Self-adaptive adjustment method for unbundling angle of unbundling belt robot

Abstract

The invention belongs to the field of industrial automation and intelligent manufacturing, and discloses a self-adaptive adjustment method for the unbundling angle of a belt unbundling robot. Comprising the following steps: acquiring structural parameters of a steel belt; constructing a steel strip flexibility index model according to the structural parameters to calculate a flexibility index Fflex; dividing the flexibility grade of the steel strip according to the flexibility index; constructing a self-adaptive angle adjustment model based on a flexibility level, and calculating a theoretical unbundling angle theta flex; the height difference of the two ends of the steel strip is obtained through a laser ranging module, and the actual inclination angle theta measure is calculated; and the theoretical unbundling angle and the actual inclination angle are subjected to weighted fusion, a final output angle theta final is obtained, the posture of an end effector of the robot is controlled according to the final output angle, and the unbundling action is completed. According to the self-adaptive adjustment method for the unbundling angle of the unbundling belt robot, the adaptability is higher in the operation process, and the control precision is higher.

Description

Technical Field

[0001] This invention relates to the field of industrial automation and intelligent manufacturing, specifically to a method for adaptive adjustment of the unbundling angle of a unbundling robot for different steel strip flexibility. Background Technology

[0002] In steel production, hot-rolled steel coils are typically stored and transported in bundled coil form. Before proceeding to the next process, the bundles need to be automatically removed by robots. However, existing unbundling robots mostly employ fixed path and fixed angle control strategies, lacking the ability to perceive and respond to changes in the flexibility of the steel strip, making it difficult to adapt to changes in the strength and toughness of the bundles at different temperatures. Because the flexibility of steel strips varies significantly under different production processes (such as final rolling temperature and annealing), the existing fixed control mode easily leads to the following problems: First, for highly flexible steel strips, if the unbundling angle is too large or forced entry occurs, it can easily cause tearing at the steel strip edges or warping at the beginning; second, for highly rigid steel strips, if the unbundling angle is insufficient, the bundles may not be fully lifted, resulting in unbundling failure; third, the lack of real-time feedback and adaptive adjustment leads to a low success rate and a high risk of steel strip damage. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention aims to solve the problems of poor adaptability and insufficient control precision that are common in the operation of unbundling robots. It proposes a method for adaptively adjusting the unbundling angle of unbundling robots that can adapt to different steel strap flexibility.

[0004] The adaptive adjustment method for the unbundling angle of the unbundling robot according to the present invention includes the following steps: Step 1: Obtain the structural parameters of the steel strip; Step 2: Construct a steel strip flexibility index model based on structural parameters to calculate the flexibility index F. flex ; Step 3: Classify the steel strip flexibility grade according to the flexibility index; Step 4: Construct an adaptive angle adjustment model based on flexibility level and calculate the theoretical unbundling angle θ. flex ; Step 5: Obtain the height difference between the two ends of the steel strip using the laser ranging module, and calculate the actual tilt angle θ. measured ; Step Six: Weight and fuse the theoretical unbundling angle with the actual tilt angle to obtain the final output angle θ. final The robot controls the attitude of its end effector based on the final output angle to complete the unbundling action.

[0005] Furthermore, in step one, the structural parameters of the steel strip include the thickness t, the roll diameter R, the elastic modulus E, and the Poisson's ratio v.

[0006] Furthermore, in step two, the steel strip flexibility index model is as follows: F flex

[0007] Where E is the elastic modulus, t is the thickness, v is the Poisson's ratio, and R is the roll diameter.

[0008] Furthermore, in step three, the steel strip flexibility grade includes: High-flexibility steel strip: F flex <F1; Medium-flexibility steel strip: F1≤F flex <F2; High-rigidity steel strip: F flex ≥F2; Among them, F1 and F2 are preset flexibility grading thresholds.

[0009] Furthermore, in step four, the adaptive angle adjustment model is as follows:

[0010] Where θ0 is the reference angle, α is the adjustment coefficient, and F ref The value is the reference stiffness, and ε is the minimum value.

[0011] Furthermore, in step five, the actual tilt angle θ measured The calculation formula is:

[0012] Among them, d1, d 300 These represent the heights of the front and rear ends of the steel strip, respectively; L is the sensor spacing; and δ is the system error correction term.

[0013] Furthermore, in step six, the final output angle θ final The calculation formula is:

[0014] Where λ is the weighting coefficient. ∈[0,1].

