A control method and system of a spraying robot based on Mecanum wheels

Abstract

This invention provides a control method and system for a painting robot based on Mecanum wheels, belonging to the field of painting robot control technology. The method includes: acquiring the target painting path and constructing a cooperative control law; constructing a closed-loop interaction model to extract the dynamic interaction features of the system and calculate the interaction stability margin; running a dynamically weighted real-time stability evaluation model based on the interaction stability margin, dynamically weighting the overturning risk and slip risk of the system according to the current working state to obtain the interaction stability margin value, and executing a single-point hard failure rejection mechanism to determine whether the output of the dynamically weighted real-time stability evaluation model is safe; under the constraint that the output is safe, constructing a Cartesian space trajectory according to the target painting path and fitting it to generate a continuous trajectory, and combining the angular velocity of the robotic arm joints and the chassis speed of the Mecanum wheels issued by the cooperative control law to cooperatively control the painting robot to complete the painting operation. The painting robot of this invention has high mobility and cooperative control accuracy.

Description

Technical Field

[0001] This invention relates to the field of spraying robot control technology, and in particular to a spraying robot control method and system based on Mecanum wheels. Background Technology

[0002] In existing indoor painting operations, traditional robots mostly use fixed chassis or non-omnidirectional moving chassis, which suffer from insufficient mobility and limited working range. When the robotic arm operates alone, its working space is limited, and it is prone to reaching its movement limits when facing large areas or complex-shaped walls, resulting in interrupted spraying paths and uneven coatings. Mecanum wheels, as the core component for omnidirectional movement, have three degrees of freedom of movement: forward and backward, left and right, and rotation, enabling flexible movement in confined spaces.

[0003] However, most painting robots currently do not fully utilize the omnidirectional characteristics of Mecanum wheels and lack a deep collaborative control strategy between the chassis and the robotic arm, resulting in low overall operational accuracy and efficiency. Furthermore, traditional control methods are generally open-loop or static evaluations, failing to consider the closed-loop interaction between the robotic arm, chassis, lifting platform, and environment. They also lack the ability to dynamically adjust control weights based on operational conditions, making them prone to tipping, slippage, or trajectory deviations in high-altitude operations, confined spaces, or under conditions of paint hose dragging. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a control method and system for a painting robot based on Mecanum wheels, so as to solve the problems of insufficient mobility, low collaborative control accuracy and dynamic instability of traditional painting robots.

[0005] This invention provides a control method for a painting robot based on a Mecanum wheel, comprising: The target spraying path is obtained, and a cooperative control law is constructed by combining the Mecanum wheel chassis and the robotic arm to allocate the joint angular velocity of the robotic arm and the speed of the Mecanum wheel chassis according to the cooperative control law. A closed-loop interaction model is constructed to extract the dynamic interaction features of the system and calculate the interaction stability margin. The closed-loop interaction model introduces distance constraint interaction force, posture constraint interaction torque, and paint pipe dragging torque as core evaluation variables to quantify the real-time coupling effect of the dynamic motion of the robotic arm and the extension and retraction of the paint pipe on the chassis. The closed-loop interaction model includes the constraint forces between the robotic arm, slide rail, chassis, and working environment. Run a dynamically weighted real-time stability assessment model based on the aforementioned interactive stability margin, and dynamically weight the overturning risk and slip risk of the system according to the current operating status to obtain the interactive stability margin value. At the same time, execute a single-point hard failure veto mechanism; then determine whether the output of the dynamically weighted real-time stability assessment model is safe based on the interactive stability margin value. Under the constraint that the output of the dynamic weighted real-time stability evaluation model is safe, a Cartesian space trajectory is constructed according to the target spraying path and a continuous trajectory is generated by fitting. Combined with the angular velocity of the robotic arm joints and the speed of the Mecanum wheel chassis issued by the cooperative control law, the spraying robot is cooperatively controlled to complete the spraying operation.

[0006] The aforementioned Mecanum wheel-based control method for painting robots establishes a precise Mecanum wheel chassis dynamics model and cooperative control law, and introduces the concept of interactive stability margin. It establishes a closed-loop interactive model that includes the mobile chassis, slide rails, robotic arm, and environmental constraints, transforming the traditional reactive response into proactive safety control. This not only solves the problems of the painting robot's mobility and cooperative accuracy, but also fundamentally addresses the dynamic instability of the robot in large-scale, high-altitude vertical spraying operations, significantly improving the system's autonomous disaster prevention capabilities and operational reliability.

[0007] In addition, according to the above-described control method for a painting robot based on a Mecanum wheel according to the present invention, it may also have the following additional technical features: Furthermore, the distance constraint interaction force is applied along the workpiece normal, and the calculation formula for the distance constraint interaction force is:

[0008]

[0009]

[0010] In the formula: For distance-constrained interaction forces, where, DCI It is an abbreviation for Distance Constrained Interactive, which means distance-constrained interaction; This is the distance stiffness coefficient; d is the distance damping coefficient; d is the spraying distance; This refers to distance deviation; The rate of change of distance deviation; For generalized coordinates The actual spraying distance varies; This represents the rate of change of a constant target spraying distance over time. To maintain a constant distance; This represents the rate of change of the actual spraying distance at the tip of the spray gun with respect to time. Generalized velocity representing the robot; The formula for calculating the attitude constraint interaction torque is as follows:

[0011]

[0012]

[0013] In the formula: ACI stands for Attitude Constraint Interaction, representing the interaction of attitude constraints. This refers to the attitude stiffness coefficient; This is the attitude damping coefficient; This is for attitude deviation; This refers to the actual attitude angle; To maintain a constant attitude; The rate of change of attitude deviation; This represents the rate of change of the actual attitude angle of the spray gun tip with respect to time; Generalized velocity representing the robot; This indicates the rate of change of the requirement for a constant attitude with respect to time; The dragging torque of the paint pipe is approximated using a linear model, wherein the calculation formula for the dragging torque of the paint pipe is:

[0014]

[0015] In the formula: This represents the dragging torque of the paint hose, where TD is an abbreviation for Tube Dragging, indicating the dragging of the paint hose. This is the drag coefficient for the paint hose; This is the current length of the paint pipe; This is the initial length; This represents the elongation of the lacquer tube relative to its initial length.

[0016] Furthermore, the execution logic of the dynamically weighted real-time stability evaluation model includes: Based on the operational status, the overturning assessment index and the slippage assessment index are dynamically weighted to obtain the overturning weight and the slippage weight; When the Mecanum wheel chassis is detected to be stationary and the extension range of the robotic arm exceeds 70% of the rated working radius of the robotic arm, the overturning weight is automatically increased; when the Mecanum wheel chassis is detected to be in a moving operation state, the sliding weight is forcibly increased; wherein, the sum of the overturning weight and the sliding weight is 1; The overturning assessment index and the slip assessment index are continuously monitored. When any assessment index reaches zero, a single-point hard failure veto mechanism is triggered, which forces the execution of reverse pose compensation or action freeze / retraction.

