An intelligent quilting method and a quilting robot

Abstract

The present application belongs to the technical field of fabric processing equipment, and particularly relates to an intelligent quilting method and a quilting robot. The flatness of a fabric is detected by a detection device, and whether the flatness of the fabric meets quilting requirements is judged. Then, adjustment is performed based on the judgment result, so that the flatness of the fabric finally meets the quilting requirements. The present application can automatically and intelligently judge and adjust the flatness of the fabric, realize fully automatic and intelligent quilting control, and improve the quilting yield rate and quilting efficiency of the fabric.

Description

Technical Field

[0001] This invention belongs to the technical field of fabric processing equipment, specifically relating to an intelligent quilting method and a quilting robot. Background Technology

[0002] Under current technological conditions, quilting equipment controllers are embedded systems based on PLCs or microcontrollers. They are programmed with specific operating sequences according to certain production processes to complete the quilting tasks of fabrics such as quilts. To facilitate the operation of the quilting machine, manual assistance is required. This includes manual loading and unloading of materials (such as fabrics and quilts), manual securing of the clamps, manual setting of dimensions on the human-machine interface, and manual adjustment of the tension of the material based on visual judgment. While improved quilting machine designs exist that automate loading and unloading, manual assistance or manual setting is still needed for securing and tightening fabrics, judging size, and determining tension. When quilting patterns onto fabrics, especially high-end, high-quality fabrics, the quilting quality requirements are high. The patterns must be realistic, the lines smooth and wrinkle-free, and the quilting quality must be high. Therefore, a high degree of tension is required after the fabric is loaded onto the machine and before the quilting operation begins. The degree of tension affects the flatness of the fabric and directly impacts the quality of the quilting. Too loose a tension will cause wrinkles in the quilting lines, or even lead to failure; too tight a tension may cause deformation of the fabric at the clamps or damage to the fabric during operation. Currently, the tensioning process is all done manually, relying on human experience to judge and adjust the tension. Overall, existing quilting machines have low levels of automation and intelligence. Summary of the Invention

[0003] In view of this, the present invention proposes an intelligent quilting method and a quilting robot. The method utilizes a detection device to detect the flatness of the fabric and determine whether the flatness of the fabric meets the quilting requirements. Then, adjustments are performed based on the judgment results to ultimately ensure that the flatness of the fabric meets the quilting requirements. The present invention can automatically and intelligently judge and adjust the flatness of the fabric, realize fully automated and intelligent quilting control, and improve the quilting yield and efficiency of the fabric.

[0004] To achieve the above-mentioned technical objectives, the specific technical solution adopted by the present invention is as follows:

[0005] A smart quilting method for automatically smoothing and quilting fabrics using a quilting robot includes the following steps:

[0006] S101: The fabric is conveyed and laid flat onto the quilting robot;

[0007] S102: The detection device of the quilting robot detects the flatness of the fabric; and determines whether the flatness of the fabric meets the quilting requirements;

[0008] S103: If the flatness of the fabric does not meet the quilting requirements, the flatness of the fabric is adjusted based on the adjustment device of the quilting robot.

[0009] S104: Repeat steps S102-S103 until the fabric's flatness meets the quilting requirements;

[0010] S105: The fabric is quilted based on the quilting head of the quilting robot.

[0011] Furthermore, the detection device determines the smoothness of the fabric based on the deviation between the sampling points on the fabric and the reference plane. The specific method includes the following steps:

[0012] S201: Let AX + BY + CZ + D = 0;

[0013] make

[0014]

[0015]

[0016]

[0017] Therefore, Z = a1 + a2*X + a3*Y;

[0018] S202: Let P be the coordinates of each sampling point measured by the detection device. ij (X i ,Y j Z ij ), where: i=0,1,2….,k; j=0,1,2,…,m; then the surface mathematical model of the fabric is expressed as:

[0019] Z = a X1 + f;

[0020] In the formula,

[0021]

[0022] a1, a2, and a3 are three parameters to be estimated;

[0023]

[0024]

[0025] f is the deviation between the sampling point and the reference plane;

[0026] S203: Let the values ​​of a1, a2, and a3 be such that the measured value Z of the sampling point is... ijRegression value with reference plane When the sum of squares of the deviations reaches its minimum, that is... The minimum value indicates that the fabric has the highest smoothness.

