Information processing device, information processing method, information processing program, and information processing system

Abstract

To provide a technology for detecting the fullness of containers using a mobile device that can move within a warehouse, including an automated guided vehicle (AGV). [Solution] The information processing device according to the embodiment includes an interface that is movable within a warehouse and receives sensor information detecting the contents inside a compartment container from a mobile body capable of detecting compartment containers, and a processor that determines whether the compartment container is full based on the sensor information, and if it is determined that the compartment container is not full, outputs compartmentalization information indicating that the goods transported by the automated guided vehicle can be compartmentalized into the compartment container.

Description

Technical Field

[0001] Embodiments of the present invention relate to an information processing apparatus, an information processing method, an information processing program, and an information processing system.

Background Art

[0002] In warehouses and the like, sorting systems for automating the sorting of goods such as luggage are widespread. For example, as a sorting system, there is a system that sorts goods using an unmanned transport vehicle such as an AMR (Autonomous Mobile Robot). When performing automatic sorting with an unmanned transport vehicle, if the sorting container (tray, cage, mail bag) at the sorting destination is full (the state where the sorting target (goods) cannot be put into the sorting container any more = hereinafter referred to as full), it is necessary to take out the sorting container and set a new empty container. In this case, in order to automatically detect whether each sorting container at each sorting destination is full, it is common to install a sensor in each sorting container at the sorting destination to detect fullness.

[0003] For example, Patent Document 1 discloses a technique for efficiently processing more types of goods in order to meet the demand for improving the efficiency of product sorting.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] When installing sensors in sorting containers, it is necessary to install sensors for detection for the number of sorting containers, which is costly, or there is a problem that the connection wiring (connection communication) with a large number of sensors becomes complicated.

[0006] Another method involves estimating the fullness of each container based on the total volume of the divided products. However, this method has the problem that it cannot accurately determine the fullness because the accumulation state is uncertain.

[0007] This invention was made in view of the above circumstances, and its purpose is to provide a technology for detecting the fullness of a container using a mobile body that can move within a warehouse, including an automated guided vehicle. [Means for solving the problem]

[0008] The information processing device according to this embodiment includes an interface that is movable within a warehouse and receives sensor information detecting the contents inside a compartment container from a mobile body capable of detecting the compartment container, and a processor that determines whether the compartment container is full based on the sensor information, and if it is determined that the compartment container is not full, outputs compartmentalization information indicating that the goods transported by the automated guided vehicle can be compartmentalized into the compartment container. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a conceptual diagram showing an example of a warehouse system according to the first embodiment. [Figure 2] Figure 2 shows an example of a schematic configuration of an automated guided vehicle according to the first embodiment. [Figure 3] Figure 3 is a side view showing an example of the positional relationship between the automated guided vehicle and the sorting container at the sorting location according to the first embodiment. [Figure 4] Figure 4 shows an example of the information used in the full-capacity detection process according to the first embodiment. [Figure 5] Figure 5 is a flowchart illustrating an example of a full-fill detection operation for detecting when a compartmentalized container is full according to the first embodiment. [Figure 6] Figure 6 is a flowchart illustrating in more detail the operation of step ST105 according to the first embodiment. [Figure 7]Figure 7 shows an example of the distance relationship between the automated guided vehicle and the sorting container at the sorting location, as well as a side view and a top view of the sorting container. [Figure 8] Figure 8 shows an example of a side view of a full-capacity detection criterion file and a compartmentalized container. [Figure 9] Figure 9 is a flowchart illustrating in more detail the operation of step ST105 in a first modified example of the first embodiment. [Figure 10] Figure 10 is a side view showing an example of the distance relationship between the automated guided vehicle and the sorting container at the sorting location. [Figure 11] Figure 11 is a flowchart illustrating in more detail the operation of step ST105 in a second modified example of the first embodiment. [Figure 12] Figure 12 shows an example of the positional relationship between the automated guided vehicle and the sorting container at the sorting location, as well as a 2D image. [Figure 13] Figure 13 is a conceptual diagram showing an example of a warehouse system according to the second embodiment. [Figure 14] Figure 14 is a flowchart illustrating an example of a full-fill detection operation for detecting when a compartmentalized container is full according to the second embodiment. [Figure 15] Figure 15 is a flowchart illustrating in more detail the operation of step ST505 according to the second embodiment. [Figure 16] Figure 16 shows an example of the positional relationship between the drone and the compartmentalized container, as well as a two-dimensional image. [Modes for carrying out the invention]

[0010] Hereinafter, an information processing apparatus, an information processing method, an information processing program, and an information processing system will be described in detail with reference to the drawings. In the following embodiments, the parts with the same numbers are assumed to perform the same operations, and duplicate explanations will be omitted. For example, when there are a plurality of identical or similar elements, a common reference numeral may be used to describe each element without distinction, or a branch number may be used in addition to the common reference numeral to describe each element separately.

[0011] Also, in the following description, the description "A or B" means at least one of A or B, and "A, B, or C" means at least one of A, B, or C. Further, the description "A and B" also means at least one of A and B, and "A, B, and C" means at least one of A, B, and C.

[0012] [First Embodiment] (Configuration) FIG. 1 is a conceptual diagram showing an example of a warehouse system according to the first embodiment. As shown in FIG. 1, the warehouse system S includes a warehouse management system (WMS: Warehouse Management System) 1 and a warehouse processing system 2.

[0013] The warehouse processing system 2 is an example of a goods processing system and includes a warehouse operation system (WES: Warehouse Execution System) 30, an automated guided vehicle control system (WCS: Warehouse Control System) 40, automated guided vehicles 50, and an induction 51, etc.

[0014] WMS1 can be composed of one or more computers, i.e., processors, memory, and interfaces. The processor can be a CPU (central processing unit), MPU (micro processing unit), or DSP (digital signal processor). WMS1 receives order lists from the upper-level server and transmits them to WES30. The order list includes information such as the date and time of receipt, the type and quantity of each item required, the weight and dimensions of each item.

