Control Method and System for Automatic Loading and Laminating of Plywood

Abstract

The present invention provides a control method and system for automatic feeding and blank assembly of plywood. The control method for automatic feeding and blank assembly of plywood includes: obtaining the formula information of the plywood to be assembled and the demand quantity of each panel type input by the user, inputting each panel type required for the plywood to be assembled into the corresponding chain machine based on the formula information, judging whether the panels required for the plywood to be assembled are sufficient based on the inventory information of the panels on each chain machine and the demand quantity of each panel type, when it is determined that the panels required for the plywood to be assembled are sufficient, controlling the robot to perform feeding operations on the panels on each chain machine based on the formula information, and placing multiple grabbed target panels at a preset position for blank assembly to obtain the target plywood, so as to realize flexible, efficient automatic feeding and blank assembly of plywood that can meet diverse blank assembly requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic plywood feeding and blanking, and particularly relates to a control method and system for automatic plywood feeding and blanking. Background Art

[0002] In current industrial production, the blanking process of plywood is usually completed manually, which has problems such as low efficiency and high labor intensity. In order to improve the automation degree and production efficiency of the blanking process, some studies have explored methods of using robots for automatic plywood feeding and blanking. However, most of the existing methods are limited to simple single-board type blanking, lacking flexibility and adaptability.

[0003] For example, some studies use robots through vision recognition and grasping technology to arrange predefined boards according to specified quantities and sequences to complete basic blanking. However, such methods are difficult to adapt to the production requirements of different models and specifications of plywood, and require manual detailed programming and adjustment of the robots, with complex operations. In addition, some studies use manipulators in cooperation with a chain system to achieve automatic handling and blanking of boards. However, such systems rely on fixed board sizes and arrangement methods and cannot handle plywood with different specifications and flexible assembly requirements.

[0004] In the technical solution with the application number CN 202210168300.5, although it sets standard work sections, each standard work section includes a horizontal grain board station and a vertical grain board station, obtains plywood processing data and robot information, sets the number of robots on the horizontal grain board station and the vertical grain board station according to the plywood processing data and robot information, sets corresponding numbers of robots on the horizontal grain board station and the vertical grain board station, establishes an equipment cooperation table, obtains on-site installation conditions, obtains the types and quantities of equipment for corresponding cooperation use from the equipment cooperation table according to the set number of robots and on-site installation conditions, sets the equipment for cooperation use at the corresponding positions in the standard work sections, and sets the corresponding number of standard work sections according to the on-site installation conditions to complete the layout of the processing production line. However, this method also relies on fixed board sizes and arrangement methods and cannot handle plywood with different specifications and flexible assembly requirements.

[0005] Therefore, based on the above status quo, there is an urgent need for a more flexible, efficient plywood automatic feeding and blanking method that can meet diverse blanking requirements. Summary of the Invention

[0006] The present invention provides a control method and system for automatic plywood feeding and blanking to achieve flexible, efficient automatic plywood feeding and blanking that can meet diverse blanking requirements.

[0007] To solve the above problems, the present invention adopts the following technical solutions:

[0008] The present invention provides a control method for automatic feeding and blanking of plywood, which is applied to a control device and includes:

[0009] Obtaining the formula information of the plywood to be blanked and the demand quantity of each panel type entered by the user;

[0010] Based on the formula information, entering each panel type required for the plywood to be blanked into the corresponding chain machine;

[0011] Judging whether the panels required for the plywood to be blanked are sufficient based on the inventory information of the panels on each chain machine and the demand quantity of each panel type;

[0012] When it is determined that the panels required for the plywood to be blanked are sufficient, controlling a robot to perform a feeding operation on the panels on each chain machine based on the formula information, placing the grabbed multiple target panels at a preset position for blanking to obtain a target plywood.

[0013] Further, before entering each panel type required for the plywood to be blanked into the corresponding chain machine based on the formula information, it further includes:

[0014] Checking whether all fields in the formula information have been filled;

[0015] When all fields in the formula information have been filled, determining that the integrity verification of the formula information passes;

[0016] Verifying the accuracy of the formula information;

[0017] When the accuracy verification of the formula information passes, performing the step of entering each panel type required for the plywood to be blanked into the corresponding chain machine based on the formula information;

[0018] When the accuracy verification of the formula information fails, sending a prompt message for correcting the formula information to the terminal where the engineer belongs.

[0019] Preferably, verifying the accuracy of the formula information includes:

[0020] Based on each parameter of the formula information, converting the formula information into a probability matrix;

[0021] Based on the probability matrix, calculating the information entropy of each parameter and calculating the natural logarithm of the information entropy of each parameter to obtain the difference coefficient of each parameter, and the difference coefficient reflects the variation degree between parameters;

[0022] Respectively calculating the ratio of the difference coefficient of each parameter to the sum of the difference coefficients of all parameters to obtain the weight of each parameter;

[0023] Compare the formula information item by item with the optimal formula information in the database, calculate the difference value between every two parameters, and after weighted summation of the difference value between every two parameters and the corresponding weight, obtain the comprehensive difference value;

[0024] When the comprehensive difference value is less than the preset value, it is determined that the accuracy verification of the formula information passes.

[0025] Further, after judging whether the plates required for the plywood to be assembled are sufficient based on the inventory information of the upper plates of each chain machine and the demand for each plate type, it further includes:

[0026] When it is determined that the plates required for the plywood to be assembled are insufficient, determine the missing first plate;

[0027] Generate a delivery instruction for the first plate, and the delivery instruction includes the plate information of the first plate and the target production line entrance;

[0028] Send the delivery instruction to the plate warehouse, and based on the inventory location and material status of the first plate, control the plate warehouse to perform material retrieval and picking on the first plate;

[0029] Control the automatic guided vehicle in the plate warehouse to transport the picked first plate to the target production line entrance, and control the automatic guided vehicle inside the production line to distribute the first plate from the target production line entrance to the corresponding chain machine.

[0030] Further, after controlling the automatic guided vehicle in the plate warehouse to transport the picked first plate to the target production line entrance, it further includes:

[0031] Monitor whether there is a backing plate that needs to be recycled at the target production line entrance;

[0032] When there is a backing plate that needs to be recycled, preferentially transport the backing plate to the plate return position on the automatic guided vehicle in the plate warehouse;

[0033] When the automatic guided vehicle inside the production line distributes the first plate from the target production line entrance to the corresponding chain machine, control the automatic guided vehicle in the plate warehouse to transport the backing plate to the recycling area.

