Intelligent scheduling system and method for intelligent stacking robot

Abstract

The invention provides an intelligent scheduling system and method for an intelligent stacking robot, and the method comprises the steps: S1, collecting the point cloud data of a material through a depth camera, recognizing the size of the material, and obtaining the weight of the material; s2, the material volume is calculated according to the material size, and the density value is obtained by combining the material weight; stroke parameters of a pneumatic clamping jaw are calculated, and a stacking scheme is generated through an intelligent planning algorithm; s3, a pneumatic clamping jaw is controlled to be opened and closed based on the stroke parameters so as to grab materials, and the stacking scheme is executed to complete material stacking; and S4, detecting the appearance of the stacked materials, and enabling a WMS storage location scheduling module to distribute the materials to proper storage locations according to a detection result. According to the method, aiming at the morphological characteristics of the flexible materials, a dimension identification method combining convex hull analysis and abnormal value filtering is adopted, the irregular object measurement precision is improved, and the problems that in the stacking link of the multi-specification flexible bulk materials such as tea leaves, model changing is slow, damage is high, and dispatching is low in efficiency are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial robot control and intelligent logistics scheduling system, in particular to an intelligent scheduling system and method of an intelligent stacking robot, and especially to an intelligent scheduling system and method of an intelligent stacking robot for multi-specification flexible bulk materials. BACKGROUND

[0002] The existing tea product stacking technology mostly adopts fixed program robots, which has the following core defects: 1. Poor specification adaptability: only single packaging form can be adapted, and the changeover time is long, which cannot meet the flexible production needs of multi-specification materials (such as tea powder, tea extract, etc.) with mixed packaging. There are many varieties of small-batch production in the tea product industry, and traditional equipment is difficult to adapt to fluctuations in material specifications (such as packaging size deviation ) due to fixed mechanical structure.

[0003] 2. Insufficient visual recognition accuracy: 2D visual positioning has large error, and cannot accurately identify the deformation characteristics of flexible bulk materials (such as the fluctuation of tea powder bags due to filling quantity difference), and the stacking stability is poor (high collapse rate). In severe cases, it may cause damage to the material packaging.

[0004] 3. Rigid scheduling strategy: the stacking and warehousing links are disconnected, and the AGV logistics system is not linked, resulting in low equipment utilization rate. The traditional scheme lacks a dynamic scheduling model, resulting in low utilization rate of warehouse space and high material delivery delay rate.

[0005] Patent document CN220130470U discloses a tea leaf stacking device for tea leaf production and processing. The working process is to transport tea leaves through a conveyor belt. During the process, the tea leaves are smoothly slid into the storage box on the top of the weighing table with the help of the guide plate through the receiving plate. When the tea leaf quantity reaches the set weight of the weighing table, the first push plate pushes the storage box to the stacking table. Then the second push plate pushes the storage box inside the storage warehouse to the top of the weighing table for further packaging. When the number of storage boxes at the stacking table reaches three, they are pushed to the other end of the stacking table. However, this patent document still cannot solve the above technical problems.

[0006] The fundamental reason is that the existing system lacks a linkage detection mechanism, the mechanical structure does not realize self-adaptive adjustment, and an integrated scheduling model for stacking has not been built, making it difficult to adapt to the multi-specification and non-standard characteristics of flexible bulk materials such as tea leaves. SUMMARY

[0007] In view of the defects in the prior art, the purpose of the present application is to provide an intelligent scheduling system and method of an intelligent stacking robot.

[0008] The application provides an intelligent scheduling system of an intelligent stacking robot, which comprises the following: a three-dimensional size intelligent identification module, which is used for extracting size data of the material from the collected material point cloud data; a material density estimation module, which is used for calculating the material density according to the size data and the weight information of the material; a pneumatic gripper stroke adaptation module, which is used for calculating stroke parameters of the pneumatic gripper and adding a safety margin according to the size data and the material density of the material; an adaptive stacking scheme generation module, which is used for generating an optimal stacking scheme through an intelligent planning algorithm based on the size data and the safety margin of the material; a WMS storage scheduling module, which is used for screening candidate storage positions with adaptive sizes from idle storage positions according to the size data and the safety margin of the material, matching an optimal storage position through a double-layer optimization algorithm and issuing a storage allocation instruction; an IOT communication platform, which is used for completing interactive control of a WMS software system and an equipment automation control system.