[0015] WES30 (server) is an example of an information processing device that can be configured using one or more general-purpose computers. WES30 comprises a processor 301, memory 302, and interface 303. The processor 301 is a CPU, MPU, or DSP, etc. Memory 302 stores the operating programs of the processor 301, etc. Interface 303 communicates with WMS1, WCS40, induction 51, etc. The processor 301 realizes each function by executing the programs stored in memory 302.

[0016] For example, interface 303 receives an order list from WMS1. Processor 301 retrieves the order list and generates an outbound order from the order list. Then, processor 301 outputs the generated outbound order. Interface 303 transmits the outbound order to the automated guided vehicle control system 40, etc. Interface 303 also receives the outbound processing result from the automated guided vehicle control system 40 and transmits the result to WMS1. Processor 301 updates the inventory management database stored in memory 302 according to the outbound processing result.

[0017] The WCS40 can be configured with one or more general-purpose computers. The WCS40 comprises a processor 401, memory 402, and interface 403. The processor 401 is a CPU, MPU, or DSP, etc. Memory 402 stores the operating programs of the processor 401, etc. Interface 403 communicates with the WES30 and the automated guided vehicle 50. The processor 401 realizes each function by executing the programs stored in memory 402.

[0018] For example, interface 403 receives an outbound dispatch instruction from WES30. Processor 401 acquires the outbound dispatch instruction and controls the automated guided vehicle 50 based on the instruction. Processor 401 also generates and outputs an outbound processing result, including the control result of the automated guided vehicle 50. Interface 403 transmits the outbound processing result to WES30.

[0019] The automated guided vehicle (AGV) 50 can be configured with one or more general-purpose computers and includes a processor, memory, and interfaces. The processor is a CPU, MPU, or DSP. The memory stores the processor's operating programs, etc. The interfaces communicate with the AGV control system 40. The processor implements each function by executing the programs stored in the memory.

[0020] The automated guided vehicle (AGV) 50 may be any robot capable of sorting goods into containers, such as an AGV (Automated Guided Vehicle) or an AMR (Autonomous Mobile Robot). The AGV 50 may be a mobile device that can move within the warehouse. The AGV 50 acquires the goods to be sorted, which have been transported by a conveyor belt or the like, and moves them to the sorting container for those goods. The AGV 50 loads the goods into the sorting containers using a belt conveyor belt or the like provided by the AGV 50.

[0021] The induction 51 is a reader capable of reading a 2D code containing a classification target ID attached to a product, or a device that constitutes part of a sorting system. The induction 51 is connected to the WES 30 and transmits the read classification target ID to the WES 30. If the induction 51 is part of a sorting system, the induction 51 also transports the products to the automated guided vehicle 50.

[0022] Next, an example of the schematic configuration of the automated guided vehicle 50 according to the first embodiment will be described. Figure 2 shows an example of a schematic configuration of an automated guided vehicle 50 according to the first embodiment. The automated guided vehicle 50 includes a control unit 501, a full-capacity detection sensor 502, a sorting device 503, a drive unit 504, casters 505, a charging mechanism 506, and a battery 507, etc. The control unit 501 includes a processor 5011, ROM 5012, RAM 5013, an auxiliary storage device 5014, and a communication interface 5015, etc.

[0023] The processor 5011 has the function of controlling the operation of the entire automated guided vehicle 50. The processor 5011 may also be equipped with an internal cache and various interfaces. The processor 5011 performs various processes by executing programs that are pre-stored in the internal memory, ROM 5012, or auxiliary storage device 5014. For example, processor 5011 is a CPU. Note that processor 5011 may also be implemented using hardware such as an LSI, ASIC, or FPGA.

[0024] The processor 5011 performs calculations and control processes necessary for operations such as acceleration, deceleration, stopping, changing direction, and sorting goods. Based on control signals from the WCS40, the processor 5011 generates drive signals and outputs them to each unit by executing programs stored in the ROM 5012, etc.

[0025] For example, WCS40 transmits a control signal to move the automated guided vehicle (AGV) 50 from its current position to a first position (sorting position) and to sort the goods into containers. After sorting, WCS40 also transmits a control signal to move the AGV 50 from the first position to a second position (goods receiving position). The processor 5011 of the AGV 50 outputs a drive signal corresponding to the control signal transmitted from WCS40. As a result, the AGV 50 moves from its current position to the first position, and then from the first position to the second position. The processor 5011 also outputs a drive signal corresponding to the sorting instruction included in the control signal transmitted from WCS40. As a result, the AGV 50 sorts the goods into containers using the sorting device 503, which will be described later.

[0026] Furthermore, the WCS40 may transmit a control signal at once to move the automated guided vehicle (AGV) 50 from its current position to a first position, and then to move it from the first position to a second position. Alternatively, the control signal to move the AGV 50 to the first or second position may be transmitted in parts. For example, the WCS40 may set at least one waypoint and divide the control signal into a control signal to move the AGV 50 to the waypoint and a control signal to move the AGV 50 from the waypoint to the first or second position, and then transmit each control signal to the AGV 50.

[0027] Furthermore, if the automated guided vehicle 50 is an AGV, the AGV 50 reads a two-dimensional code (for example, a QR code®) which is a floor code attached to the transport area, and transmits the read information to the WCS 40.

[0028] ROM 5012 is a non-temporary computer-readable storage medium that stores the above-mentioned program. ROM 5012 also stores data and various settings used by the processor 5011 in performing various processes.

[0029] RAM5013 is memory used for reading and writing data. RAM5013 is used as a so-called work area, where it stores data that the processor 5011 will temporarily use when performing various processes.

[0030] The auxiliary storage device 5014 is a non-temporary computer-readable storage medium and may store the above-mentioned program. The auxiliary storage device 5014 also stores data used by the processor 5011 in performing various processes, data generated by processing by the processor 5011, or various setting values.

[0031] The communication interface 5015 is an interface for sending and receiving data with WCS40 and other devices via a wireless LAN access point or the like. For example, the communication interface 5015 supports wireless LAN connectivity.