[0027] The Expressed as

[0028]

[0029] S204: Based on the measured values ​​of each sampling point, differentiate with respect to a1, a2 and a3 respectively, and calculate the values ​​of a1, a2 and a3 when the minimum value of ΔZ1 is obtained;

[0030] The expression for the deviation of the sampling point relative to the reference plane is:

[0031] .

[0032] Furthermore, the method for determining whether the flatness meets the quilting requirements is as follows:

[0033] In the data array composed of the aforementioned sampling points, take f ij The sum of the largest absolute values ​​is taken as the deviation between the flatness and the reference plane.

[0034]

[0035] If f0>f threshold This indicates that the deviation does not meet the quilting requirements; where f threshold This indicates the minimum deviation required to meet the flatness requirements for quilting.

[0036] Furthermore, the method for determining whether the flatness meets the quilting requirements is as follows:

[0037] The surface of the fabric is divided into N equal partitions; the fij^2 values ​​contained in each partition are calculated and summed, expressed as S1, S2, ….., S N ;

[0038] Find S1, S2, ..., S N Let the maximum value in f be denoted as Sx. If Sx > f threshold This indicates that the deviation does not meet the quilting requirements; where f threshold This indicates the minimum deviation required to meet the flatness requirements for quilting.

[0039] Furthermore, the quilting robot is also equipped with an artificial intelligence module, and the method for learning and judging whether the flatness meets the quilting requirements is as follows:

[0040] Based on the consistency of the deviation values ​​between each sampling point and the reference plane in at least a portion of the fabric determined by the artificial intelligence module, it is determined whether the flatness meets the quilting requirements.

[0041] Furthermore, the artificial intelligence module is also used to learn and compare the consistency after the adjustment with the consistency before the adjustment to determine the effectiveness of the adjustment.

[0042] Furthermore, the present invention also proposes a quilting robot for implementing the above-mentioned intelligent quilting method, characterized in that it includes:

[0043] Quilting frames, including frame-like structures;

[0044] A movable track, installed on the quilting frame, is used to generate linear motion parallel to the upper and lower end frame bars of the frame;

[0045] Multiple clamping mechanisms are provided, with at least two clamping mechanisms arranged on the moving track and at least two clamping mechanisms arranged on the left / right end frame bars of the frame. The multiple clamping mechanisms are used to clamp the fabric.

[0046] Multiple sets of tension actuators, at least one set of tension actuators is arranged on the moving track, and at least three sets of tension actuators are arranged on the frame. The multiple sets of tension actuators are used to perform the adjustment.

[0047] The machine head guide rail is set on the quilting frame and is used to generate linear motion parallel to the frame.

[0048] A quilting machine head is movably disposed along the machine head guide rail and is used to perform quilting on the fabric;

[0049] The detection device includes a control unit and a detection unit, wherein the detection unit is used to detect the morphology of the fabric; and the control unit is used to determine the smoothness of the fabric based on the detection data of the detection unit.

[0050] The controller is used to control the movement of the moving track, clamping mechanism, tensioning actuator, head guide rail, and quilting head.

[0051] Furthermore, the detection unit is a visual sensor or a ranging sensor.

[0052] Furthermore, the detection unit is mounted on the quilting machine head.

[0053] Furthermore, the control unit communicates with the controller and includes an FPGA and a large-capacity RAM and a large-capacity FLASH DISK that communicate with the FPGA.

[0054] By adopting the above technical solution, the present invention can also bring the following beneficial effects:

[0055] This invention proposes multiple judgment algorithms and artificial intelligence judgment methods to achieve automatic and intelligent judgment and adjustment of fabric flatness, which has the technical advantages of high judgment efficiency and accurate results. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the quilting robot in a specific embodiment of the present invention;

[0058] Figure 2 This is a flowchart illustrating the flatness adjustment process in an intelligent quilting method according to a specific embodiment of the present invention.

