An intelligent control system for grey cloth handling and stacking manipulator

Abstract

The present invention belongs to the field of mechanical control technology, and specifically discloses an intelligent control system for a grey cloth handling and stacking robot, comprising: a handling information import module, a grey cloth state monitoring module, a grey cloth environment monitoring module, a handling setting analysis module, a handling control analysis module, a database, and a grey cloth handling execution terminal. The present invention realizes efficient, safe and precise control of grey cloth handling by integrating multiple functional modules. The handling information import module collects the basic properties of the grey cloth and the planned handling path, the grey cloth state monitoring module collects the grey cloth image, and the grey cloth environment monitoring module monitors the handling environment. The three together provide data support for the handling setting analysis module to determine whether the handling strategy needs to be adjusted. And new control indicators are generated through the handling control analysis module, and finally the grey cloth handling execution terminal executes precise handling instructions. The close combination of this series of modules improves the handling quality and efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of mechanical control, and in particular relates to an intelligent control system for a grey cloth handling and stacking robot. Background Art

[0002] With the development of the textile industry, the scale of grey cloth production continues to expand. In order to improve production efficiency, reduce costs and ensure the stability of product quality, it has become an inevitable trend to realize the automation of grey cloth handling and stacking, which highlights the importance of grey cloth handling and stacking robot control.

[0003] Prior art, such as a Chinese invention patent application with application number 202210877689.0, discloses a method and system for controlling a handling robot, which includes: obtaining a first image of a pallet in the area to be handled, determining a handling target in the pallet based on the first image, and controlling the handling robot to carry the handling target to a preset placement position. Furthermore, by controlling the handling robot, the objects in the pallet can be carried sequentially, and the staff does not need to lay the objects flat in the area to be handled, thus avoiding the problem of objects taking up space when laid flat. In addition, the staff only needs to transfer the pre-stacked pallets to the area to be handled and wait for handling, which improves convenience.

[0004] There is another existing technology, such as the handling and stacking servo robot arm disclosed in the Chinese invention patent application with application number 201610596869.6, which includes a control system cabinet, a rotating cabinet, a robot arm device, a mechanical gripper device and a transmission device. A turntable is provided on the control system cabinet, the rotating cabinet is arranged on the turntable, the robot arm device is arranged on the rotating cabinet, the mechanical gripper device is arranged on the robot arm device, and the transmission device includes a first transmission shaft, a second transmission shaft, a transmission pair and a transmission rope. The present invention controls the robot arm device and the mechanical gripper device through the turntable to obtain the horizontal rotational freedom, and the transmission device enables the robot arm device to obtain the vertical freedom. The mechanical gripper device is arranged on the robot arm device and has the horizontal rotational freedom. The whole device can perform rotation, movement or compound movement in the horizontal and vertical directions to complete the specified action, change the position and posture of the grasped object, and has a simple driving relationship, stable transmission, high accuracy, simple structure, and has the value of popularization and application.

[0005] With regard to the above two technical solutions, it is obvious that the current control of handling and palletizing robots still has the following deficiencies: 1. There is still a certain lack of adaptability and pertinence. It is not designed for specific types of materials or specific working environments, and cannot flexibly respond to changing handling needs.

[0006] 2. There are still some deficiencies in the grasping stability. Different grey fabrics have different characteristics. Currently, no further analysis and design of the grey fabric characteristics has been carried out, such as the grasping point design, etc., which makes it impossible to guarantee the stability and reliability of the grey fabric grasping, and thus the stacking efficiency cannot be guaranteed.

[0007] 3. There are still some deficiencies in the accuracy of the grasping design. For example, the positioning accuracy of the grasping point is insufficient. Environmental factors such as wind speed and temperature changes may affect the stability of the grasping. Currently, the grasping design has not been deeply confirmed by combining multiple influencing factors, which leads to certain deviations in the security of the subsequent movement process. Summary of the invention

[0008] In view of this, in order to solve the problems raised in the above background technology, an intelligent control system for a grey cloth handling and stacking robot is proposed.

[0009] The objective of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent control system for a grey cloth handling and stacking robot, comprising: a handling information import module for importing the planned handling path of the grey cloth handling robot, the total number of set clamping points and the material type of the grey cloth to be handled.

[0010] The grey cloth status monitoring module is used to collect three-dimensional images of the grey cloth to be transported through the high-definition camera carried by the grey cloth handling robot to obtain a three-dimensional image of the grey cloth to be transported.

[0011] The grey cloth environment monitoring module is used to perform environment monitoring through an environment monitoring terminal placed in the area where the grey cloth to be transported is located, and obtain the environment information at each monitoring time point.

[0012] The handling setting analysis module is used to import the initial setting handling indicators of the grey cloth handling robot and judge whether the handling setting is corrected. If the judgment result is corrected, the handling control analysis module is started. If the judgment result is not corrected, the initial setting clamping indicator is used as the confirmation setting clamping indicator.

[0013] The handling control analysis module is used to analyze and correct the handling control indicators and to confirm and set the clamping indicators of the grey cloth handling robot.

