Welding robot control method, robot processor, equipment and medium

Abstract

The invention provides a welding robot control method, a robot processor, equipment and a medium, the current pose of an end effector of a robot is obtained, the robot further comprises a movable base and a mechanical arm arranged on the movable base, the end effector is arranged at the tail end of the mechanical arm, and the mechanical arm is arranged on the movable base. A plurality of movable joints are arranged on the movable base and the mechanical arm, and each movable joint is used for driving the end effector to carry out pose adjustment; determining a pose control speed of the end effector based on the current pose and a target pose of the end effector; constructing an optimization objective function, and optimizing the pose change speed based on the optimization objective function to obtain the joint control speed of each movable joint; and controlling each movable joint to move at the corresponding joint control speed so as to adjust the pose of the end effector. By the adoption of the method, it is ensured that the chassis and the mechanical arm of the welding robot move coordinately, and movement stability and welding precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and more particularly, to a welding robot control method, a robot processor, a device, and a medium. Background Art

[0002] In the field of modern industrial welding, fixed and rail-mounted welding robotic arms, with their high precision and stability, are widely used in welding scenarios such as ship cabins, decks, and storage bins. They can not only improve welding efficiency, ensure quality, and reduce manual errors, but also have high automation characteristics. They can achieve various welding paths and parameters through programming, and cooperate with advanced sensors and control systems to monitor and adjust in real time to ensure the consistency of welding quality. They have become key equipment for improving production efficiency, reducing labor intensity, and safety risks in large ship manufacturing. However, such robotic arms are limited by fixed working positions or orbital movements and lack free mobility. When faced with the extensive and irregular welding areas in shipbuilding, fixed robotic arms are difficult to flexibly adapt to different position requirements, and rail-mounted robotic arms are also restricted by the length and area of the rails and cannot move freely in three-dimensional space, resulting in an extended working cycle, increased manual intervention, and a reduction in overall automation efficiency and welding quality consistency. To break through these limitations, mobile wheeled welding robots, due to their flexibility in three-dimensional space, are gradually applied to large-area complex welding scenarios such as shipbuilding and steel structure processing. They can move freely without relying on fixed rails or positions, effectively solving the limitations of traditional robots in terms of spatial coverage and flexibility, and improving the degree of welding automation and precision.

[0003] Currently, mobile wheeled welding robotic arms generally adopt a separate control method in practical applications, that is, the movement of the robotic arm and the movement of the chassis are independently controlled. This method simplifies the system design. The robotic arm moves precisely within a preset range, and the chassis is responsible for the stable movement of the device.

[0004] However, it is found in the research that this separate control method will lead to poor coordination between the movement of the chassis and the robotic arm, seriously affecting the trajectory tracking accuracy and welding quality. During the dynamic welding process, smooth transitions cannot be achieved, and discontinuous trajectories and unstable welding effects are likely to occur, making it difficult to meet the increasingly high welding quality requirements, unable to ensure the coordinated movement of the chassis and the robotic arm, and thus reducing the movement smoothness and welding precision. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a welding robot control method, a robot processor, a device, and a medium to ensure the coordinated movement of the chassis and the robotic arm of the welding robot, and improve the movement smoothness and welding precision.

[0006] In a first aspect, an embodiment of the present application provides a welding robot control method, which is applied to a robot processor, and the method includes:

[0007] Obtain the current pose of the end effector of the robot, where the robot further includes a mobile base and a robotic arm disposed on the mobile base, the end effector is disposed at the end of the robotic arm, and a plurality of movable joints are provided on the mobile base and the robotic arm, and each movable joint is used to drive the end effector to perform pose adjustment;

[0008] Determine the pose control speed of the end effector based on the current pose and the target pose of the end effector;

[0009] Construct an optimization objective function, and optimize the pose change speed based on the optimization objective function to obtain the joint control speed of each movable joint;

[0010] Control each movable joint to move at its corresponding joint control speed to adjust the pose of the end effector.

[0011] Optionally, the current pose of the end effector is:

[0012] 0 T e = 0 T b (x, y, θ) b T a a T e (k a ,q a );

[0013] Wherein, 0 T e is the current pose of the end effector relative to the world coordinate system, 0 T b is the pose of the mobile base relative to the world coordinate system, x is the abscissa of the mobile base, y is the ordinate of the mobile base, θ is the rotation angle of the mobile base, b T a is the pose of the robotic arm relative to the mobile base, a T e is the pose of the end effector relative to the robotic arm, k a is the kinematic parameter of the robotic arm, q a is the joint angle of the robotic arm.

[0014] Optionally, the determining the pose control speed of the end effector based on the current pose and the target pose of the end effector includes:

[0015] Determine the pose change speed according to the current pose and the target pose;

[0016] The pose change speed is subjected to speed constraint and direction constraint to obtain the pose control speed.

[0017] Optionally, determining the pose change speed according to the current pose and the target pose includes:

[0018] Determining the pose change speed according to the following expression

[0019]

[0020] where γ is a proportional gain for controlling the convergence speed; vec(·) is a function for converting pose error into spatial speed; b T e is the current pose; is the target pose.

[0021] Optionally, subjecting the pose change speed to speed constraint and direction constraint to obtain the pose control speed includes:

[0022] Determining the pose control speed according to the following expression

[0023]

[0024] where is the pose change speed; λ is a speed weight factor; v weld is the constant linear speed required by the welding process; is the unit vector in the tangential direction of the welding path.

[0025] Optionally, constructing the optimization objective function includes:

[0026] Performing differential dynamics modeling on the current pose of the end effector to obtain the differential dynamics model of the end effector;

[0027] According to the differential dynamics model, constructing a motion controller for solving the joint control speed through the Jacobian matrix;

[0028] Introducing a slack vector into the motion controller to obtain the optimization objective function.

[0029] Optionally, the optimization objective function is:

[0030]

[0031] where x is the decision variable, that is, the joint control speed, x = (q, δ) T, where q is the joint angle, δ is the differential of the joint angle, Q includes the joint control speed and the relaxation vector, and C includes maximizing the manipulability and the auxiliary performance task.

[0032] Optionally, optimizing the pose change speed based on the optimization objective function to obtain the joint control speed of each movable joint includes:

[0033] Combining the optimization objective function with the pose change speed to construct a constrained optimization problem;

[0034] Setting constraint conditions, and solving the constrained optimization problem according to each constraint condition to obtain the joint control speed of each movable joint.