[0034] Preferably, the controlling the robot to perform loading operations on the plates on each chain machine based on the formula information includes:

[0035] Based on the formula information and the requirements of the robot loading operation task, determine the scale, initial position and speed of the robot;

[0036] Define a fitness function according to the optimization goal of the feeding operation task. Based on the fitness function, calculate the initial fitness value for each robot and calculate the initial global fitness value of all robots;

[0037] Update the speed and position of the robot according to the current speed, individual optimal position, and global optimal position of the robot;

[0038] Based on the fitness function, calculate the current fitness value for each robot and calculate the current global fitness value of all robots;

[0039] Compare the current fitness value of each robot with the corresponding individual optimal fitness value. If the current fitness value is greater than the corresponding individual optimal fitness value, update the individual optimal position and initial fitness value of the robot;

[0040] Compare the current global fitness value of all robots with the optimal global fitness value. If the current global fitness value is greater than the optimal global fitness value, update the global optimal position and initial global fitness value of the robot;

[0041] When the initial global fitness value reaches the optimal value, generate an optimal feeding operation plan;

[0042] Based on the optimal feeding operation plan, control the robot to perform feeding operations on the plates on each of the chain machines.

[0043] Preferably, the controlling the robot to perform feeding operations on the plates on each of the chain machines based on the recipe information includes:

[0044] Based on the recipe information, control each robot to grab the plates on the corresponding chain machine, and monitor the positions and motion states of each of the robots in real time;

[0045] According to the positions, motion states, and geometric dimensions of each of the robots, dynamically calculate the stop points of each robot;

[0046] According to the vector distance constraint between the current pose and the stop point of each robot, when controlling each robot to perform feeding operations on the grabbed plates, execute avoidance measures, and the avoidance measures include deceleration or suspension.

[0047] Preferably, the controlling the robot to perform feeding operations on the plates on each of the chain machines based on the recipe information includes:

[0048] Generate multiple feeding operation tasks for performing feeding operations on the plates on each of the chain machines based on the recipe information, and orderly arrange each of the feeding operation tasks based on a preset ant colony algorithm to form a task chain related to the near field;

[0049] The task chain is divided into near-field subsets according to the task locations to obtain multiple task subsets. The task geographical locations within each task subset are close to each other and are completed collaboratively by the same robot or the same group of robots.

[0050] Each of the task subsets is assigned to each robot, and each robot is controlled to execute the corresponding task subset.

[0051] Preferably, the placing the grabbed multiple target board pieces at a preset position for blank assembly to obtain a target plywood includes:

[0052] Controlling each robot to place the grabbed multiple target board pieces at a preset position, controlling the vision sensor and lidar on the robot to start working, and detecting the edge positions of each target board piece;

[0053] Inputting the detected edge positions of each target board piece into a preset particle swarm optimization algorithm to optimize the trajectories of each target board piece and obtain the optimal adjustment trajectories of each target board piece;

[0054] According to the optimal adjustment trajectories of each target board piece, using the polynomial trajectory planning method to plan the optimal motion trajectories for each robot;

[0055] Controlling each robot to adjust the edge positions of the corresponding target board pieces according to the optimal motion trajectories, and performing blank assembly on the adjusted multiple target board pieces to obtain a target plywood.

[0056] In one embodiment, the present invention further provides a control system for automatic loading and blank assembly of plywood, including a chain machine, a robot, and a control device. The control device is respectively connected to the chain machine and the robot. Among them, the control device includes a memory and a processor. When the computer-readable instructions stored in the memory are executed by the processor, the processor executes the steps of the control method for automatic loading and blank assembly of plywood as described above.

[0057] Compared with the prior art, the technical solution of the present invention has at least the following advantages:

[0058] The control method and system for automatic feeding and blanking of plywood provided by the present invention can automatically transfer the types of board pieces required by the recipe to the corresponding chain machines by obtaining the plywood recipe information and the required number of board pieces input by the user. Based on the recipe information, the robot is controlled to perform feeding operations on the board pieces on each chain machine, and the grabbed multiple target board pieces are placed at a preset position for blanking to obtain the target plywood. Therefore, when the production demand changes, such as recipe adjustment or change of board piece type, by updating the recipe information and adjusting the feeding strategy of the robot, different production tasks can be quickly adapted, so as to meet the production requirements of plywood of different models and different recipes. Moreover, there is no need for manual detailed programming and adjustment of the robot, the operation is simple, and it does not depend on fixed board sizes and arrangement methods, realizing plywood with different specifications and flexible assembly requirements. In addition, by obtaining the inventory information of the board pieces on each chain machine in real time and comparing it with the required quantity in the recipe, it is possible to accurately judge whether the board pieces required for the plywood to be blanked are sufficient, so as to avoid production delays caused by insufficient inventory and ensure the smooth progress of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flowchart of an embodiment of the control method for automatic feeding and blanking of plywood of the present invention;

[0060] Figure 2 It is a flowchart of another embodiment of the control method for automatic feeding and blanking of plywood of the present invention;

[0061] Figure 3 It is a flowchart of another embodiment of the control method for automatic feeding and blanking of plywood of the present invention;

[0062] Figure 4 It is a block diagram of an embodiment of the control device for automatic feeding and blanking of plywood of the present invention;

[0063] Figure 5 It is a block diagram of an embodiment of the control device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0065] In some of the processes described in the specification, claims, and above-mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as S11, S12, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. Additionally, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit that "first" and "second" are of different types.

[0066] Those of ordinary skill in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the", and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or their groups. It should be understood that when we say an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more of the associated listed items.

[0067] Those of ordinary skill in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention pertains. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0068] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention, where the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0069] Please refer to Figure 1, the present invention provides a control method for automatic feeding and blanking of plywood, which is applied to the control device of the control system for automatic feeding and blanking of plywood. The control method may include the following steps:

[0070] S11. Obtain the recipe information of the plywood to be blanked and the demand for each type of board;

[0071] S12. Enter each type of board required for the plywood to be blanked into the corresponding chain machine based on the recipe information;

[0072] S13. Judge whether the boards required for the plywood to be blanked are sufficient based on the inventory information of the boards on each chain machine and the demand for each type of board;

[0073] S14. When it is determined that the boards required for the plywood to be blanked are sufficient, control the robot to perform feeding operations on the boards on each chain machine based on the recipe information, place the grabbed multiple target boards at a preset position for blanking, and obtain the target plywood.

[0074] This embodiment can provide an intuitive and easy-to-use user interface. The user can input the recipe information of the plywood to be blanked through the interface. The recipe information may include the type of board for each layer of the plywood to be blanked and the storage location of each type of board in the chain machine library, as well as input the demand for each type of board. The interface can adopt forms such as tables, drop-down menus, input boxes, etc., to facilitate the user to quickly and accurately enter information.

[0075] The information entered by the user will be stored in the database in real time for subsequent processing and calling. The database can adopt a relational database or a non-relational database, which is selected according to the data structure and query requirements. At the same time, this embodiment will manage and maintain the data to ensure the integrity and accuracy of the data.

