Conveyance control system, abnormality detection device, abnormality detection method, and abnormality detection program

Abstract

To provide a conveyance control system capable of reducing cost as a whole system by solving a problem that an automatic carrier cannot draw cases as the whole system.SOLUTION: A conveyance control system comprises a warehouse operation device and an abnormality detection device. The abnormality detection device comprises: a processor which reveals that an abnormality has occurred in a storage part in which an article to be conveyed based on a working plan is stored, acquires a demand of an abnormality analysis including the positional information of a place where the abnormality has occurred, acquires a photographic image of the storage part including the storage part as a whole based on the positional information, analyzes an abnormality content based on the photographic image of the storage part, and generates an abnormality analysis result including the analysis result as a whole; and an interface which transmits the abnormality analysis result to the warehouse operation device. The warehouse operation device comprises a processor for outputting a countermeasure instruction according to the abnormality analysis result.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a transport control system, an abnormality detection device, an abnormality detection method, and an abnormality detection program. [Background technology]

[0002] In recent years, the volume of goods such as parcels handled at distribution centers in logistics has increased, and various efforts have been made to improve the efficiency of sorting and transporting goods. One such effort is the introduction of automated guided vehicles (AGVs) that transport goods without human intervention. For example, an AGV can retrieve a case stored on a shelf and transport it to a destination, or lift a shelf containing goods and transport the goods together with the shelf to a destination.

[0003] In addition, a common method is for the automated guided vehicle to recognize the case using a barcode or two-dimensional code printed on a case label attached to a predetermined position on the case. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6957690 Summary of the Invention [Problem to be solved by the invention]

[0005] If the relative positions of the automated guided vehicle and the case deviate by more than a certain margin, the automated guided vehicle will be unable to retrieve the case. Furthermore, if the automated guided vehicle is unable to retrieve the case, an operator will have to handle the situation, which increases the cost of the entire system.

[0006] One way to solve this problem is to equip the automated guided vehicle itself with high sensing capabilities, but this method has the problem of increasing the cost of the entire system.

[0007] This invention was made with the above-mentioned circumstances in mind, and its purpose is to provide a technology that can reduce the cost of the entire system by solving the problem of automatic guided vehicles being unable to retrieve cases throughout the entire system. [Means for solving the problem]

[0008] An anomaly detection system according to an embodiment includes a warehouse operation device and an anomaly detection device. The anomaly detection device includes a processor that acquires an anomaly analysis request indicating that an anomaly has occurred in a storage unit containing items to be transported based on a work plan and including location information of the location where the anomaly has occurred, acquires a storage unit image including the storage unit based on the location information, analyzes the content of the anomaly based on the storage unit image, and generates an anomaly analysis result including the analysis result; and an interface that transmits the anomaly analysis result to the warehouse operation device. The warehouse operation device includes a processor that outputs response instructions according to the anomaly analysis result. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a conceptual diagram illustrating an example of a warehouse management system according to a first embodiment. [Figure 2] FIG. 2 is a front view showing an example of the arrangement of the case according to the first embodiment. [Figure 3] FIG. 3 is a conceptual diagram illustrating an example of a transport control system according to the first embodiment. [Figure 4] FIG. 4 is a side view and a front view showing an example of the automatic guided vehicle according to the first embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of an automatic guided vehicle according to the first embodiment. [Figure 6] FIG. 6 is a diagram showing a possible case where the position of the case is shifted from the predetermined position. [Figure 7] FIG. 7 is a block diagram illustrating an example of a drone according to the first embodiment. [Figure 8]FIG. 8 is a sequence diagram showing an example of an item transport operation according to the first embodiment. [Figure 9] FIG. 9 is a flowchart illustrating the operation of step ST106 in more detail. [Figure 10] FIG. 10 is a side view showing the positional relationship between the storage shelf and the drone when acquiring a photographed image of the shelf including the shelf label. [Figure 11] Figure 11 compares the field of view of the shelf section image taken by the drone with the field of view of the shuttle section code reader. [Figure 12] FIG. 12 shows an example of a table summarizing abnormal conditions, the determination made by the abnormality detection system, and the response of the warehouse operation system. [Figure 13] FIG. 13 is a diagram illustrating an example of a transport control system according to a first modified example of the first embodiment. [Figure 14] FIG. 14 is a side view showing a POD and a transport AMR according to a second modified example of the first embodiment. [Figure 15] FIG. 15 is a diagram showing an example of a top view when the POD position is normal and when it is abnormal. [Figure 16] FIG. 16 is a sequence diagram showing an example of an item transport operation according to the second modified example of the first embodiment. [Figure 17] FIG. 17 is a sequence diagram showing an example of an item transport operation according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] The transport control system, the anomaly detection device, the anomaly detection method, and the anomaly detection program will be described in detail below with reference to the drawings. In the following embodiments, parts with the same numbers perform similar operations, and redundant description will be omitted. For example, when there are multiple identical or similar elements, a common symbol may be used to describe each element without distinguishing between them, or a subnumber may be used in addition to the common symbol to describe each element with distinction between them.

[0011] In the following description, the term "A" or "B" means at least one of A or B, and the term "A," "B," or "C" means at least one of A, B, or C. Furthermore, the term "A" and "B" also means at least one of A and B, and the term "A," "B," and "C" means at least one of A, B, and C.

[0012] [First embodiment] (composition) FIG. 1 is a conceptual diagram showing an example of a transport management system according to the first embodiment. 1, the transport management system S includes a transport control system, which will be described later, as well as a plurality of storage shelves 3, a floor surface 4, a plurality of cases 32, and a plurality of picking stations 5. The warehouse management system S stores items in the cases 32 arranged on the storage shelves 3.

[0013] The configuration of the transport control system will be described later, but the transport control system includes an automated guided vehicle 24 that transports cases 32, and controls the transport of the cases 32 by the automated guided vehicle 24. The cases 32 may be totes or trays, or a combination of the cases 32 and totes. The cases 32 may also be cardboard boxes. FIG. 1 shows an example in which the automated guided vehicle 24 is a CTU (Case (Container) Transfer Unit).