[0032] The full-capacity detection sensor 502 is a sensor used to detect whether the compartmentalized container is full or not. For example, the full-capacity detection sensor 502 is any sensor capable of measuring distance, such as a reflection distance sensor, RIDER, 3D camera, or 2D image camera. The full-capacity detection sensor 502 outputs sensor information that measures the contents inside the compartmentalized container. For example, under the control of the processor 5011, the full-capacity detection sensor 502 measures the distance before and after compartmentalizing the products inside the compartmentalized container and outputs sensor information including the measurement results.

[0033] The sorting device 503 is a device used to sort products into sorting containers, such as a tilt tray or a cross belt. For example, under the control of the processor 5011, the sorting device 503 operates a belt to acquire products sent from a sorter or the like. Furthermore, the sorting device 503 operates the belt to sort the products acquired at the sorting position into sorting containers.

[0034] The drive unit 504 is a motor or the like, and rotates or stops the motor based on a drive signal output from the processor 5011. The power from the motor is transmitted to the caster 505 and then to the steering mechanism. With this power from the motor, the automated guided vehicle 50 moves to its destination. The caster 505 may be a tire or the like. The caster may also be integrated with the motor.

[0035] The charging mechanism 506 is a mechanism that connects the charging station and the battery 507, and the battery 507 is charged by power supplied from the charging station or the like via the charging mechanism 506.

[0036] Furthermore, the automated guided vehicle 50 may be equipped with a plurality of reflective sensors (not shown). Each reflective sensor is mounted around the automated guided vehicle 50. Each reflective sensor emits a laser beam, detects the time from when the laser beam is emitted until it reflects off an object and returns, detects the distance to the object based on the detected time, and notifies the processor 5011 of the detection signal.

[0037] The processor 5011 outputs control signals to control the movement of the automated guided vehicle (AGV) 50 based on the detection signals from the reflection sensors. For example, the processor 5011 outputs control signals such as deceleration or stopping to avoid collisions with objects, based on the detection signals from the reflection sensors. In addition to reflection sensors, the AGV 50 may also be equipped with cameras, which may capture images of 2D barcodes in the surrounding area or on the floor and output the captured images to the processor 5011. In this case, the processor 5011 analyzes the captured images and outputs control signals such as deceleration or stopping to avoid collisions with objects. Furthermore, if a detected object is on the movement path, the processor 5011 transmits information indicating an anomaly to the WCS 40 via the communication interface 5015.

[0038] Next, we will explain the positional relationship between the automated guided vehicle 50 at the sorting location and the sorting containers. Figure 3 is a side view showing an example of the positional relationship between the automated guided vehicle 50 and the compartmentalized container 60 at the compartmentalization location according to the first embodiment. As shown in Figure 3, the compartmentalization position is a position where the contents 61 inside the compartmentalization container 60 can be detected by the full-fill detection sensor 502. The full-fill detection sensor 502 detects the distance to the contents 61 inside the compartmentalization container 60.

[0039] Figure 4 shows an example of the information used in the full-capacity detection process according to the first embodiment. As described above, the full-capacity detection sensor 502 measures the distance x between itself and the contents 61. The processor 301 calculates the maximum height Ymax of the contents 61 based on this distance x (sensor information). The processor 301 then determines whether the maximum height Ymax exceeds the threshold Th1 set by the compartment container 60. If the processor 301 determines that the maximum height Ymax exceeds the threshold Th1, it determines that the compartment container 60 is full. On the other hand, if the processor 301 determines that the maximum height ymax does not exceed the threshold Th1, it determines that the compartment container 60 is not full.

[0040] (operation) Figure 5 is a flowchart illustrating an example of a full-fill detection operation for detecting when the compartmentalized container 60 is full according to the first embodiment. The operation of this flowchart is achieved by the processor 301 of WES30, the processor 401 of WCS40, and the processor 5011 of the automated guided vehicle 50 reading and executing programs stored in memory 302, memory 402, and the internal memory, ROM 5012, or RAM 5013 of the processor 5011, respectively.

[0041] This flowchart is initiated, for example, when WMS1 or WES30 receives an order list and induction 51 reads the classification IDs attached to the items to be classified based on that order list. Alternatively, if induction 51 is part of a sorter system, this flowchart may be initiated when a sorter system operator or robot induction reads the classification IDs attached to the items.

[0042] In the first embodiment, the full-capacity detection sensor 502 is a sensor capable of measuring the distance between the full-capacity detection sensor 502 and the contents 61 in two dimensions, such as a RIDER or a 3D camera.

[0043] In step ST101, the processor 301 of WES30 obtains the classification target ID. The processor 301 obtains the classification target ID transmitted by induction 51 through interface 303. The processor 301 searches the order list received from WMS1 using the obtained classification target ID to determine the classification destination and size (e.g., three-sided dimensions) information of the classification target ID, and transmits this product classification information to WCS40.

[0044] In step ST102, the processor 401 of the WCS40 acquires product classification information. For example, an operator places a product whose classification target ID has been read using the induction 51 onto the classification device 503. When the processor 5011 detects that a product has been placed on the classification device 503, it receives product classification information indicating that a product with the corresponding ID has been placed on the classification device 503 and transmits this product classification information to the WCS40. Having acquired the product classification information, the processor 401 transmits control information (product classification information), including instructions on which classification container 60 to transport the product with the classification target ID to, to the automated guided vehicle 50 via the interface 403.

[0045] Furthermore, the method of placing products onto the sorting device 503 is not limited to those described above. Any method is acceptable, such as the robot induction 51 placing the products onto the sorting device 503, or the sorter system transporting the products to the sorting device 503 of the automated guided vehicle 50 via a conveyor.

[0046] In step ST103, the processor 5011 controls the transport of the goods to the sorting location. Based on the control information, the processor 5011 moves from its current position to the first sorting location (i.e., the location where the sorting container 60 to be sorted of the goods being transported is located).

