Task allocation and trajectory cooperative control method and system for two-component spray adhesive

Abstract

The application relates to the field of automation control and robot technology, in particular to a task distribution and trajectory cooperative control method and system for double-component glue spraying; the method comprises the following steps: acquiring a spraying path of a workpiece and a kinematics model of double robots; for each point on the spraying path, the multi-physical fusion cost of each robot at the point is calculated, and the multi-physical fusion cost of each robot at multiple path points on the spraying path is calculated; the multi-physical fusion cost is integrated along the path to obtain the total work cost of each robot; a handover point that minimizes the sum of the total work costs of all the robots is found, and the spraying path is distributed to each robot based on the handover point. The application has the effects of improving the glue spraying effect and the motion stability of the glue spraying system.

Description

Technical Field

[0001] This application relates to the fields of automation control and robotics, and in particular to a method and system for task allocation and trajectory collaborative control of two-component spray adhesive. Background Technology

[0002] Two-component spray adhesive technology plays a crucial role in high-strength bonding scenarios such as aerospace structural components, wind turbine blades, composite materials, and the manufacturing of large tooling. This type of adhesive consists of two components that are mixed at the spraying end and rapidly undergo initial curing, allowing for bonding operations on the surfaces to be bonded. Its bond strength and durability are significantly superior to single-component adhesives. In the spraying of large components such as aerospace and wind turbine blades, the workpiece size often exceeds the effective working space of a single robot, necessitating the use of dual or multiple robots working collaboratively to complete the spraying task.

[0003] Reference Figure 1 When assigning tasks to multiple robots, a static geometric segmentation method is typically used. This involves manually dividing the spraying path according to geometric length or area ratio (e.g., a 50 / 50 path length), assigning each robot a corresponding spraying section. While simple and easy to implement, this method has limitations: First, it only considers the path's geometric length and doesn't account for the robot's kinematic characteristics in different spatial positions, potentially leading to some robots being assigned to areas with low motion efficiency or unfavorable postures. Second, different robots exhibit significant differences in speed and energy consumption characteristics within the workspace. An unreasonable allocation ratio can result in one robot being overloaded and having a significantly longer spraying time, while another robot finishes its task early and is forced to idle. This uneven workload not only reduces overall spraying efficiency but can also cause adhesive to remain in some areas beyond its working time, affecting spraying quality and equipment stability. Summary of the Invention

[0004] To improve the stability and coating quality in the dual-robot adhesive spraying process, this application provides a method and system for task allocation and trajectory collaborative control of two-component adhesive spraying.

[0005] In a first aspect, this application provides a method for task allocation and trajectory collaborative control of two-component spray adhesive, employing the following technical solution:

[0006] A method for task allocation and trajectory collaborative control of two-component spray adhesive includes: acquiring the spraying path of the workpiece and the kinematic model of the two robots;

[0007] For each point on the spraying path, calculate the multi-physics fusion cost of each robot at that point, and calculate the multi-physics fusion cost of each robot at multiple path points on the spraying path.

[0008] The multi-physics fusion cost is integrated along the path to obtain the total working cost of each robot; the intersection point that minimizes the sum of the total working costs of all robots is found, and the spraying path is assigned to each robot based on the intersection point;

[0009] The calculation steps for multi-physics fusion cost include: for any point on the spraying path, obtaining the robot's motion cost at that point based on the robot's posture at that point; calculating the robot's power cost at that point based on the robot's power consumption at that point; calculating the robot's glue curing risk at that point based on the time it takes for the robot to reach that point; and multiplying the robot's motion cost, power cost, and glue curing risk at that point as the multi-physics fusion cost of the robot at that point.

[0010] By calculating the multi-physics fusion cost at each point on the spraying path, the robot's kinematic constraints, energy consumption level, and glue curing risk are systematically incorporated into the multi-physics fusion cost. This makes task allocation no longer limited to simple geometric ratio division or manual division, but takes into account multiple factors such as motion stability, energy utilization efficiency, and curing time.