[0014] As shown in Fig. 1, the storage shelf 3 is a multi-level shelf that can accommodate multiple cases 32. The automated guided vehicle 24 transports the cases 32 between the storage shelf 3 and the picking station 5 to take out or bring in the items stored in the cases 32.

[0015] The plurality of picking stations 5 are stations for receiving the cases 32 transported by the automated guided vehicles 24 and proceeding to subsequent processing. The picking stations 5 may have a general configuration, and therefore detailed description thereof will be omitted here.

[0016] FIG. 2 is a front view showing an example of the arrangement of the case 32 according to the first embodiment. 2, the storage shelf 3 includes a plurality of shelf sections 31 for storing (arranging) cases 32 in the up-down direction (vertical direction). Each shelf section 31 is capable of storing a plurality of cases 32.

[0017] Furthermore, shelf unit labels 311 are attached to the shelf units 31, and store shelf unit identification information corresponding to the locations where the cases 32 are stored. Here, the shelf unit identification information is information for identifying the location of the shelf unit 31 on the storage shelf 3 and case position information (coordinates on a warehouse map) indicating the location where the cases 32 are stored. The shelf unit identification information may also include shelf unit position information (coordinates on a warehouse map) indicating the location of the shelf unit 31 (storage shelf 3) within the warehouse. For example, the shelf unit labels 311 are attached so as to face the aisle side of the shelf unit 31. The shelf unit labels 311 are sheet-like labels on which a barcode or two-dimensional code reflecting the shelf unit identification information is printed, or wireless storage media on which the shelf unit identification information is stored. The wireless storage media communicates with a wireless reader and transmits the shelf position identification information to the wireless reader.

[0018] A case label 321 on which case identification information is recorded is attached to each case 32. For example, the case 32 is an example of a storage unit in which items are stored. The case identification information is information that individually identifies the case 32. The case label 321 is a label on which a barcode or two-dimensional code reflecting the case identification information is printed, or a wireless storage medium on which the case identification information is stored. The wireless storage medium communicates with a wireless reader and transmits the case identification information to the wireless reader.

[0019] Furthermore, floor labels 411 storing floor position identification information are affixed to the floor 4. Here, the floor position identification information is identification information including position information indicating where the case is located within the warehouse. For example, the floor labels 411 are affixed to the floor 4 in a grid pattern within the warehouse. The floor labels 411 are also used when the automated guided vehicle 24 retrieves the case 32. Therefore, the floor position identification information stored in the floor labels 411 may include corresponding shelf identification information. In other words, the floor labels 411 and the shelf labels 311 may be associated with each other. The floor labels 411 are labels on which a barcode or a two-dimensional code is printed, or wireless storage media on which the floor position identification information is stored. The wireless storage media communicates with a wireless reader and transmits the case identification information to the wireless reader.

[0020] FIG. 3 is a conceptual diagram illustrating an example of a transport control system according to the first embodiment. As shown in FIG. 3, the transport control system 2 communicates with a warehouse management system (WMS) 1, receives an order list, and transmits processing results. The processing results include transport control results and inventory entry / exit processing results. The transport control system 2 may be part of the warehouse management system 1. The transport control system 2 also includes a warehouse execution system (WES) 21, an automated guided vehicle control system (WCS) 22, an anomaly detection system 23, multiple automated guided vehicles 24, a drone 25, and the like.

[0021] The warehouse management system 1 and the warehouse operation system 21 may be connected via a network. Here, the network is, for example, a LAN (local area network). The warehouse operation system 21, the automated guided vehicle control system 22, and the anomaly detection system 23 may be connected via a network. Furthermore, the automated guided vehicle control system 22 and the automated guided vehicle 24, and the anomaly detection system 23 and the drone 25 may each be connected via a network. Here, the automated guided vehicle 24 and the drone 25 may be connected to the network wirelessly.

[0022] The warehouse management system 1 can be configured with one or more computers, i.e., processors, memories, interfaces, etc. The processors are central processing units (CPUs), micro processing units (MPUs), digital signal processors (DSPs), etc. The warehouse management system 1 receives work instructions such as order lists from a higher-level server and transmits them to the warehouse operation system 21.

[0023] An order list is a list for retrieving items stored in multiple storage locations that can be used according to purpose, use, frequency, etc., and specifies one or more items. For example, an order list includes item information, order information, delivery information, etc. Item information includes the number of items, item identification information, item name, etc. Order information includes the order date and time and the orderer, etc. Delivery information includes the delivery destination, delivery date and time, recipient, etc.

[0024] The warehouse operation system 21 (warehouse operation device, i.e., warehouse operation server) can be configured with one or more general-purpose computers. The WES 21 includes a processor 211, a memory 212, and an interface 213. The processor 211 is a CPU, an MPU, a DSP, or the like. The memory 212 stores the operating program of the processor 211, etc. The interface 213 communicates with the warehouse management system 1, the automated guided vehicle control system 22, the anomaly detection system 23, etc.

[0025] The processor 211 of the warehouse operation system 21 includes an acquisition unit 2111, a generation unit 2112, and an output unit 2113. The processor 211 executes a program stored in the memory 212 to realize the functions of each unit.

[0026] For example, the acquisition unit 2111 acquires an order list from the warehouse management system 1 and also acquires inventory management data within the warehouse. The generation unit 2112 generates a work plan based on the order list and the inventory management data. The output unit 2113 outputs the generated work plan. The interface 213 transmits the work to the automated guided vehicle control system 22. The acquisition unit 2111 also acquires a transport processing result from the automated guided vehicle control system 22. Then, the output unit 2113 transmits the processing result, including the transport processing result, to the warehouse management system 1. Furthermore, the acquisition unit 2111 acquires anomaly detection information, which will be described later, from the automated guided vehicle control system 22. Then, the output unit 2113 outputs the anomaly detection information to the anomaly detection system 23.

[0027] The processor 211 is a CPU, MPU, DSP, or the like. The processor 211 is also responsible for generating work plans. The memory 212 stores the operating program of the processor 211, and the like. The memory 212 may also store an inventory database that indicates the inventory status in the warehouse. The interface 213 communicates with the warehouse management system 1, the automated guided vehicle control system 22, the anomaly detection system 23, and the like.