[0014] The database is used to store the initial three-dimensional image of the grey cloth to be transported, store the adaptive transport complexity corresponding to each transport index, and store the maximum allowable safe clamping force of the grey cloth of each material type.

[0015] The grey cloth handling execution terminal is used to control the grey cloth handling robot to execute the grey cloth clamping instruction based on the confirmed setting of the clamping index.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention realizes efficient, safe and precise control of grey cloth handling by integrating multiple functional modules. The handling information import module collects the basic properties of grey cloth and the planned handling path, the grey cloth status monitoring module collects the grey cloth image, and the grey cloth environment monitoring module monitors the handling environment. The three together provide data support for the handling setting analysis module to determine whether the handling strategy needs to be adjusted. Once adjustment is required, the handling control analysis module will generate new control indicators, and the grey cloth handling execution terminal will eventually execute precise handling instructions. The close integration of this series of modules reduces manual intervention, reduces costs, improves handling quality and efficiency, and has good flexibility and scalability.

[0017] (2) The present invention makes correction judgments on the handling settings by combining multiple dimensions such as the planned handling path of the grey cloth, the environment in the area where it is located, and the surface flatness of the grey cloth, thereby effectively solving the shortcomings of the current control of the palletizing robot in terms of adaptability and targeting, and realizing targeted design analysis of specific types and specific working environments, which facilitates flexible response to changing handling needs, thereby improving the accuracy of the control of the palletizing robot.

[0018] (3) The present invention sets a handling compensation factor for the grey cloth to be handled, analyzes the interference degree of the handling environment and the surface flatness, and then sets various judgment conditions and judges the correction items when it is necessary to make corrections to the handling settings. This can more scientifically judge the correction items in the handling process, thereby facilitating the improvement of the efficiency, stability and safety of the handling process, and also improves the pertinence of the robot control.

[0019] (4) The present invention performs correction analysis on the number of clamping points, clamping spacing and clamping force based on various judgment conditions when the correction item is clamping, and performs correction analysis on the moving speed when the correction item is movement, thereby achieving detailed and multi-directional control of the manipulator, making up for the shortcomings of the current grasping design in terms of accuracy, ensuring the stability of grasping and the effectiveness of grasping control, fully considering the influence of multiple factors on grasping control, achieving in-depth confirmation of the grasping design, and greatly improving the security in the subsequent movement process. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention.

[0022] Figure 2 It is a schematic diagram of the overall process of intelligent control of the manipulator of the present invention.

[0023] Figure 3 It is a schematic diagram of the placement posture of the grey cloth of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 and Figure 2 As shown, the present invention provides an intelligent control system for a grey cloth handling and stacking robot, the system comprising: a handling information import module, a grey cloth state monitoring module, a grey cloth environment monitoring module, a handling setting analysis module, a handling control analysis module, a database and a grey cloth handling execution terminal.

[0026] In the above, the transport setting analysis module is respectively connected to the transport information import module, the grey cloth state monitoring module, the grey cloth environment monitoring module, the database and the transport control analysis module, and the transport control analysis module is also respectively connected to the database and the grey cloth transport execution terminal.

[0027] The transport information importing module is used to import the planned transport path of the grey cloth transporting robot, the total number of set clamping points and the material type of the grey cloth to be transported.

[0028] The grey cloth state monitoring module is used to collect a three-dimensional image of the grey cloth to be transported by using a high-definition camera carried by the grey cloth transporting robot to obtain a three-dimensional image of the grey cloth to be transported.

[0029] Specifically, Figure 3 As shown, the grey cloth to be transported in the present invention is stacked horizontally. Due to the soft and slippery characteristics of the grey cloth, the transport method adopted by the present invention is the gripping and clamping method of a robot instead of the suction cup suction method, so as to ensure the stability of the transport.

[0030] The grey cloth environment monitoring module is used to perform environment monitoring through an environment monitoring terminal installed in the area where the grey cloth to be transported is located, and obtain environment information at each monitoring time point.

[0031] It should be added that the environmental monitoring terminal is integrated with multiple devices such as temperature sensors, humidity sensors and humidity meters.

[0032] Specifically, the environmental information includes but is not limited to temperature, humidity and wind speed, wherein the temperature is monitored by a temperature sensor, the humidity is monitored by a humidity sensor, and the wind speed is monitored by an anemometer.

[0033] The handling setting analysis module is used to import the initial setting handling indicators of the grey cloth handling robot, and judge whether the handling settings are corrected. If the judgment result is correction, the handling control analysis module is started; if the judgment result is not correction, the initial setting clamping indicator is used as the confirmation setting clamping indicator.

[0034] Specifically, the judgment of whether the handling setting is corrected includes: A1, based on the planned handling path of the handling robot, setting the handling compensation factor of the grey cloth to be handled, denoted as .

[0035] A2. Extract wind speed, temperature and humidity from the environmental information and calculate the environmental interference degree of the grey cloth to be transported, which is recorded as .

[0036] A3. Extracting the initial three-dimensional image of the grey cloth to be transported from the database, and then extracting the contour of each grey cloth surface by image processing technology as the initial contour.