[0011] Furthermore, the total working cost is obtained by integrating the multi-physics fusion cost along the path, and the optimal handover point is found with the goal of minimizing the sum of the total costs of the two robots, thus achieving a globally optimal task partitioning strategy. This reduces the occurrence of poor painting effects and poor equipment operation stability caused by robot posture or task load during operation.

[0012] Optionally, the step of calculating the robot's motion cost at a point based on the robot's posture at that point includes: obtaining the angles of each joint of the robot to calculate the robot's motion constraints; calculating the robot's unit path joint change rate, which is positively correlated with the ratio of the robot's joint angular velocity to its end effector linear velocity; and fusing the robot's motion constraints and unit path joint change rate to obtain the robot's motion cost, wherein the motion constraints and the unit path joint change rate are positively correlated with the robot's motion cost at that point.

[0013] Motion constraints reflect the risks when joints approach their mechanical limits, helping to prevent mechanical lock-up and accuracy degradation in advance. Meanwhile, the rate of change of joints per unit path characterizes the total angle that each joint needs to rotate under small path changes, effectively identifying areas of decreased maneuverability in the posture space. The integration of these two metrics allows motion costs to incorporate both posture constraints and flexibility information, thereby allocating the robot to areas with more comfortable postures and greater operational redundancy, reducing frequent posture switching, and minimizing equipment impact caused by the robot at its movement limits.

[0014] Optionally, the steps of obtaining the angles of each joint of the robot to calculate the motion limitation of the robot include: for any joint of any robot, determining the joint limitation of the joint based on the current angle of the joint and the rotation range of the joint, and taking the maximum value of the joint limitation corresponding to each joint as the motion limitation of the robot.

[0015] The constraint is determined based on the current angle and rotation range of each joint, and the maximum value among all joints is taken as the overall motion constraint. Local limit constraints reflect the bottleneck of the overall system motion and ensure the sensitivity and stability of the motion constraint calculation.

[0016] Optionally, the step of determining the joint limitation degree of the joint based on the current angle of the joint and the range of rotation of the joint includes: for any joint, taking the maximum angle and the minimum angle in the range of rotation of the joint as the limit angle, and determining the joint limitation degree based on the angle between the current angle of the joint and the limit angle, wherein the joint limitation degree is negatively correlated with the angle between the current angle of the joint and the limit angle.

[0017] Each joint of a robot has a range of rotation. The closer the current angle of a robot joint is to the boundary of its range of rotation, the greater the restriction on the robot's current movement. Therefore, the joint limitation is negatively correlated with the angle between the current angle and the limit angle of the joint.

[0018] Optionally, the square root of the sum of squares of the rates of change of all robot joint angles relative to the arc length of the spraying path is calculated, and this square root is taken as the rate of change per unit path joint.

[0019] The unit path joint change rate reflects the total angle that all joints of the robot need to rotate when the spray gun mounted on the end effector moves a unit distance along the path. It can effectively identify areas where the robot's posture is close to singularities, making up for the inability to detect singularity risks by relying solely on joint constraints.

[0020] Optionally, the step of obtaining the robot motion cost by integrating the robot's motion limitation and unit path joint change rate includes: using the difference between a preset coefficient and the motion limitation as a first adjustment factor, using the difference between the preset coefficient and the unit path joint change rate as a second adjustment factor, using the product of the first adjustment factor and the second adjustment factor as motion comfort, using the difference between 1 and motion comfort as a control coefficient, calculating the product of the control coefficient and a preset scaling factor, and using the sum of this product and a preset basic cost factor as the robot's motion cost.

[0021] Motion constraints and unit path joint change rate are fused into a single motion cost. Simultaneously, the flexibility and adaptability of the fusion process are improved by introducing preset scaling factors and a base cost factor.