[0028] Here, the inventory database includes management information including item information about items and storage location information about where the items are stored. The inventory database may also be updated with inventory management data. The management information is information in which item information and storage location information are associated, and indicates where an item is actually stored or where an item should be stored. When an item is being transported out, information indicating where an item is actually stored is used, and when an item is being brought in, information indicating where an item should be stored is used.

[0029] For example, the item information includes an SKU number code (item identification information), item name, and attribute information. The attribute information includes size (three-side dimensions), weight, occurrence frequency, combination frequency, package form, expiration date, and fragile item information. The storage location information includes shelf section information regarding the shelf section 31 where the item is stored and case information regarding the case 32 where the item is stored. The shelf section information is one type of storage location identification information, and is shelf section identification information that identifies the shelf section 31 of the storage shelf 3 where the item is stored and case position information (coordinates on a warehouse map) that indicates the location where the case 32 is stored. The case information is one type of storage location identification information, and includes case identification information that identifies the case 32 where the item is stored.

[0030] The automated guided vehicle control system 22 (automated guided vehicle control device, i.e., automated guided vehicle control server) can be configured with one or more general-purpose computers. The automated guided vehicle control system 22 includes a processor 221, a memory 222, and an interface 223. The processor 221 is a CPU, an MPU, a DSP, or the like. The memory 222 stores the operating program of the processor 221, etc. The interface 223 communicates with the warehouse operation system 21 and the automated guided vehicles 24.

[0031] The anomaly detection system 23 (anomaly detection device, i.e., anomaly detection server) can be configured with one or more general-purpose computers. The anomaly detection system 23 includes a processor 231, a memory 232, and an interface 233. The processor 231 is a CPU, an MPU, a DSP, or the like. The memory 232 stores the operating program of the processor 231, etc. The interface 233 communicates with the warehouse operation system 21 and the drone 25.

[0032] For example, multiple automated guided vehicles 24 are arranged in a warehouse, and transport cases 32 containing specified items to a predetermined position. The automated guided vehicles 24 transport the cases 32 to the predetermined position under the control of the automated guided vehicle control system 22. Here, in this embodiment, an example will be described in which the automated guided vehicles 24 are CTUs, but the automated guided vehicles 24 are not limited to CTUs and may be any automated guided vehicle (AGV), autonomous mobile robot (AMR), or the like. For example, the automated guided vehicles 24 may be any of a track type, a QR (registered trademark) code type, and a SLAM type. In other words, the automated guided vehicles 24 may be any robot capable of autonomous transport.

[0033] The drone 25 is installed at a predetermined location in the warehouse. In the example of Fig. 3, one drone 25 is installed, but it goes without saying that multiple drones 25 may be installed depending on the size of the warehouse.

[0034] FIG. 4 is a side view and a front view showing an example of the automated guided vehicle 24 according to the first embodiment. As shown in FIGS. 4(a) and 4(b), the automated guided vehicle 24 includes a main body 2401, a support 2402, a shuttle 2403, multiple trays 2404, a shuttle code reader 2405, a floor code reader 2406, tires 2407, and the like. The support 2402 is attached vertically to the main body 2401. The shuttle 2403 is attached to the support 2402 and moves along the support 2402 (i.e., vertically). The shuttle 2403 includes an arm that moves horizontally and uses the arm to retrieve or grab a case 32 at a destination location. The multiple trays 2404 are attached to the support 2402 and hold the cases 32 retrieved by the shuttle 2403. When the shuttle 2403 reaches a destination location such as the picking station 5, it can either send out the cases 32 held on the multiple trays 2404 or place the cases held on the multiple trays 2404 using the arm.

[0035] The shuttle section code reader 2405 reads shelf section identification information and case identification information from the barcode or two-dimensional code printed on the shelf section label 311 and the case label 321. The floor surface code reader 2406 reads floor surface position identification information from the barcode or two-dimensional code printed on the floor surface label 411 attached to the floor surface 4.

[0036] FIG. 5 is a diagram showing an example of the automated guided vehicle 24 according to the first embodiment. The automated guided vehicle 24 includes a processor 241, a ROM 242, a RAM 243, an auxiliary memory device 244, a communication interface 245, a sensing device 246, a drive unit 247, a battery 248, a charging mechanism 249, a shuttle unit 2403, a shuttle unit code reader 2405, a floor code reader 2406, tires 2407, and the like.

[0037] The processor 241 has a function of controlling the overall operation of the automated guided vehicle 24. The processor 241 may include an internal cache and various interfaces. The processor 241 realizes various processes by executing programs stored in advance in the internal memory, the ROM 242, or the auxiliary storage device 244.

[0038] For example, the processor 241 is a CPU. The processor 241 may be realized by hardware such as an LSI, an ASIC, or an FPGA. The processor 241 performs processes such as calculations and controls required for operations such as acceleration, deceleration, stopping, and direction changes. The processor 241 executes a program stored in the ROM 242 or the like based on a control signal from the automated guided vehicle control system 22 or the like, thereby generating a drive signal and outputting it to each unit.

[0039] For example, processor 241 receives a control signal from automated guided vehicle control system 22 to move from the current position to a first position (case acquisition position) and acquire a specific case. Processor 241 controls shuttle unit 2403 to travel to a destination position based on floor position identification information read from floor label 411 using floor code reader 2406, and move up and down to the destination shelf 31. After confirming that the case 32 is the specified case based on the shelf identification information and case identification information read by shuttle unit code reader 2405, processor 241 uses shuttle unit 2403 to acquire the case 32 and controls one of multiple trays 2404 to hold the acquired case 32.

[0040] Next, processor 241 receives a control signal from automated guided vehicle control system 22 to move from the first position to the second position (picking station 5). Based on floor position identification information read from floor label 411 using floor code reader 2406, processor 241 travels to the second position, which is the destination position, and sends out cases 32 held on multiple trays 2404 to picking station 5 using shuttle unit 2403, or grabs and sends them out using an arm.