[0059] Figure 3 This is a communication block diagram of the control unit and the controller in a specific embodiment of the present invention;

[0060] Figure 4 This is a flowchart illustrating the flatness judgment process in an intelligent quilting method according to a specific embodiment of the present invention.

[0061] Figure 5 This is a schematic diagram illustrating the working principle of the artificial intelligence module in a specific embodiment of the present invention;

[0062] The components include: 1. frame; 2. head guide rail; 3. clamping mechanism; 4. tensioning actuator; 5. quilting head; and 6. moving guide rail. Detailed Implementation

[0063] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0064] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0065] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0066] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0067] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0068] In one embodiment of the present invention, an intelligent quilting method is proposed for automatically smoothing and quilting fabrics based on a quilting robot, comprising the following steps:

[0069] S101: Convey and lay the fabric onto the quilting robot;

[0070] S102: A detection device based on a quilting robot detects the flatness of the fabric and determines whether the flatness of the fabric meets the quilting requirements;

[0071] S103: If the flatness of the fabric does not meet the quilting requirements, the flatness of the fabric is adjusted based on the adjustment device of the quilting robot.

[0072] S104: Repeat S102-S103 until the fabric flatness meets the quilting requirements;

[0073] S105: A quilting machine head based on a quilting robot performs quilting on 5 pairs of fabrics.

[0074] In this embodiment, the quilting robot is equipped with a quilting device, and the quilting device in this embodiment adopts a traditional technical solution.

[0075] like Figure 1 As shown, the quilting robot in this embodiment includes:

[0076] Quilting frame, including a frame-shaped frame 1;

[0077] The movable track, installed on the quilting frame, is used to generate linear motion parallel to the upper and lower frame bars of the frame 1;

[0078] Multiple clamping mechanisms 3, at least two clamping mechanisms 3 are set on the moving track, and at least two clamping mechanisms 3 are set on the left / right end frame bars of the frame body 1. The multiple clamping mechanisms 3 are used to clamp the fabric.

[0079] Multiple sets of tension actuators 4, at least one set of tension actuators 4 is set on the moving track, and at least three sets of tension actuators 4 are set on the frame 1. The multiple sets of tension actuators are used to perform adjustments.

[0080] The head guide rail 2 is set on the quilting frame and is used to generate linear motion parallel to the frame 1;

[0081] The quilting head 5 is movable along the head guide rail 2 and is used to perform quilting on the fabric;

[0082] The detection device includes a control unit and a detection unit. The detection unit is used to detect the morphology of the fabric, and the control unit is used to determine the smoothness of the fabric based on the detection data of the detection unit.

[0083] The controller is used to control the movement of the moving track, clamping mechanism 3, tensioning actuator 4, head guide rail 2, and quilting head 5.

[0084] In this embodiment, the quilting frame is a supporting structure, and the frame 1 can be set according to the specific fabric shape to be quilted; the fabric shape in this embodiment can be a polygon or rectangle that can be clamped based on the corners, and the material and type of the fabric are not limited in this embodiment. For clarity, the following description uses the fabric to be quilted as the quilting material.

[0085] In this embodiment, the clamping mechanism 3 clamps the four corners of the fabric using a clamping method; before the fabric is loaded, it is located at the entrance of the frame 1, and at least two clamping mechanisms 3 are set on the servo moving guide rail 6. The movement range of the servo moving guide rail 6 is: linear reciprocating between the left and right end frame bars of the frame 1.

[0086] like Figure 2 As shown, during loading, after the fabric is transported from the loading platform to the quilting robot, one edge, i.e., the two corners, of the fabric reaches the entrance of the frame 1 of the quilting machine (such as the left frame). Two clamping mechanisms 3 automatically and simultaneously press down on the two corners of the fabric. Driven by the synchronized servo moving guide rail 6, these two clamping mechanisms 3 move the fabric towards the end of the frame 1 (such as the right frame) until the fabric is fully stretched and transported onto the frame 1. During the stretching and transporting process, the fabric is automatically flattened. As the fabric is transported forward, the distance sensor or vision sensor located on the quilting machine head 5 detects that the fabric has been completely transported above the frame. Then, the other two clamping mechanisms 3 located at the entrance of the frame 1 automatically lower and clamp the two end corners of the fabric. Simultaneously, the fabric transport is slowly delayed and stops. The purpose of the delay is to slightly flatten the fabric again in the transport direction to ensure it is wrinkle-free in that direction. This completes the first process, the fabric loading process.