[0015] Furthermore, in step six, the robot's end effector is a rotatable unbundling tool, the rotation angle of which is determined by the final output angle θ. final control.

[0016] Furthermore, the adaptive adjustment method for the unbundling robot's unbundling angle also includes: during the unbundling process, the reaction force of the robot's end effector is monitored by a force sensor. When the reaction force is abnormal, a fine-tuning mechanism is triggered to self-correct the unbundling angle or path.

[0017] Furthermore, the adaptive adjustment method for the unbundling angle of the unbundling robot is implemented based on a PLC control platform. The variables used in each step are designed based on the PLC data block structure, and the control flow is triggered by the program judgment logic.

[0018] Compared with existing technologies, the adaptive adjustment method for the unbundling robot of this invention integrates knowledge from multiple disciplines such as materials mechanics, sensor measurement, and industrial control. It utilizes laser ranging and image recognition technology to perceive the morphology and flexibility of the steel strip, and estimates its stiffness characteristics based on a computational model. Based on this, it adaptively adjusts the operating angle of the robot's end effector to achieve precise unbundling of different types of steel strips. This invention completely abandons the fixed-angle, one-size-fits-all control mode of existing technologies. By creating an intelligent control system that integrates material characteristic quantification, flexibility level classification, theoretical model calculation, and real-time sensor feedback, it takes into account both prior material knowledge and on-site measurements. This achieves dynamic adaptive adjustment of the unbundling angle to diverse physical properties of steel strips (such as differences in flexibility due to different final rolling temperatures and annealing processes). This fundamentally solves problems such as steel strip tearing, warping, or unbundling failure caused by angle mismatch in traditional methods, significantly improving the success rate, equipment versatility, and production line intelligence. It is particularly suitable for industrial scenarios with a wide variety of steel strips, large differences in flexibility, and high requirements for automation. It helps to improve the versatility of equipment, reduce operational risks, and enhance intelligence. It is one of the key application directions of high-end manufacturing equipment control optimization and flexible processing technology. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an adaptive adjustment method for the unbundling angle of a strapping robot according to an embodiment of the present invention is shown. Figure 2 The working process of the unbundling robot is shown. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] Figure 1A flowchart illustrating the adaptive adjustment method for the unbundling angle of a strapping robot according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, the method may include the following steps: Step 1 S1: Obtain the structural parameters of the steel strip; Step 2 S2: Construct a steel strip flexibility index model based on the structural parameters to calculate the flexibility index F. flex Step 3 (S3): Classify the steel strip flexibility level according to the flexibility index; Step 4 (S4): Construct an adaptive angle adjustment model based on the flexibility level and calculate the theoretical unbundling angle θ. flex Step 5 (S5): Obtain the height difference between the two ends of the steel strip using the laser ranging module, and calculate the actual tilt angle θ. measured Step S6: Weight and fuse the theoretical unbundling angle with the actual tilt angle to obtain the final output angle θ. final The robot controls the attitude of its end effector based on the final output angle to complete the unbundling action.

[0022] The adaptive adjustment method for the unbundling angle of the unbundling robot according to an embodiment of the present invention begins by acquiring the structural parameters of the steel strip, and then quantifies its physical properties using a flexibility index model. Subsequently, flexibility levels are classified based on the quantification results, and the theoretical unbundling angle θ is calculated based on the level using an adaptive angle adjustment model. flex Meanwhile, the laser ranging module collects real-time data on the steel strip's morphology and calculates the actual inclination angle θ. measured Finally, the theoretical angle and the measured tilt angle are weighted and fused to obtain the final output angle θ. final And directly use this angle to control the robot's end effector to complete the unbundling action.

[0023] The adaptive adjustment method for the unbundling angle of the unbundling robot in this invention integrates knowledge from multiple disciplines such as materials mechanics, sensor measurement, and industrial control. It utilizes laser ranging and image recognition technology to perceive the morphology and flexibility of the steel strip, and estimates its stiffness characteristics based on a computational model. Accordingly, it adaptively adjusts the operating angle of the robot's end effector to achieve precise unbundling of different types of steel strips. Its working process is an intelligent control flow that integrates multi-source information fusion. The core principle is to deeply integrate the theoretical predictions of the materials mechanics model with the robot's real-time environmental perception, enabling the robot to perceive the hardness and posture of the steel strip like a skilled worker, determine the most suitable cutting angle, and execute precise actions. This significantly improves its adaptability to different steel strips, the success rate and reliability of unbundling operations, and systematically solves the fundamental problem of the mismatch between the unbundling angle and the physical properties of the steel strip, achieving a leap from fixed execution to intelligent adaptation.