[0047] In step ST104, the processor 401 acquires a sectioning position stop signal. When the automated guided vehicle 50 arrives at a sectioning position, the processor 5011 stops the automated guided vehicle 50 at the sectioning position and generates a sectioning position stop signal. The processor 5011 then transmits the sectioning position stop signal to the WES30 via the communication interface 5015 and WCS40.

[0048] In step ST105, the processor 301 performs full-capacity detection processing. Upon receiving the classification stop signal, the processor 301 transmits a full-capacity detection instruction to the automated guided vehicle 50 via interface 303 and WCS40. For example, the processor 5011 controls the full-capacity detection sensor 502 to detect the distance to the object.

[0049] Figure 6 is a flowchart illustrating in more detail the operation of step ST105 according to the first embodiment. In step ST201, the processor 5011 measures the distance Z(x,y) using the full-capacity detection sensor 502. For example, the processor 401 transmits a full-capacity detection instruction to the automated guided vehicle 50 via the interface 403. The processor 5011 controls the full-capacity detection sensor 502 to detect the distance to the contents 61. The full-capacity detection sensor 502 detects the contents 61 of the compartmentalized container 60. The full-capacity detection sensor 502 then measures the distance Z(x,y) between itself and the contents 61. Here, the distance Z(x,y) is a two-dimensional distance that can be represented in two dimensions.

[0050] Figure 7 shows an example of the distance relationship between the automated guided vehicle 50 and the compartmentalized container 60 at the compartmentalized location, as well as a side view and a top view of the compartmentalized container 60. As shown in the top view of the compartmentalized container 60 in Figure 7, the full-fill detection sensor 502 measures the distance Z(x,y) between the full-fill detection sensor 502 and the contents 61, with the position directly below the full-fill detection sensor 502 being the origin (x,y)=(0,0). The processor 5011 measures the distance Z(x,y) to each of the contents 61 within the compartmentalized container 60.

[0051] In step ST202, processor 301 acquires two-dimensional distance data. Processor 5011 transmits the two-dimensional distance data, including the distance Z(x,y) of each contents 61, to WES30 via communication interface 5015 and WCS40.

[0052] In step ST203, the processor 301 converts the distance Z(x,y) into height information. First, the processor 301 calculates the vertical distance D1(x,y) from the height of the full-capacity sensor 502 to the contents 61 from the distance Z contained in the 2D distance data. The processor 301 calculates the vertical distance D1 expressed by equation (2) using the relationship in equation (1) below. Note that the vertical distance D1 represents the vertical distance between the full-capacity sensor 602 and the contents 61.

[0053] Z(x,y) 2 =D1(x,y) 2 +x 2 +y 2 (Formula 1) D1(x,y) = √(Z(x,y)) 2 -x 2 -y 2 ) (Formula 2)

[0054] The processor 301 then calculates the height Y(x,y) of the contents 61 from the bottom surface of the compartment container 60, which is represented by the following equation 3.

[0055] Y(x,y) = D2 - D1(x,y) (Equation 3) Here, distance D2 is the vertical length between the full-capacity detection sensor 502 and the bottom surface of the compartmentalized container 60, and is a predetermined value depending on the location where the compartmentalized container 60 is placed. This distance D2 is stored in memory 302.

[0056] The processor 301 then calculates the height Y(x,y) of each component 61 and calculates the maximum height Ymax of the component 61, which is expressed by the following equation (4).

[0057] Ymax = MAX(Y(x,y)) (Equation 4) In step ST204, the processor 301 determines whether the container is full or not. The processor 301 determines whether the maximum height Ymax exceeds the full level (threshold Th1). If it determines that it does not exceed the full level, the processor 301 determines that the container 60 is not full. On the other hand, if it determines that the maximum height Ymax exceeds the full level, the processor 301 determines that the container 60 is full.

[0058] As described above, the processor 301 determines whether the compartmentalized container 60 is full based on the sensor information (two-dimensional distance data).

[0059] Here, the fullness level (threshold Th1) of the compartment container 60 may be constant, but it can also be set for each placement location of the compartment container 60. For example, a fullness detection criterion file representing the set value (threshold) for each compartment container 60 is stored in memory 302. Then, the processor 301 refers to this fullness detection criterion file and uses the fullness level set for each compartment container 60 to determine whether the compartment container 60 is full.

[0060] Figure 8 shows an example of a side view of the full-capacity detection criterion file and the compartmentalized container 60. As shown in Figure 8, the full-capacity detection criteria file includes a setting value Hi for each of the 60 designated containers. i is any integer less than or equal to the total number of designated containers.

[0061] Furthermore, the example in Figure 8 shows examples of sorting destinations S1, S2, and S3. S1 and S2 show cases where the sorting container 60 is the same but installed at different heights, while S3 shows a case where the sorting container 60 is different from that of sorting destinations S1 and S2. For example, sorting destination S1 has a setting value H1, sorting destination S2 has a setting value H2, and sorting destination S3 has a setting value H3. The processor 301 may refer to this full-capacity detection criterion file to determine whether the sorting container 60 is full. In one embodiment, the full-capacity detection criterion file is shown as an example that includes height as a setting value, but it is not limited to height and may store any information.

[0062] Alternatively, the processor 301 may perform the full-container detection process multiple times. For example, the processor 301 may change the stopping position of the automated guided vehicle 50 to obtain 2D distance data and calculate the maximum height Ymax at various positions. Then, the processor 301 may use the maximum value among the multiple maximum heights Ymax to determine whether the compartment container 60 is full. Alternatively, the processor 301 may use the average value of the multiple maximum heights Ymax to determine whether the compartment container 60 is full.

[0063] Returning to Figure 3, in step ST106, the processor 301 determines whether sorting is possible. If it is determined in step ST105 that the sorting container 60 is not full, the processor 301 determines that the product can be sorted into the sorting container 60. Then, the process proceeds to step ST109. On the other hand, if it is determined in step 106 that the sorting container 60 is full, the processor 301 determines that sorting is not possible. Then, the process proceeds to step ST107.