[0076] Secondly, this embodiment will parse out each type of board required for the plywood to be blanked and its quantity according to the recipe information entered by the user. Then, according to the layout of the production line and the configuration of the chain machine, the corresponding type of board will be assigned to the corresponding chain machine. For example, if the recipe includes hard wood boards and soft wood boards, the system will assign the hard wood boards to chain machine A and the soft wood boards to chain machine B.

[0077] This embodiment can send the assigned board type information to the corresponding chain machine through the communication interface with the chain machine. After receiving the information, the chain machine will start to prepare and convey the corresponding board according to the preset program and parameters. At the same time, the control device will monitor the working state of the chain machine in real time to ensure the smooth supply of the board.

[0078] In addition, the control device will obtain the inventory information of the panels on each chain machine in real time, including data such as the type, quantity, and location of the panels. Then, the obtained inventory information is compared and analyzed with the demand in the recipe. If the inventory quantity of a certain panel type is greater than or equal to the demand, it is considered that the panel is sufficient; if the inventory quantity is less than the demand, it is considered that the panel is insufficient. The control device will generate corresponding prompt information or alarm signals according to the judgment result.

[0079] Finally, this embodiment can plan the feeding task for the robot according to the recipe information and the inventory situation of the panels. The task planning includes determining parameters such as the movement path, grasping sequence, and placement position of the robot to ensure that the robot can complete the feeding operation efficiently and accurately.

[0080] The control device sends the feeding task instructions and control parameters through the communication interface with the robot. After receiving the instructions, the robot will automatically move to the side of the chain machine according to the planned path and parameters, and grasp the target panel. Then, the robot places the grasped panel at the preset blanking position and stacks and arranges it according to the requirements of the recipe. During the blanking process, this embodiment will also monitor the position and state of each layer of panels in real time to ensure that the alignment and tightness of the panels meet the requirements. If it is found that the panel is not placed in place or there are quality problems, the actions of the robot will be adjusted in time or the feeding task will be re-planned.

[0081] For example, assume that a furniture manufacturing enterprise needs to produce a batch of customized plywood. The user enters the recipe information of the plywood to be blanked through the system interface, including the types, thicknesses, and demands of the hard boards and soft boards. The control device distributes the hard boards and soft boards to the corresponding chain machines according to the recipe information, and obtains the inventory information of the chain machines in real time to judge whether the required panels are sufficient. After confirming that the panels are sufficient, the robot is controlled to perform the feeding operation. The robot grasps the hard boards and soft boards from the chain machines according to the planned path and sequence, and places them at the preset blanking position for precise stacking and arrangement, and finally obtains the plywood that meets the requirements.

[0082] The control method for automatic feeding and blanking of plywood provided by the present invention can automatically transfer the types of board pieces required by the formula to the corresponding chain machines by obtaining the plywood formula information input by the user and the number of board pieces required. Based on the formula information, the robot is controlled to perform feeding operations on the board pieces on each chain machine, and the grabbed multiple target board pieces are placed at preset positions for blanking to obtain the target plywood. Therefore, when the production demand changes, such as formula adjustment or board piece type change, by updating the formula information and adjusting the feeding strategy of the robot, different production tasks can be quickly adapted, so as to meet the production requirements of plywood of different models and different formulas. Moreover, there is no need for manual detailed programming and adjustment of the robot, the operation is simple, and it does not depend on fixed board sizes and arrangement methods, realizing plywood with different specifications and flexible assembly requirements. In addition, by obtaining the inventory information of the board pieces on each chain machine in real time and comparing it with the required quantity in the formula, it is possible to accurately judge whether the board pieces required for the plywood to be blanked are sufficient, so as to avoid production delays caused by insufficient inventory and ensure the smooth progress of the production process.

[0083] In one embodiment, this control system can be composed of 12 robots, 4 automatic guided vehicles RGV, 4 heavy-duty lifting and transplanting devices, 4 heavy-duty hydraulic lifting platforms, 24 heavy-duty chain machines, three sets of rear-section conveying lines and a board piece warehouse distribution system. Among them, the heavy-duty hydraulic lifting platform is used to realize the docking with the automatic guided vehicle in the front-end board piece warehouse to realize material supply and withdrawal. The heavy-duty lifting and transplanting device is used to realize the material supply and withdrawal connection between the heavy-duty hydraulic lifting platform and the automatic guided vehicle inside the production line. The automatic guided vehicle inside the production line can be used to supply and withdraw materials for the heavy-duty chain machines. The heavy-duty chain machines are used to supply materials for the robots. The robots are used to unstack from the heavy-duty chain machines and supply materials to the rear-section conveying lines.

[0084] Specifically, workers can input or select the formula information of the plywood to be processed through the production line system interface, and the system automatically verifies the integrity and accuracy of the formula information. If the formula information is incorrect, the system will prompt the workers to make corrections. If there are no board pieces of the corresponding board piece type on the chain machine and the storage location is empty, the production line system will automatically generate distribution instructions. These instructions include the specific information of the required board pieces and the distribution destination.

[0085] After receiving the instructions, the board piece warehouse automatically conducts material retrieval and picking. The system will optimize the picking path and time according to the inventory location and material status. The automatic guided vehicle in the board piece warehouse accurately delivers the required board pieces to the entrance of the designated production line.

[0086] During the delivery process, the system monitors the status of materials in real time to ensure that data transmission is not lost. Meanwhile, during the delivery process of the automated guided vehicle in the panel warehouse, the system monitors in real time whether there are pallets to be recycled at the entrance of the production line. If there are pallets to be recycled, the automated guided vehicle will automatically give priority to transporting the pallets to the pallet return position on the vehicle, and then continue to complete the delivery task. After the delivery is completed, it will switch to the task of transporting the pallets to the recycling area.

[0087] The automated guided vehicle inside the production line delivers the panels from the production line entrance to the corresponding chain machine. The automated guided vehicle can efficiently complete the internal transportation task according to the preset route, has precise positioning ability, and can accurately place the panels at the designated position of the chain machine. This process does not require manual intervention, greatly improving production efficiency and accuracy.

[0088] When the required panels arrive, the robot starts to perform the automatic loading operation. The robot accurately grabs and places the panels at the designated position according to the recipe information and the demand for each panel type. During the process of configuring the panels, the robot will comprehensively consider the recipe requirements, the avoidance of adjacent robots, and the conveyor line signals.

[0089] In addition, the system can also optimize the configuration order and path of the panels through improved algorithms to improve production efficiency. At the same time, the reasonable scheduling of the robot tasks can be achieved through programming to avoid robots entering the same area at the same time due to unreasonable task allocation, and the system can reasonably arrange the tasks and working areas of each robot according to the order tasks and the working status of the robots to ensure that the robots work in an orderly manner without interference. In addition, according to the specific situation of the panels, workers can switch whether the robot needs to perform the automatic edge alignment and placement function through the system interface. When this function is enabled, the robot will automatically detect and adjust the position of the panel edge to make it accurately aligned. This function further improves the quality and consistency of the products.