[0037] A4. Extract the contour of each grey cloth surface from the three-dimensional image of the grey cloth to be transported by image processing technology, record it as the monitoring contour, and calculate the surface flatness of the grey cloth to be transported, record it as .

[0038] It should be added that image processing technology can be specifically used for extraction using technologies such as edge detection, and contour extraction is a relatively mature image processing method. Its specific extraction principle and extraction process will not be described here.

[0039] A5. Calculate the handling complexity of the grey fabric to be transported ,

[0040] , and The reference handling environment interference and the handling grey fabric surface flatness are set respectively. and They represent the weights corresponding to the interference degree of the handling environment and the surface smoothness of the handled grey fabric, .

[0041] It should be added that the flatness of the grey cloth directly affects the stability and success rate of clamping. If the grey cloth is not flat, it may lead to failure of clamping or damage to the grey cloth. At the same time, the uneven grey cloth is more likely to deform during transportation, which increases the difficulty of transportation. This is a direct contact transportation complexity influence. The transportation environment interference refers to the influence of wind speed and other factors on transportation during transportation. Under the influence of the same wind speed, uneven grey cloth will directly increase the complexity of transportation. Therefore, the flatness of the grey cloth has a more intuitive impact. Therefore, the weight corresponding to the transportation environment interference is greater than the weight corresponding to the surface flatness of the grey cloth. For example, and The values ​​can be 0.6 and 0.4 respectively.

[0042] In a specific embodiment, The specific value can be 0.5. The specific value can be 0.8.

[0043] A6. Extract the reference-adaptive handling complexity corresponding to each initially set handling index from the database. If the reference-adaptive handling complexity corresponding to a certain initially set handling index is smaller than the handling complexity of the grey cloth to be handled, correction is made as the judgment result, otherwise no correction is made as the judgment result.

[0044] The embodiment of the present invention performs correction judgments on the transport settings by combining multiple dimensions such as the planned transport path of the grey cloth, the environment in the area, and the surface flatness of the grey cloth, thereby effectively solving the shortcomings of the current control of the palletizing robot in terms of adaptability and targeting, and realizes targeted design analysis of specific types and specific working environments, which facilitates flexible response to changing transport needs, thereby improving the accuracy of the control of the palletizing robot.

[0045] Furthermore, regarding setting the handling compensation factor of the grey cloth to be handled in step A1, it includes: A11, extracting the handling path features from the planned handling path of the handling robot, including the length of the handling path, the number of turning sections, and the length and bending angle of each turning section.

[0046] A12. Sum the lengths of the turning sections to obtain the total length of the bending sections, and divide the sum by the length of the transport path to obtain the bending path length ratio.

[0047] A13. Compare the turning angle of each turning section with the set interference turning angle, record the turning section with a turning angle smaller than the set interference turning angle as an interference section, count the number of interference sections, and divide it by the number of turning sections to obtain an interference section ratio.

[0048] It should be added that the specific value of the interference bending angle can be set as .

[0049] A14. Extract the interval lengths between the turning sections from the planned transport route and determine the ratio of complex sections.

[0050] A15. Set the weights of the bending path length ratio, interference section ratio and complex section ratio, and obtain the handling compensation factor of the grey cloth to be handled by weighted summation. .

[0051] It should be added that the characteristics of the turning path will affect the posture change of the grey cloth during the handling process. If the robot does not fully consider the impact of the subsequent path on the stability of the grey cloth when gripping, it may cause the grey cloth to deviate or fall. For example, on the turning path, the grey cloth may be affected by lateral force, and the clamping force needs to be appropriately increased to ensure that the grey cloth does not slip. Therefore, it is necessary to select the three parameter indicators of the bending path length ratio, the interference section ratio and the complex section ratio to set the handling path compensation factor.

[0052] It should also be added that the bending path length ratio reflects the length proportion of the turning part in the transport path. A longer bending path means more path changes, which increases the complexity of transportation. Therefore, this weight is set higher to reflect its important impact on the complexity of transportation, and the interference section ratio reflects the presence of a smaller bending angle in the path, that is, the part with a sharper turn. A smaller bending angle may cause instability of the grey cloth during transportation, increasing the difficulty of transportation. Although this factor is also very important, it is slightly smaller than the bending path length ratio, so the weight of the interference section ratio is set second, and the complex section ratio reflects the part of the path where there may be multiple continuous turns or other complex terrains. Although this type of section will also affect the complexity of transportation, its impact is usually not as significant as the bending path length ratio and the interference section ratio. Therefore, this weight can be set to the minimum. For example, the weights of the bending path length ratio, the interference section ratio and the complex section ratio can be taken as 0.5, 0.3 and 0.2 respectively.

[0053] Understandably, the determination of the complex road section ratio in step A14 includes: U1, traversing all adjacent turning road sections, starting from the first turning road section, and checking each turning road section and its adjacent next turning road section one by one.

[0054] U2. If the length of the interval between adjacent turning sections is 0, the adjacent turning sections are recorded as a level I complex section group, and the number of level I complex section groups is counted and recorded as .