[0064] Furthermore, if the processor 301 determines that the container is separable, it determines how much of the container 60 is filled based on the maximum height Ymax. The processor 301 may determine how much of the container 60 is filled at several stages, such as empty, about 1 / 3 full, or about 2 / 3 full.

[0065] In step ST107, the processor 301 notifies that the container is full. The processor 301 notifies the terminal or other device held by the worker via the interface 303 that the container 60 is full. The terminal may be controlled to display that the container 60 is full.

[0066] In step ST108, the worker removes the full compartment container 60 and sets an empty compartment container 60 in place. This process is performed by the worker and is shown with a dotted line in Figure 3. The worker notifies WES30, using a terminal or similar device, that an empty compartment container 60 has been set.

[0067] Furthermore, after notifying that the container is full, the processor 301 may control the automated guided vehicle 50 to move to another container 60. In other words, the processor 301 may, of course, perform dynamic allocation.

[0068] In step ST109, the processor 301 determines whether the product is separable. The processor 301 retrieves product information from memory that is associated with the separable ID. The product information includes information such as the product dimensions (length, width, and height), weight, and product attributes (whether the product is fragile or not). The processor 301 then determines whether the product is separable. The processor 301 refers to the product attributes of the contents 61 that have been separated in the separation container 60 and determines whether it is separable based on the contents of the separation container and the size of the product. For example, if the processor 301 determines that the product would significantly exceed the size of the separation container 60 after being separated, it determines that it cannot be separated. Alternatively, the processor 301 determines whether it is separable based on the product attributes. For example, if the contents 61 in the separation container 60 are fragile, it is not possible to separate a heavy product. Furthermore, the processor 301 determines whether it is separable based on 2D distance data and the attributes of the product to be separated. For example, if the product is fragile, there is a possibility that the product will be damaged if the distance x is large. In such cases, the processor 301 determines that it cannot classify the product.

[0069] If it is determined that the container can be separated, the process proceeds to step ST110. On the other hand, if it is determined that the container cannot be separated, the process proceeds to step ST107. The processor 301 may be configured to transport the automated guided vehicle 50 to another container 60 instead of notifying that the container is full.

[0070] Here, if the target product is sufficiently small, the process in step ST109 may be skipped. In other words, if it is determined in step ST106 that it can be separated, the process may proceed directly to step ST110.

[0071] In step ST110, the processor 401 controls the loading of goods into the sorting container 60. The processor 301 transmits sorting-capable information to the WCS 30 via interface 303, indicating that the goods can be sorted into the sorting container 60. Based on this information, the processor 301 transmits control instructions, including sorting instructions, to the automated guided vehicle control system 40 and the automated guided vehicle 50 via interface 303. The processor 5011 drives the sorting device 503 to load the goods into the sorting container 60.

[0072] In step ST111, the processor 401 performs a full-container detection process. After the product is placed in the compartmentalized container 60, the processor 401 performs the full-container detection process again. Note that the full-container detection process may be the same as the full-container detection process described in step ST105. Therefore, a redundant explanation is omitted here.

[0073] In step ST112, the processor 301 determines whether the items are separable. The processor 301 determines whether the items are separable based on the distance x and a threshold. The determination process in step ST112 may be the same as the determination process described in step ST106. Therefore, a redundant explanation is omitted here.

[0074] In step ST113, the processor 301 determines whether processing has finished. The processor 301 refers to the order list and determines whether processing has finished. If it determines that processing has not finished, processing returns to step ST101. On the other hand, if it determines that processing has finished, the full-capacity detection operation is terminated.

[0075] (Effects of the first embodiment) According to the first embodiment described above, a full-capacity detection sensor 502 is placed on the automated guided vehicle 50. This reduces costs by requiring full-capacity detection sensors to be installed on fewer automated guided vehicles 50 than on the number of compartmentalized containers 60. Furthermore, since only one full-capacity detection sensor 502 needs to be placed on each automated guided vehicle 50, complicated wiring is avoided, and multi-node connections using Wi-Fi® or Bluetooth® are not required.

[0076] Furthermore, the WES30 processor 301 determines whether the compartmentalized container 60 is full based on the two-dimensional distance data from the full-capacity detection sensor 502. This allows for accurate understanding of the state of the compartmentalized container 60.

[0077] [First modified example of the first embodiment] In the first embodiment, an example was described in which the full-capacity detection sensor 502 determines whether the compartmentalized container 60 is full using two-dimensional distance data. In the first modification of the first embodiment, an example is described in which the full-capacity detection sensor 502 determines whether the compartmentalized container 60 is full using the reflection distance between it and the contents 61.

[0078] (composition) The configuration in the first modified example of the first embodiment may be the same as that of the first embodiment. Therefore, redundant explanations are omitted here.

[0079] (operation) The full-capacity detection operation in the first modified example of the first embodiment is the same as described with reference to Figure 3, except for step ST105. Therefore, redundant explanations are omitted here.

[0080] In the first modified example of the first embodiment, the full-capacity detection sensor 502 is a sensor capable of measuring the reflective distance between the full-capacity detection sensor 502 and the contents 61, such as a reflective distance sensor.

[0081] Figure 9 is a flowchart illustrating in more detail the operation of step ST105 in a first modified example of the first embodiment. In step ST301, the processor 5011 measures the reflection distance X using the full-capacity detection sensor 502. For example, the processor 401 transmits a full-capacity detection instruction to the automated guided vehicle 50 via the interface 403. The processor 5011 controls the full-capacity detection sensor 502 to detect the reflection distance X to the contents 61. The full-capacity detection sensor 502 detects the contents 61 of the compartmentalized container 60. Then, the full-capacity detection sensor 502 measures the reflection distance X between itself and the contents 61.

[0082] In step ST302, the processor 301 acquires the reflection distance data. The processor 5011 transmits the reflection distance data, including the reflection distance X of each component 61, to the WES30 via the communication interface 5015 and the WCS40.