[0035] The constraint conditions include:

[0036]

[0037] X - ≤x≤X + ;

[0038] where J is the Jacobian matrix, x is the joint control speed, is the target speed of the end effector, A and are both preset values for realizing joint position limit avoidance, X + is the maximum value of the decision variable, and X - is the minimum value of the decision variable.

[0039] Optionally, the end effector is a welding torch; after controlling each movable joint to move at its corresponding joint control speed to adjust the pose of the end effector, the method further includes:

[0040] Performing a welding task through the end effector.

[0041] In a second aspect, an embodiment of the present application provides a robot processor, and the robot processor includes:

[0042] A current pose acquisition module, configured to acquire the current pose of the end effector of the robot, where the robot further includes a mobile base and a robotic arm disposed on the mobile base, the end effector is disposed at the end of the robotic arm, and a plurality of movable joints are disposed on the mobile base and the robotic arm, and each movable joint is used to drive the end effector to perform pose adjustment;

[0043] A pose control speed determination module, configured to determine the pose control speed of the end effector based on the current pose and the target pose of the end effector;

[0044] A joint control speed determination module, configured to construct an optimization objective function, and optimize the pose change speed based on the optimization objective function to obtain the joint control speeds of the movable joints;

[0045] A pose adjustment module, configured to control each movable joint to move at its corresponding joint control speed to adjust the pose of the end effector.

[0046] Optionally, the current pose of the end effector is:

[0047] 0 T e = 0 T b (x, y, θ) b T a a T e (k a , q a );

[0048] Wherein, 0 T e is the current pose of the end effector relative to the world coordinate system, 0 T b is the pose of the mobile base relative to the world coordinate system, x is the abscissa of the mobile base, y is the ordinate of the mobile base, θ is the rotation angle of the mobile base, b T a is the pose of the robotic arm relative to the mobile base, a T e is the pose of the end effector relative to the robotic arm, k a are the kinematic parameters of the robotic arm, q a are the joint angles of the robotic arm

[0049] Optionally, the determining the pose control speed of the end effector based on the current pose and the target pose of the end effector includes:

[0050] Determining a pose change speed according to the current pose and the target pose;

[0051] Performing speed constraint and direction constraint on the pose change speed to obtain the pose control speed.

[0052] Optionally, the determining the pose change speed according to the current pose and the target pose includes:

[0053] Determining the pose change speed according to the following expression

[0054]

[0055] Among them, γ is the proportional gain, which is used to control the convergence speed; vec(·) is a function that converts the pose error into a spatial velocity; b T e is the current pose; is the target pose.

[0056] Optionally, the obtaining of the pose control velocity by performing velocity constraint and direction constraint on the pose change velocity includes:

[0057] Determine the pose control velocity according to the following expression

[0058]

[0059] Among them, is the pose change velocity; λ is the velocity weight factor; v weld is the constant linear velocity required by the welding process; is the unit vector in the tangential direction of the welding path.

[0060] Optionally, the constructing of the optimization objective function includes:

[0061] Perform differential dynamics modeling on the current pose of the end effector to obtain the differential dynamics model of the end effector;

[0062] According to the differential dynamics model, construct a motion controller for solving the joint control velocity through the Jacobian matrix;

[0063] Introduce a slack vector into the motion controller to obtain the optimization objective function

[0064] Optionally, the optimization objective function is:

[0065]

[0066] Among them, x is the decision variable, that is, the joint control velocity, x = (q, δ) T , q is the joint angle, δ is the differential of the joint angle, Q includes the joint control velocity and the slack vector, and C includes maximizing manipulability and auxiliary performance tasks.

[0067] Optionally, the obtaining of the joint control velocities of the movable joints by optimizing the pose change velocity based on the optimization objective function includes:

[0068] Combine the optimization objective function with the pose change velocity to construct a constrained optimization problem;

[0069] Set constraint conditions, and solve the constraint optimization problem according to each constraint condition to obtain the joint control speeds of the movable joints.

[0070] The constraint conditions include:

[0071]

[0072] X - ≤x≤X + ;

[0073] where J is the Jacobian matrix, x is the joint control speed, is the target speed of the end effector, A and are both preset values for achieving joint position limit avoidance, X + is the maximum value of the decision variable, X - is the minimum value of the decision variable.

[0074] Optionally, the end effector is a welding torch; the device further includes:

[0075] A welding task execution module, configured to, after controlling the movable joints to move at their corresponding joint control speeds to adjust the pose of the end effector, execute a welding task through the end effector.

[0076] In a third aspect, an embodiment of the present application provides a computer device, including: a processor, a memory, and a bus, where the memory stores machine-readable instructions executable by the processor. When the computer device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the welding robot control method in any optional implementation manner in the first aspect are executed.

[0077] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the welding robot control method in any optional implementation manner in the first aspect are executed.

[0078] The technical solutions provided by the present application include but are not limited to the following beneficial effects:

[0079] The present application first obtains the current pose of the end effector, integrates the movable joints of the mobile base and the robotic arm, and perceives the pose of the two as a unified whole, breaking the limitations of traditional modular control, avoiding the coordinate conversion error between the chassis and the robotic arm, enabling the robot to dynamically perceive its own state based on the overall kinematic model, and providing reliable initial data support for precise control in complex environments, especially suitable for scenarios that require multi-degree-of-freedom coordination such as ship curved surface welding.

[0080] Then, based on the difference between the current pose and the target pose, the spatial velocity of the end effector is calculated in real time through the kinematic model, and the welding process constraints are incorporated. This not only realizes the direct mapping from the task objective to the motion instruction, but also ensures that the end effector converges to the target pose along the optimal path by dynamically adjusting the velocity direction and amplitude. At the same time, it meets the strict requirements of the welding process for velocity stability and trajectory accuracy, effectively avoiding the problem of discontinuous trajectories caused by the disconnection between the chassis and the manipulator movement in traditional independent control.

[0081] Next, by constructing an optimization function that includes multiple objectives such as joint motion smoothness, manipulator controllability, and chassis movement constraints, the pose control velocity of the end effector is converted into the collaborative motion instructions of the mobile base and each joint of the manipulator. The optimal solution with constraints is solved through algorithms such as quadratic programming, significantly improving the overall motion smoothness and energy efficiency of the robot and extending the service life of the equipment.