[0055] U3. If the length of the interval between adjacent turning sections is less than the length of any turning section in the adjacent turning sections, the adjacent turning sections are recorded as a level II complex section group, and the number of level II complex section groups is counted and recorded as .

[0056] U4, record the number of turning sections as , calculate the complex section ratio, denoted as ,

[0057] .

[0058] It should be added that the level I complex section group indicates that there is no connection between the adjacent turning sections. In this case, it indicates that the turning directions between the adjacent turning sections are inconsistent, that is, the grey cloth handling robot will experience continuous turning, which has a greater impact on the stability of the grey cloth handling robot. That is, the level I complex section group represents the most complex part, that is, there is no interval execution section between the adjacent turning sections, and the level II complex section group indicates that there is no connection between the adjacent turning sections. The connecting section is short, which will also cause certain difficulties in handling. Therefore, the complex sections are divided into level I and level II, and the number and ratio of the two are counted respectively, so as to show the complexity of the path in a more detailed and comprehensive way.

[0059] It should also be added that the calculation formula of the complex section ratio reflects the ratio of the number of Class I complex section groups to the number of turning sections as the correction factor, that is, As a correction factor, the ratio of the number of level II complex road section groups to the number of turning road sections is corrected and then used as the complex road section ratio.

[0060] In a specific embodiment, when calculating the complex section ratio, it is taken into account that the ratio of the Level I complex section group can more accurately reflect the most difficult part of the path. Since the Level I complex section group is the most difficult situation to handle, its ratio can be used as an important indicator to measure the overall path complexity. By using the ratio of the Level I complex section group as a correction factor, the distribution of difficult areas in the path can be better reflected when calculating the impact of the Level II complex section group, thereby making the final transportation compensation factor closer to the actual situation. That is, by using the ratio of the Level I complex section group as a correction factor to correct the ratio of the Level II complex section group, the complexity of the transportation path can be more accurately reflected.

[0061] Further, regarding the calculation of the interference degree of the transport environment of the grey cloth to be transported in step A2, the method includes: A21, calculating the average wind speed corresponding to each monitoring time point as the transport wind speed, recorded as .

[0062] A22. Set the transport wind speed compensation factor, recorded as ,Will As the expected transport wind speed, .

[0063] A23. Calculate the average values ​​of temperature and humidity at each monitoring time point to obtain the average temperature and average humidity, which are recorded as and .

[0064] A24. Calculate the interference degree of the transport environment of the grey cloth to be transported ,

[0065] , , and They represent the referenced interference wind speed, interference temperature and interference humidity respectively. , and are the weights corresponding to wind speed, temperature and humidity respectively, .

[0066] It should be added that due to its material characteristics, grey fabrics are easily affected by the environment, especially the temperature and humidity changes in a short period of time will also have a certain impact on the handling settings of grey fabrics. When the humidity in the air increases, the natural fibers will absorb moisture, causing the grey fabric to expand, increase in weight, and may change its dimensional stability. The increase in temperature may accelerate the impact of humidity on the grey fabric, because higher temperatures are usually accompanied by higher absolute humidity. High temperatures may also cause certain types of grey fabrics to become soft, reducing their rigidity and making it more difficult to handle. The wind speed directly affects the stability during the handling process, so the three environmental parameters of temperature, humidity and wind speed are selected to analyze the interference degree of the handling environment.

[0067] It should also be added that the impact of wind speed on handling operations is very significant. For example, strong winds will increase the swing amplitude of the robot, which will increase the difficulty of handling and the degree of damage to the grey cloth. Therefore, the weight corresponding to the wind speed is set to the largest. Changes in temperature may affect the physical properties of the grey cloth. For example, some materials may become softer at high temperatures and harder at low temperatures, which will affect the control during the handling process. The impact of humidity on grey cloth is mainly reflected in materials with strong hygroscopicity. In a high humidity environment, the grey cloth may absorb moisture, resulting in an increase in weight and more likely to stick together, increasing the difficulty of handling. Therefore, the weights of temperature and humidity are set equal. For example, , and The values ​​are 0.4, 0.3 and 0.3 respectively.

[0068] In a specific embodiment, The specific value of is mainly set according to the operation area. If the transportation is only carried out indoors, The value is 0.5 m / s. If the transport is carried out outdoors, The value is 5 m / s, and and The values ​​of these two are mainly set in combination with the material type of the grey cloth to be transported. For example, if the material type of the grey cloth to be transported is cotton grey cloth, The value can be 30℃. The value can be 60%. For example, if the material type of the grey cloth to be transported is synthetic fiber grey cloth, The value can be 25℃. The value is 70%.

[0069] It needs to be explained that cotton fiber has good hygroscopicity and can absorb moisture from the air and maintain a certain moisture content. This means that in a high humidity environment, cotton grey fabrics are more likely to absorb moisture from the air, causing the weight of the grey fabric to increase, the size to change, and even the structure to become loose. Therefore, in order to prevent these adverse effects, the ambient humidity of cotton grey fabrics is usually controlled at a relatively low level. Compared with cotton grey fabrics, most synthetic fibers, such as polyester, nylon, etc., have poor hygroscopicity. This means that synthetic fiber grey fabrics are relatively insensitive to changes in ambient humidity. Although high humidity may still have a certain impact on some synthetic fiber grey fabrics, the degree of impact is usually not as significant as that of cotton grey fabrics. Therefore, the interference humidity of synthetic fiber grey fabrics can be set higher.