[0017] Furthermore, the methods for constructing cooperative control laws include: Define the objective function and set the constraints, and solve the optimal solution using the Lagrange multiplier method to obtain the chassis speed and the angular velocity of the robotic arm joints. The chassis speed includes forward and backward, left and right translational and rotational components, which correspond to the rotational speed and direction of the Mecanum wheel. The expression for the objective function is:

[0018] The constraint expression is:

[0019] In the formula, The angular velocity of the robotic arm joints; v b Chassis speed; W This is the chassis motion weighting coefficient; For the Jacobian matrix of the robotic arm; The Jacobian matrix represents the influence of the chassis on the final velocity. The desired velocity at the terminal; where, a It is an abbreviation for arm, meaning robotic arm; b It is an abbreviation for "base," meaning chassis. de "Desired" is an abbreviation for "desired," meaning something is expected or desired.

[0020] Furthermore, the collaborative control of the painting robot also includes boundary compensation, and the boundary compensation methods include: Establish a reference window for the workspace boundary of the robotic arm, and determine the boundary state of the robotic arm's workspace by calculating the statistics within the reference window. When the robot arm is detected to be approaching the limit position of the robot arm's workspace boundary state, the compensation movement of the Mecanum wheel chassis is automatically triggered to adaptively adjust the motion parameters of the Mecanum wheel. The methods for detecting and identifying extreme positions include: A multi-dimensional boundary reference window is established, and the monitoring dimensions of the multi-dimensional boundary reference window include the joint angle of the robotic arm, the position of the spray gun end, and the distance between the spray gun end and the wall. The ratio of the second-order statistic to the mean of the monitoring data within the multi-dimensional boundary reference window is calculated in real time to achieve quantitative judgment of the extreme state. When the statistic exceeds the preset threshold, the extreme position is reached.

[0021] Furthermore, the state detection method for the reference window includes: Define the reference unit as the angle range of each joint of the robotic arm, the end position limit, and the distance threshold from the wall to establish the boundary state reference window of the robotic arm's workspace. The second-order statistic and mean ratio within the reference window are calculated to obtain the reference window detection results. The second-order statistic is used to determine whether the joint state of the robotic arm is uniform, and the mean ratio is used to determine whether the states of the left and right reference units are consistent. When the reference window detection result exceeds the set threshold, the Mecanum wheel chassis compensation motion is triggered. The direction of the compensation speed is to reduce the load on the robotic arm, and its magnitude is proportional to the degree of saturation of the robotic arm.

[0022] Furthermore, in the step of generating the continuous trajectory of the spray gun at the end of the painting robot, the trajectory planning method includes: The spray gun trajectory is planned in Cartesian space using the linear interpolation method. The spray gun trajectory between the starting point and the target point is divided into several segments and several interpolation points are obtained. The coordinates of each interpolation point are calculated. The joint angles corresponding to each interpolation point are solved by the inverse kinematics equation of the robotic arm, and the joint space trajectory is fitted by a high-order polynomial interpolation method to ensure smooth motion. By combining the Mecanum wheel chassis compensation speed, the robot pose is updated in real time through a discrete-time model to ensure precise synchronization between chassis compensation motion and robotic arm motion. Among these, several interpolation points are non-uniformly spaced. In the step of dividing the spray gun trajectory between the starting point and the target point into several segments and obtaining several interpolation points, the method for dividing the interpolation points includes: When the planned path is in a wide redundancy area where the robotic arm and chassis work together, the interpolation point spacing is increased to reduce computation and improve work efficiency. When the planned path is in a region with strict process constraints, the interpolation point spacing is reduced, and high-order polynomial fitting is used to ensure smooth end-spray gun speed and uniform coating overlap. The wide redundancy area includes open and flat walls, while the region with strict process constraints includes narrow bottleneck areas.

[0023] Another aspect of the present invention provides a control system for a painting robot based on a Mecanum wheel, the system comprising: The acquisition module is used to acquire the target spraying path and combine the Mecanum wheel chassis and the robotic arm to construct a cooperative control law, so as to allocate the joint angular velocity of the robotic arm and the speed of the Mecanum wheel chassis according to the cooperative control law. The extraction module is used to construct a closed-loop interaction model to extract the dynamic interaction features of the system and calculate the interaction stability margin. The closed-loop interaction model introduces distance constraint interaction force, posture constraint interaction torque, and paint pipe dragging torque as core evaluation variables to quantify the real-time coupling effect of the dynamic motion of the robotic arm and the extension and retraction of the paint pipe on the chassis. The closed-loop interaction model includes the constraint forces between the robotic arm, slide rail, chassis, and working environment. The judgment module is used to run a dynamically weighted real-time stability assessment model based on the interactive stability margin, and dynamically weight the overturning risk and slip risk of the system according to the current operating status to obtain the interactive stability margin value, while executing a single-point hard failure veto mechanism; then, it judges whether the output of the dynamically weighted real-time stability assessment model is safe based on the interactive stability margin value. The spraying module is used to construct a Cartesian space trajectory and fit it to generate a continuous trajectory based on the target spraying path under the constraint that the output of the dynamic weighted real-time stability evaluation model is safe. Combined with the angular velocity of the robotic arm joints and the speed of the Mecanum wheel chassis issued by the cooperative control law, the module is used to cooperatively control the spraying robot to complete the spraying operation.

[0024] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described Mecanum wheel-based spraying robot control method.

[0025] In another aspect, the present invention provides a data processing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described Mecanum wheel-based spraying robot control method. Attached Figure Description

[0026] Figure 1 This is a flowchart of the painting robot control method based on Mecanum wheels in the first embodiment of the present invention; Figure 2 This is a simulation diagram of the spray gun end trajectory generated by spatial linear interpolation in the first embodiment of the present invention; The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0027] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] To address the shortcomings of traditional painting robots, such as insufficient mobility, failure of open-loop stability analysis in complex physical interactions, and inability of static evaluation to adapt to highly dynamic and variable operating conditions, this application provides a control method and system for a painting robot based on Mecanum wheels. Based on a precise chassis dynamics model and cooperative control law, this invention innovatively constructs a closed-loop interaction model including distance-constrained interaction forces, posture-constrained interaction torques, and paint hose dragging torques, proposing an interaction stability margin capable of quantifying coupled effects. Furthermore, it develops a dynamic weighted and single-point hard failure rejection real-time evaluation model based on the interaction stability margin. This method not only achieves adaptive cooperative operation between the Mecanum wheel chassis and the robotic arm, fully leveraging the advantages of omnidirectional movement, but also achieves a leap in system stability evaluation from "offline static envelope" to "online real-time calculation," endowing the robot with a high degree of autonomous disaster prevention capability in complex interactive environments, and comprehensively improving the quality, efficiency, and absolute safety of painting operations.