[0082] Finally, by sending the control velocity instructions of each joint in real time, the mobile base and the manipulator are driven to move collaboratively, enabling the end effector to smoothly adjust its pose according to the planned trajectory, significantly improving the welding trajectory tracking accuracy and anti-interference ability.

[0083] In summary, this application models the mobile base and the manipulator as a whole, uses multi-sensor fusion to accurately sense the end pose, dynamically calculates the pose control velocity with process constraints, generates collaborative joint velocities through multi-objective optimization, realizes the deep linkage between the base and the manipulator, ensures the coordinated movement of the welding robot chassis and the manipulator, and improves the motion smoothness and welding accuracy.

[0084] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative efforts.

[0086] Figure 1 Shows the flowchart of a welding robot control method provided in Embodiment 1 of the present invention;

[0087] Figure 2 Shows the flowchart of a method for determining the pose control velocity provided in Embodiment 1 of the present invention;

[0088] Figure 3Shows the flowchart of a method for constructing an optimization objective function provided in Embodiment 1 of the present invention;

[0089] Figure 4 Shows the schematic diagram of the pose adjustment process of an end effector provided in Embodiment 1 of the present invention;

[0090] Figure 5 Shows the schematic diagram of the change in the movement path of the end of the welding torch before optimization and the influence of the joint speed gain on the end trajectory provided in Embodiment 1 of the present invention;

[0091] Figure 6 Shows the schematic diagram of the change in the movement path of the end of the welding torch after optimization and the influence of the joint speed gain on the end trajectory provided in Embodiment 1 of the present invention;

[0092] Figure 7 Shows the schematic diagram of the movement trajectory of the end of the welding torch provided in Embodiment 1 of the present invention;

[0093] Figure 8 Shows the schematic diagram of the movement trajectory of the end of the second welding torch provided in Embodiment 1 of the present invention;

[0094] Figure 9 Shows the simulation schematic diagram of a welding robot performing a long straight weld provided in Embodiment 1 of the present invention;

[0095] Figure 10 Shows the simulation schematic diagram of a welding robot performing a vertical weld provided in Embodiment 1 of the present invention;

[0096] Figure 11 Shows the simulation schematic diagram of a welding robot performing vertical welding provided in Embodiment 1 of the present invention;

[0097] Figure 12 Shows the simulation schematic diagram of a welding robot performing overhead welding provided in Embodiment 1 of the present invention;

[0098] Figure 13 Shows the simulation schematic diagram of a welding robot performing a fillet weld at a right angle provided in Embodiment 1 of the present invention;

[0099] Figure 14 Shows the simulation schematic diagram of a second welding robot performing a fillet weld at a right angle provided in Embodiment 1 of the present invention;

[0100] Figure 15 Shows the schematic diagram of the structure of a robot processor provided in Embodiment 2 of the present invention;

[0101] Figure 16 Shows the schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. Detailed implementation manners

[0102] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. Components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present invention provided in the accompanying drawings here is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0103] Embodiment 1

[0104] For ease of understanding of this application, the following will combine Figure 1 the content described in the flowchart of a welding robot control method provided in Embodiment 1 of the present invention shown below to describe Embodiment 1 of this application in detail.

[0105] See Figure 1 as shown below, Figure 1 which shows a flowchart of a welding robot control method provided in Embodiment 1 of the present invention. Among them, applied to a robot processor, the method includes steps S101 to S105:

[0106] S101: Obtain the current pose of the end effector of the robot. Among them, the robot further includes a mobile base and a robotic arm provided on the mobile base, the end effector is provided at the end of the robotic arm, and a plurality of movable joints are provided on the mobile base and the robotic arm, and each movable joint is used to drive the end effector to adjust its pose.

[0107] Specifically, the real-time pose of the end effector is obtained through an integrated sensor system and positioning technology. The mobile base of the robot is a two-wheel differential drive chassis, on which a 7-degree-of-freedom robotic arm is installed. At the end of the robotic arm, an end effector for performing welding tasks, that is, a welding torch, is provided. Specifically, the mobile chassis uses the built-in LiDAR sensor to perform simultaneous localization and mapping to achieve global environment perception and self-position calibration; receives the angles of each axis fed back by the robotic arm through joint encoders, and combines its kinematic model to calculate the position and pose of the end effector in the base coordinate system. The robot processor combines computer vision technology to identify and position the welding target, further correcting the pose data of the end effector to ensure that the obtained current pose (including three-dimensional coordinates and spatial orientation) accurately reflects its actual state in the world coordinate system, providing reliable initial parameters for subsequent control.

[0108] S102: Determine the pose control speed of the end effector based on the current pose and the target pose of the end effector.

[0109] Specifically, first convert the current pose and the target pose (generated by welding path planning) into homogeneous transformation matrices, obtain the pose error through matrix operations, and use position-based servo control (PBS) to convert the pose error into an initial spatial velocity (including linear velocity and angular velocity).

[0110] To adapt to the requirements of welding tasks, two constraints are introduced: one is the constant welding speed constraint, by superimposing a constant linear velocity component (such as 5 mm / s) aligned with the tangential direction of the welding path, to ensure that the end effector moves stably along the weld direction at a set speed, avoiding uneven speeds caused by fluctuations in pose error; the other is the attitude alignment constraint, align the tool axis of the welding torch (such as the z-axis) with the path tangent direction, and the normal axis (such as the x-axis) with the weld normal, and adjust the target attitude through a rotation matrix to ensure the consistency of the welding angle. The finally generated pose control speed is a comprehensive speed command that combines the position convergence requirement and welding process parameters, providing a target orientation for subsequent joint speed optimization.

[0111] S103: Construct an optimization objective function, and optimize the pose change speed based on the optimization objective function to obtain the joint control speed of each movable joint.

[0112] Specifically, in this step, the pose control speed of the end effector is mapped to the motion commands of each joint through a quadratic programming (QP) algorithm. First, construct an optimization objective function that includes joint speed smoothness, maximization of maneuverability, and auxiliary tasks (such as obstacle avoidance) to minimize joint motion energy consumption and sudden impacts; at the same time, set multi-dimensional constraint conditions, including:

[0113] Kinematic constraints establish the mapping relationship between the end - effector velocity and joint velocities through the overall manipulator Jacobian matrix, ensuring the coordinated linkage between the chassis virtual joints (corresponding to the movement of the mobile chassis) and the actual joints of the robotic arm; physical limit constraints restrict the joint velocities and positions within a safe range to avoid mechanical overload or excessive movement; task - priority constraints, such as dynamically adding obstacle - avoidance logic in path planning, adjust joint movements to avoid risks when obstacles are detected. By solving this QP problem, the spatial velocity requirements of the end - effector are decomposed into the angular velocities of the chassis wheels (based on the unicycle kinematic model) and the angular velocities of the joints of the robotic arm, generating joint control velocities that meet multi - objective optimization and realizing the flexible linkage between the mobile chassis and the robotic arm.