[0090] Among them, the entrance inspection process of the heavy-duty hydraulic lifting platform is as follows: When there are no panels on each line body, and the panels of the panel warehouse delivery trolley are to be delivered to this line body and are the panels required by the line body, the panels of the panel warehouse delivery trolley are allowed to enter the heavy-duty hydraulic lifting platform. If the panel types of all line bodies are already the required panel types, the panels of the panel warehouse delivery trolley will also be directly prohibited from entering the heavy-duty hydraulic lifting platform.

[0091] When the heavy-duty hydraulic lifting platform receives the panel and enters the heavy-duty lifting and transplanting, it will request the automated guided vehicle inside the production line to perform the feeding operation. If there is already a pallet on the automated guided vehicle, it will first complete the feeding task and then perform the pallet return task. After receiving the material, it will use the positions of each chain machine pre-stored in the system for precise positioning, and then start the feeding process.

[0092] In one embodiment, before entering the types of each panel required for the plywood to be assembled according to the formula information into the corresponding chain machine, the following steps are further included:

[0093] Check whether all fields in the formula information have been filled;

[0094] When all fields in the formula information have been filled, determine that the integrity verification of the formula information passes;

[0095] Verify the accuracy of the formula information;

[0096] When the accuracy verification of the formula information passes, perform the step of entering the types of each panel required for the plywood to be assembled according to the formula information into the corresponding chain machine;

[0097] When the accuracy verification of the formula information fails, send a prompt message for correcting the formula information to the terminal of the engineer.

[0098] After receiving the formula information entered by the user, first perform a data integrity check to ensure that each necessary field in the formula information has been filled. The necessary fields may include panel type, demand quantity, size, glue type, etc. For example, through a preset field list, verify whether there is valid data for each field one by one.

[0099] If it is found that there are unfilled fields, the control device will immediately feedback to the user, indicating which fields are missing, and prompt the user to supplement the complete information to ensure that the data basis for subsequent processing is complete.

[0100] Once all fields are confirmed to be filled, the control device marks the integrity verification of the formula information as passed.

[0101] After the integrity verification passes, the control device further verifies the accuracy of the formula information, including checking whether the field data conforms to the preset format and logical rules. For example, the demand quantity field should be a positive integer, the size field should conform to the actual production range, and the panel type should exist in the predefined type list.

[0102] In addition to format and range checks, logical consistency checks will also be performed. For example, if the formula requires a certain special treatment (such as waterproof treatment), the corresponding panel type and glue type should be compatible with it.

[0103] If the accuracy verification of the formula information also passes, enter the types of each panel required for the plywood to be assembled into the corresponding chain machine according to the formula information.

[0104] If the accuracy verification of the formula information fails, the control device will send a detailed error report and correction tips to the terminal of the engineer. The report will indicate which fields have problems and the recommended correction directions to ensure that the engineer can promptly discover and correct the errors, avoiding production delays or quality issues caused by incorrect information.

[0105] This embodiment can ensure the high quality of the formula information through integrity checking and accuracy verification, provide reliable data support for the production process, promptly discover and correct error information, avoid production delays and quality problems caused by incorrect formulas, and improve production efficiency and product quality.

[0106] In one embodiment, verifying the accuracy of the formula information includes:

[0107] Based on each parameter of the formula information, convert the formula information into a probability matrix;

[0108] Based on the probability matrix, calculate the information entropy of each parameter, and calculate the natural logarithm of the information entropy of each parameter to obtain the difference coefficient of each parameter, where the difference coefficient reflects the degree of variation between parameters;

[0109] Calculate the ratio of the difference coefficient of each parameter to the sum of the difference coefficients of all parameters respectively to obtain the weight of each parameter;

[0110] Compare each parameter of the formula information with the optimal formula information in the database item by item, calculate the difference value between every two parameters, and after weighted summing the difference value between every two parameters and the corresponding weight, obtain the comprehensive difference value;

[0111] When the comprehensive difference value is less than the preset value, it is determined that the accuracy verification of the formula information passes.

[0112] In this embodiment, each parameter in the formula information can be first standardized so that its value range is between 0 and 1.

[0113] Then convert the standardized parameter values into a probability matrix. Each element in the probability matrix represents the relative importance or occurrence probability of the corresponding parameter in the formula information. For example, if there are three parameters A, B, and C in the formula information, and their standardized values are 0.2, 0.5, and 0.3 respectively, the probability matrix can be expressed as [0.2, 0.5, 0.3].

[0114] Information entropy is an index to measure the uncertainty of parameters. For each parameter, calculate its information entropy based on the probability matrix. The calculation formula of this information entropy is:

[0115] ;

[0116] wherein, n is the number of parameters, and the is a probability matrix, representing the probability value of the i-th parameter under the j-th index, and the is the information entropy.

[0117] The coefficient of variation is the natural logarithm of the information entropy, and the calculation formula is D(j)=ln( ), where D(j) is the coefficient of variation. The coefficient of variation reflects the degree of variation between parameters. The larger the coefficient of variation, the greater the degree of variation between parameters.

[0118] Calculate the sum of the coefficients of variation of all parameters, and calculate the ratio of the coefficient of variation of each parameter to the sum of the coefficients of variation of all parameters to obtain the weight of each parameter. This weight is a measure of the relative importance of the parameter in the formula.

[0119] Finally, compare each parameter in the formula information with the corresponding parameter in the optimal formula information in the database, and calculate the difference value between them. The difference value can be calculated using the absolute difference or the relative difference. Multiply the difference value between every two parameters by the corresponding weight, and then sum them up to obtain the comprehensive difference value. The comprehensive difference value reflects the overall difference degree between the formula information and the optimal formula information.

[0120] wherein, the preset value is a threshold for judging the accuracy of the formula information, and can be set according to actual production experience and requirements. For example, it can be set to 0.1.

[0121] When the comprehensive difference value is less than the preset value, it indicates that the difference between the formula information and the optimal formula information is small, and it can be considered that the accuracy of the formula information has been verified and meets the production requirements.

[0122] This embodiment can accurately evaluate the accuracy of the formula information through the information entropy and the coefficient of variation, avoiding the subjectivity and errors of human judgment, and comprehensively considering the differences and weights of each parameter in the formula information, and can comprehensively evaluate the difference degree between the formula information and the optimal formula, improving the reliability of the verification result. At the same time, when the accuracy verification of the formula information fails, the key parameters affecting the accuracy can be found according to the comprehensive difference value and weight analysis, providing a guiding direction for the optimization of the formula. In addition, ensuring the accuracy of the formula information helps to improve production efficiency and product quality, reduce production delays and product defects caused by formula errors, and reduce production costs.