[0083] In step ST303, the processor 301 determines whether the container is full or not. The processor 301 determines whether half of the reflection distance X (i.e., the distance between the fullness detection sensor 502 and the contents 61) is greater than or equal to the fullness level (threshold Th1). If it is determined to be greater than or equal to the fullness level, the processor 301 determines that the compartment container 60 is not full. On the other hand, if it is determined to be less than the fullness level, the processor 301 determines that the compartment container 60 is full.

[0084] Figure 10 is a side view showing an example of the distance relationship between the automated guided vehicle 50 and the sorting container 60 at the sorting location. In the first modification of the first embodiment, the threshold Th1 is set relative to the distance between the full-capacity detection sensor 502 and the contents 61. In this case, the processor 301 determines whether the distance between the full-capacity detection sensor 502 and the contents 61 is shorter than the threshold Th1. If the distance between the full-capacity detection sensor 502 and the contents 61 is shorter than the threshold Th1, the processor 301 determines that the compartmentalized container 60 is full. On the other hand, if the distance between the full-capacity detection sensor 502 and the contents 61 is longer than the threshold Th1 (example in Figure 6), the processor 301 determines that the compartmentalized container 60 is not full.

[0085] (Effects of the first modification of the first embodiment) According to the first modification of the first embodiment described above, a full-capacity detection sensor 502 is placed on the automated guided vehicle 50. This reduces costs by requiring full-capacity detection sensors to be installed on fewer automated guided vehicles 50 than on the partitioned containers 60. Furthermore, since only one full-capacity detection sensor 502 needs to be placed on each automated guided vehicle 50, complicated wiring or multi-node connections via WiFi or Bluetooth are not required.

[0086] Furthermore, the WES30's processor 301 determines whether the compartmentalized container 60 is full based on the reflection distance between the full-capacity detection sensor 502 and the contents 61. This allows for accurate determination of the state of the compartmentalized container 60.

[0087] [Second variation of the first embodiment] In the first embodiment, an example was described in which the full-capacity detection sensor 502 determines whether the compartmentalized container 60 is full using two-dimensional distance data. In the second modification of the first embodiment, a case is described in which the full-capacity detection sensor 502 is a two-dimensional image camera.

[0088] (composition) The configuration in the second modified example of the first embodiment may be the same as that of the first embodiment. Therefore, redundant explanations are omitted here.

[0089] (operation) The full-capacity detection operation in the second modified example of the first embodiment is the same as described with reference to Figure 3, except for step ST105. Therefore, redundant explanations are omitted here.

[0090] In the second modified example of the first embodiment, the full-capacity detection sensor 502 is a two-dimensional image camera capable of capturing a two-dimensional image of the contents 61.

[0091] Figure 11 is a flowchart illustrating in more detail the operation of step ST105 in a second modified example of the first embodiment. In step ST401, the processor 5011 captures a two-dimensional image using the full-capacity detection sensor 502. For example, the processor 401 transmits a full-capacity detection instruction to the automated guided vehicle 50 via the interface 403. The processor 5011 controls the full-capacity detection sensor 502 to capture a two-dimensional image showing the contents 61.

[0092] Figure 12 shows an example of the positional relationship between the automated guided vehicle 50 and the sorting container 60 at the sorting location, as well as a two-dimensional image. As shown in Figure 10, the fullness detection sensor 502 captures a two-dimensional image of the contents 61 inside the compartmentalized container 60.

[0093] In step ST402, the processor 301 acquires two-dimensional image data. The processor 5011 transmits the two-dimensional image data of each contents 61 to the WES30 via the communication interface 5015 and WCS40.

[0094] In step ST403, the processor 301 determines whether the container is full or not. The processor 301 uses machine learning, such as deep learning, to obtain the maximum height Ymax of the contents 61 from the 2D image. Note that a general method can be used for learning using deep learning, so a detailed explanation is omitted here. The processor 301 then determines whether the maximum height Ymax exceeds the threshold Th1 set by the compartment container 60. If the processor 301 determines that the maximum height Ymax exceeds the threshold Th1, it determines that the compartment container 60 is full. On the other hand, if the processor 301 determines that the maximum height Ymax does not exceed the threshold Th1, it determines that the compartment container 60 is not full.

[0095] (Effects of the second modification of the first embodiment) According to the second modification of the first embodiment described above, a full-capacity detection sensor 502 is placed on the automated guided vehicle 50. This reduces costs by requiring the installation of full-capacity detection sensors 502 on fewer automated guided vehicles 50 than on the number of compartmentalized containers 60. Furthermore, since only one full-capacity detection sensor 502 needs to be placed on each automated guided vehicle 50, complicated wiring or multi-node connections via WiFi or Bluetooth are not required.

[0096] Furthermore, the WES30 processor 301 determines whether the compartment container 60 is full based on the two-dimensional image data from the full-capacity detection sensor 502. This allows for accurate determination of the state of the compartment container 60.

[0097] [Second Embodiment] In the first embodiment, an example was described in which a full-capacity detection sensor 502 was installed on an automated guided vehicle 50. In the second embodiment, an example was described in which a full-capacity detection sensor 502 was installed on a drone instead of an automated guided vehicle 50.

[0098] (composition) Figure 13 is a conceptual diagram showing an example of a warehouse system S according to the second embodiment. As shown in Figure 13, the second embodiment differs from the first embodiment in that the warehouse processing system 2 includes a drone control system 70 and a drone 80.

[0099] The drone control system 70 can be configured with one or more general-purpose computers. The drone control system 70 comprises a processor 701, memory 702, and interface 703. The processor 701 is a CPU, MPU, or DSP, etc. Memory 702 stores the operating program of the processor 701, etc. Interface 703 communicates with the WES30 and the drone 80. The processor 701 realizes each function by executing the program stored in memory 702.

[0100] For example, interface 703 receives an outbound order from WES30. Processor 401 acquires the outbound order and generates a sorting container confirmation order based on the outbound order. Then, processor 701 transmits the sorting container confirmation order to drone 80. Processor 701 receives sensor information regarding sorting container 60 from drone 80 via interface 703 and transmits this sensor information to WES30.