[0114] S104: Control each movable joint to move at its corresponding joint control velocity to adjust the pose of the end - effector.

[0115] Specifically, in the real - time control stage, the joint velocity commands are executed at a high - frequency control period (such as 200Hz) to drive the coordinated movement of the mobile chassis and the robotic arm. The chassis adjusts the wheel speeds according to the virtual joint velocities through the differential or omnidirectional motion model to achieve translational / rotational position and pose; the robotic arm synchronously adjusts the joint angles according to the angular velocities of each axis to ensure that the end - effector torch moves smoothly along the planned path.

[0116] During the process, the pose error and joint states are monitored in real - time through a feedback mechanism. If path occlusion or error exceeds the limit occurs, an exception - handling logic is triggered: the former dynamically avoids obstacles through QP constraints, and the latter pauses the welding and replans the path.

[0117] In an optional implementation, the current pose of the end - effector is:

[0118] 0 T e = 0 T b (x, y, θ) b T a a T e (k a , q a );

[0119] Wherein, 0 T e is the current pose of the end - effector relative to the world coordinate system, 0 T b is the pose of the mobile base relative to the world coordinate system, x is the abscissa of the mobile base, y is the ordinate of the mobile base, θ is the rotation angle of the mobile base, b T a is the pose of the robotic arm relative to the mobile base,a T e is the pose of the end effector relative to the robotic arm, k a are the kinematic parameters of the robotic arm, q a are the joint angles of the robotic arm.

[0120] Specifically, a simulation model of a welding robot is also constructed. The model uses an omnidirectional chassis (implemented by Mecanum wheels) to demonstrate the overall operation effect of a welding robot with omnidirectional movement ability. The robotic arm and the simulation model of the welding robot are both provided as part of the Python Robotics Toolbox to facilitate research on control and path planning.

[0121] In an alternative embodiment, refer to Figure 2 as shown Figure 2 shows a flowchart of a method for determining the pose control speed provided in Embodiment 1 of the present invention. Among them, determining the pose control speed of the end effector based on the current pose and the target pose of the end effector includes steps S201 to S202:

[0122] S201: Determine the pose change speed according to the current pose and the target pose.

[0123] Specifically, by quantifying the difference between the current pose and the target pose, a basic speed command for driving the movement of the end effector is generated. In specific implementation, first, the current pose 0 T e of the end effector relative to the world coordinate system is b T e converted to the pose b T e of the end effector relative to the mobile base. The pose of the end effector relative to the world coordinate system and the target pose of the end effector relative to the mobile base are represented as homogeneous transformation matrices (4×4 matrices). Through matrix inversion and multiplication operations, a pose error matrix is obtained.

[0124] First, according to the target pose of the end effector relative to the mobile base, a welding path point sequence is generated and the tangential vector of each point is calculated Example: For a straight weld, is a constant; for a curved weld, it needs to be updated in real time During this process, the z-axis (tool direction) of the welding torch is always aligned with the path tangent while the x-axis is aligned with the weld normal. The attitude part is adjusted through the rotation matrix to ensure consistent welding angles.

[0125] S202: Perform speed constraint and direction constraint on the pose change speed to obtain the pose control speed.

[0126] Specifically, the basic speed command is corrected by superimposing process constraints to ensure that the welding process meets the process requirements. In terms of speed constraint, a constant linear speed v weld (such as 3 mm / s) required by the welding process is introduced, and it is projected onto the tangential direction of the welding path through a weight factor λ (dynamic adjustment range 0.8 - 1.2) to form an additional speed component

[0127] After this component is superimposed with the basic speed, the end effector can maintain a stable welding speed when approaching the target pose, avoiding uneven welds caused by deceleration. The direction constraint is achieved through an attitude alignment algorithm, aligning the welding torch tool axis (such as the z-axis) with the path tangent direction and the normal axis (such as the x-axis) with the weld normal. The target attitude is adjusted through the rotation matrix to ensure that the welding angle error is controlled within the expectation.

[0128] In an optional implementation, determining the pose change speed according to the current pose and the target pose includes:

[0129] Determine the pose change speed according to the following expression

[0130]

[0131] where γ is the proportional gain used to control the convergence speed; vec(·) is a function that converts pose error into spatial speed; b T e is the current pose; is the target pose.

[0132] Specifically, this expression quantifies the difference between the current pose and the target pose and converts it into the pose change speed of the end effector Its core function is to provide a basic speed command for robot control, driving the end effector to move from the current pose to the target pose, ensuring that the robot can dynamically adjust the movement speed according to the pose error, laying a foundation for subsequent speed constraint, direction constraint, and joint speed optimization, and ultimately achieving precise adjustment of the end effector pose to meet the requirements of position and attitude accuracy for tasks such as welding.

[0133] In an alternative embodiment, the obtaining of the pose control velocity by performing velocity constraint and direction constraint on the pose change velocity includes:

[0134] Determining the pose control velocity according to the following expression

[0135]

[0136] wherein, is the pose change velocity; λ is the velocity weight factor; v weld is the constant linear velocity required by the welding process; is the unit vector in the tangential direction of the welding path.

[0137] Specifically, the function of this expression is to synthesize the pose error convergence requirement and the welding process constraint, so that during the movement of the end effector towards the target pose, its movement speed and direction simultaneously meet the welding process requirements. Specifically, it can not only ensure that the end effector gradually approaches the target pose (driven by ), but also ensure that it moves along the tangential direction of the welding path at a constant linear velocity v weld (guaranteed by ), thereby improving the welding quality, avoiding welding defects caused by unstable speed or direction deviation, and realizing the organic unity of pose adjustment and welding process requirements.

[0138] Furthermore, the end effector is forced to move along the path direction at a set speed through the additional term . Dynamically adjust λ so that the linear velocity component of the synthesized velocity always satisfies v weld .

