A seam tracking cooperative control method and device for a dual-arm welding robot

Abstract

The application discloses a kind of welding seam tracking cooperative control method and device for double-arm welding robot, method includes: according to the position error and speed error of each joint of robot, combined with immediate correction gain, historical cumulative gain and trend prediction gain, sliding mode control item is obtained;According to the pre-constructed robot dynamics model, front feed-back torque compensation cooperative control law is obtained;According to random disturbance compensation item, cooperative error correction mechanism, sliding mode control item and front feed-back torque compensation cooperative control law, first matrix is obtained;According to first algorithm, first matrix is adjusted, and second matrix is obtained;First algorithm includes improved multiverse optimization algorithm;Improvement includes introducing local search mechanism based on individual fitness difference guide, and based on the optimal fitness change rate of generation Dynamic adjustment wormhole existence probability;The actual and expected joint motion parameters of robot are acquired, and welding seam tracking cooperative control is realized according to actual and expected joint motion parameters combined with second matrix.

Description

Technical Field

[0001] This invention relates to the field of robot collaborative control technology, and in particular to a weld seam tracking collaborative control method and device for a dual-arm welding robot. Background Technology

[0002] Welding is a crucial process in smart construction, but the arc light, fumes, high temperatures, noise, and potential radiation involved in welding make the manual welding environment extremely harsh, and the consistency of manual welding quality is also difficult to guarantee. Therefore, robot-based automated welding has become a major direction in current smart manufacturing.

[0003] In intelligent manufacturing, the large number of assembled components and complex spatial structures, coupled with the need for double-sided collaborative welding in many processes, have led to the widespread application of dual-arm welding robots in intelligent construction due to their high redundancy degrees of freedom and collaborative capabilities. However, the strong coupling, dynamic nonlinearity, and multi-degree-of-freedom coordination issues inherent in dual-arm welding robots present numerous technical challenges in weld seam collaborative tracking. Current research primarily focuses on single-robot weld seam tracking; tracking of regular or continuous weld seams is relatively mature. For example, existing research proposes a robotic arm trajectory tracking method based on improved integral terminal sliding mode control, which constructs a non-singular fast integral terminal sliding mode surface and designs an exponential reaching law that fuses the hyperbolic tangent function. While this existing method demonstrates good performance in single-arm control, its controller structure is relatively simple and lacks optimization and adaptive tuning of controller parameters, making it difficult to extend its application to complex multi-robot collaborative operation scenarios. Meanwhile, in the current intelligent manufacturing industry, which features a wide variety of components, complex spatial structures, and diverse assembly forms, welding is mostly done manually or through manual instruction. Welding accuracy is greatly affected by human factors, making it difficult to ensure the consistency and quality stability of complex welds. Furthermore, the labor intensity is high, long-term operation efficiency is low, and it is not suitable for high-intensity production with strong repetitiveness.

[0004] Therefore, a new technical solution is urgently needed to address the technical problem of how to perform weld seam tracking and collaborative control in dual-arm welding robots. Summary of the Invention

[0005] This invention provides a method and apparatus for weld seam tracking and collaborative control of a dual-arm welding robot, which solves the technical problem of how to perform weld seam tracking and collaborative control of a dual-arm welding robot.

[0006] To achieve the above objectives, the present invention provides a weld seam tracking collaborative control method for a dual-arm welding robot, comprising:

[0007] The sliding mode control term is obtained by combining the position and velocity errors of each joint of the robot with the real-time correction gain, historical cumulative gain and trend prediction gain; the feedforward-feedback torque compensation cooperative control law is obtained based on the pre-built robot dynamics model.

[0008] The first matrix is ​​obtained by using the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law; the second matrix is ​​obtained by tuning the first matrix according to the first algorithm.

[0009] The first algorithm includes an improved multiverse optimization algorithm; the improvements include the introduction of a local search mechanism guided by individual fitness differences, and dynamic adjustment of the probability of wormhole existence based on the intergenerational optimal fitness change rate.

[0010] The robot's actual and expected joint motion parameters are obtained, and the weld seam tracking collaborative control is achieved by combining the actual and expected joint motion parameters with the second matrix.

[0011] Preferably, the sliding mode control term, obtained by combining the position and velocity errors of each robot joint with real-time correction gain, historical cumulative gain, and trend prediction gain, includes:

[0012] Define the position error of each joint of the robot separately. and speed error ,include:

[0013] ;

[0014] in, For robots The expected trajectory of the joints, For robots The actual trajectory of the joint; For robots The expected velocity of the joint, For robots The actual speed of the joint; For robots Positional error; For robots Speed ​​error; ;

[0015] According to position error and speed error The sliding mode control quantity is obtained by combining the instantaneous correction gain, historical cumulative gain, and trend prediction gain. ,include:

[0016] ;

[0017] in, Indicates instantaneous correction gain; Indicates historical cumulative gain; Indicates the trend prediction gain;

[0018] Input control is performed using hypersurface functions, based on sliding mode control quantities. Obtain sliding mode control terms ,include:

[0019] ;

[0020] in, Represents the joint gain coefficient; This represents a hypersurface function.

[0021] Preferred, pre-built robot dynamics models include:

[0022] The dynamic model of a six-degree-of-freedom welding robot includes:

[0023] ;

[0024] in, and These represent the robot's joint position, joint angular velocity, and joint angular acceleration, respectively. Represents the inertia matrix; Represents the Coriolis force matrix; Represents the gravity vector; This indicates the joint torque that controls the input. This indicates an unknown external disturbance;

[0025] Robot dynamics models that consider errors include:

[0026] ;

[0027] By combining the six-degree-of-freedom welding robot dynamics model with the error-considered robot dynamics model, a pre-constructed robot dynamics model is obtained, including:

[0028] ;

[0029] in, ; , and These are the nominal model parameters; , and These are the actual model parameters; , and This represents the error in the nominal model parameters.

[0030] Preferably, the feedforward-feedback torque compensation cooperative control law obtained from the pre-constructed robot dynamics model includes:

[0031] Based on the nominal model parameters in the pre-built robot dynamics model , and Constructing a feedforward-feedback torque compensation cooperative control law, including:

[0032] ;

[0033] in, Represents robots Joint acceleration; Represents robots Angular velocity of the joint; Representative robot The inertia matrix of the robot, i.e. of ; Representative robot The Coriolis force matrix, i.e., the robot of ; Representative robot The gravity vector, i.e., the robot of ; Represents robots The feedforward-feedback torque compensation collaborative control law;

[0034] symbol The operation rules include:

[0035] ;

[0036] in, and Represents a matrix; This represents the solution obtained by performing operations on two matrices.