[0009] Preferably, the process of extracting the size data of the material by the three-dimensional size intelligent identification module comprises the following steps: performing outlier filtering on the original point cloud data, and removing noise points deviating from the mean value by more than 3 times the standard deviation through a Z-score algorithm; projecting the pretreated point cloud on the XY plane, constructing a convex hull model to fit the material outer contour, determining the material length by calculating the maximum distance between the convex hull vertices, and calculating the width by the projection difference perpendicular to the longest axis direction; directly extracting the maximum coordinate difference of the point cloud in the Z-axis direction to obtain the material height.

[0010] Preferably, the pneumatic gripper stroke adaptation module extracts the maximum size of the material in the length direction as the gripper opening and closing reference, dynamically increases the bidirectional safety margin according to the density on the basis of the maximum size, and compensates for the shape change when the flexible material is grabbed.

[0011] Preferably, the process of generating the optimal stacking scheme through the intelligent planning algorithm comprises the following steps: introducing a safety margin parameter to correct the actual size of the material; automatically simulating two placing directions, respectively calculating the number of materials that can be accommodated in a single layer, and selecting the direction with a larger accommodation capacity as the optimal placing way; determining the maximum stacking layer number according to the ratio of the tray height limit to the corrected height of the material, in combination with the maximum allowed layer number threshold.

[0012] Preferably, the automatic simulation of the two placing directions comprises an original direction and The number of single-layer accommodated materials is calculated by integer division of the tray size and the corrected material size.

[0013] Preferably, the WMS storage scheduling module obtains idle storage data through a WMS interface, establishes a standardized model containing storage code, area, layer height, size limit and access priority, and selects a candidate storage with a size that fits from the idle storage by taking the length, width and height of the goods obtained by shape detection as input and introducing a safety margin parameter to correct the actual demand.

[0014] Preferably, the WMS storage scheduling module selects the optimal storage by using a double-layer optimization algorithm, sorts according to the access storage group constraint, calculates the height utilization rate, and selects the storage that best matches the height demand of the goods, and issues a storage allocation instruction through the WMS interface and returns a scheduling result containing storage information.

[0015] According to the intelligent scheduling method of the intelligent stacking robot provided by the application, the method comprises the following steps: Step S1: collecting material point cloud data by a depth camera and identifying the size of the material to obtain the weight of the material; Step S2: calculating the volume of the material according to the size of the material, obtaining the density value in combination with the weight of the material, calculating the stroke parameter of the pneumatic gripper, and generating a stacking scheme through an intelligent planning algorithm; Step S3: controlling the pneumatic gripper to open and close based on the stroke parameter to grasp the material, and executing the stacking scheme to complete the stacking of the material; Step S4: detecting the shape of the stacked material, and making the WMS storage scheduling module distribute the material to a suitable storage to complete warehousing according to the detection result.

[0016] Preferably, the calculation process of the size of the material comprises: Z-score algorithm is used to remove noise points deviating from the mean value by more than 3 times the standard deviation; A convex hull model is constructed in the XY plane to calculate the length and width of the material; The height of the material is obtained according to the maximum coordinate difference in the Z-axis direction.

[0017] Preferably, the stacking scheme comprises the material placement direction, the layout of each layer, the number of each layer, the total number of layers, the total accommodation capacity and the distribution of the remaining space.

[0018] Compared with the prior art, the application has the following beneficial effects: 1. The present application adopts a size recognition method combining convex hull analysis and outlier filtering according to the morphological characteristics of flexible materials, improving the measurement accuracy of irregular objects; through multi-direction simulation and dynamic adaptation of safety margin, the intelligence and safety balance of the stacking scheme are realized; at the same time, the size data and multi-scene requirements are integrated to form an integrated solution from recognition to execution, solving the problems of slow type change, high damage and low efficiency in scheduling of tea and other multi-specification flexible bulk materials in the stacking link, and reducing manual intervention, improving the flexible adaptation capability of the automatic production line.

[0019] 2. The present application constructs a matching algorithm of "stacking posture The AGV automatically distributes to the adaptive storage location according to the warehouse shape detection data (height recognition accuracy ), so that the warehouse space utilization rate is improved to , the equipment utilization rate is improved to , solving the problems of slow type change, high damage and low efficiency in scheduling of tea and other multi-specification flexible bulk materials in the stacking link.