[0139] Example: If the initial velocity generated based on position-based servo (PBS) is then the velocity is corrected to

[0140]

[0141] wherein, θ is the included angle between and .

[0142] In an alternative embodiment, as shown in Figure 3 , Figure 3 shows a flowchart of a method for constructing an optimization objective function provided in Embodiment 1 of the present invention. The constructing of the optimization objective function includes steps S301 to S303:

[0143] S301: Performing differential dynamics modeling on the current pose of the end effector to obtain the differential dynamics model of the end effector.

[0144] Specifically, based on differential kinematics, the base velocity and joint angular velocity are determined as well as the spatial velocity v of the end effector e The relationship between them is specifically realized by constructing a virtual moving base and introducing an infinitesimal transformation into the expression of the current pose of the end effector to represent the pose of the instantaneous motion of the base:

[0145]

[0146] where 0 T e is the current pose of the end effector relative to the world coordinate system, δ θ and δ d respectively represent the infinitesimal rotation and forward translation of the nonholonomic constraint base. It can be regarded as a simple rotational-translational robot, and its virtual joint coordinate q b is ‖q b ‖→0 and and where is the set of integers, 0 T0 is the pose of the moving base relative to the world coordinate system, b T b is the pose of the virtual moving base relative to the moving base, b T a is the pose of the robotic arm relative to the virtual moving base, a T e is the pose of the end effector relative to the robotic arm, k a is the kinematic parameter of the robotic arm, q a is the joint angle of the robotic arm.

[0147] The finally obtained expression is similar to the forward kinematics of a serial chain robotic arm, and the differential kinematics is expressed as:

[0148]

[0149] This equation maps the velocities 0 υ e of each axis of the mobile manipulation system to the spatial velocity of the end effector, where the joint angle n is the number of virtual joints and actual joints, 02 is a matrix, q a is the joint angle of the robotic arm. The overall manipulator Jacobian matrix 0 J e (·) is expressed in the world coordinate system. The relationship between the velocity of the virtual base joint and the angular velocity of the wheel is:

[0150]

[0151] where R is the radius of the wheel, are the angular velocities of the right and left wheels of the robot respectively, W is the distance between the two wheels, is the differential angular velocity of q b and is the differential linear velocity of q b For some control behaviors, it is useful to express velocities in different coordinate systems. For example, in the base coordinate system or the end-effector coordinate system, the corresponding Jacobian matrices b J e (q) and e J e (q) can be configured respectively.

[0152] S302: According to the differential dynamics model, construct a motion controller for solving the joint control velocity through the Jacobian matrix.

[0153] Specifically, use the overall manipulator Jacobian matrix to construct a motion controller for solving the target joint velocity at time t:

[0154]

[0155] where (·) + represents the Moore-Penrose pseudoinverse, is the joint velocity that enables the end-effector of the mobile manipulation system to achieve the required spatial velocity, and the target velocity This controller represents the most basic reactive and overall control form of the mobile manipulation system, v x is the linear velocity in the x direction, v y is the linear velocity in the y direction, v z is the linear velocity in the z direction, ω x is the angular velocity in the x direction, ω y is the angular velocity in the y direction, ω z is the angular velocity in the z direction. represents the target pose of the end-effector relative to the mobile base at time t.

[0156] The target joint velocity is decomposed into the base virtual joint velocity These velocities are converted into the velocities of the left and right wheels through the unicycle kinematic model (using the relationship between the velocities of the aforementioned virtual base joints and the angular velocities of the wheels), while corresponds to the joint velocity of the robotic arm.

[0157] S303: Introduce a slack vector into the motion controller to obtain the optimization objective function.

[0158] Specifically, to achieve the overall control of the mobile manipulator, the controller outputs joint velocities to achieve the target velocity of the desired end effector minus a slack vector (intentional error deviation) δ, where the slack components are used to allow the controller to better minimize the cost and meet specific constraints.

[0159] In an alternative embodiment, the optimization objective function is:

[0160]

[0161] where x = (q, δ) T is the decision variable, i.e., the joint control velocity, Q includes the joint control velocity and the slack vector, and C includes maximizing the manipulability and auxiliary performance tasks; f o (x) is the expression of the optimization objective function, q is the joint angle, and δ is the differential of the joint angle.

[0162] Specifically, by comprehensively considering the joint control velocity, the slack vector, the manipulability, and the auxiliary tasks, this function provides an optimized decision for robot control. During the robot's motion, it can balance various factors: using Q to constrain the change range of the joint control velocity to ensure smooth motion and reduce mechanical losses; through C, it preferentially satisfies the manipulability and auxiliary tasks (such as avoiding obstacles in a complex path), enabling the robot to find the optimal motion plan in multi-objective scenarios (such as accurately reaching the pose during welding while avoiding surrounding components), and improving the overall control performance and task execution quality.

[0163] In an alternative embodiment, as shown in Figure 4 shown, Figure 4 FIG. shows a schematic diagram of the pose adjustment process of an end effector provided in Embodiment 1 of the present invention. Among them, optimizing the pose change velocity based on the optimization objective function to obtain the joint control velocities of each movable joint includes:

[0164] Combining the optimization objective function with the pose change velocity to construct a constrained optimization problem.

[0165] Specifically, the optimization objective function is combined with the pose change speed to construct a constrained optimization problem: In this step, the optimization objective function including elements such as joint speed, relaxation vector, maximization of manipulability, and auxiliary performance tasks is associated and integrated with the pose change speed determined based on the current pose and the target pose. By establishing a mathematical connection between the two, a comprehensive constrained optimization model framework is formed, enabling subsequent optimization to not only meet the speed requirements of end-effector pose control but also take into account other optimization objectives, laying a foundation for precise control of joint motion.

[0166] The aforementioned optimization objective function is:

[0167]

[0168] Add the acceleration constraint \(a\) to the above optimization objective function max or the jerk limit \(j\) max :

[0169]

[0170] where is the joint angular acceleration, and the constrained optimization problem is extended through the optimization variable is the joint angular velocity to achieve smooth motion.

[0171] Set the constraint conditions, and solve the constrained optimization problem according to each constraint condition to obtain the joint control speeds of each movable joint.

