefficient based on the input vector f0:

[0146] Among them, the model architecture of the preset intelligent impedance calculation model constructed based on RFWNN includes: an input layer, a fuzzification and wavelet layer, a rule layer, a waveform layer, and an output layer. Based on this, the specific implementation of the above steps S22-31 and S22-32 can be achieved through the following steps 1) to 4):

[0147] 1) Input the input vector into the input layer.

[0148] Among them, each input node of the input layer corresponds to an input variable, receives each movement tracking error generated by the nine-axis linkage robot of the segment pasting robot, and provides raw data for the data processing of subsequent layers. Specifically, the input signal of this input layer is usually represented in the form of an input vector. Exemplarily, the input signal is , where each input variable in the vector corresponds to a movement tracking error. Exemplarily, x 1 is the position tracking error , x 2 is the speed tracking error , x 3 is the acceleration tracking error , x 4 is the force error .

[0149] 2) Through the fuzzification and wavelet layer, with the help of the Gaussian membership function, determine the output result of the wavelet nodes of the fuzzification and wavelet layer.

[0150] The fuzzification and wavelet layer calculates the membership degree of each signal of the input layer through the Gaussian membership function, and uses the wavelet transform combination feedback mechanism for signal processing. Among them, the Gaussian membership function satisfies the constraint of the following formula (8):

[0151] Formula (8)

[0152] Among them, represents the th membership function of the th input variable, m is the number of input variables in the input signal, n is the number of fuzzy rules, where and They are the center and width of the Gaussian membership function respectively. Among them, the fuzzy rule refers to the relationship between the input and output described in the form of "IF-THEN". Exemplarily, in the fuzzy rule: if the contact force is very large and the speed is very fast, then increase the damping coefficient. At this time, "the contact force is very large" and "the speed is very fast" are fuzzy sets of an input variable, defined by the Gaussian membership function, and "increase the damping coefficient" is the fuzzy set of the output variable. Among them, the fuzzy rule can be represented by: It is represented, and n represents the nth fuzzy rule.

[0153] Based on the above formula (8), the output results of each wavelet node of the fuzzification and wavelet layer can be determined Satisfy the following formula (9):

[0154] Formula (9)

[0155] Among them, and are the translation and dilation factors of the mother wavelet respectively, is the input of the wavelet node. The input of this wavelet node can be calculated by the following formula (10):

[0156] Formula (10)

[0157] Among them, is the information storage parameter, is the previous value of the mother wavelet function in the fuzzification and wavelet layer.

[0158] 3) The rule layer calculates the activation degree of each fuzzy rule of the rule layer according to the output result of the wavelet node.

[0159] Among them, each neural node in the rule layer calculates the activation intensity of the fuzzy rule associated with the neural node, and combines the calculated activation intensity with the output result of the above wavelet node for weighted processing, providing a feature representation after rule matching for the subsequent waveform layer and output layer.

[0160] Specifically, the number of nodes in the rule layer is equal to the number of fuzzy rules and wavelet nodes. Each node in the rule layer corresponds to a fuzzy rule, and each node in the rule layer corresponds to a wavelet node. The activation degree of each rule can be calculated by using the AND (product) operation , and the activation degree of each fuzzy rule can be calculated by the following formula (11):

[0161] Formula (11)

[0162] Correspondingly, the jThe output of the wavelet layer can be calculated by the following formula (12):

[0163] Formula (12)

[0164] where is the output result of the j -th wavelet layer, is the weight of the output of the j -th rule part, is the output result of the defuzzification and each wavelet node of the wavelet layer:

[0165] 4) Using the waveform layer and the output layer, perform defuzzification based on the activation degree of each fuzzy rule, and output the variable impedance coefficient f 0.

[0166] where the waveform layer can calculate the corresponding output result by executing the following formula (13) y :[[]]END]]

[0167] Formula (13)

[0168] On this basis, the output layer can defuzzify the output y of the waveform layer according to the following formula (14), and finally adaptively output the variable impedance coefficient:

[0169] Formula (14)

[0170] In the process of executing the above steps 1) to 4), the present application defines a learning cost function for measuring the learning cost of the RFWNN model. By this learning cost function, the difference between the predicted output of the RFWNN model and the variable impedance coefficient of the actual target output is measured, which is equivalent to the loss function in the model training process. The model parameters of the RFWNN model are optimized by minimizing the difference between the predicted output and the variable impedance coefficient of the actual target output.