[0030] To facilitate understanding of the present invention, several embodiments are given below. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be more thorough and complete.

[0031] Example 1 Please see Figure 1 The figure shows a painting robot control method based on Mecanum wheels in the first embodiment of the present invention, the method including steps S101 to S104: S101. Obtain the target spraying path and construct a cooperative control law by combining the Mecanum wheel chassis and the robotic arm, so as to allocate the joint angular velocity of the robotic arm and the speed of the Mecanum wheel chassis according to the cooperative control law.

[0032] As a specific example, firstly, a Mecanum wheel chassis dynamics model is established. The model construction method specifically includes steps S1011 to S1014: S1011. Establish system parameters.

[0033] In this embodiment, the system parameters include the total mass of the vehicle. m Moment of inertia about the Z-axis of the center of mass I z Longitudinal distance from center of gravity to front axle l x Lateral distance from the center of gravity to the left and right wheel axles l y Mass of a single wheel m w Moment of inertia of a single wheel about its axis I w Wheel radius Rand the coefficient of friction μ between the wheel and the ground; where, w It is an abbreviation for wheel, meaning a wheel, specifically a Mecanum wheel.

[0034] S1012, Define motion variables.

[0035] In this embodiment, the motion variables include the linear velocity of the vehicle's center of mass. v angular velocity of the vehicle body oh , No. i The rotational angular velocity ω of each wheel i and motor torque control input τ i ,in, v = , v x In the vehicle coordinate system, this represents the linear velocity component of the vehicle's center of mass along the longitudinal axis of the vehicle body. v y Let be the linear velocity component of the vehicle's center of mass along the vehicle's transverse axis in the vehicle coordinate system; in the th... i The rotational angular velocity ω of each wheel i middle, i =1,2,3,4, where: 1 corresponds to the front left wheel, 2 corresponds to the front right wheel, 3 corresponds to the rear left wheel, and 4 corresponds to the rear right wheel.

[0036] S1013. Based on Newton's second law and Euler's equations, establish the equations for the translation of the vehicle body, the rotation of the vehicle body, and the rotation of the wheels.

[0037] Specifically, the expression for the car body's translation equation is: ; In the formula: F x The component of the net external force on the vehicle body along the X-axis in the vehicle coordinate system; F y This represents the component of the net external force on the vehicle body along the Y-axis in the vehicle coordinate system. Secondly, the expression for the equation of motion of the vehicle body is: ; In the formula, M Z The net external torque about the Z-axis; The derivative of the vehicle's angular velocity; The equation for the rotation of the wheel is expressed as follows: ; In the formula, Let be the tangential reaction force exerted by the ground on the wheel, where t It is an abbreviation for tangential, meaning tangential direction. i Wheel serial number; R The radius of the wheel; S1014. Based on the kinematic constraints of the Mecanum wheel, establish the relationship between force and torque transmission.

[0038] Specifically, the relationship between force and torque transmission is as follows: ; Among them, F t,1 F represents the tangential reaction force of the first wheel. t,2 F represents the tangential reaction force of the second wheel. t,3 F represents the tangential reaction force of the third wheel. t,4 This represents the tangential reaction force of the fourth wheel; T Indicates transpose; The Jacobian matrix is ​​expressed as:

[0039] Furthermore, the Mecanum wheel chassis dynamics model also considers rolling friction and viscous damping to accurately describe the chassis's motion under the action of motor torque. The formula for calculating rolling friction torque is as follows: The formula for calculating viscous damping torque is: In the formula, The coefficient of rolling friction; For the normal load of the wheel, where, n It is an abbreviation for "normal," meaning normal or directional. Let ω be the viscous damping coefficient of the wheel, where damping represents damping; R is the wheel radius; and ω is the viscous damping coefficient. i For the first i The rotational angular velocity of each wheel; sgn ( oh i ) is the friction direction correction function.

[0040] As a concrete example, a four-wheel Mecanum wheel chassis is selected, equipped with an AUBO-i5 six-DOF robotic arm, and system parameters are established. The system parameters include: the total mass of the vehicle. m =200kg, moment of inertia about the center of mass I z =10 kg·m², distance from the center of mass to the front and rear axles l x =0.5m, distance to the left and right wheel axles l y =0.4m, wheel radius R =0.15m. The parameters of the robotic arm DH (Denavit–Hartenberg, DH) are shown in Table 1.

[0041] Table 1: DH parameters corresponding to the robotic arm

[0042] Secondly, a kinematic model of the robotic arm is established based on the DH parameters, and then the mapping relationship between the end pose of the robotic arm and the joint angle is solved by forward and inverse kinematic equations.

[0043] The technical solution in this embodiment is based on the principle of "robotic arm control and chassis compensation." It designs an optimization objective function centered on end-effector trajectory tracking, and allocates motion tasks between the robotic arm and the chassis through constraints. The Mecanum wheel chassis adaptively adjusts motion parameters according to the robotic arm's working state. When the robotic arm approaches the workspace boundary, it compensates for insufficient working range through omnidirectional movement, ensuring the continuity of the end-effector spray gun trajectory. Specifically: The method for constructing the cooperative control law includes: defining the optimization objective function and setting constraints, and solving for the optimal solution using the Lagrange multiplier method to obtain the chassis speed and the angular velocity of the robotic arm joints. The chassis speed includes forward and backward, left and right translational and rotational components, which correspond to the control of the rotational speed and direction of the Mecanum wheel.

[0044] In this embodiment, the expression for the objective function is: The constraint expression is: In the formula, The angular velocity of the robotic arm joints; v b Chassis speed; W This is the chassis motion weighting coefficient; J a For the Jacobian matrix of the robotic arm; J b The Jacobian matrix represents the influence of the chassis on the final velocity. v de The desired velocity at the terminal; where, a It is an abbreviation for arm, meaning robotic arm; b It is an abbreviation for "base," meaning chassis. de "Desired" is an abbreviation for "desired," meaning something is expected or desired.

