Bin sorting method and bin sorting apparatus

Abstract

To provide a bin distribution method capable of improving efficiency in picking work when performing the picking work for moving articles to a delivery bin from a storage bin using a plurality of picking robots.SOLUTION: A bin distribution method distributes bins where objects of picking work by a picking robot can be stored, and comprises the steps for: acquiring picking instruction data for instructing the picking work of the objects; acquiring an evaluation index related to the picking work for each of the objects and each of the picking robots; and distributing bins to a first picking robot of a plurality of picking robots on the basis of the picking instruction data and the evaluation index.SELECTED DRAWING: Figure 18

Description

[Technical Field]

[0001] The present disclosure relates to a bin sorting method and a bin sorting apparatus. [Background Art]

[0002] Conventionally, an information processing method for assigning picking processing to a picking means is known (see Patent Document 1). The information processing method of Patent Document 1 generates a picking work list for picking work of picking articles from shelves transported by an automated guided vehicle based on an order list acquired from an external device, and calculates, for each picking means based on work logs related to past picking work, the processing capacity for processing the picking work included in the picking work list, and allocates the picking work included in the generated picking work list to each picking means based on the processing capacity. The picking means is a worker or a robot. [Prior Art Documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Unexamined Patent Publication No. 2023-017393 [Summary of the Invention] [Problem to be Solved by the Invention]

[0004] The present disclosure provides a bin sorting method and a bin sorting apparatus that can improve the efficiency of picking work when performing picking work of moving articles from storage bins to shipping bins using a plurality of picking robots. [Means for Solving the Problem]

[0005] One aspect of the present disclosure is a bin sorting method for sorting bins into which objects to be picked by a picking robot can be stored, the method comprising: acquiring picking instruction data for instructing the picking operation of the objects; acquiring evaluation indicators for the picking operation for each object and each picking robot; and sorting the bins to a first picking robot among a plurality of picking robots based on the picking instruction data and the evaluation indicators.

[0006] One aspect of the present disclosure is a bin sorting device comprising a processor for sorting bins capable of storing objects to be picked by a picking robot, wherein the processor acquires picking instruction data for instructing the picking operation of the objects and evaluation indicators for the picking operation for each object and each picking robot, and sorts the bins to a first picking robot among a plurality of picking robots based on the picking instruction data and the evaluation indicators. [Effects of the Invention]

[0007] According to this disclosure, when performing picking operations that involve moving items from storage bins to retrieval bins using multiple picking robots, the efficiency of the picking operation can be improved. [Brief explanation of the drawing]

[0008] [Figure 1] A schematic diagram showing an example of the appearance of a warehouse management system according to the first embodiment. [Figure 2] A diagram showing an example of the environment inside a warehouse. [Figure 3] A diagram showing an example of a picking station where workers are positioned. [Figure 4] This diagram shows an example of a picking station equipped with orthogonal robots, which are picking robots. [Figure 5] This diagram shows an example of a picking station equipped with a multi-jointed picking robot. [Figure 6] Diagram illustrating a configuration example of a bin [Figure 7] Diagram illustrating an example of a bin and a transfer robot that transfers bins [Figure 8] Diagram illustrating a layout concept of cameras [Figure 9] Block diagram illustrating a configuration example of a host management device [Figure 10] Block diagram illustrating a configuration example of a robot control system [Figure 11] Diagram illustrating an example of picking instruction data [Figure 12] Diagram illustrating an example of robot configuration information [Figure 13] Diagram illustrating an example of target object information [Figure 14] Diagram illustrating an example of picking success rate information [Figure 15] Diagram illustrating an example of tact time information [Figure 16] Diagram illustrating an example of picking result data [Figure 17] Diagram illustrating an example of storage bin coordinates and delivery bin coordinates [Figure 18] Flowchart illustrating an operation example of a host management device [Figure 19] Flowchart illustrating a detailed example of sorting processing [Figure 20] Diagram illustrating a movement example of bins during picking work [Figure 21] Block diagram illustrating a configuration example of a host management device according to a second embodiment [Figure 22] Block diagram illustrating a configuration example of a robot control system according to a second embodiment [Figure 23] Diagram illustrating updated picking instruction data I1A [Figure 24] Diagram illustrating a concept of hand movement time in the X and Y directions according to the positional relationship between storage bins and delivery bins [Figure 25] Diagram illustrating a concept of movement speed of the hand in the Z direction [Figure 26] Diagram illustrating a concept of movement of a target object according to weight [Figure 27]Flowchart showing an example of the operation of a host management device [Figure 28] Top view of the interior of a shipping bin and a storage bin [Figure 29] Diagram showing an example of an arrangement pattern for arranging storage bins and shipping bins in a bin arrangement area [Figure 30] Diagram showing an example of the movement flow of a hand and a target object during picking Mode for Carrying Out the Invention

[0009] Hereinafter, embodiments will be described in detail with reference to the drawings as appropriate. However, overly detailed descriptions may be omitted in some cases. For example, detailed descriptions of already well-known matters and duplicate descriptions for substantially the same configuration may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. The accompanying drawings and the following description are provided to allow those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter recited in the claims.

[0010] Background Leading to the First Embodiment In the information processing method of Patent Document 1, a picking means picks an article from a shelf and places it into a tray, but no assumption is made for picking articles from storage bins that store products and storing them in shipping bins from which products are shipped. Furthermore, although a picking robot is assumed as the picking means, there is no intent to accommodate a plurality of picking robots. Therefore, for example, when each picking robot has strengths and weaknesses in picking depending on the characteristics of products, no consideration is given to which picking robot among the plurality of picking robots the storage bin should be moved close to.

[0011] In the first embodiment, a bin distribution method and a bin distribution device capable of improving the efficiency of picking work when performing picking work for moving articles from storage bins to shipping bins using a plurality of picking robots will be described.

[0012] (First Embodiment) <Warehouse Management System Configuration> Figure 1 shows an example configuration of the warehouse management system 5 in the first embodiment. The warehouse management system 5 comprises a warehouse management device 10, a higher-level management device 20, a robot control system 30, a robot device 40, and a camera 60. The robot device 40 includes a transport robot 40C and a picking robot 40P.

[0013] The warehouse management system 10 is a device for facilitating logistics within a warehouse and is also called a Warehouse Management System (WMS). The warehouse management system 10 manages goods such as inventory. The warehouse management system 10 has inventory management functions and inbound / outbound management functions. In its inventory management function, the warehouse management system 10 manages information regarding the status of currently stored inventory. This information includes, for example, the storage location, arrival date, quantity, expiration date, color, size, etc. In its inbound / outbound management function, the warehouse management system 10 manages records of goods being received and dispatched. The warehouse management system 10 can manage inbound / outbound schedules and record actual inbound / outbound transactions. The warehouse management system 10 also has functions to streamline operations associated with inbound / outbound transactions, such as creating picking lists and slips for goods.

[0014] The higher-level management device 20 operates as a Warehouse Execution System (WES) and has warehouse management and control functions. The higher-level management device 20 is an intermediate device between the warehouse management device 10 and the robot control system 30. Furthermore, the higher-level management device 20 can grasp on-site work data such as inventory management, receiving, and picking of goods in real time. In short, the higher-level management device 20 is a device for comprehensively controlling people, goods, and equipment within the warehouse.

[0015] The higher-level management device 20 has both a work management function and an equipment control function. In its work management function, the higher-level management device 20 visualizes the work status of the robot device 40 or the worker, enabling real-time progress monitoring. It can also transmit predetermined instructions to terminals carried by the worker (e.g., wearable devices, voice terminals) or terminals installed in the work area (e.g., displays). In its equipment control function, the higher-level management device 20 can control various devices in the warehouse (e.g., the robot device 40 or cameras 60).

[0016] Furthermore, the warehouse management device 10 and the higher-level management device 20 are integrated, and one may perform the functions of the other.

[0017] The robot control system 30 operates as a Warehouse Control System (WCS). The robot control system 30 enables optimally scheduled loading and unloading by controlling various equipment in the warehouse (e.g., robot equipment 40 or camera 60) in real time. The robot control system 30 can monitor the operation of equipment in real time and transmit predetermined instruction information to the equipment.

[0018] The robotic device 40 contributes to the automation of various tasks within the warehouse. The robotic device 40 includes multiple transport robots 40C and multiple picking robots 40P.

[0019] The transport robot 40C transports bins 70 in which various items managed within the warehouse are stored. Bins 70 include storage bins 71 for storing incoming items and outgoing bins 72 for storing outgoing items. Note that bins 70 may be automated guided vehicles (AGVs), and bins 70 themselves may operate as the transport robot 40C, meaning bins 70 may be self-propelled.

[0020] The picking robot 40P picks various items stored in the storage bin 71 in response to instructions from the robot control system 30, moves them to the retrieval bin 72, and stores them. The picking robot 40P has an arm and a hand. The arm is capable of accessing predetermined positions inside the bin 70 and picking items within the bin 70. The hand is attached to the end of the arm. The picking robot 40P may include multiple picking robots of different types, 40PA, 40PB, 40PC.