[0171] Specifically, the learning cost function E(t) can satisfy the constraint of the following formula (15):

[0172] Formula (15)

[0173] where is the gain function of the speed error , is the gain function of the force error , which can be set according to requirements and can be variable or constant. Among them, the speed error can be calculated by the following formula (16):

[0174] Formula (16)

[0175] Force error It can be calculated by the following formula (17):

[0176] Formula (17)

[0177] Furthermore, the gradient descent algorithm can be used to adjust the network weights of the neural network model of the RFWNN to optimize the cost function E(t) . Specifically, assume the following parameter vector p (see the following formula (18)) contains the parameters that need to be adjusted in the RFWNN network model:

[0178]

[0179] Among them, represents the weight of the nth fuzzy rule, represents the mean of the Gaussian membership function of the mth input variable in the nth fuzzy rule, represents the width (also called the standard deviation) of the Gaussian membership function of the mth input variable in the nth fuzzy rule, represents the size parameter of the wavelet of the mth input variable in the nth fuzzy rule, which is used to control the width of the waveform, represents the translation parameter of the wavelet of the mth input variable in the nth fuzzy rule, which is used to control the position of the waveform, represents the recursive parameter of the mth input variable in the nth fuzzy rule, which is used to store historical information.

[0180] Specifically, the parameters to be adjusted can be updated by the following formula (19):

[0181] Formula (19)

[0182] Among them, , , respectively represent t+ at time 1, t time and t-1 the parameter vectors at time p . represents the learning rate of the fuzzy processing function part of the RFWNN, which is used to control the update step size of the fuzzy part parameters (membership function weight, center, width), represents the learning rate of the wavelet output part of the RFWNN, which is used to control the update step size of the wavelet part parameters (scale, translation, recursive parameter). Among them, the wavelet parameters need to be adjusted more finely, is much smaller than 。 is the momentum term coefficient of the RFWNN model, which is used to accelerate the model convergence speed and prevent oscillation. is the gradient of the cost function with respect to the parameter calculated by the chain rule.

[0183] Furthermore, the specific weight update rule can be expressed as the following formula (20) and formula (21):

[0184] For the parameters of the fuzzy part (such as: ), they can be updated by the following formula (20):

[0185] Formula (20)

[0186] For the parameters of the wavelet part (such as ), they can be updated by the following formula (21):

[0187] Formula (21)

[0188] Selecting the embodiment of the present application, the RFWNN neural network model can be trained based on the above formula (6), so that the trained RFWNN model can accurately output the variable impedance coefficient of the segment-pasting nine-axis linkage robot when dealing with the current environment based on the input desired trajectory and actual control parameters. Then, with the help of this variable impedance coefficient, the current pose of the segment-pasting nine-axis linkage robot is adjusted, so that the nine-axis linkage robot moves to the target pose expected to be reached in the desired trajectory for segment pasting, thereby achieving high-precision, high-reliability, and high-efficiency completion of the segment-pasting task.

[0189] In some possible embodiments, refer to the flowchart as Figure 3 shown. After the preset intelligent impedance calculation model outputs the corresponding variable impedance coefficient based on the input desired trajectory x d (t) and the current actual control parameters, the above step S23 can be executed to perform variable impedance processing on the desired trajectory f 0, and obtain the expected trajectory x d (t) of this desired trajectory x d (t) Under the action of this variable impedance coefficient f 0, and determine the corresponding expected pose x a based on this expected trajectory x a x I 。​

[0190] As an implementation manner, it can be as Figure 4 shown, by making the variable impedance coefficient f 0 output by the preset intelligent impedance calculation model and the acting force error e f perform variable impedance solution on the desired trajectory x d (t) to calculate the expected trajectory corresponding to the desired trajectory x a . Specifically, based on the deformation of the above formula (5), the following formula (22) can be obtained:

[0191] Formula (22)

[0192] Substitute the desired trajectory x d (t) , the acting force error e f , and the variable impedance coefficient f 0 into this formula (22), and then the variable impedance solution formula can be obtained, that is, the following formula (23) can be obtained:

[0193] Formula (23)

[0194] Among them, is the expected acceleration corresponding to the desired trajectory, is x a corresponding acceleration, that is, the actual acceleration generated by the segment pasting robot through the variable impedance characteristic. Since this is the second derivative of the expected trajectory x a , based on this, the expected trajectory x a can be calculated by inverse integration.

[0195] Furthermore, execute step S24, and perform inverse kinematics solution of the robotic arm according to the expected trajectory x a to determine the expected pose x a obtained after the nine-axis linkage robot for segment pasting moves according to the expected trajectory , and determine the expected joint angle at the next moment corresponding to each of the motion joints and the expected moving position at the next moment of each of the moving axes based on the expected pose .