[0022] Optionally, the step of calculating the robot's power cost at a point based on the robot's power consumption at that point includes: for any point in the spraying path, obtaining the robot's arm span when spraying that point, the robot's movement speed when spraying that point, and the curvature of that point in the spraying path; and calculating the power cost based on the robot's arm span, movement speed, and the curvature of that point in the spraying path, wherein the power cost is positively correlated with the robot's arm span, movement speed, and the curvature of that point in the spraying path.

[0023] By taking into account factors directly related to energy consumption, such as the robot's arm span, movement speed, and path curvature, this application enables energy conservation and consumption reduction during task allocation, thus helping to lower equipment operating costs.

[0024] Optionally, the horizontal distance between the end spray gun and the robot base can be used as the robot's arm span.

[0025] Optionally, in the step of calculating the risk of glue curing at a point based on the time it takes for the robot to arrive at that point, the time it takes for the robot to arrive at that point is positively correlated with the risk of glue curing.

[0026] Secondly, this application provides a task allocation and trajectory collaborative control system for two-component spray adhesive, employing the following technical solution:

[0027] A task allocation and trajectory coordination control system for two-component spray adhesive includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the task allocation and trajectory coordination control method for two-component spray adhesive described above is implemented.

[0028] The above-described method for task allocation and trajectory collaborative control of two-component spray adhesive is used to generate a computer program and store it in a memory so that it can be loaded and executed by a processor. Thus, a system can be built based on the memory and processor for easy use.

[0029] This application has the following technical advantages:

[0030] A multi-physics integrated cost model was constructed, which integrates the robot's motion cost, power cost, and adhesive curing risk. By integrating this multi-physics integrated cost along the spraying path, the system can intelligently find the optimal task handover point that minimizes the total working cost of the two robots, thereby achieving multi-robot load balancing and improving spraying efficiency, quality, and system stability. Attached Figure Description

[0031] Figure 1 This is a rendering of the traditional character allocation method and path division in the background technology of this application.

[0032] Figure 2This is a flowchart of a method for task allocation and trajectory collaborative control of two-component spray adhesive according to an embodiment of this application.

[0033] Figure 3 This is a flowchart of step S2 in the task allocation and trajectory collaborative control method for two-component spray adhesive in the embodiments of this application.

[0034] Figure 4 This is a flowchart of step S21 in the task allocation and trajectory collaborative control method for two-component spray adhesive according to an embodiment of this application.

[0035] Figure 5 This is a diagram illustrating the effect of the task allocation and trajectory collaborative control method for two-component spray adhesive in an embodiment of this application. Detailed Implementation

[0036] This application discloses a task allocation and trajectory collaborative control method for two-component spray adhesive. For any point in the spraying path, the robot's posture at that point, the robot's power consumption at that point, and the time required for the robot to spray to that point are analyzed to construct a multi-physics fusion cost. Based on this, the optimal work handover point is found, thereby minimizing the cost of the entire spraying process and improving spraying efficiency and stability.

[0037] Reference Figure 2 The task allocation and trajectory collaborative control method for two-component spray adhesive includes steps S1-S4.

[0038] S1: Obtain the spraying path of the workpiece and the kinematic model of the two robots.

[0039] In this embodiment, the first step is to construct an accurate digital twin environment as the basis for subsequent task allocation and simulation. Specifically, this involves acquiring the computer-aided design of the workpiece to be coated (…). A 3D model is generated, and a predefined set of spraying trajectory points is extracted from it. To obtain a continuous path that can be differentiated, the trajectory point set is then processed, for example, by using B-spline interpolation or polynomial fitting, and then smoothed to finally generate a continuous and standardized 3D spraying path curve.

[0040] Simultaneously, the kinematic models of the two robots participating in the collaborative operation are collected. Specifically, this can be achieved by collecting their... ( The parameters are used to accurately describe its linkage structure. In addition, the robot's technical specifications need to be obtained to determine key performance parameters such as the motion limits of each joint and the maximum reach. Furthermore, the key positional information, such as the base coordinates and cleaning station coordinates of each robot in the workpiece coordinate system, needs to be calibrated. Finally, based on the properties of the two-component adhesive itself, the operable time after mixing the two-component adhesive is recorded. This time defines the maximum permissible duration from adhesive mixing to equipment blockage due to curing.