[0020] 3. The present application integrates multiple technologies, from bottom to top, the bottom adopts a device data real-time acquisition system, compatible with mqtt and Siemens S7 protocol and other industrial communication protocols, detects the length, width and height specifications of the material through a visual camera, estimates the density combined with the weight, and intelligently calculates the appropriate stacking formula; after stacking is completed, the length, width and height data of the shape detection are calculated again to obtain the appropriate storage location information, and the linkage scheduling system is transported to the appropriate material storage area.

[0021] 4. The data acquisition platform in the present application adopts the industry's general industrial data transmission protocol, and is encapsulated as an IOT platform on this basis, increasing device connection configuration management, acquisition attribute group management, acquisition attribute configuration, etc., which belongs to the infrastructure platform; the interaction control between the WMS software system and the device automation control system is completed through the IOT platform.

[0022] 5. Each module of the system in the present application forms a closed loop through data chain transmission: three-dimensional size recognition provides basic parameters for all subsequent modules; stacking scheme generation relies on accurate size data to realize space optimization; density estimation combines size and weight to realize material property analysis; gripper stroke calculation is based on size data to ensure the reliability of grabbing; storage scheduling is based on size calculation to select the appropriate shape detection scheme, and transportation scheduling is carried out according to the shape detection result; the system coordinates the timing of each module through unified control logic, realizes full automation from material recognition to warehouse processing execution, is suitable for logistics sorting, warehouse stacking, flexible manufacturing and other scenes, and significantly improves processing efficiency and adaptation accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0023] Other features, objects, and advantages of the application will become apparent from the following detailed description of non-limiting embodiments, when read in connection with the following accompanying drawings: Figures la-c Figure 1 is a schematic diagram of an intelligent stacking robot and a vertical warehouse hoist docking device in an embodiment of the present application.

[0024] Figure 2 Figure 2 is a schematic diagram of an automatic control system for a conveying line in the present application.

[0025] Figure 3 Figure 3 is a code used when extracting flexible material parameters in an embodiment of the present application.

[0026] Figure 4 Figure 4 is a schematic diagram of high, medium, and low storage location classification and storage implementation in a warehouse storage location storage group management in the present application.

[0027] Figure 5 Figure 5 is a schematic diagram of IOT platform data read and write configuration in the present application.

[0028] Figure 6 Figure 6 is a schematic diagram of a preset stacking formula template and stacking feedback in an embodiment of the present application.

[0029] Figure 7 Figure 7 is a schematic diagram of using width and material density to achieve flexible stroke of pneumatic clamping jaw for grabbing in an embodiment of the present application.

[0030] Figure 8 Figure 8 is a schematic diagram of a system collaboration process of the present application.

[0031] Figure 9 Figure 9 is a flowchart of three-dimensional size calculation of a material in the present application.

[0032] Figure 10 Figure 10 is a stacking optimization decision tree of the present application.

[0033] Figure 11 Figure 11 is a workflow diagram of a pneumatic clamping jaw stroke adaptation module of the present application.

[0034] Figure 12 Figure 12 is a workflow diagram of a WMS storage location scheduling module in the present application. DETAILED DESCRIPTION

[0035] The present application will be described in detail below with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of changes and improvements can be made. These are within the scope of the present application.

[0036] Example 1 As shown in Figure 1, it is a schematic diagram of the intelligent stacking robot and the three-dimensional warehouse hoist docking device in the embodiment, wherein Figure la It is a top view, Figure lb It is a schematic diagram of the three-dimensional structure, Figure lc It is a side view; An intelligent scheduling system of an intelligent stacking robot, comprising: A three-dimensional size intelligent identification module: As Figure 2 shown, the material density is calculated through the depth camera (positioning error ) installed on the right side of the grabbing position and the weight linkage, and the pneumatic gripper stroke (adjustment range ) of the robot is dynamically adjusted to realize no downtime changeover (changeover time minutes) of 30kg bags to 60kg bags and other specification materials, and the material breakage rate is 0; As Figure 3 shown, this module takes point cloud data collected by a depth camera as input, and realizes accurate extraction of length, width and height parameters of flexible materials through multi-step data processing: 1. Point cloud preprocessing: filter out abnormal values from the original point cloud data, and use Z-score algorithm to remove noise points (such as redundant points generated by material surface wrinkles) deviating from the mean value by more than 3 times the standard deviation, to ensure data validity; 2. Convex hull analysis: project the preprocessed point cloud on the XY plane, construct a convex hull model to fit the material outer contour, determine the material length (longest axis) by calculating the maximum distance between convex hull vertices, and calculate the width (shortest axis) by the projection difference perpendicular to the longest axis direction, to adapt to the irregular shape of flexible materials; 3. Height calculation: directly extract the maximum coordinate difference of point cloud in Z axis direction to get the material height, avoiding measurement deviation caused by local protrusions; 4. Result output: accurately output the length, width and height parameters to 1mm, providing basic data for subsequent processes.