[0196] In the embodiments of the present application, posture is a general term for position and posture. From a macroscopic perspective, the posture can be the position and posture of the entire segment pasting robot. From a microscopic perspective, the posture specifically refers to the position and posture of each motion joint, and the position and posture of each moving axis. When the posture is specifically a general term for the position and posture of each motion joint and each moving axis, the trajectory and position are expressed in the form of a vector, and each element in the vector corresponds to the value of the nine degrees of freedom of the nine-axis linkage robot, that is, the degrees of freedom of six motion joints and three moving axes. That is, illustratively, the posture can be expressed in the form of a vector as follows: =[M1,M2,M3,M4,M5,M6,X,Y,Z], where M1~M6 correspond to the degrees of freedom of the six motion joints, and X, Y, and Z are the degrees of freedom of the three moving axes.

[0197] Based on this, in some possible embodiments, Figure 4 As shown, by Solve the inverse kinematics of the robot arm to determine the expected trajectory of the nine-axis linkage robot for segment pasting. Expected pose after moving x I . In the embodiment of the present application, the forward kinematics solution can be understood as determining the actual movement trajectory of the robot based on the actual position and posture of the robot. The inverse kinematics solution is opposite to the forward kinematics solution, which is to reversely determine the corresponding position and posture of the robot based on the movement trajectory of the robot. Specifically, it can be the target position and posture of the robot's motion joints and moving axes, and the solution can achieve the joint angles of the motion joints and the moving positions of the moving axes corresponding to the target position.

[0198] In the embodiment of the present application, the expected posture Specifically, it can be expressed as the expected displacement vector of each joint in the joint space. It can represent the expected velocity vector of each joint in the joint space. and The inverse kinematic solution is obtained, where The end position of the nine-axis linkage robot arm for pasting pipe segments. The position of the nine-axis linkage robot for segment pasting at the current joint angle. As an implementation method, based on the Newton numerical iteration method, the inverse kinematic solution process can be expressed as the following formula (24):

[0199] Formula (24)

[0200] in, is the generalized inverse corresponding to the current joint angle, which is calculated by the following formula (25):

[0201] Formula (25)

[0202] Wherein, J is the nine-axis Jacobian matrix, is its transpose matrix, and the vector product method is used for solution.

[0203] Based on this, during the execution of steps S24 and S25, inverse kinematic solution can be performed through steps S51 to S57 as shown in Figure 5 to determine the expected joint angles at the next moment corresponding to each motion joint and the expected moving positions at the next moment of each moving axis:

[0204] S51. Obtain the set expected pose X I . The expected pose X I here is the expected trajectory determined by the above step S23 x a and the expected pose obtained by inverse kinematic solution x I ;

[0205] S52. Set the starting joint angles and the starting moving axis positions. The starting joint angles and the starting moving axis positions can be initial values and are represented by vectors = [0 0 0 0 0 0 0 0 0].[[]END]]

[0206] S53. Perform forward kinematic solution to determine the actual joint angles at the current moment and the current pose corresponding to the actual moving axis positions at the current moment. Exemplarily, the actual pose x R can be directly obtained as the current pose.

[0207] S54. Calculate the pose difference △X I between the current pose and the expected pose. Specifically, the pose difference can be calculated in the form of vector subtraction.

[0208] S55. Perform inverse kinematic solution to update the current actual joint angles and the current actual moving axis positions to obtain the target pose X I(K+1) = X I(K) +△X I at the next moment. Specifically, the target pose at the next moment can be determined by using the above formula (24).

[0209] S56. Determine whether the pose difference < ɛ. If it is greater than ɛ, return to step S53 and perform iterative calculation and judgment , where ɛ represents the allowable error, which can be flexibly set according to actual requirements and is not strictly limited in this application. If it is less than ɛ, it means that the difference between the actual pose and the expected pose of the segment-pasting nine-axis linkage robot is small enough, and the segment-pasting accuracy is high enough. At this time, step S57 can be executed to generate a force control signal based on the target pose X I(K+1) The corresponding target joint angles and the positions of the target moving axes

[0210] As a possible implementation, during the execution of the above step S57 or step S25, as shown Figure 4 , by inputting the expected pose x I , the actual pose , and the actual speed into the controller constructed based on the dynamic surface fuzzy mechanism, the controller constructed based on the dynamic surface fuzzy mechanism generates a force control signal based on the input information . Among them, the operating principle of this controller is to construct a force control signal generator using the actual dynamic model of the segment-pasting nine-axis linkage robot