[0041] S2: For each point on the spraying path, calculate the multi-physics fusion cost of each robot at that point.

[0042] Reference Figure 3 Step S2 includes steps S21-S24.

[0043] S21: For any point on the spraying path, obtain the robot's motion cost at that point based on the robot's posture at that point.

[0044] Reference Figure 4 Step S21 includes steps S211-S213.

[0045] S211: Obtain the angles of each joint of the robot and calculate the robot's motion constraints.

[0046] For any joint of any robot, the joint constraint is determined based on the current angle of the joint and the range of rotation of the joint. The maximum value of the joint constraint corresponding to each joint is taken as the motion constraint of the robot.

[0047] The steps for determining joint limitation include: for any joint, taking the maximum and minimum angles within the joint's range of motion as the limiting angles, and determining the joint limitation based on the angle between the current angle and the limiting angle, wherein the joint limitation is negatively correlated with the angle between the current angle and the limiting angle.

[0048] Robots typically consist of multiple rotating joints, which coordinate to spray adhesive at multiple angles. During robot operation, each movable joint has a certain range of motion, and the joint can only rotate within this range. When a joint is at the limit of its range of motion, the robot's movement is restricted, and it may even lock up mechanically. Furthermore, as the movement approaches its limits, mechanical vibration or decreased precision may occur.

[0049] Therefore, in this embodiment, the joint constraint degree of each joint is calculated, and the motion constraint degree of the robot is determined based on the maximum joint constraint degree.

[0050] The formula for calculating joint restriction can be expressed as:

[0051] In the formula, Represents robots No. Each joint is in the spraying trajectory Joint constraints at points; Represents robots No. Each joint is in the spraying trajectory Angle of the point; This indicates the first [item] obtained from the robot's technical specifications. The minimum angle within the range of rotation of a joint; This indicates the first [item] obtained from the robot's technical specifications. The maximum angle of rotation of each joint.

[0052] In the formula, when a joint is at the midpoint of its rotation range, If this part is 1, then the final calculated joint constraint is 0. When a joint is at its minimum rotational angle, Approaching 0, The value approaches -1, and thus the final calculated joint constraint approaches 1. Additionally, if the joint is at its maximum range of motion... As the joint approaches 1, the final calculated joint constraint approaches 1. Regardless of whether the joint is at its minimum or maximum angle, the direction of movement of the current joint is restricted to a certain extent. Therefore, as the joint approaches the limit angle (the maximum and minimum angles of the rotation range), the joint constraint increases.

[0053] S212: Calculate the unit path joint change rate of the robot, which is positively correlated with the ratio of the robot's joint angular velocity to its end effector linear velocity.

[0054] In robot kinematics, a classic case is that all joints are in their respective comfort zones, and the result of motion limitation approaches 0. However, this may cause the spray gun at the robot's end effector to lock in a certain direction, losing the ability to move in one or more directions, and reducing operability to 0. In this case, motion limitation cannot identify this risk. Therefore, this embodiment introduces unit path joint change rate to capture this risk.

[0055] Specifically, the square root of the sum of squares of the rates of change of all robot joint angles relative to the arc length of the spraying path is calculated, and this value is normalized to obtain the unit path joint change rate.

[0056] The formula for calculating the rate of change of a unit path joint can be expressed as: In the formula, Represents robots In the spraying trajectory Rate of change of a point per unit path joint; Represents robots The Each joint at the path point The angle at which it is located; Indicates the first The angle of each joint relative to the path arc length The rate of change of represents the angle of rotation of the joint when the spray gun advances one unit length; This indicates the total number of robot joints. The larger the value, the greater the angle that the joint needs to rotate in order for the spray gun to move a small distance along the path.