[0101] The drone 80 can be configured with one or more general-purpose computers and includes a processor 801, memory 802, and interface 803, etc. The processor 801 is a CPU, MPU, or DSP, etc. The memory 802 stores the operating program of the processor 801, etc. The interface 803 communicates with the drone control system 70. The processor 801 realizes each function by executing the program stored in the memory 802.

[0102] For example, interface 803 receives a container sorting confirmation instruction from the drone control system 70. Processor 801 acquires the container sorting confirmation instruction and moves to the container sorting 60 included in the instruction. Then, processor 801 acquires sensor information using the full-capacity detection sensor 804. Interface 803 then transmits this sensor information to the drone control system 70.

[0103] (operation) Figure 14 is a flowchart illustrating an example of a full-fill detection operation for detecting when the compartmentalized container 60 is full according to the second embodiment. The operation of this flowchart is achieved by the processor 301 of the WES30, the processor 701 of the drone control system 70, and the processor 801 of the drone 80 each reading and executing the programs stored in memory 302, memory 402, and memory 802, respectively.

[0104] This flowchart is initiated, for example, when WMS1 or WES30 receives an order list and induction 51 reads the classification IDs attached to the items to be classified based on that order list. Alternatively, if induction 51 is part of a sorter system, this flowchart may be initiated when a sorter system operator or robot induction reads the classification IDs attached to the items.

[0105] In the second embodiment, the full-capacity detection sensor 804 provided by the drone 80 is a two-dimensional image camera capable of capturing two-dimensional images.

[0106] Steps ST501 to ST504 may be the same operations as steps ST101 to ST104, which were explained with reference to Figure 5. Therefore, redundant explanations are omitted here.

[0107] In step ST505, the processor 301 performs full-capacity detection processing. Upon receiving the section stop signal, the processor 301 transmits a full-capacity detection instruction to the drone 80 via interface 303 and the drone control system 70.

[0108] Figure 15 is a flowchart illustrating in more detail the operation of step ST505 according to the second embodiment. In step ST601, the processor 801 captures a two-dimensional image of the compartment container 60. The processor 301 retrieves the location information of the compartment container 60 based on the order list. The processor 301 then transmits this location information to the drone 80 via the interface 303 and the drone control system 70. The processor 801 of the drone 80 moves above the compartment container 60 based on the location information. After moving above the compartment container 60, the processor 801 uses the full-capacity detection sensor 804, which is a two-dimensional image camera, to capture a two-dimensional image including the contents 61 inside the compartment container 60.

[0109] Figure 16 shows an example of the positional relationship between the drone 80 and the compartmentalized container 60, as well as a two-dimensional image. As shown in Figure 16, the drone 80, which has moved above the compartment container 60, uses the full-container detection sensor 804 to capture a two-dimensional image of the contents 61 inside the compartment container 60.

[0110] In step ST602, the processor 301 acquires two-dimensional image data. The processor 801 transmits the two-dimensional image data of each contents 61 to the WES 30 via the interface 803 and the drone control system 70.

[0111] In step ST603, the processor 801 determines whether the imaging of all compartment containers 60 has been completed. The processor 801 determines whether the imaging of all compartment containers 60 has been completed based on the location information. If it is determined that imaging is complete, the process proceeds to step ST604. On the other hand, if it is determined that imaging is not complete, the process returns to step ST601.

[0112] In step ST604, the processor 301 determines whether the container is full or not. The processor 301 uses machine learning, such as deep learning, to obtain the maximum height Ymax of the contents 61 from the 2D image. The processor 301 then determines whether the maximum height Ymax exceeds the threshold Th1 set by the compartment container 60. If the processor 301 determines that the maximum height Ymax exceeds the threshold Th1, it determines that the compartment container 60 is full. On the other hand, if the processor 301 determines that the maximum height Ymax does not exceed the threshold Th1, it determines that the compartment container 60 is not full. This determination of fullness is the same as the one explained using Figure 12.

[0113] Steps ST506 to ST513 may be the same operations as steps ST106 to ST113, which were explained with reference to Figure 5. Therefore, redundant explanations are omitted here.

[0114] (Effects of the second embodiment) According to the second embodiment described above, a full-capacity detection sensor 804 is placed on the drone 80. This reduces costs by requiring the full-capacity detection sensor 804 to be installed on fewer drones 80 than on the compartmentalized containers 60 or the AMR 50. Furthermore, since the drone 80 can move freely within the warehouse, it is easier to photograph the contents 61 inside the compartmentalized containers 60 than if the full-capacity detection sensor 502 were installed on the automated guided vehicle 50. Therefore, it is possible to determine whether all compartmentalized containers 60 are full.

[0115] Furthermore, the WES30's processor 301 determines whether the compartmentalized container 60 is full based on the two-dimensional image data from the full-capacity detection sensor 804 equipped on the drone 80. This allows for an accurate understanding of the state of the compartmentalized container 60.

[0116] [Other embodiments] The first and second embodiments may be implemented in combination. For example, a full-capacity detection sensor 502 and a full-capacity detection sensor 804 may be placed on the automated guided vehicle 50 and the drone 80, respectively, and the processor 301 may perform full-capacity detection processing based on both the sensor information from the full-capacity detection sensor 502 and the full-capacity detection sensor 804.

[0117] Furthermore, although the embodiment describes the division using an automated guided vehicle 50, it may also be used with a general sorter. When applying this embodiment to a general sorter, it is sufficient to install a full-capacity detection sensor 502 in at least one sorting cell module.

[0118] The program according to this embodiment may be transferred while stored in an electronic device (computer) as WES30, or it may be transferred without being stored in an electronic device. In the latter case, the program may be transferred via a network, or it may be transferred while stored in a storage medium. The storage medium is a non-temporary tangible medium. The storage medium is a medium that can be read by the computer as WES30 (computer-readable medium). The storage medium can be any medium that is capable of storing a program and can be read by a computer, such as an optical disc (e.g., CD-ROM), a magnetic disc, or a semiconductor memory (e.g., a memory card), and its form is not limited.