[0211] Among them, in the ideal state, the nominal dynamic model of the segment-pasting nine-axis linkage robot can be expressed by the following formula (27):

[0212] Formula (27)

[0213] Among them, is the joint displacement vector of the corresponding moving joint, or the displacement vector of the moving axis, or the displacement vector composed of the combination of the moving joint and the moving axis is q 's velocity vector, is q 's acceleration vector is the joint torque or force control signal applied by the segment-pasting nine-axis linkage robot is the inertia matrix, is the Coriolis force and centrifugal force matrix, is the gravity matrix, is the friction force vector, or it can also be the actual environmental acting force

[0214] Due to the existence of modeling errors and external disturbances in the actual environment. Therefore, the above inertia matrix, Coriolis force and centrifugal force matrix, gravity matrix, and friction force vector will all have uncertainties. Separating this uncertainty, the following formula (28) can be obtained:

[0215] Formula (28)

[0216] Among them, For the segment, paste the actual inertia matrix of the nine-axis linkage robot, For the uncertainty matrix of this actual inertia matrix, For the actual Coriolis force and centrifugal force matrix of the nine-axis linkage robot for segment pasting, For the uncertainty matrix of this actual Coriolis force and centrifugal force matrix, For the actual gravity matrix of the nine-axis linkage robot for segment pasting, For the uncertainty matrix of this actual gravity matrix, For the actual friction force matrix, For the uncertainty matrix of this actual friction force matrix, and each uncertainty matrix is unknown.

[0217] Furthermore, based on the formula (28), the actual dynamic equation of the nine-axis linkage robot for segment pasting can be determined, and this actual dynamic equation is as shown in the following formula (29):

[0218] Formula (29)

[0219] Combined with the above formula (27), the formula (29) can be rewritten as the following formula (30):

[0220] Formula (30)

[0221] Wherein, Represents the uncertainty and interference effect of the dynamics of the nine-axis linkage robot for segment pasting, and satisfies the constraint of the following formula (31):

[0222]

[0223] Formula (31)

[0224] Furthermore, replace the q in the formula with the actual pose of the nine-axis linkage robot for segment pasting , and replace the in the formula with the actual speed of the nine-axis linkage robot for segment pasting , thus, the actual dynamic model of the nine-axis linkage robot of the segment pasting machine can be obtained as the following formula (32):

[0225] Formula (32)

[0226] Wherein, , specifically representing the unknown dynamic uncertainty and interference effect.

[0227] On this basis, a force control signal generator can be constructed using the actual dynamic model constrained by the above formula (32) to generate a force control signal based on the input expected pose x I , actual pose , actual velocity to generate a force control signal .

[0228] In the embodiment of the present application, in order to eliminate uncertainties and disturbance terms, the present application proposes a controller constructed based on a dynamic surface fuzzy mechanism, as shown Figure 6 in the figure, by adjusting the gain of the dynamic surface through the internal fuzzy mechanism and virtual control mechanism to generate an accurate force control signal

[0229] As an implementation manner, the above step S25 can be specifically implemented through the following steps

[0230] S25-1. Calculate the difference between the expected pose and the actual pose to generate a first error surface ;

[0231] S25-2. Perform virtual control on the first error surface to obtain a virtual control vector , and based on the actual velocity and the virtual control vector , generate a second error surface ;

[0232] S25-3. Based on the virtual control vector , the second error surface , combined with the actual dynamic model of the segment pasting nine-axis linkage robot, generate the force control signal .

[0233] When performing step S25-1, the difference between the expected pose x I , actual pose can be calculated according to the following formula (33) to determine the pose difference :

[0234] Formula (33)

[0235] This pose difference can be determined as the first error surface, which specifically represents the position error of each degree of freedom in the joint space, and the derivative corresponding to the derivative obtained by taking the derivative corresponds to the velocity error, satisfying the constraint of formula (34):

[0236] Formula (34)

[0237] Furthermore, in order to make the pose difference approach 0, the virtual control law corresponding to the following formula (35) can be adopted to perform virtual control on this pose difference :

[0238] , where >0 Formula (35)

[0239] To avoid the disadvantages of multi-surface sliding mode control, can be passed through a first-order filter with a time constant of t ( t＞0 ) (this first-order filter can satisfy the constraints of the following formula (36)) for transmission:

[0240] Formula (36)

[0241] In this way, the filtered virtual control vector can be output through this virtual control law. This is smoother than and is more convenient for the actual controller to generate accurate force control signals based on .