[0070] It can be understood that the setting of the transport wind speed compensation factor in step A22 includes: H1, counting the number of monitoring time points at which the wind speed is higher than the transport wind speed, and comparing it with the number of monitoring time points, and recording the ratio as .

[0071] H2. Calculate the standard deviation of the wind speed at each monitoring time point and use the result as the wind speed dispersion, denoted as .

[0072] H3. Extract the highest wind speed from the wind speeds at each monitoring time point , set the handling wind speed compensation factor ,

[0073] , , and They represent the weights corresponding to the ratio of the number of monitoring time points, wind speed dispersion and wind speed difference, respectively. , is the wind speed difference for setting the reference.

[0074] It should be added that This ratio reflects how often the wind speed exceeds the transport wind speed during the entire monitoring process, indicating that the error when using the average value as the transport wind speed calculation is large. Wind speed dispersion reflects the fluctuation of wind speed. A large dispersion indicates that the wind speed is unstable. The wind speed difference can be understood as the difference between the maximum wind speed and the average wind speed. It reflects the amplitude of the wind speed change from one aspect, and also reflects the degree of deviation when using the average value as the transport wind speed. Because grey cloth is more sensitive to wind speed, the weight corresponding to the number of monitoring time points is set to be > the weight corresponding to the wind speed dispersion > the weight corresponding to the wind speed difference. For example, , and The possible values ​​are 0.45, 0.35 and 0.3 respectively.

[0075] In a specific embodiment, The specific value of is set according to the specific thickness of the grey cloth. For example, when transporting ordinary grey cloth, that is, grey cloth of regular thickness, The value can be 1.2m / s, when transporting light and thin fabrics. The acceptable value is 0.6m / s. When transporting heavy grey fabrics, The acceptable value is 1.5m / s.

[0076] Furthermore, regarding the calculation of the surface flatness of the grey cloth to be transported in step A4, it includes: A41, using image registration technology to align the monitoring contour and the initial contour of the same grey cloth surface, and perform overlap comparison to obtain the overlap area of ​​each grey cloth surface, and compare it with the area of ​​the corresponding monitoring contour of each grey cloth surface to obtain the overlap ratio of the monitoring contour of each grey cloth surface.

[0077] In a specific embodiment, the contour matching algorithm includes but is not limited to an iterative closest point ICP algorithm, a scale-invariant feature transformation algorithm, a scale space extremum algorithm and other algorithms, which are not specifically limited here. For example, the iterative closest point ICP algorithm can be selected as the contour matching algorithm described in the present invention, and the use of the contour matching algorithm for contour alignment is a relatively mature technology, and its specific alignment process is not described in detail here.

[0078] A42. Compare the monitored contour overlap ratio of each grey cloth surface with the set reference contour overlap ratio, record the grey cloth surface with a greater overlap ratio than the set reference contour overlap ratio as a flat surface, count the number of flat surfaces, and divide it by the number of grey cloth surfaces to obtain the flatness ratio.

[0079] It should be added that the specific value of the reference contour overlap ratio can be set to 0.85.

[0080] A43. Calculate the average of the monitoring profile overlap ratios of each grey cloth surface to obtain the average monitoring profile overlap ratio, set the weights of the flatness ratio and the monitoring profile overlap ratio, and calculate the surface flatness of the grey cloth to be transported by weighted summation.

[0081] It should be added that the flatness of the grey fabric has an important influence on the clamping setting. The flatness of the grey fabric is not only related to the stability and safety during the handling process, but also directly affects the success rate of the clamping operation and the quality of the grey fabric. For example, if the grey fabric is very flat, you can choose to set the clamping point at the edge or key position of the grey fabric, because the shape of the grey fabric is relatively regular and it is easier to determine the best clamping position. If the grey fabric is wrinkled or uneven, you need to adjust the position of the clamping point according to the actual situation, and you may even need to increase the number of clamping points to ensure the stability of the grey fabric during the handling process. At the same time, a moderate clamping force can be used for a flat grey fabric, because the surface consistency of the grey fabric is good and no additional force is required to maintain the shape, while an uneven grey fabric may require a greater clamping force to overcome wrinkles or deformation to prevent slippage or deformation during handling. Therefore, the surface flatness deviation of the grey fabric is also considered when judging whether to correct the handling setting.

[0082] It should also be added that the flatness ratio refers to the proportion of all grey cloth surfaces whose overlap ratio is greater than the set reference contour overlap ratio. The flatness ratio reflects the overall flatness of the grey cloth, and the monitoring contour overlap ratio refers to the ratio of the overlap area between the monitoring contour and the initial contour to the monitoring contour area. The monitoring contour overlap ratio reflects the flatness of the grey cloth on a specific surface. Because the handling needs to be set considering the overall flatness of the grey cloth, the weight of the flatness ratio is set to be greater than the weight of the monitoring contour overlap ratio. For example, the weight of the flatness ratio can be 0.55, and the weight of the monitoring contour overlap ratio can be 0.45.