[0024] In a preferred embodiment, in step S1, the structural parameters of the steel strip may include the thickness t of the steel strip, the coil diameter R, the elastic modulus E, and the Poisson's ratio v. These structural parameters comprehensively consider the influence of material rigidity and geometric shape on the deformation ability during the unbundling process. As the basic variables for subsequent angle adjustment, they ensure the accuracy and comprehensiveness of the input of the flexibility model. The thickness t and the coil diameter R are the key geometric parameters affecting the bending stiffness, while the elastic modulus E and the Poisson's ratio v define the basic mechanical properties of the material, jointly constituting the basis for precisely quantifying flexibility. This embodiment can uniquely determine the flexibility by using four easily obtainable parameters, namely the thickness t, the coil diameter R, the elastic modulus E, and the Poisson's ratio v, eliminating the need for expensive on-line tensile tests and greatly reducing the cost.

[0025] According to the present invention, in a preferred embodiment, in step S2, the steel strip flexibility index model is: [[ID=?]] F flex

[0026] Where E is the elastic modulus, reflecting the strain degree of the steel strip under the action of unit stress; t is the thickness, and the cubic term reflects the strong influence of the thickness on the bending stiffness; v is the Poisson's ratio, describing the ratio of transverse to longitudinal strain; R is the initial coil diameter of the steel strip, and the larger it is, the stronger the flexibility.

[0027] Based on the principles of material mechanics, this model scientifically integrates material properties and geometric features into a comprehensive index, achieving an accurate and quantitative assessment of the flexibility or rigidity of the steel strip, providing a reliable basis for subsequent intelligent decision-making. The multiplication of the cube of the thickness t and the reciprocal of the coil diameter R makes F flex The magnification factor of the "actual bending difficulty" much higher than that of a single linear thickness, providing a high signal-to-noise ratio input for subsequent non-linear angle mapping. This F flex index is used to quantify the deformation response ability of the steel strip during operation and serves as the basic input for subsequent control strategies (such as angle adjustment). Compared with the existing method of only judging strategies through image or position information, this index system realizes the digital modeling of material properties and is the key support for the flexible control logic.

[0028] In a preferred embodiment, in step S3, to achieve a differential response for the unbundling control, two flexibility grading thresholds F1 and F2 can be preset, and the steel strip flexibility grades are divided into: high-flexibility steel strip: F flex < F1; medium-flexibility steel strip: F1 ≤ F flex < F2; high-rigidity steel strip: F flex≥F2; where F1 and F2 are preset flexibility grading thresholds. This embodiment transforms continuous physical indicators into discrete control strategy levels, simplifying the control logic and enabling the system to adopt differentiated and most suitable angle strategies for steel strips with different properties, greatly enhancing the system's applicability and control efficiency. Its three-interval grading maps infinite material combinations to finite control strategies, requiring only two comparisons from the PLC to complete the grading determination, significantly reducing computation time.

[0029] According to a preferred embodiment of the present invention, in step four S4, the adaptive angle adjustment model is as follows:

[0030] Where θ0 is the reference angle, which can be set as the initial angle under the standard unbundling posture; α is the adjustment coefficient, which determines the sensitivity of angle changes; F ref For reference stiffness values, F is typically taken from the stiffness of a moderately flexible steel strip. flex The value; ε is the minimum value, used to avoid the denominator being zero or the logarithm being negative.

[0031] This embodiment utilizes the properties of a logarithmic function to achieve a non-linear, smooth adjustment of the angle as flexibility changes. This model ensures that the highly flexible steel strip achieves a larger angle to prevent warping, while the highly rigid steel strip achieves a smaller angle to avoid interference, thus optimizing the accuracy and stability of the control action.

[0032] According to a preferred embodiment of the present invention, in step five S5, the laser ranging module may include two laser ranging sensors, respectively used to measure the height of the front and rear ends of the steel strip. The data collected by the laser ranging module can be normalized and offset calibrated, and the actual tilt angle θ measured The calculation formula is:

[0033] Among them, d1, d 300 These represent the heights of the front and rear ends of the steel strip, respectively; L is the sensor spacing; and δ is the system error correction term.