[0037] Preferably, the first matrix obtained based on the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law includes:

[0038] Define the error between the end effectors of the two robots and the desired weld point. and And the error between the two robot end effectors. ,include:

[0039] ;

[0040] ;

[0041] ;

[0042] in, Represents robots The actual end position; Indicates the desired end position;

[0043] Based on the current error magnitude, perform collaborative error compensation for the robot, including:

[0044] ;

[0045] ;

[0046] ;

[0047] in, Indicates the collaborative error compensation coefficient; This represents the cooperative error compensation term for robot 1; This represents the cooperative error compensation term for robot 2; Indicates the gain coefficient;

[0048] The output of the composite weld seam tracking controller, i.e., the first matrix U, is obtained based on the random disturbance compensation term, cooperative error correction, sliding mode control term, and feedforward-feedback torque compensation cooperative control law, including:

[0049] ;

[0050] in, This represents the random disturbance compensation term.

[0051] Preferably, the second matrix is ​​obtained by tuning the first matrix according to the first algorithm, including:

[0052] The gain parameters of the first matrix are tuned according to the first algorithm and the fitness function. The gain parameters include real-time correction gain, historical cumulative gain, and trend prediction gain. In each iteration of the first algorithm, a set of candidate gain parameters are scored according to the fitness function. The probability of wormhole existence is dynamically adjusted based on the intergenerational optimal fitness change rate. Through a local search mechanism guided by individual fitness differences, the gain parameters that minimize the fitness function are continuously searched. The obtained gain parameters are assigned to the first matrix U to obtain the second matrix.

[0053] fitness function Used to quantify the performance of composite weld seam tracking controllers, including:

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] in, This represents the maximum tracking error term; This represents the average tracking error term; This indicates the maximum torque input item; This represents the average torque input item; and As weight; This indicates normalization processing; Represents robots The maximum independent tracking error; This represents the robot's maximum cooperative tracking error; This indicates the robot's maximum input torque; Represents robots The average independent tracking error; This represents the average cooperative tracking error of the robots; This represents the average input torque of the robot.

[0060] Preferred local search mechanisms guided by individual fitness differences include:

[0061] For any pair of universes and Differences in adaptability Defined as:

[0062] ;

[0063] in, and Representing the universe and fitness value;

[0064] Define fitness difference threshold :

[0065] ;

[0066] in, Indicates the initial threshold; Indicates the current iteration number; Indicates the maximum number of iterations;

[0067] Using the differences in fitness across universes as a guiding signal for local searches, if the universe and the universe Differences in fitness between Less than the fitness difference threshold Then determine the universe and the universe They are in similar regions and undergo local perturbations;

[0068] Define local update factor :

[0069] ;

[0070] in, This represents the parameter that controls the effect of Euclidean distance; Represents the universe and The Euclidean distance between them;

[0071] For universes that meet the conditions for adding local perturbations, a small perturbation is added to guide the local search. The formula for updating the universe's position includes:

[0072] ;

[0073] in, This represents a constant that controls the update magnitude; The coefficient representing the random disturbance; This is a random disturbance term that follows a standard normal distribution, used to introduce randomness; Represents the updated universe Location; Represents the universe before the update Location; Represents the universe The location before the update; Represents the universe The location before the update.

[0074] Preferably, the dynamic adjustment of the wormhole existence probability based on the intergenerational optimal fitness change rate includes:

[0075] The difference between the fitness of the current generation's optimal solution and the fitness of the previous generation's optimal solution is defined as the fitness change. ,include:

[0076] ;

[0077] in, This indicates the optimal fitness of the current generation; This indicates the optimal fitness of the previous generation;

[0078] Define fitness change threshold Used to determine whether to use linear decay or exponential decay, including:

[0079] ;

[0080] in, Indicates the initial threshold; A coefficient representing the effect of the fitness change rate on the threshold; Indicates the nonlinear attenuation coefficient;

[0081] Define the update mechanism for the wormhole existence probability (WEP), including:

[0082] When the fitness change is greater than or equal to the fitness change threshold, WEP is updated using a linear decay method:

[0083] ;

[0084] When the fitness change is less than the fitness change threshold, WEP is updated using an exponential decay method:

[0085] ;

[0086] in, Indicates the current iteration number; Indicates the maximum number of iterations; This refers to the parameter that controls the decay rate; Indicates the maximum value of WEP; This represents the minimum value of WEP; Indicates the first The probability of the wormhole existing in the next iteration; Represents the natural exponential function; This represents the attenuation sensitivity coefficient.

[0087] Preferably, the weld seam tracking collaborative control is achieved by combining actual and expected joint motion parameters with a second matrix, including:

[0088] A link coordinate system and a DH parameter table are established based on the DH parameter method and the actual structure of the robot.

[0089] The mapping relationship between the operation space trajectory and the joint space command is realized by solving the forward and inverse kinematics.

[0090] The robot's actual second matrix is ​​obtained based on the actual and expected joint motion parameters, link coordinate system, DH parameter table, mapping relationship, and second matrix; the joint motion parameters include trajectory, velocity, and acceleration;

[0091] The actual second matrix is ​​applied to the robot's joint actuators to achieve collaborative control for weld seam tracking.

[0092] The present invention also provides a weld seam tracking collaborative control device for a dual-arm welding robot, which is used in the method of the present invention. The device is characterized in that it includes a first module, a second module, a third module and a fourth module.

[0093] The first module is used to obtain the sliding mode control term based on the position and velocity errors of each joint of the robot, combined with the real-time correction gain, historical cumulative gain, and trend prediction gain; and to obtain the feedforward-feedback torque compensation cooperative control law based on the pre-built robot dynamics model.

[0094] The second module is used to obtain the first matrix based on the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law;

[0095] The third module is used to tune the first matrix according to the first algorithm to obtain the second matrix; the first algorithm includes an improved multiverse optimization algorithm; the improvement includes the introduction of a local search mechanism guided by individual fitness differences, and dynamic adjustment of the probability of wormhole existence based on the intergenerational optimal fitness change rate;

[0096] The fourth module is used to obtain the robot's actual and expected joint motion parameters, and to achieve weld seam tracking collaborative control based on the actual and expected joint motion parameters combined with the second matrix.