[0083] The handling control analysis module is used to analyze and correct the handling control index and serve as a confirmation setting clamping index for the grey cloth handling robot.

[0084] Specifically, analyzing and correcting the handling control index includes: B1. determining the correction items, including one or more of clamping and moving.

[0085] B2. If the correction item is gripping, extract the number of initial gripping points from the index value of each initial handling index and record it as .

[0086] B3. The total number of gripping points set by the grey cloth handling robot is recorded as ,Will As an associated variable, .

[0087] B4. Calculate and correct the number of gripping points , , Indicates the round-up symbol. is a natural constant.

[0088] B5. Extract the length of the grey cloth to be transported from the image of the grey cloth to be transported ,Will and The ratio of is taken as the corrected clamping spacing, denoted as .

[0089] B6. Extract the maximum safe gripping force allowed for the material type of the grey cloth to be transported from the database and record it as , extract the initial setting gripping force from the index value of each initial setting handling index, and record it as , calculate the corrected gripping force , ,Will , and The correction item is the correction control indicator for gripping.

[0090] B7. If the correction item is movement, extract the initial setting movement speed from the index value of each initial setting handling index and record it as , calculate the corrected moving speed , , To set the reference handling compensation factor, The correction item is the correction control index when moving.

[0091] In a specific embodiment, The value can be 0.3.

[0092] B8. If the correction item is gripping and moving, confirm the correction handling control indicators for gripping and moving.

[0093] The embodiment of the present invention performs correction analysis on the number of clamping points, clamping spacing and clamping force based on various judgment conditions when the correction item is clamping, and performs correction analysis on the moving speed when the correction item is movement, thereby achieving detailed and multi-directional control of the manipulator, making up for the shortcomings of the current grasping design in terms of accuracy, ensuring the stability of grasping and the effectiveness of grasping control, fully considering the influence of multiple factors on grasping control, realizing in-depth confirmation of the grasping design, and greatly improving the security in the subsequent movement process.

[0094] Further, regarding the determination of the correction item in step B1, it includes: B11, extracting the handling compensation factor of the grey cloth to be handled , Transportation environment interference , surface flatness .

[0095] B12. Definition To judge condition 1, define To judge condition 2, define This is judgment condition 3.

[0096] B13. If only condition 1 is met, move will be used as a correction item.

[0097] B14. If only condition 2 is met, clamping and moving are used as correction items.

[0098] B15. If only condition 3 is met, clamping is used as a correction item.

[0099] B16. If two or all of the judgment conditions 1, 2 and 3 are met, clamping and moving will be used as correction items.

[0100] Understandably, when the path is complex, adjusting the moving speed is usually the first measure to be considered, because this can most directly improve the safety and stability during the handling process, that is, when only judgment condition 1 is met, the handling can be optimized by correcting the moving speed alone. If the interference degree of the handling environment is high, it means that the handling environment may not be conducive to handling. At this time, it is necessary to adjust the clamping and moving methods at the same time. If there is only poor surface flatness, it means that the grey cloth may easily deviate during the handling process, and the clamping method needs to be adjusted. Therefore, only judgment condition 1 is met, and the movement is used as the correction item. Only judgment condition 2 is met, and the clamping and movement are used as the correction items. Only judgment condition 3 is met, and the clamping is used as the correction item.

[0101] The embodiment of the present invention sets a handling compensation factor for the grey cloth to be handled, analyzes the interference degree of the handling environment and the surface flatness, and then sets various judgment conditions and judges the correction items when correction of the handling settings is needed. This can more scientifically judge the correction items in the handling process, thereby facilitating improving the efficiency, stability and safety of the handling process, while also improving the targetedness of the robot control.

[0102] Furthermore, regarding the correction handling control indicators for confirming the gripping and moving in step B8, the following are included: B81. If only the judgment condition 2 is met, As an associated variable, .

[0103] B82. If judgment conditions 1 and 2 are met, As an associated variable, .

[0104] B83. If judgment conditions 1 and 3 are met, As an associated variable, .

[0105] B84. If judgment conditions 2 and 3 are met, As an associated variable, .

[0106] B85. If judgment condition 1, judgment condition 2 and judgment condition 3 are all met at the same time, As an associated variable, , To set the reference handling complexity.

[0107] It should be added that when and At this time, the handling complexity In a critical state, ,therefore The value is a real number greater than 0. For ease of analysis and comparison, The value can be 0.2.

[0108] B86. Based on the associated variables, the correction item is confirmed in the same way as the correction item for the correction of the handling control index when the correction item is clamping.

[0109] B87, will As the corrected moving speed, , is an associated variable, , will correct the movement speed, As a corrective handling control indicator for movement.