[0021] The picking robot 40PA is an orthogonal suction robot. An orthogonal suction robot can move its arm in two orthogonal directions along a horizontal plane (e.g., X and Y directions) and one direction perpendicular to the horizontal plane (Z direction), and picks up items by suctioning them to its hand. The picking robot 40PA can recognize the surface of an item based on the image captured by the camera 60, using the robot control system 30. The picking robot 40PA is particularly good at picking box-shaped items. Here, "good at" means, for example, a high picking success rate or a short cycle time, as will be described later.

[0022] The picking robot 40PB is an orthogonal two-fingered robot. The orthogonal two-fingered robot can move its robot arm in the X, Y, and Z directions as described above, and picks items using the two fingers of its hand. The picking robot 40PB can recognize the shape of an item based on images captured by the camera 60, via the robot control system 30. The picking robot 40PB is particularly adept at picking heavy items.

[0023] The picking robot 40PC is an articulated robot. An articulated robot has multiple rotational mechanisms as joints in its arm, allowing it to approach objects in various directions by rotating. The picking robot 40PC picks items using two fingers of its hand. The picking robot 40PC can recognize the three-dimensional shape of an item based on images captured by the camera 60, using the robot control system 30. The picking robot 40PC is particularly adept at picking items with irregular shapes. It should be noted that the strengths of each robotic device 40 are merely examples, and their strengths will vary depending on various factors such as the type of robot, size, and shape of its hand.

[0024] Therefore, for each type of picking robot 40P, there are items that can be picked and items that cannot be picked. Each type of picking robot 40P can complement the characteristics of other types of picking robots 40P regarding pickability. For example, item 1 can be picked by picking robots 40PA, 40PB, and 40PC. Item 2 can be picked by picking robots 40PA and 40PB. Item 3 can be picked by picking robots 40PB and 40PC. Item 4 can be picked by picking robots 40PC and 40PA.

[0025] In Figure 1, the warehouse management device 10 generates picking instruction data based on the picking list and transmits it to the higher-level management device 20. The higher-level management device 20 includes a robot configuration database (robot configuration DB) 20D1 and an object database (object DB) 20D2. The robot configuration database 20D1 holds robot configuration information related to the configuration of robots. The object database 20D2 holds object information related to objects. The higher-level management device 20 has a distribution function that distributes bins 70 to one of the multiple picking robots 40P based on the picking instruction data from the warehouse management device 10 and evaluation indicators related to the picking work. The higher-level management device 20 also controls the operation of the robot device 40 and the operation of the camera 60 via the robot control system 30.

[0026] <Warehouse environment> Figure 2 shows an example of the environment inside warehouse 50.

[0027] Warehouse 50 is, for example, an automated warehouse. Storage shelves 51 for storing bins 70 are arranged inside warehouse 50. Storage shelves 51 are partitioned planarly or spatially and can accommodate a large number of bins 70. A transport robot 40C receives transport instructions from, for example, a higher-level control device 20 and transports the bins 70 stored in storage shelves 51 to one of several picking stations PS. Storage shelves 51 contain storage bins 71, and storage bins 71 may be transported from storage shelves 51 to a predetermined picking station PS at a predetermined time. Outbound bins 72 are located, for example, around storage shelves 51 or at an outbound station, and may be transported to a predetermined picking station PS at a predetermined time. Storage bins 71 and outbound bins 72 are transported to picking stations sorted by the sorting function of the higher-level control device 20.

[0028] Each picking station PS is equipped with either a picking robot 40P or a worker 45 to perform picking tasks. Picking station PS0 is a picking station where picking is performed by a human. Picking station PS1 is a picking station where picking is performed by a picking robot 40P. There may be three or more picking stations PS. In addition, there may be multiple picking stations PS1 for the picking robot 40P.

[0029] Once the picking operation at the picking station PS is completed, the transport robot 40C receives a transport instruction from, for example, the higher-level control device 20 and transports the bin 72 to a designated location (for example, the shipping station). The shipping station is a spatial area where, for example, the items are inspected and packed into boxes for delivery.

[0030] Picking is the process of picking up an item from storage bin 71, moving the item 80 to the outbound bin 72, and placing it in the outbound bin 72. In other words, picking is a pick-and-place operation.

[0031] <Picking station configuration> Figure 3 shows an example of picking station PS0 with worker 45. Figure 4 shows an example of picking station PS1 (PS11) with a Cartesian robot, picking robot 40PA or picking robot 40PB. Figure 5 shows an example of picking station PS1 (PS12) with an articulated robot, picking robot 40PC.

[0032] As shown in Figures 4 and 5, in the picking station PS1 (PS11, PS12) where the picking robot 40P is located, four bins 70 are arranged around the center of the picking robot in two in the X direction and two in the Y direction (i.e., in a 2x2 shape). The four bins 70 include at least one storage bin 71 and at least one outbound bin 72. By arranging multiple bins 70 in a 2x2 shape in this way, the warehouse management system 5 can perform picking operations using other bins 70 even when replacing one bin 70, thus enabling efficient picking operations and shortening the overall cycle time. Note that the arrangement is not limited to a 2x2 shape; for example, it may be arranged in shapes such as 2x3, 3x2, or 3x3, allowing more bins 70 to be placed around the picking robot 40P simultaneously.

[0033] <Bin structure> Figure 6 shows an example of the configuration of the bin 70. The bin 70 has a roughly rectangular parallelepiped shape, and items can be loaded and unloaded from the top. Therefore, the picking robot 40P and the worker 45 load and unload items into the bin 70 from the top. Figure 7 shows an example of the bin 70 and the transport robot 40C that transports the bin 70. In Figure 7, the transport robot 40C moves the bin 70 by pushing or pulling it, but the bin 70 may also be placed on top of the transport robot 40C and then moved. Note that different transport robots 40C may be used for transporting storage bins and for transporting out bins, or the same transport robot 40C may be used.

[0034] <Camera placement> Figure 8 shows an image of the camera 60's placement. The camera 60 is positioned to capture images of the inside of the bin 70. The camera 60 is used, for example, to recognize the state inside the bin 70. The camera 60 may be installed in the picking station PS, for example, as camera 60A, inside or near the picking robot 40P. Alternatively, the camera 60 may be placed outside the picking station PS, for example, as camera 60B. The image G1 captured by the camera 60 shows, for example, the items stored in the storage bin 71 and the retrieval bin 72, making it possible to understand how the items are arranged. The image G1 captured by camera 60A also shows whether the storage bin 71 and the retrieval bin 72 are present or not, so it is possible to determine from the image G1 whether the storage bin 71 and the retrieval bin 72 have arrived at the picking station PS where they are being transported. Note that only one camera 60 may be provided, as camera 60A, or multiple cameras may be provided, as camera 60B.

[0035] <Configuration of the higher-level management device> Figure 9 is a block diagram showing an example configuration of the higher-level management device 20. The higher-level management device 20 comprises a processor 21, memory 22, input device 23, communication device 24, and input / output interface 25.

[0036] The processor 21 may be configured using, for example, a Central Processing Unit (CPU) or a Digital Signal Processor (DSP). The processor 21 may also be configured using various integrated circuits (for example, a Large Scale Integration (LSI) or a Field Programmable Gate Array (FPGA)). The processor 21 implements various functions by executing programs held in memory 22. The processor 21 comprehensively controls each part of the higher-level management device 20 and performs various processes. The processing by the processor 21 may be performed, for example, in the cloud.

[0037] The processor 21 acquires picking instruction data to instruct the picking operation of the target object. The processor 21 acquires evaluation indicators for the picking operation for each target object and each picking robot. The evaluation indicators include, for example, the picking success rate, which indicates the success rate of the picking operation for each target object and each picking robot, and the cycle time, which is the time required for the picking operation for each target object and each picking robot. Based on the picking instruction data and the evaluation indicators, the processor 21 allocates the bins 70 to one of the multiple picking robots 40P. In this way, the processor 21 allocates the bins 70 that can store the target objects to be picked by the picking robots 40P.

[0038] Memory 22 includes a primary storage device (e.g., Random Access Memory (hereinafter referred to as "RAM") or Read Only Memory (hereinafter referred to as "ROM")). Memory 22 may also include a secondary storage device (e.g., a Hard Disk Drive (hereinafter referred to as "HDD") or Solid State Drive (hereinafter referred to as "SSD")) or a tertiary storage device (e.g., an optical disc or SD card). Memory 22 may also be an external storage medium and may be detachable from the higher-level management device 20. Memory 22 stores various data, information, programs, or logs. Memory 22 has a robot configuration database 20D1 and an object database 20D2.

[0039] Memory 22 stores, for example, picking instruction data, robot configuration information, object information, picking success rate information (described later), cycle time information (described later), and picking result data (described later). Memory 22 pre-stores, for example, robot configuration information and object information. Memory 22 may also store, for example, the picking success rate information and cycle time information obtained as calculation results, and picking result data obtained from an external device.