[0242] Furthermore, step S25-2 can be implemented through the following process:

[0243] First, construct the second error surface through the following formula (37) :

[0244] Formula (37)

[0245] Furthermore, take the derivative of this second error surface through the following formula (38):

[0246] Formula (38)

[0247] Then, substitute the actual dynamic model of the segment pasting nine-axis linkage robot into the formula (36) and formula (38) above, that is, substitute the actual dynamic model constrained by the above formula (32) into the formula (36) and formula (38), and the following formula (39) can be obtained:

[0248] Formula (39)

[0249] where t is the filtering time constant.

[0250] Further, as an implementation, the virtual control vector , the second error surface are substituted into a preset force control signal generation formula to output the force control signal . Specifically, in the above formula (39), by controlling the second error surface to approach 0, the pose difference between the actual pose and the expected pose can be made to approach 0, so as to achieve precise control of the pose of the segment-pasting nine-axis linkage robot.

[0251] Specifically, the preset force control signal generation formula can be obtained from the above formula (39) by controlling →0, to obtain the following formula (40), that is, the preset force control signal generation formula satisfies the constraint of the following formula (40):

[0252] Formula (40)

[0253] Wherein, is the actual Coriolis force and centrifugal force matrix of the segment-pasting nine-axis linkage robot, is the actual gravity matrix of the segment-pasting nine-axis linkage robot, is the transposed matrix of the actual Jacobian matrix of the segment-pasting nine-axis linkage robot, is the actual environmental acting force of the segment-pasting nine-axis linkage robot, is the actual inertia matrix of the segment-pasting nine-axis linkage robot, is the second error surface 's gain parameter, is the virtual control law, t is the filtering time constant.

[0254] In the embodiments of the present application, in order to further optimize the parameters of the controller, a closed-loop system equation is proposed to optimize the parameters of the controller. First, the above formula (40) can be substituted into formula (39) to obtain the following formula (41):

[0255] Formula (41)

[0256] Further, the above formula (38) can be substituted into formula (34) to obtain the following formula (42):

[0257] Formula (42)

[0258] That is, formula (42) is the analytical expression of the closed-loop system.

[0259] Further, if the boundary layer error is defined for:

[0260] Formula (43)

[0261] Then, by differentiating the boundary layer error, we can obtain the following formula (44):

[0262] Formula (44)

[0263] Thus, after substituting the above formulas (42) and (43) into formula (44), the following formula (45) can be obtained:

[0264] Formula (45)

[0265] in, .

[0266] Furthermore, the above formula (41), formula (42) and formula (43) can generate the dynamic surface equation shown in the following formula (46):

[0267] Formula (46)

[0268] Therefore, the following closed-loop system equation can be obtained, that is, the following formula (47):

[0269] Formula (47)

[0270] In the embodiment of the present application, the gain parameter of the first error surface can be adjusted The second error surface Gain parameter , in order to reduce the impact of unknown dynamic uncertainty and interference effect U on actual control. To achieve intelligent control and optimization of the nine-axis linkage robot through redundant manipulator inverse kinematics algorithm combined with fuzzy gain dynamic surface control and cyclic fuzzy wavelet neural network, it can adapt to the complex and changeable construction environment and ensure the safety and quality of the pipe segment pasting process.

[0271] Based on the method described in the first aspect, in the second aspect, the present application provides an adaptive force-position hybrid control device for a nine-axis linkage robot for pipe segment pasting, wherein the device is applied to a nine-axis linkage robot for pipe segment pasting, wherein the nine-axis linkage robot for pipe segment pasting includes multiple motion joints and multiple moving axes, which can be Figure 7 As shown, the device 70 includes:

[0272] Input module 701, used to obtain the desired trajectory , the actual control parameters at the current moment, the actual control parameters include: the actual posture at the current moment , actual speed , actual environmental acting force , where the expected trajectory is the set moving trajectory of the segment pasting nine-axis linkage robot;

[0273] The variable impedance coefficient determination module 702 is configured to determine the variable impedance coefficient existing when the segment pasting nine-axis linkage robot moves along the expected trajectory according to a preset intelligent impedance calculation model f 0, where the preset intelligent impedance calculation model is a neural network model constructed based on the actual control parameters;

[0274] The expected trajectory determination module 703 is configured to determine, based on the variable impedance coefficient f 0, the expected trajectory of the segment pasting nine-axis linkage robot under the influence of the variable impedance coefficient f 0; ;

[0275] The inverse kinematics solution module 704 is configured to perform inverse kinematics solution of the robotic arm according to the expected trajectory to determine the expected pose obtained after the segment pasting nine-axis linkage robot moves along the expected trajectory , and based on the expected pose to determine the expected joint angle at the next moment corresponding to each of the motion joints and the expected moving position at the next moment of each of the moving axes; The position control module 705 is configured to generate a force control signal