[0097] The present invention has the following beneficial effects:

[0098] This invention presents a cooperative control method for weld seam tracking in dual-arm welding robots. By integrating sliding mode control, feedforward-feedback torque compensation, cooperative error correction mechanisms, and random disturbance compensation terms, it achieves high-precision weld seam tracking for multiple welding robots, significantly improving the dynamic response capability and control accuracy of the multi-robot system in weld seam tracking tasks, and enhancing the system's stability and robustness in complex trajectory welding processes. By introducing a local search mechanism guided by individual fitness differences and improving the multiverse optimization algorithm by dynamically adjusting the wormhole existence probability based on the intergenerational optimal fitness change rate, it effectively enhances the global search capability and local convergence performance during the optimization process, overcoming the problem of traditional algorithms easily getting trapped in local optima. This improves the accuracy and convergence speed of controller parameter tuning. Based on the improved algorithm, adaptive parameter tuning of the weld seam tracking controller is achieved, realizing intelligent adaptation of control parameters to various welding trajectories and working conditions. This ensures that the dual-robot system can achieve high-precision tracking control under different complex welding tasks, effectively improving the consistency of welding quality and the system's anti-interference capability. This invention helps to achieve higher-precision weld seam tracking control for dual welding robots under complex trajectories, thereby improving production efficiency. The method of this invention can adapt to welding tasks with complex spatial structures, varied task paths, and the potential need for double-sided collaborative welding. It can replace traditional manual welding and ensure the long-term reliability and stability of welding.

[0099] The weld seam tracking and collaborative control device for a dual-arm welding robot of the present invention, used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0100] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0101] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0102] Figure 1 This is a schematic diagram of the method flow of a preferred embodiment of the present invention.

[0103] Figure 2 This is a flowchart of a local search mechanism based on individual fitness differences, according to a preferred embodiment of the present invention.

[0104] Figure 3 This is a flowchart of a preferred embodiment of the present invention, showing the dynamic adjustment of the probability of wormhole existence based on the intergenerational optimal fitness change rate.

[0105] Figure 4This is a schematic diagram of the link coordinate system according to a preferred embodiment of the present invention.

[0106] Figure 5 This is a schematic diagram of the disturbance signal of robot 1 according to a preferred embodiment of the present invention.

[0107] Figure 6 This is a schematic diagram of the disturbance signal of robot 2 according to a preferred embodiment of the present invention.

[0108] Figure 7 This is a schematic diagram comparing the circular trajectory errors of a preferred embodiment of the present invention.

[0109] Figure 8 This is a schematic diagram comparing the rectangular trajectory errors of a preferred embodiment of the present invention.

[0110] Figure 9 This is a schematic diagram comparing the errors of the D-shaped trajectory according to a preferred embodiment of the present invention.

[0111] Figure 10 This is a schematic diagram comparing the errors of the plum blossom-shaped trajectory according to a preferred embodiment of the present invention.

[0112] Figure 11 This is a schematic diagram comparing the input torque of the circular trajectory according to a preferred embodiment of the present invention.

[0113] Figure 12 This is a schematic diagram comparing the rectangular trajectory input torque of a preferred embodiment of the present invention.

[0114] Figure 13 This is a schematic diagram comparing the input torque of the D-shaped trajectory according to a preferred embodiment of the present invention.

[0115] Figure 14 This is a schematic diagram comparing the input torque of the plum blossom-shaped trajectory according to a preferred embodiment of the present invention.

[0116] Figure 15 This is a schematic diagram comparing the circular trajectories of a preferred embodiment of the present invention.

[0117] Figure 16 This is a schematic diagram comparing rectangular trajectories according to a preferred embodiment of the present invention.

[0118] Figure 17 This is a schematic diagram comparing the D-shaped trajectories of a preferred embodiment of the present invention.

[0119] Figure 18 This is a schematic diagram comparing the plum blossom-shaped trajectory of a preferred embodiment of the present invention.

[0120] Figure 19 This is a schematic diagram of a circular trajectory according to a preferred embodiment of the present invention.

[0121] Figure 20 This is a schematic diagram of a rectangular trajectory according to a preferred embodiment of the present invention.

[0122] Figure 21 This is a schematic diagram of a D-shaped trajectory according to a preferred embodiment of the present invention.

[0123] Figure 22 This is a schematic diagram of a plum blossom-shaped trajectory according to a preferred embodiment of the present invention. Detailed Implementation

[0124] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0125] See Figure 1 In a preferred embodiment of the present invention, a weld seam tracking collaborative control method for a dual-arm welding robot is provided, comprising:

[0126] F1. The sliding mode control term is obtained by combining the position and velocity errors of each joint of the robot with the instantaneous correction gain, historical cumulative gain and trend prediction gain; the feedforward-feedback torque compensation cooperative control law is obtained based on the pre-built robot dynamics model.

[0127] In a preferred embodiment of the present invention, the sliding mode control term is obtained by combining the position and velocity errors of each joint of the robot with the instantaneous correction gain, historical cumulative gain, and trend prediction gain, including:

[0128] Define the position error of each joint of the robot separately. and speed error ,include:

[0129] ;

[0130] in, For robots The expected trajectory of the joints, For robots The actual trajectory of the joint; For robots The expected velocity of the joint, For robots The actual speed of the joint; For robots Positional error; For robots Speed ​​error; ;

[0131] According to position error and speed error The sliding mode control quantity is obtained by combining the instantaneous correction gain, historical cumulative gain, and trend prediction gain. ,include:

[0132] ;

[0133] in, For real-time gain correction; Indicates historical cumulative gain; Indicates the trend prediction gain;

[0134] Input control is performed using hypersurface functions, based on sliding mode control quantities. Obtain sliding mode control terms ,include:

[0135] ;

[0136] in, This represents the joint gain coefficient, with a value ranging from 100 to 300. In a preferred embodiment of the present invention, it is assumed that one robot has 6 joints, and two robots have a total of 12 joints. The value is 200; This represents a hypersurface function.