[0041] Furthermore, the processor 241 receives a control signal for moving from the second position to the first position from the automated guided vehicle control system 22. The processor 241 controls the automated guided vehicle 24 to acquire the cases 32 from which items have been picked from the picking station 5 based on the case identification information, and to hold the cases 32 on the multiple trays 2404. The automated guided vehicle 24 then controls the automated guided vehicle 24 to move to the first position, which is the destination position, based on the floor surface position identification information, and to send the cases 32 held on the multiple trays 2404 to the shelf section 31 based on the shelf position identification information.

[0042] As a result, the automated guided vehicle 24 retrieves the case 32 from a predetermined shelf section 31 of the storage shelf 3 in response to the control signal, or returns the case 32 to a predetermined shelf section 31 of the storage shelf 3.

[0043] Furthermore, the automated guided vehicle control system 22 may transmit control signals all at once to move the automated guided vehicle 24 from its current position to a first position, from the first position to a second position, and from the second position to the first position. Alternatively, the control signal for moving the automated guided vehicle 24 to the first position or the second position may be transmitted separately. For example, the automated guided vehicle control system 22 may set at least one waypoint and transmit the control signal separately into a control signal for moving the automated guided vehicle 24 to the waypoint and a control signal for moving the automated guided vehicle 24 from the waypoint to the first position or the second position, and transmit each control signal to the automated guided vehicle 24.

[0044] The ROM 242 is a non-transitory computer-readable storage medium that stores the above programs. The ROM 242 also stores data and various setting values ​​that the processor 241 uses when performing various processes.

[0045] The RAM 243 is a memory used for reading and writing data, and is used as a so-called work area for storing data that is temporarily used when the processor 241 performs various processes.

[0046] The auxiliary storage device 244 is a non-transitory computer-readable storage medium and may store the above programs. The auxiliary storage device 244 also stores data used by the processor 241 when performing various processes, data generated by the processes of the processor 241, various setting values, and the like.

[0047] The communication interface 245 is an interface for transmitting and receiving data to and from the automated guided vehicle control system 22, etc., via a wireless LAN access point, etc. For example, the communication interface 245 supports wireless LAN connection.

[0048] The sensing device 246 includes a plurality of reflective sensors and a photographing camera. Each reflective sensor is attached to the periphery of the automated guided vehicle 24. Each reflective sensor emits a laser beam, detects the time from when the laser beam is emitted until the laser beam is reflected off an object and returns, detects the distance to the object based on the detected time, and notifies the processor 241 of a detection signal. Based on the detection signal from the sensing device 246, the processor 241 outputs a control signal for controlling the travel of the automated guided vehicle 24. For example, based on the detection signal from the sensing device 246, the processor 241 outputs a control signal such as deceleration or stop to avoid a collision with the object.

[0049] The photographic camera photographs the surroundings and outputs the photographed image to the processor 241. The processor 241 analyzes the photographed image and outputs a control signal such as deceleration or stop to avoid collision with an object. The processor 241 also analyzes the photographed image and detects the storage shelf 3 and the case 32. The processor 251 also analyzes the photographed image and detects the barcode or two-dimensional code printed on the shelf label 311, the case label 321, and the floor label 411, and reads shelf identification information, case identification information, or floor position information from the barcode or two-dimensional code. In other words, the photographic camera may operate as the shuttle section code reader 2405 and the floor code reader 2406.

[0050] The drive unit 247 is a motor or the like, and rotates or stops the motor based on a drive signal output from the processor 241. The power of the motor is transmitted to the tires 2407 and then to the steering mechanism. The power from such a motor moves the automated guided vehicle 24 to its destination.

[0051] Battery 248 supplies the necessary power to drive unit 247 etc. Charging mechanism 249 is a mechanism that connects a charging station and battery 248, and battery 248 is charged with power supplied from the charging station etc. via charging mechanism 249.

[0052] As described above, the case 32 placed on the shelf 31 is picked up by the automated guided vehicle 24. Normally, the shelf label 311 and the case label 321 are lined up in a predetermined position that is aligned vertically with the floor label 411 attached. However, there are cases where the case 32 shifts or falls from the shelf 31 for some reason. Possible reasons for the case 32 shifting or falling include the case 32 moving due to a protrusion on the shelf 31, or the case 32 shifting or falling due to an earthquake or the like. The following describes a case where the case 32 shifts to the right side from the predetermined position toward the shelf 31.

[0053] FIG. 6 is a diagram showing a possible case where the position of the case 32 is shifted from the predetermined position. The automated guided vehicle 24 moves to the floor label 411 associated with the shelf label 311, which is the case acquisition position, and then moves the shuttle unit 2403 up or down to the shelf unit 31 on which the case 32 is placed. The shuttle unit code reader 2405 then detects the shelf unit label 311 and the case label 321 and reads the shelf unit identification information and the case identification information. At this time, the field of view of the shuttle unit code reader 2405 is the range indicated by the dotted line in FIG. 6.

[0054] FIG. 6 shows four examples of how far a case 32 is from a case pick-up position x1. Example 1 in FIG. 6 shows an example where the distance X1 between the shelf label 311 (or floor label 411) and the case label 321 affixed to the case 32 is less than the first distance M1. Here, the distance is assumed to be the distance from the center position of each label to the center position of the case 32. In such a case, the automated guided vehicle 24 can pick up the case 32 while positioned at the floor label 411. Here, the first distance M1 may be a predetermined distance determined according to the specifications of the shuttle unit 2403 of the automated guided vehicle 24.

[0055] Case 2 in FIG. 6 illustrates an example in which the distance X1 between the shelf label 311 and the case label 321 affixed to the case 32 is equal to or less than the second distance M2. Here, it is assumed that the second distance M2 is equal to or greater than the first distance M1. In such a case, the automated guided vehicle 24 can acquire the case 32 after autonomously moving to the second distance M2 from the floor label 411. Alternatively, even if the automated guided vehicle 24 is not capable of autonomous operation, the automated guided vehicle control system 22 can acquire the case 32 after autonomously moving to the second distance M2. Here, the second distance M2 may be a predetermined distance determined according to the specifications of the automated guided vehicle 24.