[0045] As a concrete example, the specific implementation process of the cooperative control law includes: setting the chassis motion weighting coefficient W=10, prioritizing the robotic arm as the main control unit. When the desired speed of the robotic arm's end effector... v de =[0.2,0,0] T At m / s, the chassis speed is obtained by optimizing the objective function and constraints. v b and the angular velocity of the robotic arm joints This is to achieve lateral compensated movement by correspondingly controlling the Mecanum wheel. v b =[0,0.1,0] Tm / s; rad / s, T This indicates transpose.

[0046] S102. Construct a closed-loop interaction model to extract the dynamic interaction features of the system and calculate the interaction stability margin.

[0047] The closed-loop interaction model includes the constraints between the robotic arm, slide rail, chassis, and working environment. Specifically, the closed-loop interaction model introduces distance constraint interaction force, posture constraint interaction torque, and paint pipe dragging torque as core evaluation variables to quantify the real-time coupling effect of the robotic arm's dynamic motion and the paint pipe's extension and retraction on the chassis.

[0048] As a concrete example, most existing analytical frameworks for mobile robot stability are open-loop and only consider the robot itself, with the underlying assumption that "the robot moves in free space without external interaction constraints." However, the essence of spray painting is a continuous closed-loop interaction between the robot, the workpiece, and the working environment. The spray gun must maintain a constant distance and normal orientation from the workpiece surface, and the paint pipeline applies a continuous dragging torque to the robot. These interaction constraints directly alter the system's dynamic characteristics and stability boundaries.

[0049] To overcome the shortcomings of existing technologies that exhibit stability in open-loop computation but instability in closed-loop interaction, this invention introduces distance constraint interaction force, attitude constraint interaction torque, and paint pipe dragging torque to construct a closed-loop interaction model, specifically including steps S1021 to S1024: S1021, Calculate the distance constraint interaction force.

[0050] Let the actual spraying distance at the end of the spray gun be... ,in, With generalized coordinates The requirement for a constant spraying distance is variable. Then the distance deviation The formula for calculating the distance constraint interaction force generated along the workpiece normal is:

[0051]

[0052]

[0053] In the formula: For distance-constrained interaction forces, where, DCI It is an abbreviation for Distance Constrained Interactive, which means distance-constrained interaction; The distance stiffness coefficient (unit: N / m) is determined by the spraying process requirements. In the high-pressure spraying scenario of this embodiment, the value range is [value range missing]. ; This is the distance damping coefficient (unit: N·s / m), used to suppress distance fluctuations, with a value range of [value range missing]. d represents the spraying distance; For distance deviation, The rate of change of distance deviation, due to A constant ( Therefore ; This represents the rate of change of a constant target spraying distance over time. To maintain a constant distance, For generalized coordinates The actual spraying distance varies. This represents the rate of change of the actual spraying distance at the tip of the spray gun with respect to time. The generalized speed representing the robot.

[0054] S1022, Calculate the attitude constraint interaction torque.

[0055] Let the actual attitude angle of the spray gun tip be... In this embodiment, the actual attitude angle is the rotation angle around the X and Y axes, used to describe the normal deviation. A constant attitude requirement is... Then the attitude deviation The formula for calculating the generated attitude constraint interaction torque is:

[0056]

[0057]

[0058] In the formula: ACI stands for Attitude Constraint Interaction, representing the interaction of attitude constraints. This is the attitude stiffness coefficient (unit: N·m / rad), with a value range of [value range missing]. ; The attitude damping coefficient (unit: N·m·s / rad) has a range of values ​​of [value range missing]. ; This is for attitude deviation; This refers to the actual attitude angle; To maintain a constant attitude; The rate of change of attitude deviation, due to Since it is a constant, , This represents the rate of change of the actual attitude angle of the spray gun tip with respect to time. Generalized velocity representing the robot; This indicates the rate of change of the constant attitude requirement with respect to time.

[0059] S1023. Calculate the dragging torque of the paint pipe.

[0060] The drag torque of the paint pipe is related to the robotic arm configuration, chassis, and potential lifting height, and varies with the generalized coordinate system. Changes. This embodiment uses a linear model approximation (which can be calibrated experimentally in engineering), and the calculation formula is:

[0061] In the formula: This represents the dragging torque of the paint hose, where TD is an abbreviation for Tube Dragging, indicating the dragging of the paint hose. This is the drag coefficient for the paint hose; This represents the elongation of the paint tube relative to its initial length. , This is the current length of the paint tube. This is the initial length.

[0062] S1024, Calculate the interactive stability margin.

[0063] The distance constraint interaction force, attitude constraint interaction torque, and paint pipe drag torque obtained from the above solution are used as external environment interaction disturbance terms for the calculation of the interaction stability margin (ISM) below.

[0064] Traditional stability assessments only evaluate the forces acting on the chassis itself. This invention, however, introduces the Tip Evaluation Index (TEI) and Slip Evaluation Index (SEI), which consider distance constraint interaction forces, posture constraint interaction moments, and paint hose dragging moments, to evaluate the overall stability of the painting robot. Furthermore, the ISM value of the Mecanum wheel robot is calculated in real-time using a weighted average of the Tip Evaluation Index and Slip Evaluation Index. This accurately quantifies the real-time coupling effect of the robotic arm's dynamic motion and the paint hose extension / retraction on the Mecanum wheel robot, providing core data support for subsequent advanced stability and disaster prevention measures.

[0065] S103. Run the dynamic weighted real-time stability assessment model based on interactive stability margin, and dynamically weight the overturning risk and slip risk of the system according to the current operating status to obtain the interactive stability margin value. At the same time, execute the single-point hard failure veto mechanism; then judge whether the output of the dynamic weighted real-time stability assessment model is safe based on the interactive stability margin value.

[0066] As a specific example, traditional stability assessments often employ static minimum methods or offline envelope methods, which cannot adapt to the frequently changing operating conditions of painting robots in actual operations. This invention abandons the traditional static assessment method, achieving a leap from "offline static envelope" to "online real-time calculation" to realize disaster prevention. The assessment process of the assessment model of this invention specifically includes steps S1031 to S1034: S1031. Dynamic weighted logic settings for overturning weight and slip weight based on job status.

[0067] The system adjusts the weighting coefficients of TEI and SEI in the integrated ISM in real time based on the motion state of the Mecanum wheel chassis and the configuration of the robotic arm. Taking large-scale static operation and cooperative mobile operation as examples, the specific weighting strategy is as follows. Among them, operation A is a large-scale static operation, and operation B is a cooperative mobile operation.

[0068] Condition A: When the Mecanum wheel chassis is detected to be stationary and the robotic arm extends beyond 70% of its rated working radius to cover the distant painting area, the system determines that the main instability risk is overturning caused by a shift in the center of gravity. At this time, the system automatically increases the overturning weight to 0.8~0.9, while correspondingly decreasing the slip weight.