[0172] Specifically, set the constraint conditions, and solve the constrained optimization problem according to each constraint condition to obtain the joint control speeds of each movable joint: This step ensures that the solution process conforms to the principles of robot kinematics and physical limitations by introducing kinematic constraints (such as the relationship constructed by the augmented manipulator Jacobian matrix joint position limit avoidance constraints (implemented by \(A\) and ) and decision variable range constraints (\(X\) - ≤ \(x\) ≤ \(X\) + ). Under these constraints, use an optimization algorithm to solve the constructed constrained optimization problem, and finally obtain the control speeds of each movable joint, so that the joint motion not only meets the requirements of end-effector pose control but also ensures the safe and stable operation of the robot, achieving precise control of multi-objective coordination.

[0173] The said constraint conditions include:

[0174]

[0175] X - ≤ \(x\) ≤ \(X\) + ;

[0176] Among them, is the kinematic constraint, J is the preset Jacobian matrix, x is the joint control speed, is the target speed of the end effector, A and are used to implement the joint position limit constraint, A and are both preset values, X - ≤x≤X + is the decision variable range constraint, X + is the maximum value of the decision variable, X - is the minimum value of the decision variable.

[0177] In the kinematic constraint, J is the augmented manipulator Jacobian matrix, which establishes the mapping relationship between the decision variable x (including information such as joint control speed) and the target speed of the end effector. Through , the pose control speed requirements of the end effector are accurately mapped to the motions of each joint, ensuring that the end can move at the desired speed and trajectory, meeting the accuracy requirements for position and pose in tasks such as welding.

[0178] In the joint position limit avoidance constraint, the matrix A and the vector define the constraint conditions related to the joint position. With the help of and X - ≤x≤X + , it is prevented that the joints exceed the position limit or other parameters such as speed exceed the limit, avoiding mechanical structure damage, prolonging the service life of the robot, and reducing safety accidents caused by abnormal motion at the same time. In the decision variable range constraint, the value range of the decision variable x is limited, X + is the maximum value of the decision variable, X - is the minimum value of the decision variable. Through the above constraints, it is ensured that parameters such as the joint control speed are within a safe and reasonable range, enabling the end controller to determine the joint control speed x from the initial pose according to its desired end pose (target pose ), as well as the QP optimization objective (optimization objective function) and the limiting conditions (constraint conditions), and moving at each joint control speed to achieve each joint angle q, and then achieving the target pose, avoiding control instability caused by excessive speed or other parameter abnormalities.

[0179] Combining the optimization objective function with the pose change speed to construct a constrained optimization problem is essentially, on the premise of satisfying the robot kinematic relationship, joint physical limitations, and safe operating range, to solve the control speed of each movable joint through an optimization algorithm, so that the robot movement not only meets the task requirements (such as end - pose control), but also ensures the safety and stability of the system. By combining the optimization objective function (such as considering joint speed smoothness, maximization of manipulability, etc.) and solving the joint control speed under the above - mentioned constraints, the robot can take into account multiple performance indicators during the movement process and improve the overall control performance. For example, it can achieve smooth and efficient movement in a complex welding path.

[0180] In an alternative embodiment, the end - effector is a welding torch; after controlling each movable joint to move at its corresponding joint control speed to adjust the pose of the end - effector, the method further includes:

[0181] Performing a welding task through the end - effector.

[0182] Specifically, after the end - effector completes the pose adjustment, control the welding torch to perform the welding task: when the pose adjustment of the mobile base and the robotic arm is completed through the aforementioned control method, and the welding torch reaches the expected welding position and posture, the system sends an instruction to the welding torch to start the welding operation according to the preset welding process parameters (such as current, voltage, welding speed, etc.), and perform the welding operation on the workpiece, thereby transforming the pose control result into actual welding processing and realizing the complete process from pose adjustment to task execution.

[0183] In practical applications, the welding robot is located at the origin of coordinates, and the end of the welding torch is about 0.5 meters above the ground. The robot base is designed with wheels and can move on a plane. The welding torch is located on the robotic arm at a certain angle and is not in contact with the ground currently. The purpose of the experiment is to precisely move the end of the welding torch from the current position to the target point (4.5, 0, 0) by controlling the robot's movement, that is, to translate 4.5 meters along the X - axis direction while keeping the Y - axis and Z - axis coordinates unchanged. The relative position between the target point and the robot helps to observe the robot's movement trajectory. Study the precise positioning, path planning, and task execution capabilities of the mobile robotic arm, especially when performing complex tasks such as welding, to ensure the stability, accuracy, and response speed of the robot during operation.

[0184] See Figure 5 as shown Figure 5 shows a schematic diagram of the change in the movement path of the end of the welding torch before optimization and the influence of joint speed gain on the end trajectory provided by Embodiment 1 of the present invention. See Figure 6 as shown Figure 6Fig. shows the change of the optimized motion path of the torch end in Embodiment 1 of the present invention and the influence of joint speed gain on the end trajectory. Here, the horizontal axis represents the moving distance in the X direction (m), and the vertical axis represents the height of the torch end (m). The target coordinate reached by the torch end is (4.5, 0.1). The black dashed line represents the shortest path, the blue curve is the path before optimization, and the red curve represents the optimized path. It can be seen from the figure that the torch end only starts to move when the chassis reaches 4 m during the movement, which is manifested as a sudden change in the path. In contrast, the red curve represents the optimized path, and the movement process of the torch end becomes smooth and continuous, indicating that through QP optimization, the movement of the robot is more stable and efficient, avoiding unnecessary mutations. Figure 5 The influence of different joint speed gains on the end trajectory is also analyzed. By comparing the cases of different gain values Y (0.0001, 0.01, 0.1, 1.0 respectively), it is found that when the joint speed gain is set to 0.0001 (the trajectory corresponds to the red curve) and 0.01 (the trajectory corresponds to the green curve), the trajectory maintains a smooth curve, showing the optimal control effect; while when the gain increases to 1.0 (the trajectory corresponds to the purple curve) and 0.1 (the trajectory corresponds to the blue curve), the trajectory shows an out-of-control phenomenon, indicating that too high a joint speed gain will lead to unstable movement of the robot. Therefore, choosing an appropriate gain value is crucial for ensuring precise control of the end effector. Experimental verification shows that this control method can significantly reduce the mutation of the end trajectory, ensure that the torch maintains a stable welding speed and posture during complex movements, improve the welding accuracy and efficiency of large-scale and irregular welds, and is especially suitable for scenarios with high requirements for flexibility and precision such as shipbuilding and steel structure processing.