[0056] Case 3 in Figure 6 shows an example in which the distance X1 between the shelf label 311 and the case label 321 affixed to the case 32 is greater than the second distance M2. In this case, the shuttle code reader 2405 can detect the case 32 but cannot detect the case label 321. In such a case, the processor 241 of the automated guided vehicle 24 transmits abnormality detection information to the automated guided vehicle control system 22. Here, the abnormality detection information includes shelf identification information, floor surface identification information, etc.

[0057] Case 4 in Figure 6 shows an example in which the distance X1 between the shelf label 311 and the case label 321 affixed to the case 32 is greater than the third distance M3. Here, it is assumed that the third distance M3 is greater than the second distance M2. In this case, the shuttle code reader 2405 cannot detect the case 32 either. In such a case, the processor 241 of the automated guided vehicle 24 will transmit abnormality detection information including the shelf identification information, floor identification information, etc. to the automated guided vehicle control system 22.

[0058] 6, there may be cases where the case 32 has fallen or is not present and is therefore not on the shelf section 31. In such cases as well, the automated guided vehicle 24 transmits the abnormality detection information to the automated guided vehicle control system 22.

[0059] FIG. 7 is a block diagram showing an example of the drone 25 according to the first embodiment. The drone 25 includes a processor 251, a ROM 252, a RAM 253, an auxiliary memory device 254, a communication interface 255, a camera 256, a drive unit 257, a battery 258, a charging mechanism 259, and the like.

[0060] The processor 251 has a function of controlling the overall operation of the drone 25. The processor 251 may include an internal cache and various interfaces. The processor 251 executes programs stored in advance in the internal memory, the ROM 252, or the auxiliary storage device 254 to realize various processes.

[0061] For example, the processor 251 is a CPU. The processor 251 may be realized by hardware such as an LSI, an ASIC, or an FPGA. The processor 251 performs processes such as calculations and controls required for operations such as acceleration, deceleration, stopping, and direction changes. The processor 251 executes a program stored in the ROM 252 or the like based on a control signal from the abnormality detection system 23 or the like, thereby generating a drive signal and outputting it to each part.

[0062] For example, the processor 251 receives a control signal to move from the current position to a first position (where the abnormality has occurred) from the anomaly detection system 23. The processor 251 moves to the destination position and transmits an image captured by the camera 256 to the anomaly detection system 23.

[0063] The ROM 252 is a non-transitory computer-readable storage medium that stores the above-mentioned programs. The ROM 252 also stores data and various setting values ​​that the processor 251 uses when performing various processes.

[0064] The RAM 253 is a memory used for reading and writing data, and is used as a so-called work area for storing data that is temporarily used when the processor 251 performs various processes.

[0065] The auxiliary storage device 254 is a non-transitory computer-readable storage medium and may store the above programs. The auxiliary storage device 254 also stores data used by the processor 251 when performing various processes, data generated by the processes in the processor 251, various setting values, and the like.

[0066] The communication interface 255 is an interface for transmitting and receiving data to and from the anomaly detection system 23, etc., via a wireless LAN access point, etc. For example, the communication interface 255 supports wireless LAN connection.

[0067] The camera 256 is a photographic camera that photographs the surroundings. The camera 256 may be a general photographic camera mounted on a drone. The camera 256 may further include various devices such as a 3D camera, RiDER, or LRF (Laser Range Finder). The camera 256 outputs the photographed image to the processor 251. The processor 251 analyzes the photographed image and outputs a control signal for moving to a predetermined position. The processor 251 also analyzes the photographed image and detects the storage shelf 3 and the case 32.

[0068] The driving unit 257 is a motor or the like, and rotates or stops the motor based on a driving signal output from the processor 251. The power of the motor is transmitted to the propeller 2571. The drone 25 moves to the destination using the power from such a motor.

[0069] Battery 258 supplies the necessary power to drive unit 257 etc. Charging mechanism 259 is a mechanism that connects a charging station and battery 258, and battery 258 is charged with power supplied from the charging station etc. via charging mechanism 259.

[0070] (operation) FIG. 8 is a sequence diagram showing an example of an item transport operation according to the first embodiment. The operation of this sequence is realized by processor 211 of warehouse operation system 21, processor 221 of automatic guided vehicle control system 22, and processor 231 of anomaly detection system 23 reading and executing programs stored in memory 212, memory 222, and memory 232, respectively.

[0071] This sequence begins, for example, when the warehouse operation system 21 receives an order list, the processor 211 creates a work plan, and transmits the work plan to the automated guided vehicle control system 22.

[0072] In step ST101, the processor 211 performs a transport task in a normal manner. For example, the processor 211 transmits a control signal to the automatic transport control system 22 based on the task plan, and the automatic control transport system 22 transmits a control signal to the automatic transport vehicle 24. That is, the automatic transport vehicle 24 receives the control signal and performs a normal operation (normal operation), such as acquiring the case 32 and transporting the case 32 to the picking station 5.

[0073] Now, suppose that an abnormal condition occurs in case 32. For example, due to Case 3 or Case 4 described with reference to Fig. 6, or due to the absence of case 32 on shelf 31, automated guided vehicle 24 transmits abnormality detection information to automated guided vehicle control system 22. Here, as described above, the abnormality detection information includes floor position identification information read by floor label 411, shelf identification information read by shelf label 311, etc.

[0074] In step ST102, the processor 221 receives the abnormality detection information transmitted by the automated guided vehicle 24.

[0075] In step ST103, the processor 221 transmits the abnormality information. The processor 221 acquires the case identification information of the case 32 in which the abnormality has occurred from the memory 222 based on the floor surface position identification information and the shelf identification information included in the abnormality detection information. Then, the processor 221 generates abnormality information including the case identification information and the abnormality detection information corresponding to the case 32 in which the abnormality has occurred, and transmits the abnormality information to the warehouse operation system 21.

[0076] In step ST104, the processor 211 transmits an abnormality analysis request. When the processor 211 receives the abnormality information, it transmits an abnormality analysis request including the abnormality information. Here, the abnormality analysis request only needs to include at least position information about the location where the abnormality occurred (shelf position information or floor position identification information included in the shelf identification information) and information indicating that an abnormality has occurred in the case 32 stored on the shelf 31 of the storage shelf 3 (abnormality detection information).