[0123] Please refer to Figure 2 , in an embodiment, after judging whether the plates required for the plywood to be assembled are sufficient based on the inventory information of the upper plates of each chain machine and the demand for each plate type, it further includes:

[0124] S15. When it is determined that the required panels for the plywood to be assembled are insufficient, determine the missing first panel;

[0125] S16. Generate a delivery instruction for the first panel, where the delivery instruction includes the panel information of the first panel and the target production line entrance;

[0126] S17. Send the delivery instruction to the panel warehouse, and based on the inventory location and material status of the first panel, control the panel warehouse to conduct material retrieval and picking for the first panel;

[0127] S18. Control the automatic guided vehicle in the panel warehouse to transport the picked first panel to the target production line entrance, and control the automatic guided vehicle inside the production line to distribute the first panel from the target production line entrance to the corresponding chain machine.

[0128] When it is found that the inventory quantity of a certain panel type is less than the required quantity, it will be determined that this panel is the missing first panel. For example, if 10 hardwood boards are required for the plywood to be assembled, and there are only 8 hardwood boards in the storage location on the chain machine, then the hardwood board is the missing first panel.

[0129] Generate a delivery instruction according to the determined missing panel information. The delivery instruction contains detailed information about the panel, such as panel type, specification, quantity, etc., as well as the location information of the target production line entrance, ensuring that the panel can be accurately delivered to the designated location. At the same time, format the delivery instruction into a format that can be recognized and executed by the panel warehouse and the automatic guided vehicle, such as data formats like JSON and XML, for subsequent transmission and processing.

[0130] The control device sends the generated delivery instruction to the management system of the panel warehouse through the network or other communication methods. After receiving the instruction, the warehouse management system will retrieve the materials in the warehouse according to the panel information in the instruction.

[0131] The warehouse management system controls the picking equipment or staff in the warehouse to accurately pick the required first panel according to the inventory location and material status of the panel, such as storage area, shelf number, material availability, etc. For example, an automated picking robot can quickly locate the storage location of the hardwood board according to the instruction and pick it out from the shelf.

[0132] Control the automatic guided vehicle in the panel warehouse to plan an optimal transportation path according to the target production line entrance location in the delivery instruction. The automatic guided vehicle transports the picked first panel to the target production line entrance safely and efficiently along the planned path.

[0133] The control system of the production line takes over the distribution task of the panel, controls equipment such as automatic guided vehicles or conveyor belts inside the production line, and further distributes the first panel from the production line entrance to the corresponding chain machine. For example, the automatic guided vehicle inside the production line can identify the position of the chain machine and accurately place the hard wooden board in the feeding area of the chain machine to ensure the smooth progress of the production process.

[0134] This embodiment can, through automated and intelligent material retrieval, picking, and distribution processes, quickly respond to the material requirements of the production line, improve material supply efficiency, reduce production delays caused by material shortages, also reduce the workload of manual material picking and handling, lower labor costs and labor intensity, and at the same time avoid errors and omissions that may occur in manual operations. In addition, it can monitor the inventory situation and production line requirements in real time, adjust the material distribution plan in a timely manner, and improve the flexibility of production and the response speed to market changes.

[0135] In one embodiment, after the automatic guided vehicle that controls the panel warehouse transports the picked first panel to the target production line entrance, it further includes:

[0136] Monitoring whether there is a backing plate that needs to be recycled at the target production line entrance;

[0137] When there is a backing plate that needs to be recycled, preferentially transport the backing plate to the plate return position on the automatic guided vehicle of the panel warehouse;

[0138] When the automatic guided vehicle inside the production line distributes the first panel from the target production line entrance to the corresponding chain machine, control the automatic guided vehicle of the panel warehouse to transport the backing plate to the recycling area.

[0139] This embodiment can install sensors, such as photoelectric sensors or proximity sensors, at the target production line entrance for real-time monitoring of whether there is a backing plate. The sensor will send the detected signal to the control equipment of the production line. In addition to sensor detection, image acquisition devices such as industrial cameras can also be used to take real-time pictures of the production line entrance area. Through image processing and recognition algorithms, judge whether there is a backing plate in the image, as well as the quantity and status of the backing plate.

[0140] After the control equipment receives the sensor signal or the image recognition result, it performs data processing and judgment. If it detects the existence of a backing plate, it determines that there is a backing plate that needs to be recycled and triggers the subsequent recycling process.

[0141] The control equipment generates a recycling instruction according to the detected backing plate information. The recycling instruction includes the recycling path of the backing plate, the position information of the plate return position, etc.

[0142] Send the recycling instruction to the automated guided vehicle (AGV) in the panel warehouse. After receiving the instruction, the AGV will automatically plan an optimal transportation route based on the path and location information in the instruction, and transport the backing plate to the plate return position on the AGV. The plate return position on the AGV is a dedicated area for placing the backing plates to be recycled, and is designed with corresponding fixing devices or markings to ensure that the backing plates can be placed safely and accurately.

[0143] In the task scheduling of the AGV, set the priority of the backing plate recycling task higher than other tasks, such as the panel delivery task. This can ensure that the backing plates can be recycled in a timely manner, avoiding affecting the normal operation of the production line and the turnover of materials.

[0144] The control device communicates and coordinates with the AGV in the panel warehouse in real time. When the AGV inside the production line successfully delivers the first panel to the chain machine, the control device will send a signal to the AGV in the panel warehouse to instruct it to start performing the backing plate recycling task.

[0145] The AGV in the panel warehouse starts from the plate return position according to the path information in the recycling instruction, and transports the backing plate to the recycling area along the planned path. The recycling area is a dedicated area for storing the recycled backing plates, and is usually equipped with corresponding storage facilities or equipment, such as recycling bins, recycling racks, etc.

[0146] After the backing plate is successfully transported to the recycling area, the recycling status information of the backing plate will be updated, and data such as the recycling time and quantity of the backing plate will be recorded. This data can be used for subsequent inventory management and material analysis to help the enterprise better understand the usage and recycling situation of the backing plates.

[0147] This embodiment can accelerate the turnover speed of materials by recycling the backing plates in a timely manner, reduce the accumulation of backing plates at the entrance of the production line, and avoid affecting the normal entry of panels and the smooth progress of the production process. At the same time, the recycling and reuse of the backing plates can reduce the demand for new backing plates, lower the enterprise's material procurement cost, and also reduce the economic losses caused by the waste of backing plates.

[0148] Please refer to Figure 3 , in one embodiment, the controlling the robot to perform the loading operation on the panels on each of the chain machines based on the recipe information includes:

[0149] S141. Determine the scale, initial position, and speed of the robot based on the recipe information and the requirements of the robot loading operation task;

[0150] S142. Define a fitness function according to the optimization objective of the loading operation task. Based on the fitness function, calculate the initial fitness value for each robot, and calculate the initial global fitness value for all robots;

[0151] S143. Update the speed and position of the robot according to the current speed, individual optimal position, and global optimal position of the robot;

[0152] S144. Calculate the current fitness value for each robot based on the fitness function, and calculate the current global fitness value of all robots;

[0153] S145. Compare the current fitness value of each robot with the corresponding individual optimal fitness value. If the current fitness value is greater than the corresponding individual optimal fitness value, update the individual optimal position and initial fitness value of the robot;

[0154] S146. Compare the current global fitness value of all robots with the optimal global fitness value. If the current global fitness value is greater than the optimal global fitness value, update the global optimal position and initial global fitness value of the robot;

[0155] S147. When the initial global fitness value reaches the optimal value, generate an optimal loading operation plan;

[0156] S148. Control the robot to perform loading operations on the plates on each of the chain machines based on the optimal loading operation plan.