[0087] The detection device then performs intelligent identification and adjustment of the fabric's flatness. The quilting head 5, following a pre-defined route, begins scanning left and right (X-direction) and then moves in the original transmission direction (Y-direction). Based on internal sampling points or feature surfaces, it performs distance scanning on the fabric located below the detection unit (which can be a distance sensor), storing the scan data and relevant point coordinates in the control unit's memory. After scanning, an algorithm is activated to learn, analyze, and judge the fabric surface flatness, and autonomously decides whether flatness adjustment is needed. If adjustment is required, the controller instructs the tension actuator 4 to make a micro-adjustment. After adjustment, the quilting head 5 restarts point distance scanning to re-judge the flatness. If the flatness meets the quilting requirements, the process ends.

[0088] In the third process, according to the pre-set pattern, the quilting machine head 5 drives the quilting device to start the quilting operation.

[0089] In this embodiment, the scanning process of the detection unit is as follows: After receiving the material loading completion signal, the controller starts the material width scanning program. The controller controls the servo motor of the servo moving guide rail 6 to perform micro-motion until the torque value output by the torque sensor installed on the servo motor is higher than the set torque value, T thresholdThen, the servo moving guide 6 stops micro-movement, and the fabric has completed the above-mentioned tensioning process. Next, the detection unit begins to sweep horizontally, that is, it moves the distance measuring sensor in the X direction. The distance measuring sensor is vertically downward and measures the distance between the distance measuring sensor and the fabric surface. When the distance measuring sensor moves out of the fabric range with the quilting head 5, the distance measurement data shows a significant change, which means that it has exceeded the fabric width. Based on this, the controller initially determines the initial fabric width, which is the initial tension width T0.

[0090] After the fabric width is initially determined, the control unit activates the intelligent tension judgment module program, which begins to identify and calculate the flatness data of the fabric surface. When the flatness does not meet the requirements, the controller automatically adjusts the tension. After the adjustment is completed, it indicates that the flatness of the fabric surface has reached the requirements for high-quality quilting. At this point, the tension actuator 4 is locked to ensure that the flatness no longer changes.

[0091] It is important to note that the primary principle for determining the initial sampling point is that the data must be scanned at half of the initial tension width T0, that is, at the midline of the material in the X direction (parallel to the Y direction), because the data in the middle area of ​​the material in the X direction has the greatest impact on the overall flatness.

[0092] In this embodiment, the detection unit is a visual sensor or a ranging sensor.

[0093] In this embodiment, as Figure 3 As shown, the control unit communicates with the controller, including an FPGA and a large-capacity RAM and a large-capacity FLASH DISK that communicate with the FPGA.

[0094] In one embodiment, such as Figure 4 As shown, the control unit judges the flatness of the fabric based on the deviation between the sampling points (point array) on the fabric and the reference plane, and uses the least squares method to determine the reference plane and calculate the flatness error. That is, when determining the evaluation criterion, the sum of the squares of the distances from each point on the actual surface being measured to the least squares plane should be minimized.

[0095] The specific method includes the following steps:

[0096] S201: Let AX + BY + CZ + D = 0;

[0097] make

[0098]

[0099]

[0100]

[0101] Therefore, Z = a1 + a2*X + a3*Y;

[0102] S202: Let P be the coordinates of each sampling point measured by the detection device. ij (X i ,Y j Z ij ), where: i=0,1,2,….,k; j=0,1,2,…,m; then the mathematical model of the fabric surface is expressed as:

[0103] Z = a X1 + f;

[0104] In the formula,

[0105]

[0106] a1, a2, and a3 are three parameters to be estimated;

[0107]

[0108]

[0109] f is the deviation between the sampling point and the reference plane;

[0110] S203: Let the values ​​of a1, a2, and a3 be such that the measured value Z at the sampling point... ij Regression value with reference plane When the sum of squares of the deviations reaches its minimum, that is... The minimum value indicates the highest level of fabric smoothness;

[0111] Will Expressed as

[0112]

[0113] S204: Based on the measured values ​​of each sampling point, differentiate with respect to a1, a2 and a3 respectively, and calculate the values ​​of a1, a2 and a3 when the minimum value of ΔZ1 is obtained;

[0114] The expression for the deviation of the sampling point relative to the reference plane is:

[0115] .