[0057] The rate of change of a unit path joint reflects the change at path points. The unit path joint rotation rate is the total angle required for the robot's joints to rotate one unit length along the spraying path. As the robot approaches a singular posture, the unit path joint rotation rate increases. Subsequently, the unit path joint rotation rate is normalized to obtain a normalized result, indicating that the robot's posture is very uncomfortable and close to a singular posture.

[0058] S213: The robot motion cost is obtained by combining the robot's motion limitation and the unit path joint change rate, wherein the motion limitation and the unit path joint change rate are positively correlated with the robot's motion cost at that point.

[0059] The difference between the preset coefficient and the motion limitation is used as the first adjustment factor. The difference between the preset coefficient and the unit path joint change rate is used as the second adjustment factor. The product of the first adjustment factor and the second adjustment factor is used as the motion comfort. The difference between 1 and the motion comfort is used as the control coefficient. The product of the control coefficient and the preset scaling factor is calculated. The sum of this product and the preset basic cost factor is used as the motion cost of the robot.

[0060] Specifically, for any point on the spraying path, the formula for calculating the robot's motion cost at that point can be expressed as:

[0061] In the formula, Represents robots Points on the spraying path The cost of movement at the location; Represents robots Points on the spraying path Movement restriction at the location; Represents robots Points on the spraying path The normalized result of the unit path joint change rate at the location, with a value range of 0-1; The preset scaling factor is used to define the penalty weight for inefficient states; in this embodiment, it is set to 1.0, but in other embodiments it can be adjusted according to the actual production situation. The preset basic cost factor represents the basic cost required for the robot to move a unit distance in the most comfortable posture. In this embodiment, it is set to 1.0, but in other embodiments, it can also be adjusted based on the experience of the staff.

[0062] Reflects the robot Points on the spraying path The motion restriction at this location is preset to a coefficient of 1 in this embodiment. The first adjustment factor reflects the comfort of the robot's posture; similarly, Represents robots Points on the spraying path The normalized result of the rate of change of the unit path joint at the location; The second adjustment factor reflects the degree of flexibility the robot can move. The product of the two factors represents the robot's movement comfort. This part serves as a control coefficient to adjust the robot's motion cost. The robot's motion cost is lower when it is in a comfortable and flexible state, that is, when the motion is less restricted and the normalized result of the unit path joint change rate is small.

[0063] S22: Calculate the robot's power cost at that point based on the robot's power consumption at that point.

[0064] For any point in the spraying path, obtain the robot's arm span, the robot's movement speed, and the curvature of that point in the spraying path. Calculate the power consumption cost based on the robot's arm span, movement speed, and the curvature of that point in the spraying path. The power consumption cost is positively correlated with the robot's arm span, movement speed, and the curvature of that point in the spraying path.

[0065] The formula for calculating power consumption cost can be expressed as:

[0066] In the formula, Represents robots Points on the spraying path The energy cost at the location; The lever sensitivity factor is preset by staff based on their work experience; in this embodiment, it is set to 3.0. Represents robots At the waypoint Arm span at the location; Represents robots The maximum arm span, a parameter that can be obtained from the robot's technical specifications, is a fixed parameter after the robot is manufactured. Represents robots Points on the spraying path The tangential movement speed at the point is preset by the staff before spraying; Indicates points on the spraying path The curvature at that point.

[0067] During robotic painting, if the robot has a large reach when painting a certain point, meaning a large horizontal distance between the robot's end effector and the base, the torque between the robot's end effector and the base will increase. Therefore, the robot motors need to generate more torque to drive the robot. Similarly, when the robot turns at high speed along the path, the motors also need to output large torques, resulting in higher energy consumption and higher power consumption costs.

[0068] S23: Calculate the risk of glue curing at the point based on the time it takes for the robot to arrive at that point.

[0069] During the robotic application of two-component adhesive, a small amount of adhesive residue may remain at the nozzle of the spray gun. As the adhesive cures and the amount of residual adhesive increases, the spray gun may become clogged, thus affecting the application. Therefore, in this embodiment, the risk of adhesive curing at each point along the spraying path is determined based on the spraying time.