[0040] The input device 23 may include various buttons, keys, keyboards, touch panels, microphones, or other input devices. The input device 23 accepts input of various data or information. The input device 23 is operated, for example, by an administrator who manages the higher-level management device 20.

[0041] The communication device 24 communicates various data or information according to a wired or wireless communication method. The communication device 24 may be connected to the network NT in a communication-enabled manner. The communication method used by the communication device 24 may include a Local Area Network (Wide Area Network), a WAN (Wide Area Network), a mobile phone network, or power line communication. The communication device 24 communicates with external devices (e.g., warehouse management device 10, robot control system 30). For example, the communication device 24 receives picking instruction data from the warehouse management device 10. For example, the communication device 24 transmits picking instruction data to the robot control system 30 and receives picking result data from the robot control system 30.

[0042] The input / output interface 25 performs input and output of information and data between the processor 21, memory 22, input device 23, and communication device 24.

[0043] <Configuration of the robot control system> Figure 10 is a block diagram showing an example configuration of a robot control system 30. The robot control system 30 comprises a processor 31, a memory 32, an input device 33, a communication device 34, and an input / output interface 35.

[0044] The processor 31 may be configured using, for example, a CPU or a DSP. The processor 31 may also be configured using various integrated circuits (e.g., LSI or FPGA). The processor 31 implements various functions by executing programs held in the memory 32. The processor 31 comprehensively controls each part of the robot control system 30 and performs various processes.

[0045] The processor 31 controls the operation of equipment or devices in the warehouse (e.g., transport robot 40C, picking robot 40P, camera 60) via the communication device 34. For example, the processor 31 may control the transport of bins 70 by the transport robot 40C by controlling its movement. The processor 31 may control the picking operation by the picking robot 40P by controlling the movement of the picking robot 40P's arm and hand. The processor 31 may control the picking operation based on picking instruction data from the higher-level management device 20.

[0046] The processor 31 acquires various information via the input device 33 or the communication device 34. For example, the processor 31 acquires captured images taken by the camera 60 and detection information detected by the sensor. The processor may detect various events based on the acquired captured images or detection information. For example, the processor 31 may recognize whether or not a bin 70 has arrived at each picking station PS, the coordinates where the bin 70 is located at the picking station, and information about the items stored inside the bin 70 (e.g., presence, shape, weight, material, size of the items). The processor 31 may generate at least a portion of the picking result data based on the recognized information.

[0047] Memory 32 includes primary storage devices (e.g., RAM or ROM). Memory 32 may also include secondary storage devices (e.g., HDD or SSD) or tertiary storage devices (e.g., optical disc or SD card). Memory 32 may also be an external storage medium and may be detachable from the robot control system 30. Memory 32 stores various data, information, or programs.

[0048] The input device 33 may include various buttons, keys, keyboards, touch panels, microphones, sensors, or other input devices. The input device 33 accepts input of various data or information. The input device 33 may be operated, for example, by an administrator or worker 45 who manages the robot control system 30. Sensors may be provided, for example, inside or around each picking robot 40P. Sensors may be provided, for example, inside or around each transport robot 40C.

[0049] The communication device 34 communicates various data or information according to a wired or wireless communication method. The communication method used by the communication device 34 may include LAN, WAN, mobile phone network, or power line communication. The communication device 34 communicates with an external device (e.g., a higher-level management device 20).

[0050] The communication device 34 is connected to the camera 60, picking robot 40P, and transport robot 40C via wired or wireless connection. The communication device 34 sends imaging instructions to the camera 60 and acquires the images captured by the camera 60. The communication device 34 sends operation instructions to each picking robot 40P for the robot arm or hand to operate. The communication device 34 acquires information such as the position, angle, or posture of the robot arm or hand from each picking robot 40P. The communication device 34 sends transport instructions for the bin 70 to the transport robot 40C and acquires information such as the current position from the transport robot 40C. The communication device 34 is also connected to the network NT and, for example, is connected to the warehouse management device 10 via the network NT. The communication device 34 may also be connected to the camera 60, picking robot 40P, and transport robot 40C via the network NT.

[0051] The input / output interface 35 performs input and output of information and data between the processor 31, memory 32, input device 33, and communication device 34.

[0052] The robot control system 30 may be provided as a single unit, multiple units may be provided, or one unit may be provided for each piece of equipment in the warehouse, such as the robot device 40.

[0053] <Details of information handled by the higher-level management device> Next, we will explain the details of the information handled by the higher-level management device.

[0054] Figure 11 shows an example of picking instruction data I1. Picking instruction data I1 is instruction information related to picking instructed by the warehouse management device 10. Picking instruction data I1 includes information on task (work) ID, task type, storage bin ID, outbound bin ID, and time. Each task ID is a unique ID (identification information). The task type is information indicating the work content to be performed by the robot device 40 or worker 45. This work content includes information such as the ID of the object to be picked and the number of items to be picked for each object. The storage bin ID is the ID of the storage bin 71 transported from the storage shelf 51. The outbound bin ID is the ID of the outbound bin. The time information is the data issuance time when the picking instruction data was issued.

[0055] Figure 12 shows an example of robot configuration information I2. Robot configuration information I2 is stored in the robot configuration database 20D1. Robot configuration information I2 includes information on structure type ID, hand type ID, and robot installation coordinates. The structure type ID is information indicating the type of structure of the picking robot 40P (e.g., Cartesian robot, vertical articulated robot, or other structure type). The hand type ID is information indicating the type of hand (e.g., suction, bifingered, polyfingered, or other hand type). The robot installation coordinate information indicates the position where the picking robot is installed, and is shown by X and Y coordinates, for example, (X,Y)=(12,3).

[0056] Figure 13 shows an example of object information I3. Object information I3 is stored in the object database 20D2. Object information I3 stores information about the shape of object 80, the material of object 80, the weight of object 80, and the size of object 80. The shape of object 80 is, for example, a box, cylinder, or bag. The material of object 80 is, for example, paper, metal, or plastic.

[0057] Figure 14 shows an example of picking success rate information I4. Picking success rate information I4 includes information on the picking success rate for each object category and each robot ID. The picking success rate indicates the probability of success in picking an object. The picking success rate may be pre-stored in memory 22 for each object category and each robot ID. The picking success rate may be calculated by the processor 21 for each object category and each robot ID and stored as the calculation result in memory 22. The robot ID is identification information indicating the type of picking robot 40P. The type of picking robot may be determined, for example, by the hand type, or by a combination of the hand type and the structural type. The object category is classified according to the characteristics of the object 80 that may affect the picking operation (e.g., shape, size, weight, size). Since the object category is determined for each object, it can be said that the picking success rate is determined for each object and each picking robot 40P. Furthermore, instead of categorizing objects by their characteristics, they may be categorized according to the type of each individual object.

[0058] Figure 14 stores information on the picking success rate based on robot IDs (e.g., ID1, ID2, ID3) and object categories (e.g., A, B, C, D). Robot ID1 indicates, for example, a suction type. Robot ID2 indicates, for example, a two-finger type. Robot ID3 indicates, for example, a three-finger type. Furthermore, the picking success rate information I4 in Figure 14 can be used as a predicted value when the picking operation is actually performed.

[0059] Figure 15 shows an example of takt time information I5. Takt time information I5 includes takt time information for each object category and each robot ID. The takt time may be pre-stored in memory 22 for each object category and each robot ID. The takt time may be calculated by the processor 21 for each object category and each robot ID and stored in memory 22 as the calculation result. Since the object category is determined for each object, it can be said that the takt time is determined for each object and each picking robot 40P.

[0060] The cycle time in Figure 15 represents the time required for the picking operation. Here, the cycle time may be the time from the moment the picking of a predetermined object 80 from the storage bin 71 begins until the object 80 is moved and placed in the output bin 72. In other words, it may be the time required to transfer one object from the storage bin 71 to the output bin 72. Alternatively, the cycle time may be the time from the moment the first object 80 is picked from the storage bin 71 until the placement of the last object in the output bin 72 is completed, for all objects 80 specified in the picking instruction data. In other words, it may be the time from the start to the completion of placement in one output bin 72. Furthermore, the cycle time may be the time from the acquisition of the picking instruction data until the placement of the last object in the output bin 72 specified in the picking instruction data is completed. In this case, the cycle time may include the time it takes to move the bins 70.

[0061] Figure 15 stores cycle time information based on robot IDs (e.g., 1, 2, 3) and object categories (e.g., A, B, C, D). Furthermore, the cycle time information I5 in Figure 15 can be used as a predicted value for when the picking operation is actually performed.

[0062] There are categories for objects that are box-shaped and lightweight, such as boxes containing medicine. There are also categories for objects that are box-shaped and heavy, such as cardboard boxes containing bottled beverages. There are also categories for objects that are bag-shaped and lightweight, such as towels. There are also categories for objects that are bag-shaped and heavy, such as bags containing sugar. Finally, there are categories for objects that have irregular shapes, such as bags with uneven surfaces.