[0069] Condition B: When the Mecanum wheel chassis is in a mobile operation state, the mobile operation state includes the mobile state and the position adjustment state. The system determines that due to the dynamic interaction between the ground and the wheels and the influence of inertial forces, the risk of lateral slippage is increased. The system forcibly increases the slippage weight to the dominant position (such as above 0.7) to focus on monitoring the grip stability during the movement process.

[0070] S1032. Real-time stability index calculation is performed based on overturning weight and slip weight to obtain the weighted comprehensive interactive stability margin.

[0071] Specifically, the formula for calculating the overall interaction stability margin is as follows:

[0072] in, ; Furthermore, the formula for calculating TEI is as follows:

[0073]

[0074]

[0075]

[0076] The formula for calculating SEI is as follows:

[0077]

[0078]

[0079] In the formula: This is the weighted overall interactive stability margin; It is a real-time overturning assessment index based on the current interaction force correction, where the current interaction force includes distance constraint interaction force, attitude constraint interaction torque and paint pipe dragging torque; It is a real-time slip evaluation index based on the current interaction force correction, where the current interaction force includes distance constraint interaction force, attitude constraint interaction torque and paint pipe drag torque; Indicates the overturning weight. Indicates the slip weight. For the total overturning moment, Where g is the total mass of the robot; g is the acceleration due to gravity. The coordinates of the system's center of gravity (cog) projected onto the horizontal plane are: This refers to the inertial force during operation, where dyn is an abbreviation for dynamic, representing dynamics; For the height of the slide rail, For the equivalent mass of a six-axis robotic arm, The horizontal extension length of the robotic arm. The quality of the end effector is determined by the nozzle and piping. ee It is an abbreviation for End-Effector; This represents the critical rollover torque of the chassis. The front and rear track width. β Let be the convergence constant. The maximum horizontal shear force under a closed-loop interactive environment; The critical static friction force; The coefficient of friction between the Mecanum wheel and the ground is given.

[0080] S1033, when and If any one of the conditions is zero, a single-point hard failure veto (HFV) mechanism is executed to prevent false safety interaction stability margins.

[0081] In this embodiment, when When the value is zero, the painting robot has already overturned, and no further calculation is needed. .when When the value is zero, the painting robot has slipped. To ensure absolute safety, this model introduces a single-point hard failure veto logic, which means that the total score after weighted averaging is not considered, but a veto is applied to the core single indicator.

[0082] S1034. Based on the HFV mechanism, and under the premise of safety, real-time... The value is used for autonomous disaster prevention.

[0083] when If the value falls below a preset warning threshold or a hard failure rejection signal is triggered, the dynamic weighted real-time stability assessment model outputs "unsafe" and initiates a hazard avoidance maneuver. The system immediately activates a highly autonomous disaster prevention program. In this embodiment, the warning threshold is 0.1. This program ignores the current target spraying path instructions and prioritizes hazard avoidance maneuvers. These maneuvers include reverse pose compensation or action freeze / retraction. Specifically, the reverse pose compensation method includes controlling the chassis to perform an emergency translation in the opposite direction of the robotic arm's extension to quickly recover the offset center of gravity. The action freeze / retraction method includes forcibly stopping the robotic arm's further extension and guiding it to retract into a safe configuration.

[0084] Only after the stability index returns to the safe range is the system allowed to resume normal collaborative painting operations. Through the painting robot control method and system based on Mecanum wheels of this invention, the painting robot achieves ultimate risk avoidance and real-time disaster prevention in complex interactive environments and variable working conditions.

[0085] S104. Under the constraint that the output of the dynamic weighted real-time stability evaluation model is safe, a Cartesian space trajectory is constructed according to the target spraying path and a continuous trajectory is generated by fitting. Combined with the angular velocity of the robotic arm joints and the speed of the Mecanum wheel chassis issued by the cooperative control law, the spraying robot is cooperatively controlled to complete the spraying operation.

[0086] This step combines the previously calculated chassis speed and robotic arm joint angular velocity with... By combining precise trajectory interpolation and workspace monitoring, high-precision spraying operations are achieved. The specific implementation process includes steps S1041 to S1045: S1041. Constructing Cartesian space trajectory planning and pose transformation. The system achieves pose transformation between various coordinate systems based on Cartesian space trajectory planning and joint space trajectory fitting. These coordinate systems include the world coordinate system, chassis coordinate system, robotic arm end effector coordinate system, and target trajectory coordinate system. Specifically, pose transformation between coordinate systems is achieved through homogeneous transformation matrices, and the real-time transformation relationships between these coordinate systems are maintained using the TF2 library.

[0087] S1042. Based on Cartesian space trajectory planning and pose transformation, perform non-uniform spacing trajectory interpolation and fitting to obtain the initial spraying path. The spray gun trajectory is planned in Cartesian space using linear interpolation, dividing the spray gun trajectory between the starting point and the target point into several segments and obtaining several interpolation points. ),in: x i = x 0+△ x . i ; y i = y 0+△ y . i ; z i = z 0+△ z ,△ x for x Axis single interpolation increment; △ y for y Axis single interpolation increment; △ z for z The single-cycle interpolation increment is calculated. The interpolation points are segmented using a non-uniform strategy based on the spatial redundancy and process constraints of the target spraying path. Specifically, the segmentation strategy for wide redundancy regions is to increase the spacing between interpolation points to reduce computation and improve efficiency. The segmentation strategy for regions with strict process constraints is to automatically switch to "shortest path planning mode," reducing the spacing between interpolation points and using high-order polynomial fitting to ensure smooth end-spray gun speed and uniform coating overlap. In this embodiment, the wide redundancy region is an open, flat wall surface, and the region with strict process constraints is a narrow bottleneck area.

[0088] S1043, Path search cost function for bottleneck scenarios based on initial spraying path optimization.

[0089] For typical bottleneck scenarios such as door and window edges and column corners, the system optimizes the path search cost function G to prioritize both spraying continuity and obstacle avoidance safety. Its expression is: G = l 1 G craft + l 2 G smooth + l 3 G obstacle + l 4 G kinematic + l 5 G length In the formula:G The optimized path search cost function; G craft To ensure compliance with spraying process regulations, the distance and angle between the spray gun and the wall must be controlled to meet process requirements. G smooth To maintain the continuity of motion, the spray gun's speed and acceleration are constrained to remain constant. G obstacle To avoid the safety risks associated with obstacle avoidance, a safe distance must be maintained between the robot and obstacles, including doors, windows, and pillars. G kinematic To compromise on motion feasibility, the constraint path conforms to the motion limits of the chassis and the robotic arm; G length is the path length cost, and is an auxiliary optimization term. l 1. l 2. l 3. l 4. l 5 represents the weighting coefficient for each sub-item. In bottleneck scenarios, the weighting coefficients are set to meet the following conditions: l 1. l 2. l 3. l 4>> l 5. Prioritize ensuring coating quality and obstacle avoidance safety, while de-emphasizing the optimization priority of path length.