[0276] according to the expected pose and the actual pose , and based on the force control signal to control each of the motion joints to move to the expected joint angle and control each of the moving axes to move to the expected moving position. ;

[0277] Combining with the second aspect, in a second possible embodiment, the input module is further configured to:

[0278] Perform forward kinematics solution of the robotic arm for the actual pose and the actual speed to determine the actual trajectory of the segment pasting nine-axis linkage robot at the current moment ;

[0279] Determine the movement tracking error of the segment pasting nine-axis linkage robot according to the actual trajectory and the expected trajectory ;

[0280] Input the mobile tracking error into the preset intelligent impedance calculation model, and determine that the output result of the preset intelligent impedance calculation model is the variable impedance coefficient f 0.

[0281] Combined with the second possible embodiment of the second aspect, in the third possible embodiment, the mobile tracking error includes: position tracking error , speed tracking error , acceleration tracking error , force error , and the preset intelligent impedance calculation model satisfies the constraint of the variable impedance coefficient formula;

[0282] The variable impedance coefficient formula is:

[0283]

[0284] Wherein, is the variable impedance coefficient, is the actual inertia matrix varying with time, is the nominal parameter of the actual inertia matrix, is the actual damping matrix varying with time, is the nominal parameter of the actual damping matrix, is the actual stiffness matrix varying with the actual situation, is the nominal parameter of the actual stiffness matrix, is the stability coefficient.

[0285] Combined with the third possible embodiment of the second aspect, in the fourth possible embodiment, the variable impedance coefficient determination module is further configured to:

[0286] Based on the variable impedance coefficient formula, construct an RFWNN neural network model to obtain the preset intelligent impedance calculation model;

[0287] Input the mobile tracking error in the form of an input vector into the preset intelligent impedance calculation model, and the preset intelligent impedance calculation model adaptively outputs the variable impedance coefficient based on the input vector f 0.

[0288] Combined with the fourth possible embodiment of the second aspect, in the fifth possible embodiment, the preset intelligent impedance calculation model includes: an input layer, a fuzzification and wavelet layer, a rule layer, a waveform layer, and an output layer; the variable impedance coefficient determination module is further configured to:

[0289] Input the input vector into the input layer;

[0290] The output result of the wavelet nodes of the fuzzification and wavelet layer is determined by means of a Gaussian membership function through the fuzzification and wavelet layer;

[0291] The activation degree of each fuzzy rule of the rule layer is calculated by the rule layer according to the output result of the wavelet nodes;

[0292] Defuzzification processing is performed by using the waveform layer and the output layer based on the activation degree of each fuzzy rule, and the variable impedance coefficient is output f 0.

[0293] Combined with the second aspect, in the sixth possible embodiment, the position control module is further configured to:

[0294] According to the expected pose and the actual pose generate a force control signal , including:

[0295] For the expected pose and the actual pose find the difference to generate a first error surface ;

[0296] Perform virtual control on the first error surface to obtain a virtual control vector , and based on the actual speed and the virtual control vector , generate a second error surface ;

[0297] Based on the virtual control vector , the second error surface , combined with the actual dynamic model of the segment pasting nine-axis linkage robot, generate the force control signal .

[0298] Combined with the sixth possible embodiment of the second aspect, in the seventh possible embodiment, the position control module is further configured to:

[0299] Substitute the virtual control vector , the second error surface into a preset force control signal generation formula, and output the force control signal ;

[0300] The preset force control signal generation formula satisfies:

[0301]

[0302] Wherein, The actual Coriolis force and centrifugal force matrix of the nine-axis linkage robot for segment pasting, The actual gravity matrix of the nine-axis linkage robot for segment pasting, The transposed matrix of the actual Jacobian matrix of the nine-axis linkage robot for segment pasting, The actual environmental acting force of the nine-axis linkage robot for segment pasting, The actual inertia matrix of the nine-axis linkage robot for segment pasting, For the second error surface The gain parameter of, For the virtual control law, where t is the filtering time constant;

[0303] Among them, the virtual control law Satisfies:

[0304]

[0305] Among them, Is the gain parameter of the first error surface.

[0306] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0307] In a third aspect, an exemplary embodiment of the present application further provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program that can be executed by the at least one processor, and when the computer program is executed by the at least one processor, it is used to cause the electronic device to execute the method according to the embodiments of the present application. Specifically, as a preferred embodiment, the electronic device may be a segment pasting robot. As an example, the electronic device may be a nine-axis linkage robot for segment pasting.