[0063] For example, when the object has a simple shape such as a box, object recognition based on the captured image is easy and fast, and grasping by the hand is also easy, which tends to shorten the cycle time. On the other hand, when the object has an irregular shape, object recognition based on the captured image is difficult and slow, and approaching by the arm and grasping by the hand must be done carefully, which tends to lengthen the cycle time.

[0064] Figure 16 shows an example of picking result data I6. Picking result data I6 is data obtained as a result of performing a picking operation. Picking result data I6 includes information such as task ID, status, storage bin ID, outbound bin ID, storage bin coordinates, outbound bin coordinates, robot ID, bin movement time, cycle time, and time. Here, the storage bin ID is the ID of the storage bin 71 transported from the storage shelf 51. Here, the outbound bin ID is the ID of the outbound bin 72 transported from the storage shelf 51. Here, the storage bin coordinates are the coordinates of the position where the storage bin 71 was placed upon arrival at the destination picking station PS. Here, the outbound bin coordinates are the coordinates of the position where the outbound bin 72 was placed upon arrival at the destination picking station PS. Here, the robot ID is the ID of the picking robot 40P that performed the picking operation of the object 80. Here, the bin movement time is the time required from the time of receipt of the picking instruction data I1 to the time when the movement of bin 70 is completed. The takt time here refers to the actual time required for the picking operation (pick and place), i.e., the measured value of the takt time. The time information indicates the time when the picking result data I6 was issued.

[0065] The picking result data I6 is derived (e.g., detected or calculated) by the robot control system 30 and transmitted to the higher-level management device 20, that is, it is fed back as result information for the picking instruction data. The higher-level management device 20 acquires the picking result data I6 from the robot control system 30 and stores the picking result data I6 in the memory 22. The higher-level management device 20 may also have the processor 21 update the picking success rate information and cycle time information based on the fed-back picking result data I6.

[0066] Figure 17 shows an example of storage bin coordinates and outbound bin coordinates. In Figure 17, for example, three storage bins 71 and one outbound bin 72 are arranged around a picking robot 40PC located at picking station PS12. The storage bin coordinates of the three storage bins 71 are (X,Y)=(20,2), (20,3), and (21,3), respectively. The outbound bin coordinate of the one outbound bin 72 is (X,Y)=(21,2).

[0067] <How the warehouse management system works> Figure 18 is a flowchart showing an example of the operation of the higher-level management device 20.

[0068] The communication device 24 receives picking instruction data from the warehouse management device 10 (S11). The processor 21 calculates the picking success rate and cycle time for each object category and each picking robot based on the picking instruction data, robot configuration information, and object information (S12). For example, the processor 21 calculates the picking success rate and cycle time when picking the object instructed by the picking instruction data through simulation. Note that the picking success rate and cycle time do not need to be calculated each time, but data recorded in memory 22 may be used.

[0069] The robot control system 30 manages information related to picking operations for each picking robot 40P. The processor 31 recognizes the task amount for each picking robot 40P, for example, by using a sensor as an input device 33. The task amount is, for example, the amount of work that the picking robot 40P must perform (for example, the number of objects that need to be picked). For example, the robot control system 30 can recognize the number of remaining objects for each picking robot 40P based on the number of objects that have been instructed to be picked by the picking robot 40P and the number of objects that have been picked, which is derived from the detection information of the picking robot 40P's sensors and images captured by the camera 60. The processor 31 transmits the task amount information for each picking robot 40P to the higher-level management device 20 via the communication device 34. The processor 21 of the higher-level management device 20 acquires the task amount information for each picking robot 40P via the communication device 24. Alternatively, the processor 21 of the higher-level management device 20 may recognize the task amount for each picking robot 40P itself.

[0070] The processor 21 performs a sorting process (S13) to allocate the destination of the bin 70 to one of the multiple picking robots 40P based on the picking success rate and cycle time obtained in step S12, and the amount of tasks. The sorting process is equivalent to the process of determining which picking robot 40P will perform the picking work for the object 80 instructed by the picking instruction data. Details of the sorting process will be described later.

[0071] The processor 21 instructs the transport robot 40C to place the storage bin 71 containing the object specified in the picking instruction data into the destination picking robot 40P (S14). In this case, the processor 21 may instruct the transport robot 40C to transport the storage bin 71 from its storage location to the desired placement location for the picking operation, based on the storage bin ID. The desired placement location for the storage bin 71 here is the picking station PS where the destination picking robot 40P is located, and is in the vicinity of this picking robot 40P.

[0072] The processor 21 instructs the transport robot 40C to place the bin 72 containing the object 80 specified in the picking instruction data into the picking robot 40P (S14). In this case, the processor 21, via the communication device 24, instructs the transport robot to move the bin 72 from its storage location to the intended placement location for the picking operation, based on the bin ID. The intended placement location for the bin 72 is the picking station PS where the intended picking robot 40P is located, and is in the vicinity of this picking robot 40P. In steps S13 and S14, the storage bin 71 and the bin 72 are transported to the same picking station PS.

[0073] The processor 21 determines whether the transport of the storage bin 71 and the retrieval bin 72 to the predetermined placement locations has been completed (S15). In this case, the processor 21 may recognize that the transport of the storage bin 71 and the retrieval bin 72 to the predetermined placement locations has been completed by receiving arrival information via the communication device 24 indicating that the transport robot 40C has arrived at the predetermined placement location. Alternatively, the processor 21 may acquire images from the camera 60 via the communication device 24 and recognize that the transport to the predetermined placement locations has been completed based on the images.

[0074] If the storage bin 71 and the retrieval bin 72 have not been transported to their designated locations (No. in step S15), the processor 21 repeats the process in step S15. In other words, the processor 21 waits until the storage bin 71 and the retrieval bin 72 have been transported to their designated locations.

[0075] When the storage bin 71 and the retrieval bin 72 have been transported to their designated locations (Yes in step S15), the processor 21 instructs the picking robot 40P, located at the destination picking station PS, via the communication device 24 to pick the objects 80 from the storage bin 71 to the retrieval bin 72 (S16).

[0076] At step S14, the processor 21 may have already sent instructions for picking to the destination picking robot 40P via the communication device 24 and the robot control system 30.

[0077] In this case, the robot control system 30 may receive, via the communication device 34, notification information from the transport robot 40C indicating the completion of transport, and detection information from the sensor acting as the input device 33 indicating the completion of transport by the transport robot 40C (i.e., information indicating the detection of the storage bin 71 or the outbound bin 72 around the picking robot 40P). Based on the acquired information, the processor 31 determines the timing for the picking robot 40P to pick the object from the storage bin 71. The communication device 34 then notifies the picking robot 40P at the destination of the picking timing. This allows the picking robot 40P to pick the object at the appropriate time, even if the picking operation instruction was given early.

[0078] The picking robot 40P at the destination performs the picking operation according to the picking instructions from the higher-level control device 20. The picking operation is captured by the camera 60. The processor 31 of the robot control system 30 determines whether the picking robot 40P succeeded in the picking operation based on the images captured by the camera 60 (S17). For example, if the processor 31 recognizes, as a result of image recognition of the images captured by the camera 60, that the object that was stored inside the storage bin 71 has moved inside the output bin 72 and is stored there, it determines that the picking operation was successful. The communication device 34 transmits picking result data, including picking success / failure information indicating the success or failure of the picking operation, to the higher-level control device 20. The processor 21 of the higher-level control device 20 may then determine whether the picking operation was successful or not by receiving the picking result data via the communication device 24.

[0079] Furthermore, the robot control system 30 may use sensors to detect when the picking operation by the picking robot 40P at the destination has been completed, and transmit this detection information to the higher-level management device 20. The processor 21 of the higher-level management device 20 may then receive the picking operation completion information via the communication device 24 and determine that the picking operation was successful.

[0080] If it is determined that the picking operation of the target object has been successful (Yes in step S17), the processor 21 instructs the transport robot 40C via the communication device 24 to transport the storage bin 71 and the retrieval bin 72 to the specified locations (S18). The specified location for the storage bin 71 is, for example, any location on the storage shelf 51. The specified location for the retrieval bin 72 is, for example, any location on the retrieval station.

[0081] On the other hand, if it is determined that the picking operation of the object has failed (No. in step S17), the processor 21 proceeds to step 16 until it fails N times. In other words, in step S16, the processor 21 repeats the picking operation of the object up to N times. If the picking operation of the object has failed N times (No. for the Nth time in step S17), the processor 21 instructs the transport robot 40C via the communication device 24 to transport the storage bin 71 in which the object that failed to be picked is stored, and the output bin 72 in which the object is transferred and stored, to the picking station PS0, which is the human work area (S19).

[0082] One possible reason for N failures in the object picking process is that the results of the simulation for deriving the picking index value, described later, in the sorting process in step S13, are incorrect.

[0083] After step S19, the worker 45 picks the target object at the picking station PS0. At this time, the processor 21 transmits work instructions for the picking operation to a display or the like installed in the worker 45's work area (picking station PS0) via the communication device 24. This display receives and displays the work instructions for the picking operation. This allows the worker 45 to confirm detailed information regarding the picking operation of the target object.