[0090] S1044. The target painting path is obtained based on the optimized path search cost function and the discrete-time model of the Mecanum wheel chassis. The robot pose is updated in real time according to the target painting path to ensure that the chassis compensation motion and the robotic arm motion are accurately synchronized.

[0091] In the process of incorporating Mecanum wheel chassis speed compensation, the robot pose is updated in real time using a discrete-time model. The calculation function for the robot pose update is:

[0092] Among them: Among them, Sampling time, The vehicle heading angle is the angle between the X-axis of the vehicle coordinate system and the X-axis of the global coordinate system (counterclockwise is positive); k represents the k-th sampling time; x [k], y [k]) represents the global coordinates of the vehicle body at the k-th sampling time; i [k] represents the vehicle heading angle at the k-th sampling time; v [ k [] represents the generalized velocity vector at the k-th moment; x [k+1] represents the global X coordinate of the vehicle body at the (k+1)th sampling time; y[k+1] represents the global Y-coordinate of the vehicle at the (k+1)th sampling time; i [k+1] represents the vehicle heading angle at the (k+1)th time.

[0093] As a specific example, to ensure the continuity of the end-effector spray gun trajectory, the system establishes a reference window for the workspace boundary of the robotic arm. When the robotic arm is detected to be approaching its limit position (such as the joint angle reaching its limit, the distance between the end effector and an obstacle being less than 0.3m, etc.), the compensating motion of the Mecanum wheel chassis is automatically triggered.

[0094] Specifically, a reference window for the robotic arm's workspace boundary is established. Statistics within this window are calculated to determine the boundary state of the robotic arm's workspace. When the robotic arm is detected approaching the limit position of the workspace boundary state, the compensating motion of the Mecanum wheel chassis is automatically triggered to adaptively adjust the motion parameters of the Mecanum wheel. In other words, the reference unit is defined as the range of angles of each joint of the robotic arm, the limit of the end effector position, and the threshold distance from the wall to establish the reference window for the boundary state of the robotic arm's workspace. The second-order statistics and the mean ratio within the reference window are calculated to obtain the reference window detection results. The second-order statistics are used to determine whether the joint state of the robotic arm is uniform, and the mean ratio is used to determine whether the states of the left and right reference units are consistent. When the reference window detection result exceeds a set threshold, the compensating motion of the Mecanum wheel chassis is triggered. The direction of the compensating speed is to reduce the load on the robotic arm, and its magnitude is proportional to the degree of saturation of the robotic arm.

[0095] The methods for detecting and determining the extreme positions of the workspace boundary state of a robotic arm include: A multi-dimensional boundary reference window is established, wherein the monitoring dimensions of the multi-dimensional boundary reference window include the joint angle of the robotic arm, the position of the spray gun end, and the distance between the spray gun end and the wall. The ratio of the second-order statistic to the mean of the monitoring data within the multi-dimensional boundary reference window is calculated in real time to achieve quantitative judgment of the extreme state. When the statistic exceeds the preset threshold, the extreme position is reached.

[0096] As a specific example, in actual work, the spraying area also includes some special scenarios, such as wall corners, door and window edges, and indoor columns. When spraying is required in special scenarios, the method for detecting and identifying the extreme position includes: judging in real time whether the distance between the end spray gun and the obstacle is less than the safety threshold, and judging whether the elbow joint of the robotic arm shows a reverse folding trend; when both conditions are met, it is identified as the position limit; where, in this embodiment, the safety threshold is 0.3m.

[0097] In actual spraying, special scenarios also include the highest position of the lifting platform. When the lifting platform reaches the highest position, a composite limit detection is performed on the boundary state of the robotic arm's workspace. The detection and identification method of the limit position includes: when the lifting platform is raised to the highest position, the extension space of the robotic arm is compressed, and the system lowers the joint limit threshold of the robotic arm by 10% to 15% to identify the limit state earlier and achieve high-position safe spraying compensation.

[0098] As a concrete example, the state detection implementation process of the reference window includes: setting the limit threshold for the robotic arm joint angle to ±170°, the threshold for the distance between the end effector and the wall to 0.3m, and establishing a reference window containing 10 reference units. The second-order statistics within the reference window are then calculated. V VI and the ratio of the mean V MR Among them, second-order statistics V VI =3.2 (less than the threshold) K VI =4.76); mean ratio V MR =1.2 (in) =0.55 to K MR =1.806); when the robotic arm joint angle reaches 165°, the chassis compensation motion is triggered, with a compensation speed of 0.08 m / s, where, K VI This represents the variance index threshold, used to determine the upper limit of fluctuations in the joint motion of a robotic arm; This represents the inverse threshold of the mean ratio, used to determine whether the motion state is normal. K MR The mean ratio threshold is used to determine whether the motion state is normal. VI is an abbreviation for Variance Indicator, which represents the variance index (degree of volatility) and its threshold; MR is an abbreviation for Mean Ratio, which represents the mean ratio (state trend) and its upper and lower limits.

[0099] In this embodiment, as Figure 2As shown, a "bow"-shaped spraying path is planned, with the starting point point1 = (0.4, 0.25, 0.2) m and the target point point6 = (0.4, -0.25, -0.2) m, divided into 5 straight lines, with N = 50 interpolation points. The joint angles corresponding to each interpolation point are solved using the inverse kinematics equations of the robotic arm, and the joint trajectory is fitted using a 7th-order polynomial. The fitted joint angle curves show no abrupt changes. Combined with Mecanum wheel chassis compensation motion, the spraying operation is performed in the ROS and Gazebo simulation environment. The end-effector trajectory tracking error is less than ±0.05 mm, and the spraying uniformity meets industry standards. By combining the omnidirectional movement characteristics of the Mecanum wheels with the cooperative control algorithm, the working range of the spraying robot is effectively expanded, and the trajectory tracking accuracy is improved.

[0100] By combining the above spraying path with the closed-loop interaction model of step S102 and the dynamic weighted real-time stability evaluation model of step S103, the system performed the spraying operation while ensuring absolute safety. Experimental results show that, under the accompanying compensation motion of the chassis, the tracking error of the spray gun's end trajectory is strictly controlled within... Within a certain range, the coating uniformity perfectly meets industry standards. Compared to traditional open-loop control spraying robots, this invention improves work efficiency by more than 30% in complex indoor environments, and reduces coating uniformity error to within 5%, demonstrating significant practical value and broad prospects for promotion.