[0116] In one embodiment, the method for determining whether the flatness meets the quilting requirements is as follows:

[0117] In the data array composed of each sampling point, take f ij The sum of the largest absolute values ​​is taken as the deviation between the flatness and the reference plane.

[0118]

[0119] If f0>fthreshold This indicates that the deviation does not meet the quilting requirements; where f threshold This represents the minimum deviation required for flatness to meet the quilting requirements. In this embodiment, the sum of the absolute values ​​of the maximum and minimum error values ​​is used as the flatness index. The advantage is that the calculation is fast; the disadvantage is that it may introduce high data values ​​caused by accidental abnormal factors such as measurement errors into the final result, thus leading to incorrect judgment.

[0120] Therefore, the following embodiments are further proposed:

[0121] The calculated error values ​​are divided into regions and blocks to generate a flatness index, and then the flatness is characterized by taking the maximum value.

[0122] The method for determining whether the flatness meets the quilting requirements in this embodiment is as follows:

[0123] Divide the surface of the fabric into N equal partitions; calculate and sum the fij^2 values ​​in each partition, expressed as: S2, ….., S N ;

[0124] Find S2, ….., S N Let the maximum value in f be denoted as Sx. If Sx > f threshold This indicates that the deviation does not meet the quilting requirements; where f threshold This indicates the minimum deviation in flatness required to meet the quilting requirements. The advantage of this embodiment over the previous one is that it uses the flatness of a specific section of the fabric as an indicator of the final flatness of the entire fabric, thereby improving the reliability of the judgment and avoiding the impact of occasional abnormal test data.

[0125] In one embodiment, the quilting robot is also equipped with an artificial intelligence module, which learns to determine whether the flatness meets the quilting requirements.

[0126] The consistency of the deviation values ​​between each sampling point and the reference plane in at least a part of the fabric is determined by the artificial intelligence module to determine whether the flatness meets the quilting requirements.

[0127] In this embodiment, the artificial intelligence module is also used to learn and compare the consistency after adjustment with the consistency before adjustment to determine the effectiveness of the adjustment.

[0128] This embodiment uses an artificial intelligence learning algorithm to learn and compare the error data and its correlation of each sampling point. When the error data of each sampling point in the examined area or all areas are basically consistent, the artificial intelligence decision algorithm considers that the material has reached a certain degree of flatness; otherwise, the tension is adjusted to adjust the flatness so that the error of each sampling point in the examined area is basically consistent.

[0129] like Figure 5 As shown, the artificial intelligence module (AI learning module) for f ij When determining data consistency, it is not only for all f in the current iteration... ij The system learns, judges, and decides whether there is a certain degree of consistency in the data. This consistency is mainly reflected in whether the errors obtained from all sampling points and the reference plane are within a certain range. If they are within a certain range, it indicates good flatness; otherwise, it indicates poor flatness. Additionally, the artificial intelligence module also works with previous historical data... ij The data is compared and analyzed to see if there has been any improvement in the flatness adjustment. This allows for a comprehensive assessment of whether the material has met the flatness requirements.