[0084] Figure 19 is a flowchart showing an example of the details of the sorting process in step S13 of Figure 18.

[0085] The processor 21 of the higher-level management device 20 determines the priority of the picking robots 40P that will distribute the bins 70 based on the picking success rate and cycle time obtained in step S11. In this case, the processor 21 may determine the priority such that the smaller the picking index value (= cycle time / picking success rate), which is the value obtained by dividing the cycle time by the picking success rate, the higher the priority. The processor 21 may determine which picking robot 40P will distribute the bins 70 based on the priority and task volume of the picking robot 40P. Therefore, if the task volume of the picking robot 40P scheduled for distribution (for example, a high-priority picking robot) is large, the processor 21 may distribute the bins 70 to another picking robot 40P that is capable of picking the same or similar objects.

[0086] The processor 21 sets the picking robot with the highest priority as the destination (for example, a picking robot 40P with a suction hand) as the picking robot 40P to be assigned (S21). The processor 21 determines whether the amount of tasks assigned to the picking robot 40P is equal to or greater than a predetermined amount (S22).

[0087] If the amount of tasks is less than a predetermined amount (No. in step S22), the processor 21 determines the picking robot 40P that has been set as the destination picking robot 40P (S23).

[0088] If the amount of tasks assigned to the picking robot 40P is greater than or equal to a predetermined amount (Yes in step S22), the processor 21 determines whether the picking robot 40P with the next priority (e.g., 2nd) (e.g., a picking robot 40P with a two-fingered hand) is capable of picking the object that the assigned picking robot 40P was scheduled to pick (S24). Whether or not picking is possible can be determined, for example, by whether or not the success rate of picking the same object or objects with similar characteristics is greater than or equal to a predetermined value.

[0089] If the next priority picking robot 40P is available for picking (Yes in step S24), the processor 21 sets the next priority picking robot 40P as the destination picking robot 40P (S25). Then, the process proceeds to step S22.

[0090] If the priority is such that the next picking robot 40P is unable to pick (No. in step S24), the processor 21 determines that the picking robot 40P set as the transfer destination will be the picking robot 40P to be assigned (S23).

[0091] As shown in the operation examples in Figures 18 and 19, the higher-level control device 20 can determine which picking robot 40P is to allocate the bins 70 to based on evaluation indicators related to the picking work of each picking robot 40P (e.g., picking success rate, cycle time). Therefore, the higher-level control device 20 can determine which picking robot 40P is to allocate the bins 70 to, based on the assumption that it is capable of picking at high speed and with high accuracy.

[0092] Furthermore, the higher-level management device 20 can determine the picking robot 40P to which the task will be assigned, taking into account the workload. For example, the higher-level management device 20 can set (provisionally determine) the picking robot 40P to which the task will be assigned based on the picking index value, and then determine (formally determine) the picking robot 40P to which the task will be assigned, taking into account the workload of the set picking robot 40P. In other words, if the workload of a picking robot 40P is large, the picking work can be distributed to another picking robot 40P that is capable of picking the target objects. Therefore, the higher-level management device 20 can avoid a waiting period for picking work at the picking robot 40P that was initially set as the target. Thus, the higher-level management device 20 can shorten the overall cycle time required to pick all the objects to be picked.

[0093] Furthermore, the processor 21 of the higher-level management device 20 may store the picking success rate and cycle time in memory 22 when it has calculated them, so that they can be used for future bin 70 allocation. Alternatively, the processor 21 may store past performance values ​​of the picking success rate and cycle time in memory 22 so that they can be used for bin 70 allocation at a later time. In these cases, the higher-level management device 20 can avoid having to calculate the picking success rate and cycle time when allocating bin 70.

[0094] Next, we will explain other examples of situations where picking operations using the 40P picking robot are difficult.

[0095] Here, we assume that the picking robot 40P, which is set as the distribution destination, is unable to pick the object specified in the picking instruction data. Here, we assume that none of the picking robots 40P can pick the object, and the picking success rate is 0%.

[0096] If any of the picking robots 40P have a picking success rate of 0%, it is not possible to obtain a picking index value (cycle time / picking success rate). Therefore, there is no picking robot 40P that has the minimum picking index value. In this case, the processor 21 of the higher-level management device 20 determines the worker 45 as the picking means to be assigned.

[0097] The processor 21 instructs the transport robot 40C via the communication device 24 to transport the storage bin 71 and the retrieval bin 72 to the worker 45's position (picking station PS0). This process corresponds to step S19 in Figure 18. After this, the worker 45 performs the picking operation of the target items at the picking station PS0.

[0098] <Moving the bottles> Figure 20 shows an example of how bin 70 moves during picking.

[0099] In Figure 20, multiple storage bins 71 are referred to as "storage bin x" (where x is a natural number), and also simply as "storage x". In Figure 20, the retrieval bin 72 is also simply referred to as "retrieval". In the initial state, storage bin 1, storage bin 2, storage bin 3, and retrieval bin 72 are arranged in a 2x2 shape in the bin placement area BR around the picking robot 40.

[0100] First, in state A, a swap (picking operation) is performed, transferring items from storage bin 1 to retrieval bin 72.

[0101] In state B, storage bin 1, from which the object has been picked, moves away to the negative side in the X direction, and a new storage bin 4 approaches the position where storage bin 1 was located from the positive side in the Y direction and is placed in the original position of storage bin 1. During this movement of storage bins 1 and 4, the picking operation from storage bin 2 to the retrieval bin 72 is performed.

[0102] In state C, the storage bin 2 from which the object has been picked moves away in the positive X direction, and a new storage bin 5 approaches the position where storage bin 2 was located from the positive Y direction and is placed in the original position of storage bin 2. While storage bins 2 and 5 are moving in this manner, the picking operation from storage bin 3 to the retrieval bin 72 is performed.

[0103] Thus, in this embodiment, the movement of one or more storage bins 71 and the picking operation using one or more other storage bins 71 and the retrieval bin 72 can be performed at overlapping timings, enabling efficient transport of bins 70 and picking operations. Therefore, the warehouse management system 5 can reduce the total time of bin movement time and cycle time when picking items from multiple storage bins 71 to the retrieval bin 72.

[0104] According to the warehouse management system 5 in this first embodiment, bins 70 can be appropriately distributed to multiple picking robots 40P. In an automated warehouse having two or more types of picking robots 40P with different configurations capable of picking and movable bins 70, the warehouse management system 5 can improve the coverage rate of pickable objects and shorten the picking time. In this case, the higher-level management device 20 can minimize one or more work cycles in response to picking instruction data (storage bins, dispatch bins, quantities, etc. of the objects) according to the robot configuration, robot placement, and robot picking evaluation indicators (grasping success rate and work cycle time), output the results, and appropriately control the placement of the movable bins 70.

[0105] (The process leading to the acquisition of the second embodiment) The information processing method described in Patent Document 1 does not consider how the shelves transported by the automated guided vehicle are arranged relative to the picking means that performs the picking operation. Therefore, when there are multiple shelves used for the picking operation, the time required for the picking operation may increase depending on the relative positions of the multiple shelves.

[0106] In the second embodiment, for example, a bin arrangement control method and a bin arrangement control device that can shorten the time required for picking when a picking operation is performed using a picking robot to move an object from a storage bin to a retrieval bin will be described.

[0107] (Second embodiment) In the second embodiment, the warehouse management system controls the placement of each bin transported to the picking station. In this embodiment, the same matters as described in the first embodiment are omitted or simplified.

[0108] <Warehouse Management System Configuration> The warehouse management system 5A in the second embodiment has the same configuration as the warehouse management system 5 in the first embodiment. However, the warehouse management system 5A has a higher-level management device 20A instead of the higher-level management device 20, and a robot control system 30A instead of the robot control system 30.

[0109] Figure 21 is a block diagram showing an example configuration of the upper-level management device 20A in the second embodiment. The higher-level management device 20A has the same configuration as the higher-level management device 20 of the first embodiment. However, the higher-level management device 20A includes a processor 21A instead of processor 21.

[0110] The processor 21A has the same functions as the processor 21 of the first embodiment. The processor 21A also controls the placement of the bins 70 for a predetermined picking robot 40P. In this case, the processor 21A determines the positional relationship between the storage bin 71 and the retrieval bin 72 based, for example, on the characteristics of the object, the number and arrangement of items placed inside the bin 70, or the position of the object in the storage bin 71 and the possible position of the object after movement in the retrieval bin 72. Alternatively, in this case, the processor 21A may calculate the travel distance of the picking robot's hand based on the position of the object in the storage bin 71 and the possible position of the object after movement in the retrieval bin 72, and determine this positional relationship based on the travel distance of the hand.

[0111] The processor 21A controls the placement of the storage bins 71 and the retrieval bins 72 relative to the picking robot 40P based on the determined positional relationship. For example, the processor 21A determines the placement positions of the storage bins 71 and the retrieval bins 72 relative to the picking robot 40P. These placement positions are the placement positions of the storage bins 71 and the retrieval bins 72 in the bin placement area BR of the picking station PS where the picking robot 40P is located. The placement position of the storage bins 71 is defined by the storage bin coordinates, and the placement position of the retrieval bins 72 is defined by the retrieval bin coordinates.