[0101] In summary, the Mecanum wheel-based painting robot control method in the above embodiments of the present invention establishes a precise Mecanum wheel chassis dynamics model and cooperative control law, and introduces the concept of interactive stability margin to establish a closed-loop interactive model including the mobile chassis, slide rail, robotic arm, and environmental constraints. This transforms the traditional reactive response into proactive safety control, solving both the problems of the painting robot's mobility and cooperative accuracy, and fundamentally addressing the dynamic instability problem of the robot in large-scale, high-altitude vertical surface painting operations. This significantly improves the system's autonomous disaster prevention capability and operational reliability.

[0102] Example 2 The Mecanum wheel-based painting robot control system in the second embodiment of the present invention includes: The acquisition module is used to acquire the target spraying path and combine the Mecanum wheel chassis and the robotic arm to construct a cooperative control law, so as to allocate the joint angular velocity of the robotic arm and the speed of the Mecanum wheel chassis according to the cooperative control law. The extraction module is used to construct a closed-loop interaction model to extract the dynamic interaction features of the system and calculate the interaction stability margin. The closed-loop interaction model introduces distance constraint interaction force, posture constraint interaction torque, and paint pipe dragging torque as core evaluation variables to quantify the real-time coupling effect of the dynamic motion of the robotic arm and the extension and retraction of the paint pipe on the chassis. The closed-loop interaction model includes the constraint forces between the robotic arm, slide rail, chassis, and working environment. The judgment module is used to run a dynamically weighted real-time stability assessment model based on the interactive stability margin, and dynamically weight the overturning risk and slip risk of the system according to the current operating status to obtain the interactive stability margin value, while executing a single-point hard failure veto mechanism; then, it judges whether the output of the dynamically weighted real-time stability assessment model is safe based on the interactive stability margin value. The spraying module is used to construct a Cartesian space trajectory and fit it to generate a continuous trajectory based on the target spraying path under the constraint that the output of the dynamic weighted real-time stability evaluation model is safe. Combined with the angular velocity of the robotic arm joints and the speed of the Mecanum wheel chassis issued by the cooperative control law, the module is used to cooperatively control the spraying robot to complete the spraying operation.

[0103] In summary, the Mecanum wheel-based painting robot control system in the above embodiments of the present invention, by establishing a precise Mecanum wheel chassis dynamics model and cooperative control law, and introducing the concept of interactive stability margin, establishes a closed-loop interactive model including the mobile chassis, slide rail, robotic arm, and environmental constraints. This upgrades the traditional reactive response to proactive safety control, solving both the problems of the painting robot's mobility and cooperative accuracy, and fundamentally solving the dynamic instability problem of the robot in large-scale, high-altitude vertical surface painting operations. This significantly improves the system's autonomous disaster prevention capability and operational reliability.

[0104] Furthermore, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the methods described above.

[0105] Furthermore, embodiments of the present invention also propose a data processing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the methods described above.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0107] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0108] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0109] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0110] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A control method for a painting robot based on Mecanum wheels, characterized in that, include: The target spraying path is obtained, and a cooperative control law is constructed by combining the Mecanum wheel chassis and the robotic arm to allocate the joint angular velocity of the robotic arm and the speed of the Mecanum wheel chassis according to the cooperative control law. A closed-loop interaction model is constructed to extract the dynamic interaction features of the system and calculate the interaction stability margin. The closed-loop interaction model introduces distance constraint interaction force, posture constraint interaction torque, and paint pipe dragging torque as core evaluation variables to quantify the real-time coupling effect of the dynamic motion of the robotic arm and the extension and retraction of the paint pipe on the chassis. The closed-loop interaction model includes the constraint forces between the robotic arm, slide rail, chassis, and working environment. Run a dynamically weighted real-time stability assessment model based on the aforementioned interactive stability margin, and dynamically weight the overturning risk and slip risk of the system according to the current operating status to obtain the interactive stability margin value. At the same time, execute a single-point hard failure veto mechanism; then determine whether the output of the dynamically weighted real-time stability assessment model is safe based on the interactive stability margin value. Under the constraint that the output of the dynamic weighted real-time stability evaluation model is safe, a Cartesian space trajectory is constructed according to the target spraying path and a continuous trajectory is generated by fitting. Combined with the angular velocity of the robotic arm joints and the speed of the Mecanum wheel chassis issued by the cooperative control law, the spraying robot is cooperatively controlled to complete the spraying operation. The execution logic of the dynamically weighted real-time stability evaluation model includes: Based on the operational status, the overturning assessment index and the slippage assessment index are dynamically weighted to obtain the overturning weight and the slippage weight; When the Mecanum wheel chassis is detected to be stationary and the extension range of the robotic arm exceeds 70% of the rated working radius of the robotic arm, the overturning weight is automatically increased; when the Mecanum wheel chassis is detected to be in a moving operation state, the sliding weight is forcibly increased; wherein, the sum of the overturning weight and the sliding weight is 1; The overturning assessment index and the slip assessment index are continuously monitored. When any assessment index reaches zero, a single-point hard failure veto mechanism is triggered, which forces the execution of reverse pose compensation or action freeze / retraction.

2. The control method for a painting robot based on Mecanum wheels according to claim 1, characterized in that, The distance constraint interaction force is applied along the workpiece normal, and the formula for calculating the distance constraint interaction force is: In the formula: For distance-constrained interaction forces, where, DCI It is an abbreviation for Distance Constrained Interactive, which means distance-constrained interaction; This is the distance stiffness coefficient; d is the distance damping coefficient; d is the spraying distance; This refers to distance deviation; The rate of change of distance deviation; For generalized coordinates The actual spraying distance varies; This represents the rate of change of a constant target spraying distance over time. To maintain a constant distance; This represents the rate of change of the actual spraying distance at the tip of the spray gun with respect to time. Generalized velocity representing the robot; The formula for calculating the attitude constraint interaction torque is as follows: In the formula: ACI stands for Attitude Constraint Interaction, representing the interaction of attitude constraints. This refers to the attitude stiffness coefficient; This is the attitude damping coefficient; This is for attitude deviation; This refers to the actual attitude angle; To maintain a constant attitude; The rate of change of attitude deviation; This represents the rate of change of the actual attitude angle of the spray gun tip with respect to time; Generalized velocity representing the robot; This indicates the rate of change of the requirement for a constant attitude with respect to time; The dragging torque of the paint pipe is approximated using a linear model, wherein the calculation formula for the dragging torque of the paint pipe is: In the formula: This represents the dragging torque of the paint hose, where TD is an abbreviation for Tube Dragging, indicating the dragging of the paint hose. This is the drag coefficient for the paint hose; This is the current length of the paint pipe; This is the initial length; This represents the elongation of the lacquer tube relative to its initial length.