[0034] This embodiment presents a method for measuring and calculating the actual tilt angle, and clarifies the sensor configuration and data processing. By directly measuring the actual morphology of the steel strip surface, it provides realistic environmental feedback for the control system. The dual-sensor configuration and trigonometric function calculation are simple and reliable. Normalization and calibration processes eliminate systematic errors, ensuring the real-time performance and accuracy of the tilt angle data, and compensating for the shortcomings of purely theoretical models.

[0035] In a specific embodiment, combined with actual working conditions, assuming that the horizontal distance between the two sensing points in the Y-axis direction of the robot is L=800mm, the robot control system can collect the height data of both ends returned by the laser ranging module in real time through the PLC program, namely the front laser ranging value sensorData[1] and the back laser ranging value sensorData

[300] (the distance value recorded by the laser ranging sensor at the beginning and end of the robot's movement path, used to determine the tilt of the object), where:

[0036] Among them, 300 is the standardization scaling factor, and 60 is the physical offset calibration amount (zero offset of laser ranging installation).

[0037] The two sets of expressions above logically adjust the units and perform physical calibration on the front / back end ranging height data. Their essential function is to convert the original ranging value into "relative height" in the robot coordinate system.

[0038] The PLC program can execute the following assignment statement: "PLC_RobotoutPut".Robot rotation angle:=REAL_TO_INT( ) Ensure the robot's end effector is aligned with the tilt direction of the steel strip in real time to successfully remove the strapping.

[0039] According to a preferred embodiment of the present invention, in step six, to further improve the robustness of control, the flexibility calculation angle can be... With real-time detection angle Weighted fusion is performed, and the final output angle θ is obtained. final The calculation formula is:

[0040] Where λ is the weighting coefficient. ∈[0,1].

[0041] This embodiment, through weighted fusion, balances long-term predictions based on material properties (theoretical perspective) and short-term corrections based on real-time morphology (actual tilt angle), creating a complementary advantage. This mechanism significantly improves the system's robustness and control accuracy in the face of sensor noise or model bias.

[0042] In a preferred embodiment, in step six S6, the robot end effector is a rotatable unbundling tool, the rotation angle of which is determined by the final output angle θ. finalControl. This embodiment directly links the abstract angle output with specific mechanical actions, demonstrating the engineering feasibility of the invention. The rotatable unbundling cutter is an ideal mechanism for performing adaptive angles, ensuring that the method can be effectively and directly put into practice.

[0043] In a preferred embodiment, the adaptive adjustment method for the unbundling robot's unbundling angle may further include: during the unbundling process, monitoring the reaction force of the robot's end effector via a force sensor; when the reaction force is abnormal, triggering a fine-tuning mechanism to self-correct the unbundling angle or path. This embodiment introduces a second layer of safety assurance and optimization. When angle control still encounters unforeseen resistance, force feedback can trigger real-time correction, greatly improving the system's fault tolerance, safety, and final success rate under complex working conditions.

[0044] In another preferred embodiment, the adaptive adjustment method for the unbundling robot's unbundling angle can be implemented based on a PLC control platform. The variables used in each step are designed based on the PLC data block structure, and the control flow is triggered by corresponding program logic. This design based on PLC data blocks and standard logic ensures that the method is not merely a laboratory theory, but possesses high portability, replicability, and engineering practical value, facilitating rapid deployment in industrial settings.

[0045] In such Figure 2 In the preferred embodiment shown, the working process of the strapping removal robot 1 is illustrated: the strapping detection laser sensor 4 first scans the surface of the steel strapping and transmits the height data to the PLC system 2; based on this data and combined with the steel strapping structural parameters, the PLC system 2 calculates the flexibility index and the actual tilt angle in real time, and after weighted fusion through the built-in algorithm, outputs the optimal strapping removal angle command to the robot control system 3; finally, the robot control system 3 precisely drives the end effector 5 to rotate to the target angle, completing the adaptive and non-destructive removal of the strapping 6.