[0112] The specified picking robot 40P described above may be the picking robot 40P to be sorted as determined in the first embodiment, or it may be the picking robot 40P to be sorted as determined by another method. Furthermore, the characteristics of the object are, for example, the shape, material, weight, or size included in the object information I3 shown in Figure 13.

[0113] The processor 21A may acquire captured images from the camera 60 via the communication device 24 and acquire various detection information from sensors. Based on the captured images or detection information, the processor 21A recognizes the arrangement of items inside the bins 70 (e.g., storage bin 71, retrieval bin 72). Note that the recognition of the arrangement of items based on captured images or detection information described here may also be performed by the robot control system 30A. In this case, the processor 21A may recognize the arrangement of items inside the bins 70 by acquiring recognition information including the results of this recognition from the robot control system 30A via the communication device 24.

[0114] The processor 21A updates the picking instruction data I1 by adding the storage bin coordinates and the pickup bin coordinates to the picking instruction data I1 based on the determined placement positions of the storage bin 71 and the pickup bin 72, thereby generating the picking instruction data I1A.

[0115] Figure 22 is a block diagram showing an example configuration of the robot control system 30A according to the second embodiment. The robot control system 30A has the same configuration as the robot control system 30 of the first embodiment. However, the robot control system 30A includes a processor 31A instead of processor 31.

[0116] The processor 31A has the same functions as the processor 31 of the first embodiment. The processor 31A also recognizes, for example, the arrangement of items in the bin 70 based on captured images acquired from the camera 60 or detection information acquired from sensors. The information resulting from the recognition is transmitted as recognition information to the higher-level management device 20A by the communication device 34.

[0117] Figure 23 shows the updated picking instruction data I1A. In Figure 23, explanations of items similar to those in the picking instruction data I1 shown in Figure 4 are omitted or simplified. The picking instruction data I1A includes information on the task ID, task type, storage bin coordinates, outbound bin coordinates, and time. The storage bin coordinates are the coordinates of the position where the storage bin 71 is to be placed upon arrival at the destination picking station PS. The outbound bin coordinates are the coordinates of the position where the outbound bin 72 is to be placed upon arrival at the destination picking station PS.

[0118] <How the warehouse management system works> Next, an example of the picking operation in this embodiment will be described.

[0119] The picking operation by the picking robot 40P can be broken down into the following processes, for example. The picking operation by the picking robot 40P is performed according to control instructions from, for example, the robot control system 30. The picking operation includes, for example, the process during picking: recognition of the content of the picking instruction data, recognition of the object to be picked, movement of the arm (hand) to the object in the storage bin 71, waiting for the vibration of the arm to subside after movement, and picking the object with the hand (e.g., suction or gripping). The picking operation also includes the process during placing: recognition of the possible placement position of the picked object in the output bin 72, movement of the arm (hand) to the possible placement position of the object in the output bin 72, waiting for the vibration of the arm to subside after movement, and placing (positioning) the object to the possible placement position with the hand. The above arm movement includes movement in the XY direction and movement in the Z direction (downward and upward). Since the hand is installed at the tip of the arm, it moves in conjunction with the movement of the arm. The hand can also move independently of the arm.

[0120] Of the subdivided processes in the picking operation described above, the processes of picking, moving the arm (hand), and placing take a relatively long time. In this embodiment, the warehouse management system 5 reduces at least the movement time required for the hand that holds the object during the picking operation. The movement speed of the hand is controlled (also called trapezoidal control) so that it gradually increases from 0 at the start of movement to a target speed, and gradually decreases from the target speed to 0 at the end of movement. Factors that affect the movement time of the hand include, for example, how the object is placed in the retrieval bin 72, the size of the object, and the weight of the object.

[0121] The movement speed of the hand in the XY direction may differ depending on how the object is placed into the retrieval bin 72. The way the object is placed into the retrieval bin 72 refers to the positional relationship between the placement position of the object in the storage bin 71 and the possible placement position of the object after movement in the retrieval bin 72. For example, the longer the distance from the placement position of the object in the storage bin 71 to the possible placement position of the object in the retrieval bin 72, the longer the movement distance of the hand in the XY direction and the longer the movement time of the hand in the XY direction. On the other hand, the shorter the distance from the placement position of the object in the storage bin 71 to the possible placement position of the object in the retrieval bin 72, the shorter the movement distance of the hand in the XY direction and the shorter the movement time of the hand in the XY direction.

[0122] Figure 24 shows an image of the hand's movement time in the XY direction depending on the arrangement of the storage bin 71 and the retrieval bin 72. In Figure 24, in arrangement pattern A, the storage bin 71 and the retrieval bin 72 are arranged side by side in the X direction, and the hand needs to move a long distance over the object 80, passing over other items 85. In arrangement pattern B, the storage bin 71 and the retrieval bin 72 are arranged diagonally opposite each other, and the hand only needs to move a short distance over the object 80 without passing over other items 85.

[0123] The movement time of the arm in the Z direction may vary depending on the size of the object or the number of items in bin 70. The larger the size of the object, or the greater the number of items in storage bin 71, the faster the arm moves in the Z direction, and therefore the shorter the arm's movement time. This is because when items are stacked in the Z direction, the distance the arm travels in the Z direction is reduced. Similarly, when there are many items in the retrieval bin 72, the arm's movement time in the Z direction is also reduced because the objects are stacked in the Z direction. On the other hand, the smaller the size of the object, or the fewer the number of items in storage bin 71, or the fewer the number of items in retrieval bin 72, the faster the arm moves in the Z direction.

[0124] Figure 25 is a diagram illustrating the movement speed of the hand in the Z direction. In Figure 25, when the hand is placed on another item 85, the distance the hand travels is shorter, resulting in higher speed. When the hand is not placed on another item 85, the distance the hand travels is longer.

[0125] For example, the processor 21 determines, based on the arrangement of the recognized items, whether the object 80 is stacked on top of item 85 in the storage bin 71, that is, whether the object 80 is placed at a high position in the Z direction. If the object 80 is placed at a high position, the picking time can be suppressed even if the storage bin 71 in which the object 80 is stored and the retrieval bin 72 are placed diagonally opposite each other, rather than side by side in the X or Y direction. Similarly, the processor 21 determines, based on the arrangement of the recognized items, whether the object 80 can be stacked on top of item 85 in the retrieval bin 72, that is, whether the object 80 can be placed at a high position in the Z direction. If the object 80 can be placed at a high position, the picking time can be suppressed even if the storage bin 71 and the retrieval bin 72 in which the object is stored are placed diagonally opposite each other, rather than side by side in the X or Y direction.

[0126] Depending on the weight of the object, the travel time of the hand in each of the XYZ directions while the hand is picking the object may differ. The heavier the object, the slower the hand's movement speed in the XYZ directions while picking the object, and therefore the longer the hand's travel time. This is because the acceleration in the trapezoidal control described above is smaller with heavier weights. On the other hand, the lighter the object, the faster the hand's movement speed in each of the XYZ directions while picking the object, and therefore the shorter the arm's travel time.

[0127] Figure 26 shows an image of how objects move according to their characteristics. In Figure 26, the higher-level control device 20 can shorten the hand's movement time and thus the cycle time by making the movement distance of heavier objects shorter than that of lighter objects. The same applies when the characteristics are not heavy weight, but rather large size, slippery material, or simple shape. In this case, the higher-level control device 20 can suppress an increase in picking time by placing the storage bin 71 and the retrieval bin 72 close together, for example, adjacent in the X or Y direction. On the other hand, when the characteristics are light weight, small size, non-slippery material, or complex shape, the hand's movement speed can be increased and the hand's movement time shortened, so even if the storage bin 71 and the retrieval bin 72 are placed some distance apart, for example diagonally, an increase in picking time can be suppressed.

[0128] Figure 27 is a flowchart showing an example of the operation of the higher-level management device 20. It is assumed that at the start of processing as shown in Figure 27, each bin 70 is located near the picking robot 40P that performs the picking operation.

[0129] The processor 21 acquires an image captured by the camera 60 via the communication device 24. This image shows the items inside the storage bin 71. Based on the image, the processor 21 recognizes the arrangement of the items inside the storage bin 71. At this time, the processor 21 recognizes the position of the object within the storage bin 71 (S31).

[0130] The processor 21 acquires the image captured by the camera 60 via the communication device 24. This image shows the items in the retrieval bin 72. Based on the image, the processor 21 recognizes the arrangement of the items in the retrieval bin 72. At this time, the processor 21 recognizes the positions in the retrieval bin 72 where the items can be placed (S32).