[0137] In a preferred embodiment of the present invention, the pre-constructed robot dynamics model includes:

[0138] The dynamic model of a six-degree-of-freedom welding robot includes:

[0139] ;

[0140] in, and These represent the robot's joint position, joint angular velocity, and joint angular acceleration, respectively. Represents the inertia matrix; Represents the Coriolis force matrix; Represents the gravity vector; This indicates the joint torque that controls the input. This indicates an unknown external disturbance;

[0141] In the actual robot modeling process, due to the influence of factors such as modeling errors and physical parameter errors, the robot system exhibits model uncertainty. Robot dynamics models that consider errors include:

[0142] ;

[0143] By combining the six-degree-of-freedom welding robot dynamics model with the error-considered robot dynamics model, a pre-constructed robot dynamics model is obtained, including:

[0144] ;

[0145] in, ; , and These are the nominal model parameters; , and These are the actual model parameters; , and This represents the error in the nominal model parameters.

[0146] In a preferred embodiment of the present invention, the feedforward-feedback torque compensation cooperative control law obtained based on the pre-constructed robot dynamics model includes:

[0147] Based on the nominal model parameters in the pre-built robot dynamics model , and Constructing a feedforward-feedback torque compensation cooperative control law, including:

[0148] ;

[0149] in, Represents robots Joint acceleration; Represents robots Angular velocity of the joint; Representative robot The inertia matrix of the robot, i.e. of ; Representative robot The Coriolis force matrix, i.e., the robot of ; Representative robot The gravity vector, i.e., the robot of ; Represents robots The feedforward-feedback torque compensation collaborative control law;

[0150] To avoid directly calculating the inverse of a matrix, we define a notation. The operation rules include:

[0151] ;

[0152] in, and Represents a matrix; This represents the solution obtained by performing operations on two matrices.

[0153] F2 is derived from the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law, resulting in the first matrix. F2 specifically includes:

[0154] Define the error between the end effectors of the two robots and the desired weld point. and And the error between the two robot end effectors. ,include:

[0155] ;

[0156] ;

[0157] ;

[0158] in, Represents robots The actual end position; Indicates the desired end position;

[0159] Based on the current error magnitude, perform collaborative error compensation for the robot, including:

[0160] ;

[0161] ;

[0162] ;

[0163] in, Indicates the collaborative error compensation coefficient; This represents the cooperative error compensation term for robot 1; This represents the cooperative error compensation term for robot 2; This represents the gain coefficient, which ranges from 100 to 300, and is set to 200 in the preferred embodiment of the present invention.

[0164] The output of the composite weld seam tracking controller, i.e., the first matrix U, is obtained based on the random disturbance compensation term, cooperative error correction, sliding mode control term, and feedforward-feedback torque compensation cooperative control law, including:

[0165] ;

[0166] in, This represents the random disturbance compensation term.

[0167] In a preferred embodiment of the present invention, after the design of the output of the composite weld seam tracking controller, i.e., the first matrix U, is completed, the convergence analysis of the first matrix U is performed using the Lyapunov method, including:

[0168] Define the control input of the first matrix U as:

[0169] ;

[0170] ;

[0171] ;

[0172] in, Indicates by and The resulting row block matrix; Indicates by and The constructed row block matrix; Indicates by and The resulting row block matrix.

[0173] Define the error dynamics equation:

[0174] ;

[0175] in, This indicates acceleration error. It represents the ideal torque of inertia.

[0176] Substitute the control input into the error dynamics equation and simplify:

[0177] ;

[0178] Sliding mode control quantity Differentiation yields And substitute the simplified error dynamics equation into :

[0179] ;

[0180] in, Representation matrix The reverse.

[0181] Choose the Lyapunov function:

[0182] ;

[0183] in, This represents the constructed Lyapunov function; This represents the transpose of a matrix.

[0184] Differentiating the selected Lyapunov function yields:

[0185] ;

[0186] It is a skew-symmetric matrix and satisfies:

[0187] ;

[0188] Right now:

[0189] ;

[0190] in, Represents any real vector; This represents the time derivative of the inertia matrix.

[0191] Will Substituting and simplifying, we get:

[0192]

[0193] Choose the appropriate That can make In the preferred embodiment of this invention, the value is 200, meaning that the time derivative of the constructed Lyapunov function has negative definiteness. This rigorously proves that the closed-loop control system is asymptotically stable. This implies that the system's tracking error will converge to zero over time, thus theoretically ensuring that the first matrix U designed in this invention can achieve high-precision trajectory tracking control, eliminating the risk of system instability or divergence, and ensuring the safety and reliability of the dual-robot collaborative welding task.

[0194] F3. The first matrix is ​​tuned according to the first algorithm to obtain the second matrix. The first algorithm includes an improved multiverse optimization algorithm; the improvement includes the introduction of a local search mechanism guided by individual fitness differences, and dynamic adjustment of the wormhole existence probability based on the intergenerational optimal fitness change rate.

[0195] F3 specifically includes: tuning the gain parameters of the first matrix according to the first algorithm and the fitness function; the gain parameters include real-time correction gain, historical cumulative gain and trend prediction gain; in each iteration of the first algorithm, a set of candidate gain parameters are scored according to the fitness function, the probability of wormhole existence is dynamically adjusted based on the intergenerational optimal fitness change rate, and the gain parameters that minimize the fitness function are continuously searched through a local search mechanism guided by individual fitness differences, and the obtained gain parameters are assigned to the first matrix U to obtain the second matrix;

[0196] fitness function Used to quantify the performance of composite weld seam tracking controllers, including:

[0197] ;

[0198] ;

[0199] ;

[0200] ;

[0201] ;

[0202] in, This represents the maximum tracking error term; This represents the average tracking error term; This indicates the maximum torque input item; This represents the average torque input item; and In the preferred embodiment of the present invention, as a weighting, , , , ; This indicates normalization processing; Represents robots The maximum independent tracking error; This represents the robot's maximum cooperative tracking error; This indicates the robot's maximum input torque; Represents robots The average independent tracking error; This represents the average cooperative tracking error of the robots; This represents the robot's average input torque. The design of the fitness function directly determines the optimization direction, requiring a balance between tracking accuracy, cooperative performance, and control energy consumption.

[0203] In a preferred embodiment of the present invention, the cooperative error term is used to measure the cooperative operation accuracy between the two robots, ensuring that they can maintain a consistent motion state in the cooperative task; the independent tracking error term is used to evaluate the weld seam tracking accuracy of each robot, ensuring that it can accurately track its respective desired trajectory. A fitness function is constructed by weighted summing of the maximum tracking error term, the average tracking error term, the maximum torque input term, and the average torque input term to comprehensively reflect the overall performance of the cooperative control system.

[0204] In a preferred embodiment of the present invention, by tuning the gain parameter of the first matrix U, the optimized output gain matrix is ​​a better solution that achieves a balance between tracking accuracy, cooperative performance, and control energy consumption. Specifically, in this preferred embodiment of the present invention... =58.8414, =4.8213, =0.5247.