[0185] See Figure 7 as shown Figure 7 Fig. shows a schematic diagram of the movement trajectory of the torch end provided in Embodiment 1 of the present invention. See Figure 8 as shown Figure 8 Fig. shows a schematic diagram of the second movement trajectory of the torch end provided in Embodiment 1 of the present invention, where the path of the torch during the movement of the robot is represented by a yellow line. Figure 7 and Figure 8 respectively present the motion characteristics of the robot during the welding task from different perspectives. In Figure 7 , it can be observed that the chassis and the robotic arm of the robot move in coordination, making the trajectory of the torch end present a smooth curve rather than a sudden linear change. This indicates that the linkage algorithm makes real-time adjustments to the robotic arm during the movement of the robot, keeping the torch on a stable movement path. Figure 7Further shows the trajectory of the robot from different angles, confirming that the movement trajectory of the welding torch is continuous and natural throughout the process. Compared with the situation without using the linkage algorithm, this method can effectively reduce the drastic adjustment at the end of the welding torch, enabling it to maintain a stable trajectory throughout the welding task and improving welding accuracy and motion stability.

[0186] See Figure 9 as shown Figure 9 shows a simulation schematic diagram of a welding robot provided in Embodiment 1 of the present invention for a long straight weld. Among them, the robot welds along a straight line trajectory to ensure the continuity and strength of the weld. See Figure 10 as shown Figure 10 shows a simulation schematic diagram of a welding robot provided in Embodiment 1 of the present invention for vertical weld welding. Among them, the welding torch moves stably in the vertical direction to achieve uniform welding fusion. See Figure 11 as shown Figure 11 shows a simulation schematic diagram of a welding robot provided in Embodiment 1 of the present invention for vertical welding. See Figure 12 as shown Figure 12 shows a simulation schematic diagram of a welding robot provided in Embodiment 1 of the present invention for overhead welding. Among them, intermittent arc extinguishing is used to reduce the use of welding materials, while optimizing the heat input to prevent the workpiece from deforming due to high temperature. See Figure 13 as shown Figure 13 shows a simulation schematic diagram of a welding robot provided in Embodiment 1 of the present invention for fillet welds. See Figure 14 as shown Figure 14 shows a simulation schematic diagram of a second welding robot provided in Embodiment 1 of the present invention for fillet welds. Among them, an intermittent welding strategy is adopted to ensure sufficient connection strength between welding points, while reducing the welding time and improving production efficiency. Overall, this experiment shows that the stitch welding technology can effectively reduce material consumption, optimize heat management, and significantly improve the overall efficiency of the welding operation while ensuring welding quality.

[0187] Embodiment 2

[0188] See Figure 15 as shown Figure 15 shows a structural schematic diagram of a robot processor provided in Embodiment 2 of the present invention. Among them, the robot processor includes:

[0189] A current pose acquisition module 1501, configured to acquire the current pose of the end effector. Among them, the end effector includes a mobile base and a robotic arm disposed on the mobile base. A plurality of movable joints are provided on the mobile base and the robotic arm, and each movable joint is used to drive the end effector to perform pose adjustment;

[0190] The pose control speed determination module 1502 is configured to determine the pose control speed of the end effector based on the current pose and the target pose of the end effector;

[0191] The joint control speed determination module 1503 is configured to construct an optimization objective function and optimize the pose change speed based on the optimization objective function to obtain the joint control speeds of the movable joints;

[0192] The pose adjustment module 1504 is configured to control the movable joints to move at their corresponding joint control speeds to adjust the pose of the end effector.

[0193] In an optional implementation, the current pose of the end effector is:

[0194] 0 T e = 0 T b (x, y, θ) b T a a T e (k a ,q a );

[0195] Wherein, 0 T e is the current pose of the end effector relative to the world coordinate system, 0 T b is the pose of the mobile base relative to the world coordinate system, x is the abscissa of the mobile base, y is the ordinate of the mobile base, θ is the rotation angle of the mobile base, b T a is the pose of the robotic arm relative to the mobile base, a T e is the pose of the end effector relative to the robotic arm, k a is the kinematic parameter of the robotic arm, q a is the joint angle of the robotic arm.

[0196] In an optional implementation, the determining the pose control speed of the end effector based on the current pose and the target pose of the end effector includes:

[0197] Determining a pose change speed according to the current pose and the target pose;

[0198] Performing speed constraint and direction constraint on the pose change speed to obtain the pose control speed.

[0199] In an alternative embodiment, determining the pose change speed according to the current pose and the target pose includes:

[0200] Determining the pose change speed according to the following expression

[0201]

[0202] where γ is a proportional gain (scalar) for controlling the convergence speed; vec(·) is a function for converting pose error into spatial speed; b T e is the current pose; is the target pose.

[0203] In an alternative embodiment, performing speed constraint and direction constraint on the pose change speed to obtain the pose control speed includes:

[0204] Determining the pose control speed according to the following expression

[0205]

[0206] where is the pose change speed; λ is a speed weight factor; v weld is the constant linear speed required by the welding process; is the unit vector in the tangential direction of the welding path.

[0207] In an alternative embodiment, constructing the optimization objective function includes:

[0208] Performing differential dynamics modeling on the current pose of the end effector to obtain the differential dynamics model of the end effector;

[0209] According to the differential dynamics model, constructing a motion controller for solving the joint control speed through the Jacobian matrix;

[0210] Introducing a slack vector into the motion controller to obtain the optimization objective function.

[0211] In an alternative embodiment, the optimization objective function is:

[0212]

[0213] where x is the decision variable, i.e., the joint control speed, x = (q, δ) T , q is the joint angle, δ is the differential of the joint angle, Q includes the joint control speed and the slack vector, and C includes maximizing the manipulability and the auxiliary performance tasks.

[0214] In an alternative embodiment, optimizing the pose change speed based on the optimization objective function to obtain the joint control speeds of the movable joints includes:

[0215] Combining the optimization objective function with the pose change speed to construct a constrained optimization problem;

[0216] Setting constraint conditions, and solving the constrained optimization problem according to the constraint conditions to obtain the joint control speeds of the movable joints.

[0217] The constraint conditions include:

[0218]

[0219] X - ≤x≤X + ;

[0220] where J is the Jacobian matrix, x is the joint control speed, is the target speed of the end effector, A and are both preset values for avoiding joint position limits, X + is the maximum value of the decision variable, and X - is the minimum value of the decision variable.