[0131] The processor 21 determines the respective placement positions of the storage bin 71 and the retrieval bin 72 in the bin placement area BR of the picking station PS, based on the arrangement of items in the storage bin 71 and the arrangement of items in the retrieval bin 72. In this case, the processor 21 may determine the respective placement positions of the storage bin 71 and the retrieval bin 72 in the bin placement area BR of the picking station PS based on the arrangement of items in the storage bin 71 and the possible placement positions of items in the retrieval bin 72. Alternatively, in this case, the processor 21 may, as an example, calculate the shortest arrangement that minimizes the distance traveled from the storage bin 71 to the retrieval bin 72 when an item is picked, and determine the respective placement positions of the storage bin 71 and the retrieval bin 72 corresponding to the shortest arrangement (S33). Details of the shortest arrangement will be described later.

[0132] The processor 21 instructs the transport robot 40C via the communication device 24 to transport the storage bin 71 and the retrieval bin 72 so that they are placed in the determined positions (S34).

[0133] When the processor 21 recognizes that the storage bin 71 and the retrieval bin 72 are placed in their determined positions, it instructs the processor 21 to perform a picking operation to transfer the items from the storage bin 71 to the retrieval bin 72 (S35). The picking robot 40P's hand may or may not return to its initial position (for example, the center position of the picking robot 40P) after each picking operation is completed.

[0134] Next, we will explain the details of the shortest possible layout.

[0135] Figure 28 is a view from above of the interior of the storage bin 71 and the retrieval bin 72. In Figure 28, one item 85 is stored in the storage bin 71, and five items are stored in the retrieval bin 72. As an example, it is assumed that two items can be placed in the X direction and three items in the Y direction in both the storage bin 71 and the retrieval bin 72 in a planar manner (without overlapping vertically). In this case, the item 85, which is the target object 80 placed at the upper left placement position p1 of the storage bin 71 in Figure 28, will be picked and moved to the placement position p2, which is the lower right space of the retrieval bin 72, and lowered and stored in this position. As for the positional relationship between the storage bin 71 and the retrieval bin 72, for example, the arrangement pattern shown in Figure 29 is possible.

[0136] The placement position p1 may be recognized by the processor 21, for example, based on the captured image of the inside of the storage bin 71 and the object information. For example, the processor 21A may determine the presence or absence of the object specified in the picking instruction data based on various characteristics such as the size of the object, and also determine the placement position p1 of that object. The possible placement position p2 may be recognized by the processor 21, for example, based on the captured image of the inside of the retrieval bin 72 and the object information. For example, the processor 21A may determine the possible placement position p2 based on the arrangement of the items (e.g., empty space) and various characteristics such as the size of the object. For example, if an object of a predetermined size fits in the empty space in the retrieval bin 72, the processor 21A may determine the position of this empty space as the possible placement position p2. Furthermore, the placement position p1 and the possible placement position p2 may include a position component in the Z direction.

[0137] Figure 29 shows an example of an arrangement pattern for arranging storage bins 71 and retrieval bins 72 in the bin arrangement area BR. In Figure 29, the bin arrangement area BR is a rectangular area where bins 70 can be arranged side by side, two in the X direction and two in the Y direction. The processor 21 assumes multiple arrangement patterns for arranging storage bins 71 and retrieval bins 72 in the bin arrangement area BR. Arrangement patterns PT1, PT2, and PT3 are shown in Figure 29.

[0138] In arrangement pattern PT1, the storage bin 71 in the arrangement shown in Figure 28 is placed in the upper right region BR2 of the bin arrangement area BR, and the dispatch bin 72 in the arrangement shown in Figure 28 is placed in the upper left region BR1 of the bin arrangement area BR. In this case, the distance between the placement position p1 in the storage bin 71 and the possible placement position p2 in the dispatch bin 72 in the XY plane is the distance d1.

[0139] In arrangement pattern PT2, the storage bin 71 in the arrangement shown in Figure 28 is placed in the lower left region BR3 of the bin arrangement area BR, and the dispatch bin 72 in the arrangement shown in Figure 28 is placed in the upper left region BR1 of the bin arrangement area BR. In this case, the distance between the placement position p1 in the storage bin 71 and the possible placement position p2 in the dispatch bin 72 in the XY plane is the distance d2.

[0140] In arrangement pattern PT3, the storage bin 71 in the arrangement shown in Figure 28 is placed in the lower right region BR4 of the bin arrangement area BR, and the dispatch bin 72 in the arrangement shown in Figure 28 is placed in the upper left region BR1 of the bin arrangement area BR. In this case, the distance between the placement position p1 in the storage bin 71 and the possible placement position p2 in the dispatch bin 72 in the XY plane is the distance d3.

[0141] In the bin placement area BR, of the distances d1 to d3 between the storage bins 71 and the outgoing bins 72 in each placement pattern PT1 to PT3, distance d2 is the shortest distance. Therefore, placement pattern PT2, which is the case for distance d2, is determined to be the shortest placement. In this way, the processor 21 calculates the shortest placement that minimizes the movement distance of the object in step S33.

[0142] Figure 30 shows an example of the movement flow of the hand and the object during picking. In Figure 30, it is assumed that the hand 41 returns to its initial position after each picking operation is completed.

[0143] In the picking operation using arrangement pattern PT2 in Figure 29, first, in state A, the hand 41, which is in its initial position, moves towards the placement position p1 of the object 80 in the storage bin 71. Next, in state B, the hand 41 picks up the object 80. Next, in state C, the picked object 80 is moved to the placement position p2 in the output bin 72. Next, in state D, the object 80 is lowered and placed at the placement position p2 in the output bin 72, and the hand 41 is returned to its initial position. Therefore, the determination of the shortest arrangement in step S33 in Figure 27 may be determined not by minimizing the travel distance of the object 80 (i.e., the hand without considering the reference position), but by determining the arrangement pattern that minimizes the travel distance of the hand 41 considering its initial position during the picking operation.

[0144] Furthermore, this embodiment will be supplemented.

[0145] In this embodiment, the warehouse management system 5A controls the placement of bins 70 by utilizing at least one of the object information and the placement state of the bins 70, that is, based on at least one of the object information and the placement state of the bins 70. When utilizing object information, the processor 21A of the higher-level management device 20A arranges objects, for example, heavy objects, in a way that minimizes the distance they have to travel. Similarly, when utilizing the placement state of the bins 70, the processor 21A arranges objects, for example, heavy objects, in a way that minimizes the distance they have to travel. The same applies when utilizing both object information and the placement state of the bins 70.

[0146] Furthermore, in the placement control of bin 70, if there are multiple storage bins 71, it may not be possible to place a predetermined storage bin 71 in the desired placement position within the bin placement area BR because another storage bin 71 is already placed there or is scheduled to be placed there. The desired placement position is, for example, the position with the shortest cycle time (time required for picking). In this case, the processor 21A may select a placement position for the predetermined storage bin 71 that has the next best cycle time, rather than the optimal placement position.

[0147] Furthermore, when the processor 21A instructs the placement locations of multiple storage bins 71 and dispatch bins 72 almost simultaneously, it may calculate the cycle time for each storage bin 71 and each dispatch bin 72. The processor 21A may then instruct the placement locations of the storage bins 71 and dispatch bins 72 in such a way that the overall cycle time for a series of picking operations based on picking instruction data (for example, picking operations corresponding to a single shipping instruction) is shortened.

[0148] According to the warehouse management system 5A of this embodiment, the arrangement of storage bins 71 relative to the outbound bin 72 can be optimized based on the weight of the object to be picked, the size of the object, the number of items in bin 70, or how the object is placed in the outbound bin 72, as specified in the picking instruction data. Furthermore, by controlling the operation of the picking robot 40P, the warehouse management system 5A can smoothly perform the picking operation when each bin 70 is optimally arranged (for example, picking from the object that is as high as possible in the Z direction, or placing it in an empty space). As a result, the warehouse management system 5A can minimize the time required for the operation of the arm or hand of the picking robot 40P, shorten the cycle time during the picking operation of the picking robot 40P, and speed up the picking operation.

[0149] As an example, suppose a picking operation is performed for the same retrieval bin 72 from storage bins 71 located in area BR1 and storage bins 71 located in area BR2 within the bin placement area BR. In this case, since the picking operation is performed from different storage bins 71, the arm needs to approach different storage bins 71. Therefore, the distance the arm travels becomes longer, and the arm's travel time tends to be longer. In this case, the storage bin 71 located in area BR1 may move to area BR2 without leaving the bin placement area BR. The processor 21 of the higher-level management device 20 may select the case where the picking operation is performed while the storage bin 71 or retrieval bin 72 moves within the bin placement area BR, or where the arm approaches different storage bins 71 to perform the picking operation, whichever takes less time. The processor 21 may be able to calculate the time required for each of these cases through simulation.

[0150] In this embodiment, the processor 21A of the higher-level management device 20A primarily performs processing related to bin placement control, but this is not limited to this. For example, the processor 31A of the robot control system 30A may primarily perform processing related to bin placement control.

[0151] (Summary of the embodiment) Based on the above, this disclosure contains at least the following information. The components and other elements in parentheses are examples of those corresponding to the embodiments described above, but are not limited to these.