[0110] It should be added that ,in, Represents the movement speed correction factor. When the set judgment condition is met, it indicates that the transportation difficulty and assistance degree are relatively large at this time. Usually, it is necessary to appropriately reduce the movement speed to ensure the stability of transportation. Therefore, the movement speed correction factor is set according to the associated variable to correct the movement speed.

[0111] The database is used to store the initial three-dimensional image of the grey cloth to be transported, store the adaptive transport complexity corresponding to each transport index, and store the maximum allowable safe clamping force of the grey cloth of each material type.

[0112] The grey cloth handling execution terminal is used to control the grey cloth handling robot to execute the grey cloth clamping instruction based on the confirmed setting of the clamping index.

[0113] The embodiment of the present invention realizes efficient, safe and precise control of grey cloth handling by integrating multiple functional modules. The handling information import module collects the basic properties of grey cloth and the planned handling path, the grey cloth status monitoring module collects the grey cloth image, and the grey cloth environment monitoring module monitors the handling environment. The three together provide data support for the handling setting analysis module to determine whether the handling strategy needs to be adjusted. Once adjustment is required, the handling control analysis module will generate new control indicators, and the grey cloth handling execution terminal will eventually execute precise handling instructions. The close integration of this series of modules reduces manual intervention, reduces costs, improves handling quality and efficiency, and has good flexibility and scalability.

[0114] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent control system for a grey cloth handling and stacking robot, characterized in that: include: The handling information import module is used to import the planned handling path of the grey cloth handling robot, the total number of set gripping points and the material type of the grey cloth to be handled; The grey cloth state monitoring module is used to collect a three-dimensional image of the grey cloth to be transported through a high-definition camera carried by the grey cloth transport robot to obtain a three-dimensional image of the grey cloth to be transported; The grey cloth environment monitoring module is used to perform environment monitoring through the environment monitoring terminal placed in the area where the grey cloth to be transported is located, and obtain the environment information at each monitoring time point; The handling setting analysis module is used to import the initial setting handling indicators of the grey cloth handling robot, and judge whether the handling setting is corrected or not. If the judgment result is corrected, the handling control analysis module is started; if the judgment result is not corrected, the initial setting clamping indicator is used as the confirmation setting clamping indicator; The handling control analysis module is used to analyze and correct the handling control indicators and to confirm and set the clamping indicators of the grey cloth handling robot; A database for storing an initial three-dimensional image of the grey cloth to be transported, storing the adapted transport complexity corresponding to each transport index, and storing the permitted maximum safe clamping force of the grey cloth of each material type; The grey cloth handling execution terminal is used to control the grey cloth handling robot to execute the grey cloth clamping instruction based on the confirmed setting of the clamping index; The determining whether to correct the transport setting includes: Based on the planned transport path of the transport robot, the transport compensation factor of the grey cloth to be transported is set, which is expressed as ; The wind speed, temperature and humidity are extracted from the environmental information, and the interference degree of the transport environment of the grey cloth to be transported is calculated, which is recorded as ; Extracting an initial three-dimensional image of the grey cloth to be transported from a database, and then extracting the contour of each grey cloth surface as an initial contour by image processing technology; The contour of each grey cloth surface is extracted from the three-dimensional image of the grey cloth to be transported by image processing technology, recorded as the monitoring contour, and the surface flatness of the grey cloth to be transported is calculated, recorded as ; Calculate the handling complexity of the grey fabric to be handled , , and The reference handling environment interference and the handling grey fabric surface flatness are set respectively. and They represent the weights corresponding to the interference degree of the handling environment and the surface smoothness of the handled grey fabric, ; The adaptive handling complexity corresponding to each initial setting handling index is extracted from the database. If the adaptive handling complexity corresponding to a certain initial setting handling index is smaller than the handling complexity of the grey cloth to be handled, correction will be made as the judgment result, otherwise no correction will be made as the judgment result.

2. According to claim 1, the intelligent control system for a grey cloth handling and stacking robot is characterized by: The step of setting the handling compensation factor of the grey cloth to be handled includes: Extracting transport path features from the planned transport path of the transport robot, including the transport path length, the number of turning sections, and the length and bending angle of each turning section; The lengths of the turning sections are summed to obtain the total length of the bending sections, and then divided by the length of the transport path to obtain the bending path length ratio; Compare the bending angle of each turning section with the set interference bending angle, record the turning section with a smaller angle than the set interference bending angle as an interference section, count the number of interference sections, and divide it by the number of turning sections to obtain the interference section ratio; Extract the interval length between each turning section from the planned transport route and confirm the complex section ratio; Set the weights of the bending path length ratio, interference section ratio and complex section ratio, and obtain the handling compensation factor of the grey fabric to be handled by weighted summation .

3. According to claim 2, the intelligent control system for a grey cloth handling and stacking robot is characterized by: The determining of the complex road section ratio includes: Traverse all adjacent turning sections, starting from the first turning section, and check each turning section and its adjacent next turning section one by one; If the length of the interval between adjacent turning sections is 0, the adjacent turning sections are recorded as a level I complex section group, and the number of level I complex section groups is counted and recorded as ; If the length of the interval between adjacent turning sections is less than the length of any turning section in the adjacent turning sections, the adjacent turning sections are recorded as a level II complex section group, and the number of level II complex section groups is counted and recorded as ; The number of turning sections is recorded as , calculate the complex section ratio, denoted as , .