[0119] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. [Explanation of Symbols]

[0120] S...Warehouse System 1…Warehouse Management System 2…Warehouse processing system 30…Warehouse operation system 301… Processor 302...Memory 303… Interface 40…Automated Guided Vehicle Control System 401… Processor 402...Memory 403… Interface 50... Automated Guided Vehicles 501... Control Unit 5011… Processor 5012…ROM 5013…RAM 5014... Auxiliary storage device 5015…Communication Interface 502... Full detection sensor 503…sorting device 504... Drive unit 505... Caster 506…Charging mechanism 507...Battery 51...Induction 60…Separated container 61…Contents 70…Drone control system 701… Processor 702...Memory 703… Interface 80... Drone 801… Processor 802...Memory 803… Interface 804... Full detection sensor

Claims

1. An interface that receives sensor information detecting the contents inside a compartmentalized container from a mobile body that is movable within the warehouse and capable of detecting compartmentalized containers, A processor that determines whether the compartmentalized container is full based on the sensor information, and if it determines that the compartmentalized container is not full, outputs compartmentalized information indicating that the goods transported by the automated guided vehicle can be compartmentalized into the compartmentalized container. An information processing device equipped with the following features.

2. The moving object is the automated guided vehicle, and the sensor information is two-dimensional distance data measuring the two-dimensional distance between the full-capacity detection sensor and the contents of the automated guided vehicle. The processor calculates the vertical distance between the fullness detection sensor and the contents based on the two-dimensional distance, calculates the maximum height of the contents from the bottom surface of the compartment container based on the vertical distance, and determines whether the compartment container is full based on whether the maximum height exceeds a predetermined threshold. The information processing apparatus according to claim 1.

3. The interface receives multiple two-dimensional distance data measured at multiple locations. The processor calculates the maximum height of each of the multiple contents from each of the multiple two-dimensional distance data, and determines whether the compartmentalized container is full based on whether the maximum height with the largest value among the multiple contents exceeds a predetermined threshold. The information processing apparatus according to claim 2.

4. The interface receives multiple two-dimensional distance data measured at multiple locations. The processor calculates the maximum height of each of the multiple contents from each of the multiple two-dimensional distance data, and determines whether the compartmentalized container is full based on whether the average value of the maximum heights of the multiple contents exceeds a predetermined threshold. The information processing apparatus according to claim 2.

5. The moving object is the automated guided vehicle, and the sensor information is reflection distance data measured from the full-capacity detection sensor provided by the automated guided vehicle to the contents. The processor determines whether the compartment container is full based on whether the reflection distance exceeds a predetermined threshold. The information processing apparatus according to claim 1.

6. The moving object is the automated guided vehicle, and the sensor information is two-dimensional image data of the contents captured by a two-dimensional image camera provided by the automated guided vehicle. The processor calculates the maximum height of the contents from the bottom surface of the compartment container based on the two-dimensional image data, and determines whether the compartment container is full based on whether the maximum height exceeds a predetermined threshold. The information processing apparatus according to claim 1.

7. The mobile body is a drone, and the sensor information is two-dimensional image data of the contents captured by a two-dimensional image camera equipped on the drone. The processor calculates the maximum height of the contents from the bottom surface of the compartment container based on the two-dimensional image data, and determines whether the compartment container is full based on whether the maximum height exceeds a predetermined threshold. The information processing apparatus according to claim 1.

8. If the processor determines that the compartment container is not full, it determines whether the goods can be compartmentalized into the compartment container based on the goods information of the goods being transported by the automated guided vehicle. The information processing apparatus according to claim 1.

9. The processor further determines whether the goods can be separated into the separate containers based on the attribute information of the contents of the separate containers and the product information of the goods being transported by the automated guided vehicle. The information processing apparatus according to claim 8.

10. The processor determines whether the product can be sorted into the sorting container based on the product information of the product being transported by the automated guided vehicle, based on the attribute information included in the product information and the two-dimensional distance data included in the sensor information. The information processing apparatus according to claim 8.

11. The interface receives second sensor information that detects the contents inside the compartmentalized container after the product has been placed inside. Based on the second sensor information, it is determined whether the compartmentalized container is full. The information processing apparatus according to claim 1.

12. If the processor determines that the compartment container is not full, it determines to what extent the compartment container is filled. The information processing apparatus according to claim 1.

13. An information processing method executed by the processor of an information processing device, A mobile body that is movable within the warehouse and capable of detecting compartmentalized containers receives sensor information detecting the contents inside the compartmentalized containers, Based on the sensor information, it is determined whether the compartmentalized container is full, and if it is determined that the compartmentalized container is not full, information indicating that the goods transported by the automated guided vehicle can be compartmentalized into the compartmentalized container is output. An information processing method comprising:

14. An information processing program comprising instructions to be executed by the processor of an information processing device, wherein the instructions are: A mobile body that is movable within the warehouse and capable of detecting compartmentalized containers receives sensor information detecting the contents inside the compartmentalized containers, Based on the sensor information, it is determined whether the compartmentalized container is full, and if it is determined that the compartmentalized container is not full, information indicating that the goods transported by the automated guided vehicle can be compartmentalized into the compartmentalized container is output. An information processing program equipped with the following features.

15. Mobile and An information processing device connected via a mobile body control system connected to the aforementioned mobile body, An information processing system comprising, The aforementioned moving body is An interface for receiving control signals, A processor that moves to a compartmentalized container located in the warehouse according to the aforementioned control signal, detects the contents inside the compartmentalized container, and outputs sensor information including the detected result, The information processing device is equipped with, The interface for receiving the aforementioned sensor information, A processor that determines whether the compartmentalized container is full based on the sensor information, and if it determines that the compartmentalized container is not full, outputs compartmentalized information indicating that the goods transported by the automated guided vehicle can be compartmentalized into the compartmentalized container. An information processing system equipped with the following features.

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