[0205] See Figure 2In a preferred embodiment of the present invention, the local search mechanism guided by individual fitness differences includes:

[0206] In a preferred embodiment of the present invention, when running a local search mechanism guided by individual fitness differences, the cosmic position and the difference threshold are first initialized.

[0207] Calculate the fitness differences between universes: for any pair of universes and Differences in adaptability Defined as:

[0208] ;

[0209] in, and Representing the universe and fitness value;

[0210] Define fitness difference threshold :

[0211] ;

[0212] in, This represents the initial threshold, which ranges from 0.01 to 0.1, and is set to 0.05 in a preferred embodiment of the present invention. Indicates the current iteration number; Indicates the maximum number of iterations;

[0213] Using the differences in fitness across universes as a guiding signal for local searches, if the universe and the universe Differences in fitness between Less than the fitness difference threshold Then determine the universe and the universe They are in similar regions and undergo local perturbations;

[0214] Define local update factor :

[0215] ;

[0216] in, The parameter represents the control of the influence of Euclidean distance, and its value ranges from 1 to 10. In the preferred embodiment of the present invention, it is set to 5. Represents the universe and The Euclidean distance between them;

[0217] For universes that meet the conditions for adding local perturbations, a small perturbation is added to guide the local search. The formula for updating the universe's position includes:

[0218] ;

[0219] in, The constant representing the control update magnitude ranges from 0.5 to 2, and is set to 0.8 in the preferred embodiment of the present invention. The coefficient representing random disturbance has a value range of 0.01 to 0.1, and is 0.05 in the preferred embodiment of the present invention; This is a random disturbance term that follows a standard normal distribution, used to introduce randomness; Represents the updated universe Location; Represents the universe before the update Location; Represents the universe The location before the update; Represents the universe The location before the update.

[0220] After updating the universe's position, the new fitness is automatically calculated, and the difference threshold is dynamically adjusted. It is then determined whether this is the last generation; if so, the process ends; otherwise, the fitness differences between universes are recalculated, and subsequent steps are executed.

[0221] In a preferred embodiment of this invention, by introducing a fitness-difference-guided local search mechanism, the algorithm can dynamically adjust its search strategy, thereby establishing a balance between global and local searches. Fitness difference provides a measure of similarity between individuals, and by perturbing individuals with small fitness differences, the algorithm accelerates its local search process. Furthermore, by increasing a threshold to adjust the incentive mechanism for local search, the algorithm can further enhance its local search capability as it approaches the optimal solution, thereby improving the algorithm's convergence speed and solution quality. The dynamic triggering of local perturbation through individual fitness differences, along with threshold-controlled similarity determination and distance-weighted update strategies, enhances the algorithm's fine-grained search capability in similar solution regions, distinguishing it from traditional indiscriminate random search.

[0222] See Figure 3 In a preferred embodiment of the present invention, dynamically adjusting the probability of wormhole existence based on the intergenerational optimal fitness change rate includes:

[0223] In a preferred embodiment of the present invention, when dynamically adjusting the probability of wormhole existence based on the intergenerational optimal fitness change rate, parameters are first initialized, including the velocity of the optimal universe, optimal fitness, WEP, linear decay control coefficient, adaptive update sensitivity, and fitness change threshold.

[0224] Calculate the change in optimal fitness between two generations: Define the change in fitness as the difference between the fitness of the optimal solution in the current generation and the fitness of the optimal solution in the previous generation. ,include:

[0225] ;

[0226] in, This indicates the optimal fitness of the current generation; This indicates the optimal fitness of the previous generation;

[0227] Define fitness change threshold Used to determine whether to use linear decay or exponential decay, including:

[0228] ;

[0229] in, This represents the initial threshold, which ranges from 0.5 to 1, and is 0.8 in a preferred embodiment of the present invention. The coefficient representing the influence of the fitness change rate on the threshold ranges from 2 to 8, and is set to 5 in the preferred embodiment of the present invention. The value represents the nonlinear attenuation coefficient, which ranges from 0.1 to 1, and is 0.5 in the preferred embodiment of the present invention.

[0230] Define the update mechanism for the wormhole existence probability (WEP), including:

[0231] When the fitness change is greater than or equal to the fitness change threshold, WEP is updated using a linear decay method:

[0232] ;

[0233] When the fitness change is less than the fitness change threshold, WEP is updated using an exponential decay method:

[0234] ;

[0235] in, Indicates the current iteration number; Indicates the maximum number of iterations; The parameter representing the control of the decay rate has a value range of 1 to 2, and in the preferred embodiment of the present invention, it is 1.5; Indicates the maximum value of WEP; This represents the minimum value of WEP; Indicates the first The probability of the wormhole existing in the next iteration; Represents the natural exponential function; The value represents the attenuation sensitivity coefficient, which ranges from 0.01 to 0.05, and is 0.02 in the preferred embodiment of the present invention.

[0236] After updating WEP, record the current optimal universe position and corresponding fitness value; determine if it is the last generation. If so, end the process; otherwise, recalculate the change in optimal fitness between the two generations and execute subsequent steps.

[0237] In a preferred embodiment of the invention, by introducing a mechanism that drives WEP adjustment based on fitness changes, the algorithm can adaptively balance the capabilities of global search and local exploitation, enabling it to flexibly adjust its search strategy at different stages based on search progress. By dynamically adjusting the wormhole existence probability (WEP) through the intergenerational optimal fitness change rate, it achieves adaptive switching between linear and exponential decay mechanisms, distinguishing it from the traditional MVO fixed decay WEP adjustment strategy.

[0238] F4. Obtain the robot's actual and expected joint motion parameters, and combine the actual and expected joint motion parameters with the second matrix to achieve weld seam tracking collaborative control.

[0239] In a preferred embodiment of the present invention, the weld seam tracking collaborative control based on actual and desired joint motion parameters combined with a second matrix includes:

[0240] A link coordinate system and a DH parameter table are established based on the DH parameter method and the actual structure of the robot.

[0241] The mapping relationship between the operation space trajectory and the joint space command is realized by solving the forward and inverse kinematics.

[0242] The robot's actual second matrix is ​​obtained based on the actual and expected joint motion parameters, link coordinate system, DH parameter table, mapping relationship, and second matrix; the joint motion parameters include trajectory, velocity, and acceleration;

[0243] The actual second matrix is ​​applied to the robot's joint actuators to achieve collaborative control for weld seam tracking.