[0157] In this embodiment, the control device parses the recipe information to determine the type, quantity, and position of the required plates. According to the requirements of the loading operation task, determine the scale of the robots (i.e., the number of robots participating in the task), initial positions, and speeds. For example, if the task requires grasping plates on multiple chain machines, multiple robots may need to work together, and the initial positions and speeds of each robot are set according to the positions of the chain machines and the urgency of the task.

[0158] Among them, the fitness function is used to evaluate the completion of the robot's loading operation task, usually defined according to the optimization goal, such as minimizing the loading time, maximizing the task completion rate, etc. For example, the fitness function can be defined as the reciprocal of the time required to complete the task.

[0159] Calculate the initial fitness value for each robot, that is, the evaluation value of the robot to complete the task at the initial position and speed. At the same time, calculate the initial global fitness value of all robots, that is, the overall evaluation value of all robots to complete the task.

[0160] Update the speed and position of the robot using the particle swarm optimization algorithm according to the current speed, individual optimal position, and global optimal position of the robot. After updating the position and speed, recalculate the fitness value for each robot to evaluate its task completion situation at the new position. Calculate the current global fitness value of all robots to evaluate the overall task completion situation.

[0161] If the current fitness value of a certain robot is better than its individual optimal fitness value, then update the individual optimal position and fitness value of the robot to ensure that each robot continuously moves towards a better position during the search process.

[0162] If the current global fitness value is better than the optimal global fitness value, then update the global optimal position and fitness value to ensure that the entire population continuously approaches the global optimal solution during the search process.

[0163] When the updated initial global fitness value does not reach the optimal value, then continue to iteratively execute steps S143 - S146 until the updated initial global fitness value does not reach the optimal value. When the updated initial global fitness value reaches the preset optimal value, for example, when the change amplitude of the initial global fitness value in consecutive multiple iterations is lower than the preset threshold, then determine that the initial global fitness value reaches the optimal value and consider that the optimal loading operation plan has been found. At this time, record the position and speed of each robot as the optimal loading operation plan.

[0164] According to the generated optimal loading operation plan, control the robot to perform the loading operation. The robot grabs the panel and places it at the specified position according to the optimal path and speed to complete the loading task.

[0165] This embodiment can optimize the loading operation path and speed of the robot through the particle swarm optimization algorithm, reduce the movement time and waiting time of the robot between different task locations, and improve the production efficiency. At the same time, reasonably allocate tasks to each robot, realize the optimal allocation of resources, avoid task conflicts and resource waste between robots, and improve the utilization rate of equipment. In addition, by optimizing the task execution order and resource allocation, reduce the energy consumption and maintenance cost of the robot, reduce the production cost, and improve the economic benefits of the enterprise.

[0166] In one embodiment, controlling the robot to load the panels on each of the chain machines based on the recipe information includes:

[0167] Based on the recipe information, control each robot to grab the panel on the corresponding chain machine, and real - time monitor the position and motion state of each robot;

[0168] According to the position, motion state and geometric dimensions of each robot, dynamically calculate the stop points of each robot;

[0169] According to the vector distance constraint between the current pose of each robot and the stop point, when controlling each robot to perform the loading operation on the grabbed panel, execute avoidance measures, and the avoidance measures include deceleration or pause.

[0170] In this embodiment, based on the recipe information input by the user, the types and quantities of the board pieces to be grasped by each robot can be parsed, and this information is assigned to the corresponding robots. Through sensors and vision systems, the positions and motion states of each robot are monitored in real time. This data includes the current position, speed, acceleration, etc. of the robot to ensure that the robot performs tasks according to the predetermined path and speed.

[0171] The control device dynamically calculates the stop points according to the current position, motion state, and geometric dimensions of each robot. The calculation of the stop points takes into account the current position, speed, acceleration, and safety parameters of the robot to ensure that the robot can stop safely when necessary.

[0172] The types of stop points can include following stop points, calming stop points, and curve protection stop points. Following stop points are used to avoid collisions between robots, calming stop points are used to handle path conflicts, and curve protection stop points are used to handle special path situations.

[0173] Calculate the vector distance constraint between the current pose of the robot and the stop point. When the vector distance is less than the set deceleration threshold, control the robot to perform a deceleration operation; when the vector distance is less than the set stop threshold, the robot performs a stop operation.

[0174] According to the vector distance constraint, when the robot performs a loading operation, if a potential collision risk is detected, it will automatically perform a deceleration or pause operation to ensure safety.

[0175] In this embodiment, by monitoring in real time and dynamically calculating the stop points according to the positions, motion states, and geometric dimensions of each robot, and based on the vector distance constraint between the current pose of each robot and the stop point, avoidance measures are executed to ensure that the robot can stop safely when necessary, avoid collision accidents, and improve the safety of the production environment. In addition, the robot can automatically adjust its motion state according to real-time data, reduce unnecessary waiting and delays, and improve production efficiency.

[0176] In one embodiment, controlling the robot to perform a loading operation on the board pieces on each of the chain machines based on the recipe information includes:

[0177] Generating a plurality of loading operation tasks for performing a loading operation on the board pieces on each of the chain machines based on the recipe information, and arranging each of the loading operation tasks in an orderly manner based on a preset ant colony algorithm to form a task chain related to the near field;

[0178] Performing a near-field subset division on the task chain according to the task location to obtain a plurality of task subsets, where the tasks within each task subset are geographically close and are completed by the same robot or the same group of robots in cooperation;

[0179] Assign each of the task subsets to respective robots, and control each of the robots to execute the corresponding task subset.

[0180] First, parse the recipe information input by the user, and extract key parameters such as the type and quantity of each panel required for the plywood to be assembled, as well as the corresponding chain machine positions. According to the parsed recipe information, generate a loading operation task for each panel. Each task contains detailed information such as the type and quantity of the panel, the chain machine position, and the target position for loading.

[0181] Among them, the ant colony algorithm is an optimization algorithm that simulates the foraging behavior of ants and has good path search and optimization capabilities. The ant colony algorithm can be used to arrange the generated multiple loading operation tasks in an orderly manner.