[0077] In step ST105, the processor 231 transmits a movement instruction to the location where the abnormality has occurred to the drone 25. When the processor 231 acquires the abnormality analysis request, it sets a flight path for the drone 25 based on the current position of the drone 25 and the position information included in the abnormality analysis request, and transmits a movement instruction including a control signal according to the flight path to the drone 25. Then, the drone 25 moves to the storage shelf 3 where the abnormality has occurred based on the movement instruction.

[0078] In step ST106, the processor 251 analyzes the details of the abnormality based on the photographed image. For example, the processor 251 acquires a photographed image of the shelf part including the shelf part 31 in which the abnormality has occurred, and analyzes the details of the abnormality based on the photographed image of the shelf part.

[0079] FIG. 9 is a flowchart illustrating the operation of step ST106 in more detail. In step ST201, the processor 231 acquires shelf identification information. The processor 231 acquires a shelf photographed image from the drone 25 that has moved in front of the shelf 31 of the storage shelf 3 where the abnormality has occurred. The processor 231 then detects the shelf label 311 of the shelf 31 where the abnormality has occurred based on the shelf photographed image, and reads the shelf identification information from the barcode or two-dimensional code printed on the shelf label 311. The processor 231 then confirms that the acquired shelf identification information matches the shelf identification information included in the abnormality analysis request. By confirming this, the processor 231 can determine that the shelf photographed image is an image of the shelf 31 where the abnormality has occurred.

[0080] Here, if the processor 231 can identify the position of the case 32 in which the abnormality has occurred based on the position information, the processor 231 may only detect the shelf section label 311. Furthermore, the reading of the shelf section identification information or the detection of the shelf section label 311 may be performed by the processor 251 provided in the drone 25.

[0081] FIG. 10 is a side view showing the positional relationship between the storage shelf 3 and the drone 25 when a photographed image of the shelf section including the shelf section label 311 is acquired. 10, the drone 25 captures an image at a horizontal aerial position at a height corresponding to the position where the automated guided vehicle 24 picks up the case x1 and where the shelf label 311 is attached, i.e., the corresponding position on the shelf 31. The drone 25 may be approximately aligned or may deviate within an arbitrary range. The image of the shelf captured at this image capture position is transmitted to the anomaly detection system 23.

[0082] FIG. 11 is a diagram comparing the field of view of the shelf section photographed by the drone 25 with the field of view of the shuttle section code reader 2405. 11, the field of view of the shelf section photographed from an aerial position above case acquisition position x1 is wider than the field of view of the shuttle section code reader of automatic guided vehicle 24. Therefore, as shown in FIG. 11, the shelf section photographed image contains the entire case 32, which is shifted to the right of case acquisition position x1.

[0083] In step ST202, the processor 231 performs labeling processing on the photographed image of the shelf section. The labeling processing is, for example, processing for binarizing the photographed image and expressing connected areas of the binarized image by rectangles and coordinate positions. Here, the labeling processing may be performed using a general method in image processing, and detailed description thereof will be omitted here.

[0084] In step ST203, processor 231 determines whether the shape of the connected region in the labeled captured image matches case 32. If the shape of the connected region does not match case 32, the process proceeds to step ST204. On the other hand, if the shape of the connected region matches case 32, processor 231 determines that case 32 has shifted to either the left or right from the predetermined position. Then, the process proceeds to step ST205.

[0085] In the processing of steps ST202 and ST203, the processor 231 may use an image recognition technique to determine whether the case 32 can be detected from the photographed image of the shelf portion.

[0086] In step ST204, processor 231 determines that case 32 is not present. Processor 231 determines that case 32 is not present in the photographed image of the shelf section because the shape of the connected area does not match that of case 32. In other words, processor 231 determines that case 32 has fallen from storage shelf 3 or does not exist.

[0087] In such a case, the drone 25 searches outside the field of view or on the floor 4 to search for the case 32. For example, the processor 231 acquires a photographed image of the floor 4 taken by the drone 25. Then, the processor 231 performs the same processing as steps ST202 to ST203 on the photographed image of the floor. As a result, if the case 32 is detected, the processor 231 determines that the case 32 has fallen onto the floor 4. The processor 231 transmits the position of the case 32 that has fallen onto the floor 4 to the anomaly detection system 23. If the case 32 is not found, the processor 231 determines that the case 32 does not exist.

[0088] In step ST205, the processor 231 determines the position of the case 32. The processor 231 performs image analysis on the captured image of the shelf portion to determine the position of the case 32. Here, if the camera 256 of the drone 25 is equipped with devices such as a 3D camera, RiDer, or LRF, the position of the case 32 may be determined by further adding information detected by these sensors. The processor 231 determines whether the case 32 has shifted to the left or right toward the storage shelf 3. Note that if the drone 25 is not equipped with the above-mentioned sensors, such as a 3D camera, RiDer, or LRF, this information may not be necessary.

[0089] In step ST206, processor 231 estimates the position of case label 321. Processor 231 estimates the position of the case label based on the connected area and the position of case 32. For example, processor 231 identifies the side of case 32 from the captured image based on the connected area and the position of case 32, and estimates that case label 321 is located in the center of the side.

[0090] If the processor 231 can directly detect the case label 321 from the photographed image of the shelf section, the processor 231 may skip the processes of steps ST205 and ST206.

[0091] In step ST207, processor 231 reads case identification information based on the estimated location. Processor 231 detects the barcode or two-dimensional code printed on case label 321 and reads the case identification information stored in the barcode or two-dimensional code. Processor 231 then confirms that the case identification information matches the case identification information included in the abnormality analysis request. In other words, processor 231 confirms that the case 32 from which the identification information was obtained is the case 32 in which an abnormality has occurred.

[0092] Here, processor 231 may not be able to read the case identification information at the estimated position because case label 321 of case 32 is not attached or case 32 is facing the wrong way. In such cases, the following steps ST208 to ST209 may be skipped, and processor 231 may determine that it was not possible to acquire case identification information from case label 321 through image processing, i.e., that case label 321 is not in the attachment position.