[0308] An exemplary embodiment of the present application further provides a non-transitory computer-readable storage medium storing a computer program, where the computer program is used to cause a computer to execute the method according to the embodiments of the present application when executed by a processor of the computer.

[0309] An exemplary embodiment of the present application further provides a computer program product, including a computer program, where the computer program is used to cause a computer to execute the method according to the embodiments of the present application when executed by a processor of the computer.

[0310] Refer to Figure 8, a structural block diagram of an electronic device 800 that can be a server or a client of the present application will now be described. It is an example of a hardware device that can be applied to various aspects of the present application. The electronic device is intended to represent various forms of digital electronic computer devices, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, 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 application described and / or claimed herein.

[0311] As Figure 8 shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM 802) or a computer program loaded from a storage unit 808 into a random access memory (RAM 803). In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output interface (I / O interface 805) is also connected to the bus 804.

[0312] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 can be any type of device that can input information into the electronic device 800. The input unit 806 can receive input digital or character information, and generate key signal inputs related to the user settings and / or function controls of the electronic device. The output unit 807 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 808 can include but is not limited to a magnetic disk, an optical disk. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0313] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 executes the various methods and processes described above. For example, in some embodiments, the aforementioned segment pasting nine-axis linkage robot adaptive force-position hybrid control method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 800 via the ROM 802 and / or the communication unit 809. In some embodiments, the computing unit 801 can be configured to execute the aforementioned segment pasting nine-axis linkage robot adaptive force-position hybrid control method by any other suitable means (e.g., by means of firmware).

[0314] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0315] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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.

[0316] As used in this application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0317] For purposes of providing an interaction with a user, the systems and techniques described herein can be implemented on a computer 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 a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0318] The systems and techniques described herein can be implemented in a computing system that includes a back-end component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or in a computing system that includes any combination of such back-end, middleware, or front-end components. The components of the system can 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), and the Internet.

[0319] A computer system can include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. An adaptive force-position hybrid control method for a nine-axis linkage robot for segment pasting, characterized in that, The method is applied to a segment pasting nine-axis linkage robot, and the segment pasting nine-axis linkage robot includes a plurality of motion joints and a plurality of moving axes. The method includes: Obtain the desired trajectory , the actual control parameters at the current moment, and the actual control parameters include: the actual pose at the current moment , the actual speed , the actual environmental acting force , and the desired trajectory is the set moving trajectory of the segment pasting nine-axis linkage robot; According to the preset intelligent impedance calculation model, determine the variable impedance coefficient existing when the segment pasting nine-axis linkage robot moves along the expected trajectory 0, where the preset intelligent impedance calculation model is a neural network model constructed based on the actual control parameters; f 0 Based on the variable impedance coefficient f 0, determine that under the influence of the variable impedance coefficient f 0, the expected trajectory of the segment-pasting nine-axis linkage robot ; According to the expected trajectory Perform inverse kinematics solution of the robotic arm to determine the expected pose obtained after the segment pasting nine-axis linkage robot moves according to the expected trajectory , and based on the expected pose determine the expected joint angles at the next moment corresponding to each of the motion joints and the expected moving positions at the next moment of each of the moving axes; According to the expected pose and the actual pose , a force control signal is generated , and based on the force control signal , each of the motion joints is controlled to move to the expected joint angle, and each of the moving axes is controlled to move to the expected moving position; The motion tracking error includes: position tracking error , velocity tracking error , acceleration tracking error , force error , and the preset intelligent impedance calculation model satisfies the variable impedance coefficient formula constraint; The variable impedance coefficient formula is: wherein, is the variable impedance coefficient, is the actual inertia matrix varying with time, is the nominal parameter of the actual inertia matrix, is the actual damping matrix varying with time, is the nominal parameter of the actual damping matrix, is the actual stiffness matrix varying with actual change, is the nominal parameter of the actual stiffness matrix, is the stability coefficient.

2. The method according to claim 1, wherein The method further includes: For the actual pose and the actual speed perform the forward kinematics solution of the robotic arm to determine the actual trajectory of the segment pasting nine-axis linkage robot at the current moment ; According to the actual trajectory and the desired trajectory , determine the moving tracking error of the segment pasting nine-axis linkage robot; Input the mobile tracking error into the preset intelligent impedance calculation model, and determine that the output result of the preset intelligent impedance calculation model is the variable impedance coefficient f 0 3. The method according to claim 1, wherein The method further includes: Based on the variable impedance coefficient formula, construct an RFWNN neural network model to obtain the preset intelligent impedance calculation model; Input the moving tracking error in the form of an input vector into the preset intelligent impedance calculation model, and the preset intelligent impedance calculation model adaptively outputs the variable impedance coefficient based on the input vector. f 0.