[0070] Specifically, the formula for calculating the risk of adhesive curing can be expressed as:

[0071] In the formula, Represents robots Midpoint of the spraying path Risk of adhesive curing at the site; This indicates that the robot moves along the spraying path to... Time spent at the location; This indicates the time required for the adhesive to cure. This time is determined by the properties of the adhesive itself and can be determined based on the actual production environment. This is a risk amplification factor used to control the system's sensitivity to the risk of adhesive curing. The specific setting can be determined based on the experience of the staff; in this embodiment, it is set to 5.0. Indicated by An exponential function with base 0.

[0072] The formula shows that the longer the spraying time, the greater the risk of the adhesive curing. The time spent spraying is positively correlated with the risk of adhesive curing.

[0073] S24: The product of the robot's movement cost at that point, the robot's power cost at that point, and the risk of glue curing is taken as the multi-physics fusion cost of the robot at that point.

[0074] In this embodiment, for any point in the spraying path, the product of the motion cost, power cost, and adhesive curing risk corresponding to that point is taken as the multi-physics fusion cost.

[0075] S3: Calculate the multi-physics fusion cost of each robot at multiple path points on the spraying path, integrate the multi-physics fusion cost along the path to obtain the total working cost of each robot; find the intersection point that minimizes the sum of the total working costs of all robots, and allocate the spraying path to each robot based on the intersection point.

[0076] In step S2, the multi-physics fusion cost of each robot at each point on the path is evaluated. In this step, an optimal junction point is found that minimizes the total cost of the two robots working together.

[0077] Specifically, each point on the spraying path is considered a candidate handover point. For any candidate handover point on the spraying path, the corresponding total work cost consists of two parts: the cumulative cost of the first robot working from the start of the path to the candidate handover point, plus the cost of the robot... The cumulative cost from the handover point to the end of the path. This cumulative cost needs to be obtained by integrating the multi-physics fusion cost. In this embodiment, numerical integration methods, such as Simpson's rule, can be used to calculate the cumulative cost of each path segment.

[0078] Subsequently, the system performs a simple one-dimensional iterative search. This search process traverses all candidate intersection points on the path and calculates the total work cost corresponding to each candidate intersection point. Finally, the point that minimizes the total work cost is found; this point is the robot's endpoint. and The optimal intersection point.

[0079] After finding the optimal split point, the complete spraying path can be divided into two segments based on the split point and assigned to two robots respectively. Then, the robots can perform collaborative spraying based on the corresponding path to complete the process of spraying two-component adhesive on large workpieces.

[0080] Reference Figure 1 and Figure 5Traditional task allocation methods divide the spraying trajectory by the midpoint, resulting in different spraying rhythms for the two robots and consequently slowing down the spraying efficiency. The method in this application, however, considers multiple factors when dividing the spraying trajectory. This reduces the robot's workload in uncomfortable areas and unifies the rhythms of the two robots, thereby improving spraying efficiency and stability.

[0081] This application also discloses a task allocation and trajectory coordination control system for two-component spray adhesive, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the task allocation and trajectory coordination control method for two-component spray adhesive according to this application is implemented.

[0082] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0083] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A task assignment and trajectory coordination control method for two-component spray adhesive, characterized in that, The method comprises the following steps: acquiring a spraying path of a workpiece and a kinematics model of dual robots; calculating a multi-physical fusion cost of each robot at each point on the spraying path; calculating a multi-physical fusion cost of each robot at multiple path points on the spraying path; integrating the multi-physical fusion cost along the path to obtain a total work cost of each robot; finding a handover point that minimizes the sum of the total work costs of all robots, and allocating the spraying path to each robot based on the handover point; The calculation step of the multi-physical fusion cost comprises the following steps: acquiring a motion cost of the robot at the point according to the posture of the robot at the point; calculating a power cost of the robot at the point according to the power consumption of the robot at the point; calculating a glue solidification risk of the robot at the point according to the time taken by the robot to reach the point; and taking the product of the motion cost of the robot at the point, the power cost of the robot at the point, and the glue solidification risk as the multi-physical fusion cost of the robot at the point.