[0093] Alternatively, for example, if it is possible to photograph the back side of the case 32, the processor 231 may have the drone 25 photograph the back side of the case 32. Then, if it is possible to identify that a case label 321 is attached to the back side, the processor 231 may acquire case identification information from the identified case label 321. Then, the processor 231 determines that the case 32 is upside down. On the other hand, if it is not possible to identify a case label 321 on the back side either, the processor 231 determines that no case label 321 is attached.

[0094] In step ST208, the processor 231 determines the position of the case label 321. The processor 231 determines the position of the case 32 in which an abnormality has occurred. The processor 231 may detect the center position of the case label 321 included in the captured image as the position of the case label 321.

[0095] In step ST209, the processor 231 calculates the distance X between the shelf section label 311 and the case label 321. The processor 231 calculates the distance between the position of the shelf section label 311 and the position of the case label 321 based on the captured image.

[0096] Returning to FIG. 8 , in step ST107, processor 231 transmits the abnormality analysis result. As described above, the information in the abnormality analysis result differs depending on the determination made by processor 231. For example, if case 32 has fallen onto floor 4, the abnormality analysis result includes information indicating that case 32 has fallen onto floor 4. If case 32 is not present, the abnormality analysis result includes information indicating that case 32 does not exist. If case 32 has shifted, the abnormality analysis result includes the distance X calculated in step ST209 and the direction of shift. If the case label of case 32 is not in its designated position, the abnormality analysis result includes information indicating that the case label is not in its designated position. Furthermore, if case 32 is upside down, the abnormality analysis result includes information indicating that case 32 is placed upside down.

[0097] In step ST108, the processor 231 outputs a movement instruction to move the drone 25 to a standby position. The drone 25 that has received the instruction moves to a predetermined standby position.

[0098] In step ST109, processor 211 transmits a countermeasure instruction. Processor 211 transmits a countermeasure instruction corresponding to the abnormality analysis result to automated guided vehicle control system 22. For example, if the abnormality analysis result includes information that shelf label 311 and case label 321 are separated by distance X to the right as one faces storage shelf 3, the countermeasure instruction is an instruction to move by distance X to the right from floor label 411 as one faces storage shelf 3, and then retrieve case 32. Alternatively, the countermeasure instruction may be information that case 32 is present at a position shifted by distance X to the right as one faces the shelf from floor label 411.

[0099] FIG. 12 shows an example of a table summarizing abnormal conditions, determinations made by the abnormality detection system 23, and responses taken by the warehouse operation system 21. 12, No. 1 and No. 2 are cases where the case 32 is shifted to either the left or right of the shelf section 31. In this case, as described above, the amount of shift is determined based on the image captured by the drone 25, and the automated guided vehicle 24 is moved to either the left or right depending on the amount of shift, thereby acquiring the case 32.

[0100] No. 3 in Figure 12 is a case where the case 32 is not present on the shelf section 31, and this corresponds to a case where the drone 25 searches the floor surface 4 in step ST205 but is unable to find the case 32. In this case, instead of performing the processing from step ST108 onwards, the operator managing the warehouse operation system 21 is notified that the case 32 could not be found. The operator will then create a case 32 corresponding to the case 32 and store it in stock.

[0101] No. 4 in Figure 12 is a case where the case 32 is not present on the shelf section 31, and the case 32 is found as a result of the drone 25 searching the floor surface 4 in step ST205. In such a case, instead of performing the processing from step ST108 onwards, the operator is notified that it is necessary to retrieve the case 32. The operator retrieves the case 32, checks the contents inside the case 32, and then decides whether to return the case 32 to the storage shelf 3.

[0102] Nos. 5 and 6 in Fig. 12 are cases where the case label 321 was not found on the case 32, or the case 32 was upside down (i.e., the case label 321 was not found). In this case, instead of performing the processing from step ST109 onwards, the operator is informed that the case label 321 was not found on the case 32. The operator collects the case 32 and, at picking station 5, attaches the case label 321 to the case 32, or turns the case 32 back over, and stores the case 32 in inventory.

[0103] Returning to FIG. 8 , in step ST109, processor 211 sends a control signal to automatic transport control system 22, and automatic transport control system 22 sends a movement instruction to automatic transport vehicle 24. The movement instruction includes distance X by which case 32 is displaced from shelf label 311, and an instruction to move distance X to the left or right from floor label 411. Automatic transport vehicle 24 moves distance X from floor label 411 in accordance with the movement instruction. For example, if case 32 is displaced to the right toward storage shelf 3, automatic transport vehicle 24 moves distance X to the right from floor label 411.

[0104] In step ST110, processor 221 creates an instruction to acquire case 32, and in step ST111 transmits the instruction to acquire case 32 to automated guided vehicle 24. As a result, automated guided vehicle 24 acquires case 32 in the same manner as normal.

[0105] In step ST112, the processor 221 transmits a return instruction. The processor 221 transmits a return instruction to the automated guided vehicle 24 that has acquired the case 32, which is an instruction to return to the position of the original floor label 411. As a result, the automated guided vehicle 24 that has acquired the case 32 in which the abnormality has occurred returns to the position of the original floor label 411. Thereafter, the automated guided vehicle 24 moves to a destination position such as the picking station 5.

[0106] Then, the process returns to step ST 101. Note that the normal operation ends when all the processing of the work plan has been completed.

[0107] (Operation and effect of the first embodiment) According to the first embodiment described above, when the case 32 deviates from a state that can be acquired by the automated guided vehicle 24, the anomaly detection system 23 uses the drone 25 to determine how much the case 32 has deviated, and transmits the distance X of the deviation to the automated guided vehicle control system 22 via the warehouse operation system 21. This allows the automated guided vehicle control system 22 to move the automated guided vehicle 24 by the distance X to acquire the case 32. This eliminates the need for the automated guided vehicle 24 to have high-level sensing capabilities, allowing for a reduction in the cost of the entire system.

[0108] [First Modification of the First Embodiment] (composition) The transport management system S in the first embodiment is similar to that in the first embodiment, and therefore a duplicated description will be omitted here.