4. The method according to claim 3, wherein The preset intelligent impedance calculation model includes: an input layer, a fuzzification and wavelet layer, a rule layer, a waveform layer, and an output layer; the moving tracking error is input into the preset intelligent impedance calculation model in the form of an input vector, and the variable impedance coefficient is adaptively output by the preset intelligent impedance calculation model based on the input vector f 0, including: Input the input vector into the input layer; Through the fuzzyfication and wavelet layer, with the help of the Gaussian membership function, determine the output result of the wavelet nodes of the fuzzyfication and wavelet layer; The rule layer calculates the activation degree of each fuzzy rule of the rule layer according to the output result of the wavelet nodes; Using the waveform layer and the output layer, perform defuzzification based on the activation degree of each fuzzy rule, and output the variable impedance coefficient f 0。 5. The method according to claim 1, wherein According to the expected pose and the actual pose , a force control signal is generated , including: For the said expected pose and the said actual pose find the difference to generate a first error surface ; Perform virtual control on the first error surface to obtain a virtual control vector , and based on the actual speed and the virtual control vector , generate a second error surface ; Based on the virtual control vector , the second error surface , combined with the actual dynamic model of the segment pasting nine-axis linkage robot, to generate the force control signal .

6. The method according to claim 5, characterized in that, The method further includes: Substitute the virtual control vector , the second error surface into a preset force control signal generation formula to output the force control signal ; The preset force control signal generation formula satisfies: Among them, is the actual Coriolis force and centrifugal force matrix of the segment pasting nine-axis linkage robot, is the actual gravity matrix of the segment pasting nine-axis linkage robot, is the transposed matrix of the actual Jacobian matrix of the segment pasting nine-axis linkage robot, is the actual environmental acting force of the segment pasting nine-axis linkage robot, is the actual inertia matrix of the segment pasting nine-axis linkage robot, is the second error surface 's gain parameter, is the virtual control law, and t is the filtering time constant; Among them, the virtual control law satisfies: wherein, is the gain parameter of the first error surface, is the expected pose corresponding to the expected velocity.

7. An adaptive force-position hybrid control device for a nine-axis linkage robot for segment pasting, characterized in that, The device is applied to a segment pasting nine-axis linkage robot, and the segment pasting nine-axis linkage robot includes a plurality of motion joints and a plurality of moving axes. The device includes: An input module for obtaining a desired trajectory , the actual control parameters at the current moment, the actual control parameters including: the actual pose at the current moment , the actual speed , the actual environmental acting force , the desired trajectory being the set moving trajectory of the segment pasting nine-axis linkage robot; A variable impedance coefficient determination module, configured to determine a variable impedance coefficient existing when the segment pasting nine-axis linkage robot moves along the expected trajectory according to a preset intelligent impedance calculation model where the variable impedance coefficient is 0, and the preset intelligent impedance calculation model is a neural network model constructed based on the actual control parameters f 0 An expected trajectory determination module, configured to determine, based on the variable impedance coefficient f 0, the expected trajectory of the segment pasting nine-axis linkage robot under the influence of the variable impedance coefficient f 0 ; Inverse kinematics solution module, which is used to perform inverse kinematics solution of the robotic arm according to the expected trajectory to determine the expected pose obtained after the segment pasting nine-axis linkage robot moves according to the expected trajectory , and based on the expected pose to determine the expected joint angles at the next moment corresponding to each of the motion joints and the expected moving positions at the next moment of each of the moving axes; ​ A position control module, configured to generate a force control signal based on the expected pose and the actual pose , and control each of the motion joints to move to the expected joint angle and control each of the moving axes to move to the expected moving position based on the force control signal ; ​ The motion tracking error includes: position tracking error , velocity tracking error , acceleration tracking error , force error , and the preset intelligent impedance calculation model satisfies the variable impedance coefficient formula constraint; The variable impedance coefficient formula is: wherein, is the variable impedance coefficient, is the actual inertia matrix varying with time, is the nominal parameter of the actual inertia matrix, is the actual damping matrix varying with time, is the nominal parameter of the actual damping matrix, is the actual stiffness matrix varying with actual changes, is the nominal parameter of the actual stiffness matrix, is the stability coefficient.

8. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing a program; wherein, the program includes instructions, and when the instructions are executed by the processor, the processor executes the method according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause a computer to execute the method according to any one of claims 1-6.

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