[0244] In a preferred embodiment of the present invention, the link coordinate system is described below. Figure 4 The DH parameter table is shown in Table 1, which is obtained from the joint information of the actual robot. Figure 4 middle , and Represents the robot's base coordinate system; , and Let n represent the joint coordinate system of the robot, n=1,2,3,4,5,6.

[0245] Table 1 DH Parameter Table

[0246] ;

[0247] Table Indicates the link number; Indicates the link offset; Indicates the length of the link; Indicates the linkage torsion angle; Represents joint variables.

[0248] In a preferred embodiment of the present invention, since the two robots are identical, the DH parameter method is first used to perform kinematic modeling and analysis on one of the welding robots. Based on the link coordinate system and the DH parameter table, the pose transformation matrix from the robot base coordinate system to the end effector coordinate system, i.e., the forward kinematic equation, is derived through homogeneous transformation between adjacent coordinate systems. This equation establishes the mapping relationship between the robot's joint angles and the end effector pose, providing a mathematical model foundation for subsequent tracking control. Simultaneously, trajectory tracking control requires converting the desired trajectory in the operating space into motion commands in the joint space; this process relies on inverse kinematics solutions. In this preferred embodiment, a numerical iterative method is used to solve the robot's inverse kinematics problem, thereby providing support for the controller to generate joint commands.

[0249] This invention presents a cooperative control method for weld seam tracking in dual-arm welding robots. By integrating sliding mode control, feedforward-feedback torque compensation, cooperative error correction mechanisms, and random disturbance compensation terms, it achieves high-precision weld seam tracking for multiple welding robots, significantly improving the dynamic response capability and control accuracy of the multi-robot system in weld seam tracking tasks, and enhancing the system's stability and robustness in complex trajectory welding processes. By introducing a local search mechanism guided by individual fitness differences and improving the multiverse optimization algorithm by dynamically adjusting the wormhole existence probability based on the intergenerational optimal fitness change rate, it effectively enhances the global search capability and local convergence performance during the optimization process, overcoming the problem of traditional algorithms easily getting trapped in local optima. This improves the accuracy and convergence speed of controller parameter tuning. Based on the improved algorithm, adaptive parameter tuning of the weld seam tracking controller is achieved, realizing intelligent adaptation of control parameters to various welding trajectories and working conditions. This ensures that the dual-robot system can achieve high-precision tracking control under different complex welding tasks, effectively improving the consistency of welding quality and the system's anti-interference capability. This invention helps to achieve higher-precision weld seam tracking control for dual welding robots under complex trajectories, thereby improving production efficiency. The method of this invention can adapt to welding tasks with complex spatial structures, varied task paths, and the potential need for double-sided collaborative welding. It can replace traditional manual welding and ensure the long-term reliability and stability of welding.

[0250] In a preferred embodiment of the present invention, a weld seam tracking collaborative control device for a dual-arm welding robot is also provided for use in the method of the present invention. The device includes a first module, a second module, a third module, and a fourth module.

[0251] The first module is used to obtain the sliding mode control term based on the position and velocity errors of each joint of the robot, combined with the real-time correction gain, historical cumulative gain, and trend prediction gain; and to obtain the feedforward-feedback torque compensation cooperative control law based on the pre-built robot dynamics model.

[0252] The second module is used to obtain the first matrix based on the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law;

[0253] The third module is used to tune the first matrix according to the first algorithm to obtain the second matrix; the first algorithm includes an improved multiverse optimization algorithm; the improvement includes the introduction of a local search mechanism guided by individual fitness differences, and dynamic adjustment of the probability of wormhole existence based on the intergenerational optimal fitness change rate;

[0254] The fourth module is used to obtain the robot's actual and expected joint motion parameters, and to achieve weld seam tracking collaborative control based on the actual and expected joint motion parameters combined with the second matrix.

[0255] The weld seam tracking and collaborative control device for a dual-arm welding robot of the present invention, used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0256] Verification section:

[0257] To verify the method of this invention, a weld seam tracking simulation test was conducted. For example... Figures 5 to 6 As shown, to simulate potential sudden interference in an actual welding system, pulse signals were added as external disturbances during the test, with J1 to J6 corresponding to the pulse signals added to joints 1 to 6. The introduction of disturbances helps to further enhance the robustness and anti-interference capability of the method under non-ideal conditions, thereby providing a more comprehensive evaluation of its performance in practical applications.

[0258] like Figures 7 to 10As shown, the simulation test uses a six-DOF welding robot as the research object, and tests the algorithm tracking performance for four types of weld trajectories: circular, square, D-shaped, and plum blossom-shaped. The method of this invention is named IIDF-MVO. All indicators of this method are significantly improved compared to SMC (Sliding Mode Control) and AFSM (Adaptive Fuzzy Sliding Mode Control). Regarding the maximum independent tracking error, taking the data with the smallest error as an example, the errors of SMC and AFSM are 2.464 and 1.581 times that of this method, respectively; regarding the average independent tracking error, again taking the data with the smallest error as an example, the errors of SMC and AFSM are 2 and 1.857 times that of this method, respectively. Furthermore, since SMC and AFSM do not introduce cooperative error compensation, their maximum cooperative tracking errors are 1.571 and 1.428 times that of this method, respectively; and their average cooperative tracking errors are 2 and 1.8 times that of this method, respectively. This shows that the robot cooperation of SMC and AFSM is significantly insufficient. Compared to the other three optimization algorithms—GA (Genetic Algorithm), CSA (Chameleon Swarm Algorithm), and MVO (Multi-verse Optimization)—although all optimize the controller U in this embodiment, their optimization performance still differs. For ease of comparison and analysis, this embodiment first sums the three maximum errors and three average errors of the four trajectories, then calculates the average values, and finally compares the results based on these average values. The average values ​​obtained by the method of this invention are 4.625 mm and 0.85 mm, respectively. The average values ​​of the sum of the three maximum errors for the other three optimization algorithms are 6.55 mm, 6.475 mm, and 5.85 mm, respectively, and the average values ​​of the sum of the three average errors are 1.15 mm, 1.125 mm, and 0.95 mm, respectively. Calculations show that the average of the sum of the three maximum errors of the method of the present invention (4.625 mm) is reduced by approximately 20.9% compared to the average of the optimal comparison method (5.85 mm); while the average of the sum of the three average errors (0.85 mm) is reduced by approximately 10.5% compared to the average of the optimal comparison method (0.95 mm). These data further verify the superiority and robustness of the method of the present invention in weld seam tracking.