[0046] Under different production processes, varying final rolling temperatures, and varying flexibility of the steel coil strapping after bell-type furnace annealing, the adaptive adjustment method for the unbundling robot's unbundling angle in this embodiment of the invention is an intelligent control process that integrates multi-source information. Its core principle is to combine theoretical predictions from materials science with the robot's real-time environmental perception to achieve precise motion planning. At the perception layer, the system acquires information through two types of sensors: parameter sensors (such as laser thickness gauges and vision systems) to acquire macroscopic structural parameters of the steel strip (thickness t, coil diameter R) offline or online, combined with known material parameters (elastic modulus E, Poisson's ratio v); and environmental sensors (laser ranging modules) to measure the microscopic morphology of the steel strip surface online in real time, i.e., the height difference between the front and rear ends. At the decision layer, information is fused and processed. First, a theoretical path is obtained: based on material parameters, through the flexibility model F... flex The rigidity grade of the steel strip is calculated, and then the optimal theoretical unbundling angle θ is derived through an angle adjustment model. flex This path, based on prior knowledge, provides a basic strategy; then, the real-time path is obtained: based on laser ranging data, the current actual tilt angle θ of the steel strip is calculated geometrically. measured This approach, based on actual on-site conditions, provides real-time corrections; finally, a fusion decision is made: the theoretical angle and the actual tilt angle are weighted and fused to generate a final output angle θ that is both scientifically sound and realistic. final At the execution level, the final angle command is transmitted to the robot control system via the PLC. The robot control system then drives the end effector (such as a rotatable unbundling knife) to rotate precisely to θ. final The specified posture. During execution, the force sensor continuously monitors, and if abnormal resistance is encountered, it triggers fine-tuning of the angle or path, forming a closed-loop control to ensure smooth completion of the action. The unbundling robot unbundling angle adaptive adjustment method of this embodiment of the invention uses a material mechanics model as the "brain," laser ranging as the "eyes," and PLC and robot as the "hands and feet" throughout the entire working process. It organically combines long-term prediction and short-term feedback through a fusion algorithm, ultimately achieving precise, smooth, and adaptive unbundling operation.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for adaptively adjusting the unbundling angle of a strapping unbundling robot, characterized in that, Includes the following steps: Step 1: Obtain the structural parameters of the steel strip; Step 2: Construct a steel strip flexibility index model based on structural parameters to calculate the flexibility index F. flex ; Step 3: Classify the steel strip flexibility grade according to the flexibility index; Step 4: Construct an adaptive angle adjustment model based on flexibility level and calculate the theoretical unbundling angle θ. flex ; Step 5: Obtain the height difference between the two ends of the steel strip using the laser ranging module, and calculate the actual tilt angle θ. measured ; Step Six: Weight and fuse the theoretical unbundling angle with the actual tilt angle to obtain the final output angle θ. final The robot controls the attitude of its end effector based on the final output angle to complete the unbundling action.

2. The adaptive adjustment method for the unbundling angle of the unbundling robot according to claim 1, characterized in that, In step one, the structural parameters of the steel strip include the steel strip thickness t, roll diameter R, elastic modulus E, and Poisson's ratio v.

3. The method for adaptive adjustment of the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, In step two, the steel strip flexibility index model is as follows: F flex Where E is the elastic modulus, t is the thickness, v is the Poisson's ratio, and R is the roll diameter.

4. The adaptive adjustment method for the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, In step three, the steel strip flexibility grades include: High-flexibility steel strip: F flex <F1; Medium-flexibility steel strip: F1≤F flex <F2; High-rigidity steel strip: F flex ≥F2; Among them, F1 and F2 are preset flexibility grading thresholds.

5. The method for adaptive adjustment of the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, In step four, the adaptive angle adjustment model is as follows: Where θ0 is the reference angle, α is the adjustment coefficient, and F ref The value is the reference stiffness, and ε is the minimum value.

6. The method for adaptive adjustment of the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, In step five, the actual tilt angle θ measured The calculation formula is: Among them, d1, d 300 These represent the heights of the front and rear ends of the steel strip, respectively; L is the sensor spacing; and δ is the system error correction term.

7. The method for adaptive adjustment of the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, In step six, the final output angle θ final The calculation formula is: Where λ is the weighting coefficient. ∈[0,1].

8. The method for adaptive adjustment of the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, In step six, the robot's end effector is a rotatable unbundling tool, whose rotation angle is determined by the final output angle θ. final control.

9. The method for adaptive adjustment of the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, The adaptive adjustment method for the unbundling robot's unbundling angle also includes: during the unbundling process, the reaction force of the robot's end effector is monitored by a force sensor. When the reaction force is abnormal, a fine-tuning mechanism is triggered to self-correct the unbundling angle or path.

10. The method for adaptive adjustment of the unbundling angle of the unbundling robot according to claim 1 or 2, characterized in that, The adaptive adjustment method for the unbundling angle of the unbundling robot is implemented based on a PLC control platform. The variables used in each step are designed based on the PLC data block structure, and the program judgment logic is used to trigger the control flow.

Images