[0130] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A smart quilting method, characterized in that, The method for automated flat quilting of fabrics using a quilting robot includes the following steps: S101: The fabric is conveyed and laid flat onto the quilting robot; S102: The detection device of the quilting robot detects the flatness of the fabric; and determines whether the flatness of the fabric meets the quilting requirements; S103: If the flatness of the fabric does not meet the quilting requirements, the flatness of the fabric is adjusted based on the adjustment device of the quilting robot. S104: Repeat steps S102-S103 until the fabric's flatness meets the quilting requirements; S105: The fabric is quilted based on the quilting head of the quilting robot; The detection device determines the smoothness of the fabric based on the deviation between the sampling points on the fabric and the reference plane. The specific method includes the following steps: S201: Let AX + BY + CZ + D = 0; make Therefore, Z = a1 + a2*X + a3*Y; S202: Let P be the coordinates of each sampling point measured by the detection device. ij (X i ,Y j Z ij ), where: i=0,1,2….,k; j=0,1,2,…,m; then the surface mathematical model of the fabric is expressed as: Z = aX1 + f; In the formula, a1, a2, and a3 are three parameters to be estimated; f is the deviation between the sampling point and the reference plane; S203: Let the values ​​of a1, a2, and a3 be such that the measured value Z of the sampling point is... ij Regression value with reference plane When the sum of squares of the deviations reaches its minimum, that is... The minimum value indicates that the fabric has the highest smoothness. The Expressed as S204: Based on the measured values ​​of each sampling point, differentiate with respect to a1, a2 and a3 respectively, and calculate the values ​​of a1, a2 and a3 when the minimum value of ΔZ1 is obtained; The expression for the deviation of the sampling point relative to the reference plane is: 。 2. The intelligent quilting method according to claim 1, characterized in that, The method for determining whether the flatness meets the quilting requirements is as follows: In the data array composed of the aforementioned sampling points, take f ij The sum of the largest absolute values ​​is taken as the deviation between the flatness and the reference plane. If f0>f threshold This indicates that the deviation does not meet the quilting requirements; where f threshold This indicates the minimum deviation required to meet the flatness requirements for quilting.

3. The intelligent quilting method according to claim 1, characterized in that, The method for determining whether the flatness meets the quilting requirements is as follows: The surface of the fabric is divided into N equal partitions; the fij^2 values ​​contained in each partition are calculated and summed, expressed as S1, S2, ….., S N ; Find S1, S2, ..., S N Let the maximum value in f be denoted as Sx. If Sx > f threshold This indicates that the deviation does not meet the quilting requirements; where f threshold This indicates the minimum deviation required to meet the flatness requirements for quilting.

4. The intelligent quilting method according to claim 1, characterized in that, The quilting robot is also equipped with an artificial intelligence module, and the method for learning and judging whether the flatness meets the quilting requirements is as follows: Based on the consistency of the deviation values ​​between each sampling point and the reference plane in at least a portion of the fabric determined by the artificial intelligence module, it is determined whether the flatness meets the quilting requirements.

5. The intelligent quilting method according to claim 4, characterized in that, The artificial intelligence module is also used to learn and compare the consistency after the adjustment with the consistency before the adjustment to determine the effectiveness of the adjustment.

6. A quilting robot for performing the intelligent quilting method according to any one of claims 1-5, characterized in that, include: Quilting frames, including frame-like structures; A movable track, installed on the quilting frame, is used to generate linear motion parallel to the upper and lower end frame bars of the frame; Multiple clamping mechanisms are provided, with at least two clamping mechanisms arranged on the moving track and at least two clamping mechanisms arranged on the left / right end frame bars of the frame. The multiple clamping mechanisms are used to clamp the fabric. Multiple sets of tension actuators, at least one set of tension actuators is arranged on the moving track, and at least three sets of tension actuators are arranged on the frame. The multiple sets of tension actuators are used to perform the adjustment. The machine head guide rail is set on the quilting frame and is used to generate linear motion parallel to the frame. A quilting machine head is movably disposed along the machine head guide rail and is used to perform quilting on the fabric; The detection device includes a control unit and a detection unit, wherein the detection unit is used to detect the morphology of the fabric; The control unit is used to determine the smoothness of the fabric based on the detection data from the detection unit; The controller is used to control the movement of the moving track, clamping mechanism, tensioning actuator, head guide rail, and quilting head.

7. The quilting robot according to claim 6, characterized in that, The detection unit is a visual sensor or a distance sensor.

8. The quilting robot according to claim 6, characterized in that, The detection unit is installed on the quilting machine head.

9. The quilting robot according to claim 6, characterized in that, The control unit communicates with the controller and includes an FPGA and a large-capacity RAM and a large-capacity FLASH DISK that communicate with the FPGA.

Images