[0259] like Figures 11 to 14As shown in the figure, the input torque comparison of different control and optimization methods under four different weld seam trajectories is presented. It can be seen from the figure that the maximum input torque of all methods does not exceed the actual limit of the robot drive system (250 Nm), indicating that each method is feasible in practical applications. Although the method of this invention has a relatively high maximum input torque, its average input torque throughout the entire process is relatively low, and there is no significant difference from the lowest value. This indicates that it can maintain low energy consumption while ensuring high-precision weld seam tracking. The higher control input is mainly used to achieve more accurate trajectory tracking and cooperative control, thereby maintaining the stability and accuracy of the system even in complex trajectories and environments. Further combined with... Figures 7 to 10 As can be seen from the weld seam tracking error, the method of the present invention exhibits the best tracking accuracy under all four trajectories, meaning that its independent tracking error and cooperative error are both lower than other methods. In summary, the method of the present invention achieves a good trade-off between input torque and weld seam tracking performance. Although the maximum input torque is slightly higher, its average input torque is close to the minimum value, and its trajectory tracking performance is significantly better than other methods. This indicates that the method of the present invention can not only meet the input constraints of robot drive systems in practical applications, but also achieve high-precision weld seam tracking and cooperative control with lower energy consumption.

[0260] like Figures 15 to 18 As shown in the figure, a comparison of weld seam tracking using six methods under four different trajectories is presented, where the Y-axis and Z-axis represent the coordinate values ​​in the robot's base coordinate system. The figure reveals that although all algorithms achieve weld seam tracking, there are significant differences in tracking accuracy, especially in areas with discontinuous trajectory angles or large curvature changes. Specifically, the SMC and AFSM algorithms have relatively large tracking errors, with a high degree of deviation between their actual and expected trajectories, indicating limitations in their robustness and adaptability when dealing with complex trajectory changes. In contrast, GA and CSA show reduced tracking errors, but their control accuracy in areas with discontinuous angles is still lower than that of the method described in this invention, indicating shortcomings in handling nonlinear trajectories. In comparison, the method described in this invention demonstrates significant superiority in tracking. It not only has the smallest tracking error but also maintains high-precision tracking in critical areas such as discontinuous angles. Furthermore, through… Figure 17 Furthermore, it can be seen that the actual terminal trajectory of the method of the present invention is closest to the expected trajectory in all test scenarios, and significantly outperforms the tracking performance of the traditional MVO algorithm. This result shows that the improved strategy of the present invention for the MVO algorithm effectively enhances the algorithm's global search capability and local convergence accuracy, thereby achieving higher-precision tracking control under complex weld seam trajectories. Therefore, the experimental results not only demonstrate the superiority of the method of the present invention compared to the traditional MVO, but also further verify the effectiveness of the improved MVO algorithm of the present invention.

[0261] like Figures 19 to 22 As shown, the 3D tracking performance of the dual welding robot under four typical trajectories is further demonstrated. Figures 19 to 22 middle, , and The coordinate system represents the entire working environment. The robot in the diagram is inverted, and the origin is the midpoint of the line connecting the two robot bases. By introducing the method of this invention to optimize the controller gain matrix, the system exhibits superior tracking performance. The tracking error of the optimized system is significantly improved compared to the comparison algorithm, especially in maintaining stable tracking accuracy at trajectory turning points and curvature abrupt change regions. This further verifies the superiority of the method of this invention in solving complex system parameter optimization problems.

[0262] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A weld seam tracking collaborative control method for a dual-arm welding robot, characterized in that, include: The sliding mode control term is obtained by combining the position and velocity errors of each joint of the robot with the real-time correction gain, historical cumulative gain and trend prediction gain. The feedforward-feedback torque compensation cooperative control law is obtained based on the pre-constructed robot dynamics model; A first matrix is ​​obtained based on the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law; the first matrix is ​​then tuned according to the first algorithm to obtain a second matrix. The first algorithm includes an improved multiverse optimization algorithm; the improvement includes introducing a local search mechanism guided by individual fitness differences, and dynamically adjusting the probability of wormhole existence based on the intergenerational optimal fitness change rate; Obtain the robot's actual and expected joint motion parameters, and implement weld seam tracking collaborative control based on the actual and expected joint motion parameters combined with the second matrix; The local search mechanism guided by individual fitness differences includes: For any pair of universes and Differences in adaptability Defined as: ； in, and Representing the universe and fitness value; Define fitness difference threshold ΔF two : ； in, Indicates the initial threshold; Indicates the current iteration number; Indicates the maximum number of iterations; Using the differences in fitness across universes as a guiding signal for local searches, if the universe and the universe Differences in fitness between Less than the fitness difference threshold Then determine the universe and the universe They are in similar regions and undergo local perturbations; Define local update factor : ； in, This represents the parameter that controls the effect of Euclidean distance; Represents the universe and The Euclidean distance between them; For universes that meet the conditions for adding local perturbations, a small perturbation is added to guide the local search. The formula for updating the universe's position includes: ； in, This represents a constant that controls the update magnitude; The coefficients represent random perturbations; randn(1,dim) is a random perturbation term from a standard normal distribution, used to introduce randomness; Represents the updated universe Location; Represents the universe before the update Location; Represents the universe The location before the update; Represents the universe The location before the update; The dynamic adjustment of the wormhole existence probability based on the intergenerational optimal fitness change rate includes: The difference between the fitness of the current generation's optimal solution and the fitness of the previous generation's optimal solution is defined as the fitness change. ,include: ； in, This indicates the optimal fitness of the current generation; This indicates the optimal fitness of the previous generation; Define fitness change threshold Used to determine whether to use linear decay or exponential decay, including: ； in, This represents the initial threshold. A coefficient representing the effect of the fitness change rate on the threshold; Indicates the nonlinear attenuation coefficient; Define the update mechanism for the wormhole existence probability (WEP), including: When the fitness change is greater than or equal to the fitness change threshold, WEP is updated using a linear decay method: ； When the fitness change is less than the fitness change threshold, WEP is updated using an exponential decay method: ； in, Indicates the current iteration number; Indicates the maximum number of iterations; This refers to the parameter that controls the decay rate; Indicates the maximum value of WEP; This represents the minimum value of WEP; Indicates the first The probability of the wormhole existing in the next iteration; Represents the natural exponential function; This represents the attenuation sensitivity coefficient.