[0182] In the ant colony algorithm, each "ant" represents a task arrangement scheme, and the arrangement order of the tasks is continuously updated through an iterative process. The algorithm gradually constructs a near-field related task chain based on the relevance between tasks and the near-field relationship of geographical locations. The tasks in the task chain are arranged in a certain order, so that the geographical locations of adjacent tasks are close, which is convenient for subsequent task execution and resource allocation.

[0183] Secondly, according to the geographical location information of the tasks in the task chain, divide the task chain into multiple task subsets. The tasks within each task subset are close in geographical location. For example, the tasks located in the same area of the production line are divided into one subset.

[0184] When dividing the task subsets, consider the cooperation between tasks and resource utilization rate, and try to make the tasks within each subset be completed by the same robot or the same group of robots in cooperation, reducing the task switching and moving distance between robots.

[0185] In addition, according to the performance, position and current working status of the robots, the divided task subsets can be assigned to respective robots. When assigning, consider the load balance of the robots and the urgency of the tasks to ensure that the tasks can be executed efficiently and reasonably.

[0186] After receiving the assigned task subset, the robot performs the loading operation according to the task requirements. The control device monitors the execution situation of the robot in real time to ensure the smooth progress of the task, and makes dynamic adjustment and optimization according to the actual situation.

[0187] In this embodiment, the ant colony algorithm can be used to optimize task arrangement and near-field subset division, reducing the moving distance and time of the robot between different task locations, improving production efficiency. The robot can quickly respond to task requirements, shortening the production cycle. Reasonably allocating task subsets to each robot realizes the optimal allocation of resources, avoiding task conflicts and resource waste between robots, and improving the utilization rate of equipment. At the same time, it can flexibly adjust the task chain and task subsets according to changes in production requirements and recipe information, adapt to different production tasks and panel types, enhancing the flexibility and adaptability of production. In addition, by optimizing the task execution order and resource allocation, multiple robots are centrally controlled and cooperate in operation, feeding continuously. Automated control reduces manual intervention and error rate, significantly improving production efficiency.

[0188] In one embodiment, the step of placing the grabbed multiple target panels at a preset position for blank assembly to obtain a target plywood includes:

[0189] Controlling each robot to place the grabbed multiple target panels at a preset position, and controlling the vision sensor and lidar on the robot to start working to detect the edge positions of each of the target panels;

[0190] Inputting the detected edge positions of each of the target panels into a preset particle swarm algorithm to optimize the trajectory of each of the target panels, obtaining the optimal adjustment trajectory of each of the target panels;

[0191] According to the optimal adjustment trajectory of each of the target panels, using the polynomial trajectory planning method to plan the optimal motion trajectory for each of the robots;

[0192] Controlling each of the robots to adjust the edge positions of the corresponding target panels according to the optimal motion trajectory, and performing blank assembly on the adjusted multiple target panels to obtain a target plywood.

[0193] In this embodiment, according to the recipe information and production requirements, the grabbed multiple target panels can be allocated to each robot and specified to be placed at a preset position. The preset position is determined according to the production process and equipment layout to ensure that the panels can smoothly enter the subsequent blank assembly process.

[0194] Start the vision sensor and lidar on the robot. The vision sensor identifies the edge contour of the panel by taking pictures of the panel; the lidar accurately measures the distance between the panel edge and the preset position by emitting laser beams and receiving reflected signals. The data collected by the sensor includes information such as the position coordinates and shape features of the panel edge.

[0195] Among them, the particle swarm optimization algorithm can also be used for trajectory optimization. The algorithm takes the edge positions of each panel as input, and by simulating the movement process of the particle swarm, it searches for the optimal adjustment trajectory. Each particle in the algorithm represents a possible trajectory solution. The particles move in the search space, and the quality of each solution is evaluated according to the fitness function (such as minimizing the edge deviation). By iteratively updating the positions and velocities of the particles, it finally converges to the optimal solution, that is, the optimal adjustment trajectory of each panel.

[0196] Adopt a polynomial trajectory planning method, such as 3-5-3 polynomial interpolation, to plan a smooth and continuous motion trajectory for the robot. This method can ensure that the robot moves smoothly when adjusting the edge position of the panel, avoiding panel displacement or damage caused by excessive acceleration. For example, according to the optimal adjustment trajectory of the panel and the kinematic constraints of the robot, determine the parameters of the polynomial trajectory, such as position, velocity, and acceleration, etc., to form the optimal motion trajectory.

[0197] The robot accurately adjusts the edge position of the target panel according to the planned optimal motion trajectory to align it with the preset position. The adjusted panel is placed at the blanking position, and the robots work together to stack and arrange multiple panels according to the recipe requirements to complete the blanking operation and obtain the target plywood.

[0198] This embodiment can ensure the alignment and tightness of the panels during blanking through accurate edge position detection and optimized trajectory adjustment, improving the quality and consistency of the plywood. At the same time, the robot can quickly and accurately complete the adjustment and blanking tasks of the panels, reducing the time and labor intensity of manual operations and improving production efficiency. In addition, it can also reduce rework and material waste caused by inaccurate panel placement, reducing production costs.

[0199] Please refer to Figure 4 , in an embodiment of the present invention, a control device for automatic feeding and blanking of plywood is further provided, including:

[0200] An acquisition module 11, configured to acquire the recipe information of the plywood to be blanked entered by the user and the demand quantity of each panel type;

[0201] An input module 12, configured to input each panel type required for the plywood to be blanked into the corresponding chain machine based on the recipe information;

[0202] A judgment module 13, configured to judge whether the panels required for the plywood to be blanked are sufficient based on the inventory information of the panels on each chain machine and the demand quantity of each panel type;

[0203] The feeding module 14 is used to, when it is determined that there are sufficient board pieces required for the plywood to be assembled, control the robot to perform a feeding operation on the board pieces on each of the chain machines based on the recipe information, and place the grabbed multiple target board pieces at a preset position for assembling the plywood to obtain the target plywood.

[0204] Regarding the device in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0205] In one embodiment, the present invention also provides a control system for automatic feeding and assembling of plywood, including a chain machine, a robot, and a control device. The control device is respectively connected to the chain machine and the robot. Among them, the control device includes a memory and a processor. Computer-readable instructions are stored in the memory. When the computer-readable instructions are executed by the processor, the processor executes the steps of the control method for automatic feeding and assembling of plywood as described above.

[0206] In one embodiment, the control device provided in an embodiment of the present application is referred to Figure 5 ., this control device may be a computer device, and its internal structure may be as Figure 5 shown. In addition, this control device may also be a PLC control device, which will not be elaborated here. Among them, this computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer design is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of this computer device is used to store relevant data of the control method for automatic feeding and assembling of plywood. The network interface of this computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes the control method for automatic feeding and assembling of plywood described in the above embodiments.

[0207] In one embodiment, the present invention also proposes a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the above control method for automatic feeding and assembling of plywood. Among them, the storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0208] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above-described embodiments can be completed by instructing relevant hardware through a computer program. This computer program can be stored in a storage medium. When the program is executed, it can include the processes of the embodiments of the above various methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0209] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0210] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be subject to the appended claims.