3. The control method for a painting robot based on Mecanum wheels according to claim 1, characterized in that, Methods for constructing cooperative control laws include: Define the objective function and set the constraints, and solve the optimal solution using the Lagrange multiplier method to obtain the chassis speed and the angular velocity of the robotic arm joints. The chassis speed includes forward and backward, left and right translational and rotational components, which correspond to the rotational speed and direction of the Mecanum wheel. The expression for the objective function is: The constraint expression is: In the formula, The angular velocity of the robotic arm joints; v b Chassis speed; W This is the chassis motion weighting coefficient; For the Jacobian matrix of the robotic arm; The Jacobian matrix represents the influence of the chassis on the final velocity. The desired velocity at the terminal; where, a It is an abbreviation for arm, meaning robotic arm; b It is an abbreviation for "base," meaning chassis. de "Desired" is an abbreviation for "desired," meaning something is expected or desired.

4. The control method for a painting robot based on Mecanum wheels according to claim 1, characterized in that, The steps of collaborative control of the painting robot also include boundary compensation, and the boundary compensation methods include: Establish a reference window for the workspace boundary of the robotic arm, and determine the boundary state of the robotic arm's workspace by calculating the statistics within the reference window. When the robot arm is detected to be approaching the limit position of the robot arm's workspace boundary state, the compensation movement of the Mecanum wheel chassis is automatically triggered to adaptively adjust the motion parameters of the Mecanum wheel. The methods for detecting and identifying extreme positions include: A multi-dimensional boundary reference window is established, and the monitoring dimensions of the multi-dimensional boundary reference window include the joint angle of the robotic arm, the position of the spray gun end, and the distance between the spray gun end and the wall. The ratio of the second-order statistic to the mean of the monitoring data within the multi-dimensional boundary reference window is calculated in real time to achieve quantitative judgment of the extreme state. When the statistic exceeds the preset threshold, the extreme position is reached.

5. The control method for a painting robot based on Mecanum wheels according to claim 4, characterized in that, The methods for detecting the state of a reference window include: Define the reference unit as the angle range of each joint of the robotic arm, the end position limit, and the distance threshold from the wall to establish the boundary state reference window of the robotic arm's workspace. The second-order statistic and mean ratio within the reference window are calculated to obtain the reference window detection results. The second-order statistic is used to determine whether the joint state of the robotic arm is uniform, and the mean ratio is used to determine whether the states of the left and right reference units are consistent. When the reference window detection result exceeds the set threshold, the Mecanum wheel chassis compensation motion is triggered. The direction of the compensation speed is to reduce the load on the robotic arm, and its magnitude is proportional to the degree of saturation of the robotic arm.

6. The control method for a painting robot based on Mecanum wheels according to claim 1, characterized in that, In the step of generating the continuous trajectory of the spray gun at the end of the painting robot, the trajectory planning method includes: The spray gun trajectory is planned in Cartesian space using the linear interpolation method. The spray gun trajectory between the starting point and the target point is divided into several segments and several interpolation points are obtained. The coordinates of each interpolation point are calculated. The joint angles corresponding to each interpolation point are solved by the inverse kinematics equation of the robotic arm, and the joint space trajectory is fitted by a high-order polynomial interpolation method to ensure smooth motion. By combining the Mecanum wheel chassis compensation speed, the robot pose is updated in real time through a discrete-time model to ensure precise synchronization between chassis compensation motion and robotic arm motion. Among these, several interpolation points are non-uniformly spaced. In the step of dividing the spray gun trajectory between the starting point and the target point into several segments and obtaining several interpolation points, the method for dividing the interpolation points includes: When the planned path is in a wide redundancy area where the robotic arm and chassis work together, the interpolation point spacing is increased to reduce computation and improve work efficiency. When the planned path is in a region with strict process constraints, the interpolation point spacing is reduced, and high-order polynomial fitting is used to ensure smooth end-spray gun speed and uniform coating overlap. The wide redundancy area includes open and flat walls, while the region with strict process constraints includes narrow bottleneck areas.

7. A painting robot control system based on Mecanum wheels, characterized in that, The system includes: The acquisition module is used to acquire the target spraying path and combine the Mecanum wheel chassis and the robotic arm to construct a cooperative control law, so as to allocate the joint angular velocity of the robotic arm and the speed of the Mecanum wheel chassis according to the cooperative control law. The extraction module is used to construct a closed-loop interaction model to extract the dynamic interaction features of the system and calculate the interaction stability margin. The closed-loop interaction model introduces distance constraint interaction force, posture constraint interaction torque, and paint pipe dragging torque as core evaluation variables to quantify the real-time coupling effect of the dynamic motion of the robotic arm and the extension and retraction of the paint pipe on the chassis. The closed-loop interaction model includes the constraint forces between the robotic arm, slide rail, chassis, and working environment. The judgment module is used to run a dynamically weighted real-time stability assessment model based on the interactive stability margin, and dynamically weight the overturning risk and slip risk of the system according to the current operating status to obtain the interactive stability margin value, while executing a single-point hard failure veto mechanism; then, it judges whether the output of the dynamically weighted real-time stability assessment model is safe based on the interactive stability margin value. The spraying module, under the constraint that the output of the dynamic weighted real-time stability evaluation model is safe, constructs a Cartesian space trajectory based on the target spraying path and fits it to generate a continuous trajectory. Combined with the angular velocities of the robotic arm joints and the Mecanum wheel chassis speed issued by the cooperative control law, it collaboratively controls the spraying robot to complete the spraying operation. The execution logic of the dynamically weighted real-time stability evaluation model includes: Based on the operational status, the overturning assessment index and the slippage assessment index are dynamically weighted to obtain the overturning weight and the slippage weight; When the Mecanum wheel chassis is detected to be stationary and the extension range of the robotic arm exceeds 70% of the rated working radius of the robotic arm, the overturning weight is automatically increased; when the Mecanum wheel chassis is detected to be in a moving operation state, the sliding weight is forcibly increased; wherein, the sum of the overturning weight and the sliding weight is 1; The overturning assessment index and the slip assessment index are continuously monitored. When any assessment index reaches zero, a single-point hard failure veto mechanism is triggered, which forces the execution of reverse pose compensation or action freeze / retraction.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the Mecanum wheel-based spraying robot control method as described in any one of claims 1-6.

9. A data processing apparatus, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the painting robot control method based on Mecanum wheels as described in any one of claims 1-6.

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