[0037] Self-adaptive stacking scheme generation module As Figure 6 shown, based on three-dimensional size data and preset stacking formula (tray length limit, width limit, height limit), the optimal stacking scheme is generated through intelligent planning algorithm: Parameter adaptation: introduce a safety margin parameter (default 10mm) to correct the actual size of the material, to avoid stacking interference caused by slight deformation of flexible materials; Direction optimization: automatically simulate two placing directions (original direction and Calculate the number of single-layer accommodated materials (achieved by integer division of the tray size and the corrected material size) in each direction (rotation direction), and select the direction with larger accommodation capacity as the optimal placement mode; Layer number calculation: According to the ratio of the tray height limit and the corrected material height, combined with the maximum allowed layer number threshold, determine the maximum stacking layer number to ensure not to exceed the height limit; Scheme output: Generate a complete scheme including placement direction, layout of each layer (number of rows and columns), number of each layer, total number of layers, total accommodation capacity, and remaining space distribution, providing execution basis for automated stacking equipment.

[0038] Material density estimation module 1. Combine three-dimensional size and weight data to quickly estimate the material density: 2. Volume conversion: Convert length, width, and height parameters in millimeters to centimeters to calculate the volume of the material (length width height); 3. Density calculation: Get the density value in unit, accurate to , by the ratio of weight (obtained by sensor) and volume; Pneumatic gripper stroke adaptation module As shown in Figure 7 and Figure 11 , based on three-dimensional size and density data, automatically calculate the adapted pneumatic gripper stroke parameters: Reference selection: Extract the maximum size in the length direction of the material as the gripper opening and closing reference to ensure covering the maximum span of the material; Safety redundancy: Dynamically increase bidirectional safety margin (reference 10mm / side) based on the maximum size and density to compensate for the shape change when flexible material is grasped; Stroke output: The calculation result is accurate to 0.1cm, which is directly used as the parameter input of the gripper control system to ensure the stability of grasping.

[0039] WMS storage scheduling module based on shape detection As shown in Figure 12 , obtain idle storage data through WMS interface, and establish a standardized model containing storage code, area, layer height, size limit, and access priority. With the length, width, and height of the goods obtained by shape detection as input, introduce safety margin parameters (height 10mm, length and width 10mm) to correct the actual demand, and select the candidate storage with suitable size from the idle storage.

[0040] As shown in Figure 4 , construct a "stacking posture-warehouse height" matching algorithm, and AGV selects the storage according to the warehouse shape detection data (height recognition accuracy ) automatically distribute to the adaptive storage location, the warehouse space utilization rate is improved to , the equipment utilization rate is improved to , and the problems of slow type changing, high damage, and low efficiency in scheduling of multi-specification flexible bulk materials such as tea during the stacking process are solved.

[0041] An optimal storage location is selected by using a double-layer optimization algorithm: first, sort according to the access storage location group constraint, and prefer high-priority storage locations; then calculate the height utilization rate, and select the storage location that best matches the height requirement of the goods to reduce space waste. Finally, issue a storage location allocation instruction through the WMS interface, and return a scheduling result containing storage location information, to realize an automatic closed loop from shape detection to warehouse storage.

[0042] Each module forms a closed loop through data chain transmission: three-dimensional size identification provides basic parameters for all subsequent modules; stacking scheme generation relies on accurate size data to achieve space optimization; density estimation combines size and weight to analyze material characteristics; gripper stroke calculation is based on size data to ensure reliable grabbing; and storage scheduling is based on size calculation to select an appropriate shape detection scheme and transport scheduling according to the shape detection result. The system coordinates the timing of each module through a unified control logic, realizes full automation from material identification to warehouse processing execution, and is suitable for logistics sorting, warehouse stacking, flexible manufacturing, and other scenarios, significantly improving processing efficiency and adaptation accuracy.