4. The intelligent control system for a grey cloth handling and stacking robot according to claim 1 is characterized in that: The calculating of the interference degree of the transport environment of the grey cloth to be transported comprises: Calculate the average wind speed at each monitoring time point as the transport wind speed, recorded as ; Set the handling wind speed compensation factor, denoted as ,Will As the expected transport wind speed, ; Calculate the average values ​​of temperature and humidity at each monitoring time point to obtain the average temperature and average humidity, which are recorded as and ; Calculate the interference degree of the handling environment of the grey fabric to be handled , , , and They represent the referenced interference wind speed, interference temperature and interference humidity respectively. , and are the weights corresponding to wind speed, temperature and humidity respectively, .

5. The intelligent control system for a grey cloth handling and stacking robot according to claim 4 is characterized in that: The setting of the transport wind speed compensation factor includes: Count the number of monitoring time points where the wind speed is higher than the transport wind speed, compare it with the number of monitoring time points, and record the ratio as ; The standard deviation of the wind speed at each monitoring time point is calculated, and the calculation result is used as the wind speed dispersion, denoted as ; Extract the highest wind speed from the wind speeds at each monitoring time point , set the handling wind speed compensation factor , , , and They represent the weights corresponding to the ratio of the number of monitoring time points, wind speed dispersion and wind speed difference, respectively. , is the wind speed difference for setting the reference.

6. The intelligent control system for a grey cloth handling and stacking robot according to claim 1 is characterized by: The calculating of the surface flatness of the grey cloth to be transported comprises: The monitoring contour and the initial contour of the same grey cloth surface are aligned using image registration technology, and the overlap comparison is performed to obtain the overlap area of ​​each grey cloth surface, which is then compared with the area of ​​the corresponding monitoring contour of each grey cloth surface to obtain the overlap ratio of the monitoring contour of each grey cloth surface; The monitored contour overlap ratio of each grey cloth surface is compared with the set reference contour overlap ratio, and the grey cloth surface with a greater overlap ratio than the set reference contour overlap ratio is recorded as a flat surface. The number of flat surfaces is counted and divided by the number of grey cloth surfaces to obtain the flat ratio; The average of the monitoring profile overlap ratios of each grey cloth surface is calculated to obtain the average monitoring profile overlap ratio, the weights of the flatness ratio and the monitoring profile overlap ratio are set, and the surface flatness of the grey cloth to be transported is calculated by weighted summation.

7. The intelligent control system for a grey cloth handling and stacking robot according to claim 1 is characterized by: The analysis corrects handling control indicators, including: Determine the correction items, including one or more of gripping and moving; If the correction item is gripping, extract the number of initial gripping points from the index value of each initial handling index and record it as ; The total number of gripping points set by the grey cloth handling robot is recorded as ,Will As an associated variable, ; Calculate the number of corrected grip points , , Indicates the round-up symbol. is a natural constant; Extract the length of the grey cloth to be transported from the image of the grey cloth to be transported ,Will and The ratio of is taken as the corrected clamping spacing, denoted as ; Extract the maximum safe gripping force allowed for the material type of the grey cloth to be transported from the database and record it as , extract the initial setting gripping force from the index value of each initial setting handling index, and record it as , calculate the corrected gripping force , ,Will , and As the correction item, it is the correction handling control index when gripping; If the correction item is movement, extract the initial setting movement speed from the index value of each initial setting handling index and record it as , calculate the corrected moving speed , , To set the reference handling compensation factor, As the correction item is the correction handling control indicator when moving; If the correction item is gripping and moving, confirm the correction handling control indicators for gripping and moving.

8. The intelligent control system for a grey cloth handling and stacking robot according to claim 7 is characterized by: The judgment correction items include: Extract the handling compensation factor of the grey fabric to be handled , Transportation environment interference , surface flatness ; definition To judge condition 1, define To judge condition 2, define For judgment condition 3; If only condition 1 is met, the move is used as the correction item; If only condition 2 is met, clamping and moving are used as correction items; If only condition 3 is met, clamping is used as a correction item; If two or all of the judgment conditions 1, 2, and 3 are met, clamping and moving are used as correction items.

9. The intelligent control system for a grey cloth handling and stacking robot according to claim 8, characterized in that: The corrected handling control indicators for confirming gripping and movement include: If only condition 2 is met, As an associated variable, ; If judgment condition 1 and judgment condition 2 are met, As an associated variable, ; If judgment conditions 1 and 3 are met, As an associated variable, ; If judgment conditions 2 and 3 are met, As an associated variable, ; If the judgment conditions 1, 2 and 3 are all met at the same time, As an associated variable, , To set the reference handling complexity; Based on the associated variables, the correction item is confirmed in the same manner as the correction item for the corrected handling control index when the correction item is gripping; Will As the corrected moving speed, , is an associated variable, , will correct the movement speed, As a corrective handling control indicator for movement.

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