[0221] In an alternative embodiment, the end effector is a welding torch; after controlling the movable joints to move at their corresponding joint control speeds to adjust the pose of the end effector, the method further includes:

[0222] Performing a welding task through the end effector.

[0223] Embodiment III

[0224] Based on the same application concept, as shown in Figure 16 shown, Figure 16 shows a schematic structural diagram of a computer device provided in Embodiment III of the present invention, where, as Figure 16 shown, a computer device 1600 provided in Embodiment III of the present application includes:

[0225] A processor 1601, a memory 1602, and a bus 1603. The memory 1602 stores machine-readable instructions executable by the processor 1601. When the computer device 1600 runs, the processor 1601 communicates with the memory 1602 through the bus 1603. When the machine-readable instructions are run by the processor 1601, the steps of the welding robot control method shown in the above Embodiment I are executed.

[0226] Embodiment IV

[0227] Based on the same application concept, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the welding robot control method described in any one of the above embodiments.

[0228] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0229] The computer program product for controlling a welding robot provided by an embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the foregoing method embodiments. For specific implementation, reference can be made to the method embodiments, and will not be described herein again.

[0230] The robot processor provided by an embodiment of the present invention can be specific hardware on a device or software or firmware installed on the device. The implementation principle and the technical effects produced by the robot processor provided by an embodiment of the present invention are the same as those of the foregoing method embodiments. For a brief description, for the parts not mentioned in the robot processor embodiment, reference can be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the foregoing described systems, devices, and units can all refer to the corresponding processes in the above method embodiments, and will not be described herein again.

[0231] In the embodiments provided by the present invention, it should be understood that the disclosed robot processor and method can be implemented in other ways. The robot processor embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0232] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0233] In addition, each functional unit in the embodiments provided by the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

[0234] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0235] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0236] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, and are not intended to limit them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A control method for a welding robot, characterized in that, Applied to a robot processor, the method includes: Obtain the current pose of the end effector of the robot. The robot further includes a mobile base and a robotic arm disposed on the mobile base. The end effector is disposed at the end of the robotic arm. A plurality of movable joints are provided on the mobile base and the robotic arm, and each movable joint is used to drive the end effector for pose adjustment; Determine the pose control speed of the end effector based on the current pose and the target pose of the end effector; Construct an optimization objective function, and optimize the pose change speed based on the optimization objective function to obtain the joint control speeds of the respective movable joints; Control each movable joint to move at its corresponding joint control speed to adjust the pose of the end effector.

2. The method according to claim 1, wherein The current pose of the end effector is: 0 T e = 0 T b (x, y, θ) b T a a T e (k a , q a ) Among them, 0 T e is the current pose of the end effector relative to the world coordinate system, 0 T b is the pose of the mobile base relative to the world coordinate system, x is the abscissa of the mobile base, y is the ordinate of the mobile base, and θ is the rotation angle of the mobile base, b T a is the pose of the robotic arm relative to the mobile base, a T e is the pose of the end effector relative to the robotic arm, k a are the kinematic parameters of the robotic arm, q a are the joint angles of the robotic arm.

3. The method according to claim 1, wherein The determining the pose control speed of the end effector based on the current pose and the target pose of the end effector includes: Determine the pose change speed according to the current pose and the target pose; Perform speed constraint and direction constraint on the pose change speed to obtain the pose control speed.

4. The method according to claim 3, wherein The determining the pose change speed according to the current pose and the target pose includes: Determine the pose change speed according to the following expression Among them, γ is the proportional gain used to control the convergence speed; vec(·) is a function that converts the pose error into a spatial velocity; b T e is the current pose; is the target pose.

5. The method according to claim 3, characterized in that, The performing speed constraint and direction constraint on the pose change speed to obtain the pose control speed includes: Determine the pose control speed according to the following expression Among them, is the pose change speed; λ is the speed weight factor; v weld is the constant linear speed required by the welding process; is the unit vector in the tangential direction of the welding path.

6. The method according to claim 1, wherein The constructing the optimization objective function includes: Perform differential dynamics modeling on the current pose of the end effector to obtain the differential dynamics model of the end effector; According to the differential dynamics model, construct a motion controller for solving the joint control speed through the Jacobian matrix; Introduce a slack vector into the motion controller to obtain the optimization objective function.

7. The method according to claim 1, wherein The optimization objective function is: where x is the decision variable, i.e., the joint control speed, x = (q, δ), q is the joint angle, δ is the differential of the joint angle, Q includes the joint control speed and the slack vector, and C includes maximizing maneuverability and auxiliary performance tasks. T , q is the joint angle, δ is the differential of the joint angle, Q includes the joint control speed and the slack vector, and C includes maximizing maneuverability and auxiliary performance tasks.

8. The method according to claim 1, characterized in that, The optimizing the pose change speed based on the optimization objective function to obtain the joint control speeds of the respective movable joints includes: Combine the optimization objective function with the pose change speed to construct a constrained optimization problem; Set constraint conditions, and solve the constrained optimization problem according to each constraint condition to obtain the joint control speeds of the respective movable joints. The constraint conditions include: X - ≤x≤X + ; Among them, J is the Jacobian matrix, x is the joint control speed, is the target speed of the end effector, A and are both preset values for achieving joint position limit avoidance, X + is the maximum value of the decision variable, X - is the minimum value of the decision variable.

9. The method according to claim 1, characterized in that, The end effector is a welding torch; After controlling each movable joint to move at its corresponding joint control speed to adjust the pose of the end effector, the method further includes: Execute a welding task through the end effector.

10. A robot processor, characterized in that, The robot processor includes: A current pose acquisition module, configured to obtain the current pose of the end effector of the robot. The robot further includes a mobile base and a robotic arm disposed on the mobile base. The end effector is disposed at the end of the robotic arm. A plurality of movable joints are provided on the mobile base and the robotic arm, and each movable joint is used to drive the end effector for pose adjustment; A pose control speed determination module, configured to determine the pose control speed of the end effector based on the current pose and the target pose of the end effector; A joint control speed determination module, configured to construct an optimization objective function, and optimize the pose change speed based on the optimization objective function to obtain the joint control speeds of the movable joints; A pose adjustment module, configured to control each movable joint to move at its corresponding joint control speed to adjust the pose of the end effector.

11. A computer device, characterized in that, Comprising: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the welding robot control method according to any one of claims 1 to 9 are executed.

12. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is run by the processor, the steps of the welding robot control method according to any one of claims 1 to 9 are executed.

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