[0043] As shown in Table 1, the system components also include: Table 1 Components and Their Corresponding Functions

[0044] As shown in Figure 5 , in addition, the data acquisition platform uses the industry-standard industrial data transmission protocol, and is encapsulated as an IOT platform, which adds device connection configuration management, acquisition attribute group management, and acquisition attribute configuration, and belongs to the infrastructure platform. The IOT platform completes the interactive control between the WMS software system and the device automation control system.

[0045] The IOT platform is used to access the internal resources of the PLC, including: Reading and writing input / output (I / O) point states; Reading and writing memory area (such as M area, DB area) data; Obtaining PLC running status and fault information; Remote controlling the PLC (such as starting and stopping).

[0046] Example 2 The system mentioned above contains three operating modes: automatic stacking of raw materials into the warehouse, manual stacking into the warehouse, and whole pallet storage; the operator can select the corresponding production mode and configure the material information (or issue by WMS) on the interface; As Figure 8 and Figure 9 Intelligent scheduling method of intelligent stacking robot, comprising: Step 1, empty tray group / empty tray is put on the empty tray online point, enters the tray unstacking machine to be unstacked into a single tray and is supplied to the automatic stacking station, and the tray barcode information is scanned; Step 2, raw materials are fed from the right side telescopic belt conveyor, and stop waiting for mechanical arm grabbing when reaching the automatic stacking station; Step 3, the change type judgment is triggered by the grabbing position in the signal, and the length, width and height information of the goods is identified by vision and output to the automatic control program.

[0047] Step 4, the automatic control program calculates the material density according to the vision identification information and weight, and adds the pneumatic clamping stroke control parameters on the basis of the stacking scheme specified by the formula.

[0048] Step 5, the mechanical arm grabs the raw materials according to the grabbing stroke and stacking scheme given by the upper layer information system and stacks them on the tray; Step 6, after the raw material robot stacking is completed, the system binds and uploads the tray barcode information and the stacking raw material information to WMS.

[0049] As Figure 10 shown, step 7, WMS matches the appropriate storage group according to the height of the goods, initiates logistics scheduling instruction, and promotes to AGV connection after scheduling AGV to transport warehouse area high / medium / low storage.

[0050] Those skilled in the art know that in addition to implementing the system provided by the present application and each device, module, unit thereof in pure computer readable program code, the same function can be realized by logically programming the method steps in the form of logic gate, switch, special integrated circuit, programmable logic controller and embedded microcontroller. Therefore, the system provided by the present application and each device, module, unit thereof can be considered as a hardware component, and the devices, modules and units included therein for realizing various functions can also be considered as structures in the hardware component; the devices, modules and units for realizing various functions can also be considered as both software modules for realizing methods and structures in the hardware component.

[0051] The specific embodiments of the present application are described above. It should be understood that the present application is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essential content of the present application. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. An intelligent scheduling system for an intelligent palletizing robot, characterized in that, The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. 2.The intelligent scheduling system of the intelligent palletizing robot according to claim 1, characterized in that, The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. 3.The intelligent scheduling system of the intelligent palletizing robot according to claim 1, wherein, The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. 4.The intelligent scheduling system of the intelligent palletizing robot according to claim 1, wherein, The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition. 5.The intelligent scheduling system of the intelligent palletizing robot according to claim 4, characterized in that, The application relates to a WMS (Warehouse Management System) intelligent storage scheduling system based on three-dimensional size recognition.

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The Step S3: controlling the opening and closing of the pneumatic clamping jaw based on the travel parameters to grab the material, and executing the stacking scheme to complete the material stacking; Step S4: detecting the shape of the stacked material, and making the WMS storage scheduling module distribute the material to a suitable storage location according to the detection result to complete the warehousing. 7.The intelligent scheduling method of the intelligent palletizing robot according to claim 6, wherein, The calculation process of the material size includes: Using the Z-score algorithm to eliminate noise points deviating from the mean value by more than 3 times the standard deviation; Constructing a convex hull model in the XY plane to calculate the length and width of the material; Obtaining the height of the material according to the maximum coordinate difference in the Z-axis direction. 8.The intelligent scheduling method of the intelligent palletizing robot according to claim 6, wherein, The stacking scheme includes the material placement direction, the layout of each layer, the number of each layer, the total number of layers, the total capacity, and the remaining space distribution.

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