[0152] (Item 1) A bin sorting method for sorting objects (objects 80) to be picked by multiple picking robots (picking robots 40P) into bins (bins 70) capable of storing them, Step S11: Obtain picking instruction data (picking instruction data I1) for instructing the picking operation of the aforementioned object. Step S12: Obtain evaluation indicators for the picking operation for each object and each picking robot. Step S13: Based on the picking instruction data and the evaluation index, the bins are distributed to the first picking robot among the plurality of picking robots. A bin distribution method including the following.

[0153] As a result, the bin sorting method can recognize what kind of picking each robot should perform based on the picking instruction data, even when there are multiple picking robots and each robot has strengths and weaknesses in picking different products. It can also recognize strengths and weaknesses based on the characteristics of the target object and the picking robot using evaluation indicators. Therefore, the bin sorting method can determine the most suitable bin destination from among multiple picking robots.

[0154] (Item 2) The aforementioned evaluation indicators are: Success rate information (Picking Success Rate Information I4) that shows the success rate of the picking operation for each object and each picking robot, This includes takt information (takt time information I5) that indicates the takt time, which is the time required for the picking operation for each object and each picking robot, The bin distribution method described in item 1.

[0155] This allows the bin sorting method to determine the destination of bins that are more likely to be successfully picked and that shorten the picking time (cycle time).

[0156] (Item 3) For each of the picking robots, the step is to acquire robot configuration information (robot configuration information I2) relating to the configuration of the picking robot. The method further includes the step of obtaining object information (object information I3) relating to each of the aforementioned objects, The step of obtaining the evaluation index includes the step of calculating the evaluation index by simulation based on the robot configuration information and the object information. The bin distribution method described in item 1 or 2.

[0157] As a result, the bin distribution method can obtain evaluation indicators based on robot configuration information and object information, even if the evaluation indicators are not stored in memory or other means beforehand.

[0158] (Item 4) The step of sorting the aforementioned bins is, A step of determining the priority of the picking robot to pick the object instructed by the picking instruction data, based on the success rate information and the cycle time information, The steps include determining the first picking robot based on the aforementioned priority, The bin distribution method described in item 2.

[0159] This allows the bin sorting method to determine the destination bin by, for example, selecting bins with high priority for picking robots as candidates for bin sorting.

[0160] (Item 5) The method further includes the step of obtaining task quantity information relating to the amount of objects to be picked for each picking robot, The step of sorting the aforementioned bins is, The step of determining the first picking robot based on the priority of the picking robot and the task volume information includes: The bin distribution method described in item 4.

[0161] This allows the bin allocation method to determine the destination by taking into account, for example, the congestion of picking operations at the candidate destinations.

[0162] (Item 6) The step of sorting the aforementioned bins is, For each picking robot, the step of determining whether or not the target object can be picked based on the success rate information, If there is no picking robot capable of picking the aforementioned object, the process includes the step of distributing bins to a work area for an operator (operator 45) to pick the aforementioned object. The bin distribution method described in item 2.

[0163] This allows the bin sorting method to allocate bins to human workers from the initial sorting destination if it is estimated that picking the objects would be difficult for any picking robot.

[0164] (Item 7) The step of sorting the aforementioned bins is, A step of determining whether the first picking robot has failed to pick the object a predetermined number of times, If it is determined that the predetermined number of attempts have failed, the step includes distributing the bins to a work area for the worker to pick the object, The bin distribution method described in item 1 or 2.

[0165] This allows the bin sorting method to be modified so that even if the picking robot at the destination has difficulty picking the object, the bins are sorted to human workers, thereby increasing the probability of successful picking.

[0166] (Item 8) The process further includes the step of instructing a transport robot (transport robot 40C) to transport the bin to the position of the first picking robot, A bin distribution method described in any one of items 1 through 7.

[0167] This allows the bin sorting method to position the bins at the locations determined for the picking robots, making them ready for the picking work to be performed by the picking robots.

[0168] (Item 9) The bins include a storage bin (storage bin 71) in which the objects were stored prior to the picking operation. A bin distribution method described in any one of items 1 through 8.

[0169] This allows the bin sorting method to place the storage bins at the location of the picking robot determined as the sorting destination.

[0170] (Item 10) The bins include an outbound bin (outbound bin 72) in which the objects are stored after the picking operation. A bin distribution method described in any one of items 1 through 9.

[0171] This allows the bin sorting method to place the bins for shipment at the location of the picking robot determined as the sorting destination.

[0172] (Item 11) A bin sorting device (higher-level management device 20) equipped with a processor (processor 21) that sorts bins into which objects to be picked by a picking robot can be stored, The aforementioned processor, The system acquires picking instruction data for instructing the picking operation of the aforementioned object, and evaluation indicators for the picking operation for each object and each picking robot. Based on the picking instruction data and the evaluation index, the bins are distributed to the first picking robot among the multiple picking robots. Bin sorting device.

[0173] As a result, the bin sorting device achieves the same effect as item 1.

[0174] Although various embodiments have been described above with reference to the drawings, it goes without saying that this disclosure is not limited to such examples. It is clear to those skilled in the art that various modifications or alterations can be conceived within the scope of the claims, and these will naturally also fall within the technical scope of this disclosure. Furthermore, the components of the above embodiments may be combined in any way without departing from the spirit of the invention.

[0175] Furthermore, the above embodiment may also apply to a program that implements the functions of a bin distribution method, which is supplied to a computer (e.g., a higher-level management device 20) via a network or various storage media, and which is read and executed by the processor of this computer, as well as a recording medium on which this program is stored. [Industrial applicability]

[0176] This disclosure is useful for a bin sorting method and bin sorting device, etc., that can improve the efficiency of picking operations when performing picking operations in which items are moved from storage bins to retrieval bins using multiple picking robots. [Explanation of Symbols]

[0177] 5. Warehouse Management System 10 Warehouse management equipment 20,20A Upper management device 21,21A Processor 22 memory 23 Input Devices 24 Communication devices 25 Input / Output Interfaces 30,30A Robot Control System 31,31A Processor 22 memory 23 Input Devices 24 Communication devices 35 Input / Output Interfaces 40 Robot equipment 40C Transport Robot 40P Picking Robot 45 Workers 50 Warehouse 60 Cameras 70 bottles 71 Storage bottle 72 Outbound bottles 80 Objects 85 Goods BR bin placement area PS Picking Station p1 placement position p2 Possible placement position

Claims

1. A bin sorting method for distributing bins capable of storing objects to be picked by multiple picking robots, The steps include: obtaining picking instruction data for instructing the picking operation of the aforementioned object, A step of obtaining an evaluation index for the picking operation for each of the aforementioned objects and each of the aforementioned picking robots, A step of distributing the bins to a first picking robot among the plurality of picking robots based on the picking instruction data and the evaluation index, A bin distribution method including the following.

2. The aforementioned evaluation indicators are, Success rate information indicating the success rate of the picking operation for each object and each picking robot, This includes takt information indicating the takt time, which is the time required for picking each object and each picking robot, The method for distributing bottles according to claim 1.

3. For each of the picking robots, the step is to obtain robot configuration information relating to the configuration of the picking robot. The method further includes the step of obtaining object information relating to each of the aforementioned objects, The step of obtaining the evaluation index includes the step of calculating the evaluation index by simulation based on the robot configuration information and the object information. The bin sorting method according to claim 1 or 2.

4. The step of sorting the aforementioned bins is, A step of determining the priority of the picking robot to pick the object instructed by the picking instruction data, based on the success rate information and the cycle time information, The steps include determining the first picking robot based on the aforementioned priority order, The bin distribution method according to claim 2.

5. The method further includes the step of obtaining task quantity information relating to the amount of objects to be picked for each picking robot, The step of sorting the aforementioned bins is, The step of determining the first picking robot based on the priority of the picking robot and the task volume information includes: The method for distributing bottles according to claim 4.

6. The step of sorting the aforementioned bins is, For each picking robot, the step of determining whether or not the target object can be picked based on the success rate information, If there is no picking robot capable of picking the object, the process includes the step of distributing bins to a work area for an operator to pick the object. The bin distribution method according to claim 2.

7. The step of sorting the aforementioned bins is, It is determined whether the first picking robot has failed to pick the object a predetermined number of times. If it is determined that the predetermined number of attempts have failed, the step includes distributing the bins to a work area for the worker to pick the object, The bin sorting method according to claim 1 or 2.

8. The process further includes the step of instructing a transport robot to transport the bin to the position of the first picking robot, The bin sorting method according to claim 1 or 2.

9. The bins include storage bins in which the objects were stored prior to the picking operation. The method for distributing bottles according to claim 1.

10. The bins include an outbound bin in which the objects are stored after the picking operation. The method for distributing bottles according to claim 1.

11. A bin sorting device equipped with a processor, which sorts bins into which objects to be picked by a picking robot can be stored, The aforementioned processor, The system acquires picking instruction data for instructing the picking operation of the aforementioned object, and evaluation indicators for the picking operation for each of the aforementioned object and each of the aforementioned picking robots. Based on the picking instruction data and the evaluation index, the bins are distributed to a first picking robot among the plurality of picking robots. Bin sorting device.

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