2. The weld seam tracking collaborative control method for a dual-arm welding robot according to claim 1, characterized in that, The sliding mode control term, obtained by combining the position and velocity errors of each robot joint with real-time correction gain, historical cumulative gain, and trend prediction gain, includes: Define the position error of each joint of the robot separately. and speed error ,include: ； in, For robots The expected trajectory of the joints, For robots The actual trajectory of the joint; For robots The expected velocity of the joint, For robots The actual speed of the joint; For robots Positional error; For robots Speed ​​error; ; According to the position error and speed error The sliding mode control quantity is obtained by combining the instantaneous correction gain, historical cumulative gain, and trend prediction gain. ,include: ； in, For real-time gain correction; Indicates historical cumulative gain; Indicates the trend prediction gain; Input control is performed using a hypersurface function, based on the sliding mode control quantity. Obtain sliding mode control terms ,include: ； in, Indicates the joint gain coefficient; This represents a hypersurface function.

3. The weld seam tracking collaborative control method for a dual-arm welding robot according to claim 2, characterized in that, The pre-built robot dynamics model includes: The dynamic model of a six-degree-of-freedom welding robot includes: ； in, and These represent the robot's joint position, joint angular velocity, and joint angular acceleration, respectively. Represents the inertia matrix; Represents the Coriolis force matrix; Represents the gravity vector; This indicates the joint torque that controls the input. This indicates an unknown external disturbance; Robot dynamics models that consider errors include: ； The pre-constructed robot dynamics model is obtained by combining the six-degree-of-freedom welding robot dynamics model with the error-considered robot dynamics model, including: ； in, ; , and These are the nominal model parameters; , and These are the actual model parameters; , and This represents the error in the nominal model parameters.

4. The weld seam tracking collaborative control method for a dual-arm welding robot according to claim 3, characterized in that, The feedforward-feedback torque compensation cooperative control law obtained from the pre-constructed robot dynamics model includes: Based on the nominal model parameters in the pre-built robot dynamics model , and Constructing the feedforward-feedback torque compensation cooperative control law includes: ； in, Represents robots Joint acceleration; Represents robots Angular velocity of the joint; Representative robot The inertia matrix of the robot, i.e. of ; Representative robot The Coriolis force matrix, i.e., the robot of ; Representative robot The gravity vector, i.e., the robot of ; Represents robots The feedforward-feedback torque compensation collaborative control law; symbol The operation rules include: ； in, and Represents a matrix; This represents the solution obtained by performing operations on two matrices.

5. The weld seam tracking collaborative control method for a dual-arm welding robot according to claim 4, characterized in that, The first matrix obtained based on the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law includes: Define the error between the end effectors of the two robots and the desired weld point. and And the error between the two robot end effectors. ,include: ； ； ； in, Represents robots The actual end position; Indicates the desired end position; Based on the current error magnitude, perform collaborative error compensation for the robot, including: ； ； ； in, Indicates the collaborative error compensation coefficient; This represents the cooperative error compensation term for robot 1; This represents the cooperative error compensation term for robot 2; Indicates the gain coefficient; The output of the composite weld seam tracking controller, i.e., the first matrix U, is obtained based on the random disturbance compensation term, the cooperative error correction, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law, and includes: ； in, This represents the random disturbance compensation term.

6. The weld seam tracking collaborative control method for a dual-arm welding robot according to claim 5, characterized in that, The step of adjusting the first matrix according to the first algorithm to obtain the second matrix includes: The gain parameters of the first matrix are tuned according to the first algorithm and the fitness function; the gain parameters include the instantaneous correction gain, the historical cumulative gain, and the trend prediction gain; in each iteration of the first algorithm, the gain parameters are scored according to the fitness function as a set of candidate gain parameters, the probability of wormhole existence is dynamically adjusted based on the intergenerational optimal fitness change rate, and the gain parameters that minimize the fitness function are continuously searched through the local search mechanism guided by individual fitness differences, and the obtained gain parameters are assigned to the first matrix U to obtain the second matrix; The fitness function Used to quantify the performance of composite weld seam tracking controllers, including: ； ； ； ； ； in, This represents the maximum tracking error term; This represents the average tracking error term; This indicates the maximum torque input item; This represents the average torque input item; and As weight; This indicates normalization processing; Represents robots The maximum independent tracking error; This represents the robot's maximum cooperative tracking error; This indicates the robot's maximum input torque; Represents robots The average independent tracking error; This represents the average cooperative tracking error of the robots; This represents the average input torque of the robot.

7. The weld seam tracking collaborative control method for a dual-arm welding robot according to claim 6, characterized in that, Based on the actual and expected joint motion parameters combined with the second matrix, the weld seam tracking collaborative control is achieved, including: A link coordinate system and a DH parameter table are established based on the DH parameter method and the actual structure of the robot. The mapping relationship between the operation space trajectory and the joint space command is realized by solving the forward and inverse kinematics. The robot's actual second matrix is ​​obtained based on the actual and expected joint motion parameters, the link coordinate system, the DH parameter table, the mapping relationship, and the second matrix; the joint motion parameters include trajectory, velocity, and acceleration. The actual second matrix is ​​applied to the robot's joint actuators to achieve weld seam tracking and collaborative control.

8. A weld seam tracking collaborative control device for a dual-arm welding robot, used in the method according to any one of claims 1 to 7, characterized in that, The device includes a first module, a second module, a third module, and a fourth module; The first module is used to obtain the sliding mode control term based on the position and velocity errors of each joint of the robot, combined with the real-time correction gain, historical cumulative gain and trend prediction gain; and to obtain the feedforward-feedback torque compensation cooperative control law based on the pre-built robot dynamics model. The second module is used to obtain the first matrix based on the random disturbance compensation term, the cooperative error correction mechanism, the sliding mode control term, and the feedforward-feedback torque compensation cooperative control law; The third module is used to tune the first matrix according to the first algorithm to obtain the second matrix; the first algorithm includes an improved multiverse optimization algorithm; the improvement includes introducing a local search mechanism guided by individual fitness differences, and dynamically adjusting the probability of wormhole existence based on the intergenerational optimal fitness change rate; The fourth module is used to obtain the robot's actual and expected joint motion parameters, and to realize weld seam tracking collaborative control based on the actual and expected joint motion parameters and the second matrix.

Images