Claims

1. A control method for automatic feeding and assembly of plywood, characterized in that: Applied to control equipment, including: Obtain the formula information of the plywood to be assembled and the demand for each type of board entered by the user; Entering each type of board required for the plywood to be assembled into a corresponding chain machine based on the recipe information; Based on the inventory information of the panels on each chain machine and the demand for each type of panels, determine whether the panels required for the plywood to be assembled are sufficient; When it is determined that the required panels for the plywood to be assembled are sufficient, the robot is controlled based on the recipe information to load the panels on each of the chain machines, and the captured multiple target panels are placed at a preset position for assembly to obtain the target plywood; Wherein, before entering the types of panels required for the plywood to be assembled into the corresponding chain machine based on the recipe information, the method further includes: Check that all fields in the recipe information are filled in; When all fields in the recipe information have been filled in, determining that the integrity verification of the recipe information has passed; Verify the accuracy of the formula information; When the accuracy of the formula information is verified, the step of entering each type of board required for the plywood to be assembled into a corresponding chain machine based on the formula information is performed; When the accuracy verification of the formula information fails, a prompt message is sent to the terminal to which the engineer belongs to correct the formula information; The verifying the accuracy of the recipe information includes: Based on each parameter of the recipe information, converting the recipe information into a probability matrix; Calculating the information entropy of each parameter based on the probability matrix, and calculating the natural logarithm of the information entropy of each parameter to obtain a coefficient of variation of each parameter, wherein the coefficient of variation reflects the degree of variation between the parameters; Calculate the ratio of the coefficient of difference of each parameter to the sum of the coefficients of difference of all parameters to obtain the weight of each parameter; Compare the formula information with the optimal formula information in the database parameter by parameter, calculate the difference between every two parameters, and perform weighted summation of the difference between every two parameters and the corresponding weight to obtain a comprehensive difference value; When the comprehensive difference value is less than a preset value, it is determined that the accuracy verification of the recipe information has passed.

2. The control method for automatic plywood feeding and assembly according to claim 1 is characterized in that: After judging whether the required panels for the assembled plywood are sufficient based on the inventory information of the panels on each chain machine and the demand for each panel type, the method further includes: When it is determined that the required panels for the plywood to be assembled are insufficient, determining the first missing panel; generating a delivery instruction for the first panel, wherein the delivery instruction includes panel information of the first panel and a target production line entrance; Sending the delivery instruction to a panel warehouse, and controlling the panel warehouse to perform material retrieval and picking of the first panel based on the inventory location and material status of the first panel; The automatic guided vehicle of the panel warehouse is controlled to transport the picked first panel to the entrance of the target production line, and the automatic guided vehicle inside the production line is controlled to distribute the first panel from the entrance of the target production line to the corresponding chain machine.

3. The control method for automatic plywood feeding and assembly according to claim 2 is characterized in that: After the automatic guided vehicle controlling the panel warehouse transports the picked first panel to the entrance of the target production line, the method further includes: Monitor whether there is a pad that needs to be recovered at the entrance of the target production line; When a backing plate needs to be recovered, the backing plate is preferentially transported to the backing position on the automatic guided vehicle of the panel warehouse; When the automatic guided vehicle inside the production line delivers the first panel from the entrance of the target production line to the corresponding chain machine, the automatic guided vehicle controlling the panel warehouse transports the pad to the recycling area.

4. The control method for automatic plywood feeding and assembly according to claim 1, characterized in that: The controlling the robot to load the panels on each of the chain machines based on the recipe information includes: Based on the recipe information and the requirements of the robot feeding operation task, determine the scale of the robot, the initial position and speed of the robot; A fitness function is defined according to the optimization objective of the loading operation task, and based on the fitness function, an initial fitness value is calculated for each robot, and an initial global fitness value of all robots is calculated; Update the robot's speed and position according to the robot's current speed, individual optimal position and global optimal position; Based on the fitness function, calculate the current fitness value for each robot, and calculate the current global fitness value of all robots; Compare the current fitness value of each robot with the corresponding individual optimal fitness value. If the current fitness value is greater than the corresponding individual optimal fitness value, update the robot's individual optimal position and initial fitness value. Compare the current global fitness value of all robots with the optimal global fitness value. If the current global fitness value is greater than the optimal global fitness value, update the global optimal position and initial global fitness value of the robot. When the initial global fitness value reaches the optimal value, an optimal feeding operation plan is generated; Based on the optimal loading operation plan, the robot is controlled to perform loading operations on the panels on each of the chain machines.

5. The control method for automatic plywood feeding and assembly according to claim 1, characterized in that: The controlling the robot to load the panels on each of the chain machines based on the recipe information includes: Based on the recipe information, each robot is controlled to grab the plate on the corresponding chain machine, and the position and movement state of each robot is monitored in real time; Dynamically calculating the stopping point of each robot according to the position, motion state and geometric dimensions of each robot; According to the vector distance constraint between the current posture of each robot and the stop point, when controlling each robot to perform a loading operation on the grasped panel, avoidance measures are executed, and the avoidance measures include deceleration or pause.

6. The control method for automatic plywood feeding and assembly according to claim 1, characterized in that: The controlling the robot to load the panels on each of the chain machines based on the recipe information includes: Based on the recipe information, a plurality of feeding operation tasks for feeding the plates on each of the chain machines are generated, and based on a preset ant colony algorithm, each of the feeding operation tasks is arranged in order to form a near-field related task chain; Dividing the task chain into near-field subsets according to the task locations to obtain a plurality of task subsets, wherein the tasks in each task subset are geographically close and are collaboratively completed by the same robot or the same group of robots; Each of the task subsets is assigned to each robot, and each of the robots is controlled to execute the corresponding task subset.

7. The control method for automatic plywood feeding and assembly according to claim 1, characterized in that: Placing the captured multiple target panels at a preset position for assembly to obtain a target plywood comprises: Control each robot to place the captured multiple target panels to a preset position, and control the visual sensor and laser radar on the robot to start working to detect the edge position of each target panel; Inputting the detected edge position of each target plate into a preset particle swarm algorithm, optimizing the trajectory of each target plate, and obtaining the optimal adjustment trajectory of each target plate; According to the optimal adjustment trajectory of each target plate, the optimal motion trajectory is planned for each robot using a polynomial trajectory planning method; Each of the robots is controlled to adjust the edge position of the corresponding target panel according to the optimal motion trajectory, and the adjusted multiple target panels are assembled to obtain a target plywood.

8. A control system for automatic plywood feeding and assembly, characterized in that: The invention comprises a chain machine, a robot and a control device, wherein the control device is connected to the chain machine and the robot respectively, wherein the control device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the control method for automatic feeding and assembly of plywood as claimed in any one of claims 1 to 7.

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