2. The task allocation and trajectory coordination control method for two-component spray adhesive according to claim 1, wherein, The calculation step of the motion cost of the robot at the point according to the posture of the robot at the point comprises the following steps: acquiring an angle of each joint of the robot to calculate a motion restriction degree of the robot; calculating a unit path joint change rate of the robot, which is positively correlated with the ratio of the joint angular velocity of the robot to the linear velocity at the end; and fusing the motion restriction degree of the robot and the unit path joint change rate to obtain the motion cost of the robot, wherein the motion restriction degree and the unit path joint change rate are positively correlated with the motion cost of the robot at the point. 3.The task allocation and trajectory cooperative control method for two-component spray adhesive according to claim 2, wherein, The step of acquiring the angle of each joint of the robot to calculate the motion restriction degree of the robot comprises the following steps: for any joint of any robot, determining a joint restriction degree of the joint according to the current angle of the joint and the rotation range of the joint; and taking the maximum value of the joint restriction degrees of the joints as the motion restriction degree of the robot.

4. The task allocation and trajectory coordination control method for dual-component spray adhesive according to claim 3, wherein, The step of determining the joint restriction degree of the joint according to the current angle of the joint and the rotation range of the joint comprises the following steps: for any joint, taking the maximum angle and the minimum angle in the rotation range of the joint as limit angles, and determining the joint restriction degree according to the included angle between the current angle of the joint and the limit angles, wherein the joint restriction degree is negatively correlated with the included angle between the current angle of the joint and the limit angles.

5. The task allocation and trajectory coordination control method for dual component spray adhesive according to claim 2, wherein, The step of calculating the unit path joint change rate of the robot comprises the following steps: calculating the square root of the square sum of the change rates of all joint angles of the robot with respect to the arc length of the spraying path; and taking the square root as the unit path joint change rate.

6. The task allocation and trajectory coordination control method for dual component spray adhesive according to claim 2, wherein, The step of fusing the motion restriction degree of the robot and the unit path joint change rate to obtain the motion cost of the robot comprises the following steps: taking the difference between a preset coefficient and the motion restriction degree as a first adjustment factor, taking the difference between a preset coefficient and the unit path joint change rate as a second adjustment factor, taking the product of the first adjustment factor and the second adjustment factor as a motion comfort, taking the difference between 1 and the motion comfort as a control coefficient, calculating the product of the control coefficient and a preset scaling factor, and taking the sum of the product and a preset base cost factor as the motion cost of the robot.

7. The task allocation and trajectory coordination control method for dual component spray adhesive of claim 1, wherein, The step of calculating the power cost of the robot at the point according to the power consumption of the robot at the point comprises: for any point in the spraying path, obtaining the arm span of the robot when spraying the point, the motion speed of the robot when spraying the point, and the curvature of the point in the spraying path, and calculating the power consumption cost based on the arm span of the robot, the motion speed of the robot, and the curvature of the point in the spraying path, wherein the power consumption cost is positively correlated with the arm span of the robot, the motion speed of the robot, and the curvature of the point in the spraying path.

8. The task allocation and trajectory coordination control method for dual component spray adhesive according to claim 7, wherein, The horizontal distance between the end spray gun and the robot base is taken as the arm span of the robot.

9. The task allocation and trajectory coordination control method for dual component spray adhesive of claim 1, wherein, In the step of calculating the glue solidification risk of the robot at the point according to the time of the robot reaching the point, the time of the robot reaching the point is positively correlated with the glue solidification risk.

10. A task assignment and trajectory coordination control system for two-component spray glue, characterized in that, The method comprises the steps of: A processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, the method for task allocation and trajectory cooperative control of two-component glue spraying according to any one of claims 1-9 is realized.

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