[0109] FIG. 13 is a diagram showing an example of a transport control system 2 according to a first modified example of the first embodiment. 13 , the first modified example of the first embodiment differs from the first embodiment in that a ceiling camera 26 is used instead of the drone 25. A plurality of ceiling cameras 26 are arranged so as to be able to photograph all of the cases 32 arranged on the storage shelf 3. The ceiling cameras 26 may be configured to output the photographed images to the anomaly detection system 23.

[0110] (operation) The operation of the first modified example of the first embodiment is similar to the operation of the first embodiment described with reference to Figures 8 and 9. The difference is that when the anomaly detection system 23 receives an anomaly analysis request in step ST104, the processor 231 uses the ceiling camera 26 to acquire a photographed image of the storage shelf 3 at the location where the anomaly has occurred.

[0111] Furthermore, if it is determined in step ST205 that the case 32 is not present, the processor 231 may acquire a photographed image of the floor surface 4 and determine whether the case 32 has fallen.

[0112] As described above, by arranging multiple ceiling cameras 26 so that they can photograph all of the cases 32 placed on the storage shelf 3, it is possible to perform operations similar to those performed in the first embodiment.

[0113] (Operation and effect of the first modification of the first embodiment) According to the first modification of the first embodiment described above, when case 32 has shifted beyond a state that can be captured by automated guided vehicle 24, anomaly detection system 23 uses ceiling camera 26 to determine the extent to which case 32 has shifted, and transmits the shifted distance X to automated guided vehicle control system 22 via warehouse operation system 21. This allows automated guided vehicle control system 22 to move automated guided vehicle 24 by distance X to capture case 32. This eliminates the need for automated guided vehicle 24 to have high-level sensing capabilities, allowing for a reduction in the cost of the entire system.

[0114] [Second Modification of the First Embodiment] In a second modification of the first embodiment, PODs 33 are used in some of the storage shelves 3, and the PODs 33 are transported by the transport AMR 27. For example, the transport AMR 27 is controlled by a WCS such as the automated guided vehicle control system 22, similar to the automated guided vehicle 24.

[0115] FIG. 14 is a side view showing a POD 33 and a transport AMR 27 according to a second modification of the first embodiment. 14, a POD label 331 storing POD identification information for identifying the POD 33 is attached to the center of the bottom surface of the POD 33. The POD 33 is an example of a storage unit that stores items. The transport AMR 27 identifies the POD to be transported by reading the floor position identification information from the floor label 411 and the POD identification information from the POD label 331.

[0116] FIG. 15 is a diagram showing an example of a top view when the position of the POD 33 is normal and when it is abnormal. FIG. 15(a) is a top view showing an example when the position of the POD 33 is normal. As shown in FIG. 15(a), when the position of the POD 33 is normal, the POD label 331 and the floor label 411 are aligned when viewed from above. FIG. 15(b) is a top view showing an example when the position of the POD 33 is abnormal. When the position of the POD 33 is abnormal as shown in FIG. 15(b), the POD label 331 and the floor label 411 are misaligned. The example in FIG. 15(b) shows an example where they are misaligned by a distance X.

[0117] In this way, even when the POD 33 is displaced as in the first embodiment, it can be inferred that the POD 33 that is displaced from the predetermined position is the displaced POD 33. Therefore, by the same operation as in the first embodiment, the anomaly detection system 23 determines the distance X by which the POD 33 has been displaced, and transmits information about the distance X to the WCS that controls the transport AMR 27, such as the automated guided vehicle control system 22. As a result, even if the POD 33 is displaced more than the transport AMR 27 allows, as in the first embodiment, it can be corrected.

[0118] (operation) FIG. 16 is a sequence diagram showing an example of an item transport operation according to the second modified example of the first embodiment. The operation of this sequence is realized by processor 211 of warehouse operation system 21, processor 221 of automatic guided vehicle control system 22, and processor 231 of anomaly detection system 23 reading and executing programs stored in memory 212, memory 222, and memory 232, respectively.

[0119] This sequence begins, for example, when the warehouse operation system 21 receives an order list, the processor 211 creates a work plan, and transmits the work plan to the automated guided vehicle control system 22.

[0120] In step ST301, the processor 211 performs a transport operation in a normal manner. Step ST301 is similar to the operation in step ST101 described with reference to Fig. 8, and therefore a duplicated description will be omitted here.

[0121] Here, a case where an abnormal state occurs in the POD position will be described. For example, when the POD position is misaligned or the POD 33 does not exist as described with reference to FIG. 15, the automated guided vehicle 27 transmits abnormality detection information to the automated guided vehicle control system 22. The abnormality detection information includes floor surface identification information read by the floor surface label 411, etc.

[0122] In step ST302, the processor 221 receives the abnormality detection information transmitted by the automated guided vehicle 24.

[0123] In step ST303, the automatic guided vehicle control system 22 transmits the abnormality detection information to the warehouse operation system 21.

[0124] In step ST304, the processor 211 transmits an abnormality analysis request. When the processor 211 receives the abnormality information, it transmits an abnormality analysis request including the abnormality information. Here, the abnormality analysis request only needs to include at least the location information of the location where the abnormality occurred (POD location information included in the POD identification information) and information indicating that an abnormality has occurred in the POD 33 (abnormality detection information).

[0125] In step ST305, the processor 231 transmits a movement instruction to move the drone 25 to the location where the abnormality was detected. When the processor 231 receives the abnormality analysis request, it sets a flight path for the drone 25 based on the current position of the drone 25 and the position information included in the abnormality analysis request, and transmits a movement instruction including a control signal according to the flight path to the drone 25. Upon receiving the instruction, the drone 25 moves to the POD position included in the POD identification information and captures a POD position image. Then, the processor 251 of the drone 25 transmits the captured image to the anomaly detection system 23.

[0126] In step ST307, the processor 231 analyzes the abnormality details. The processor 231 detects an abnormality in the POD position arranged based on the captured image. For example, the processor 231 may perform processing on the POD position image similar to the analysis of the abnormality content described in detail using FIGS. 9 and 15 to determine whether an abnormality has occurred. If the determination result indicates that the POD 33 is not in the predetermined position, that the POD 33 does not exist, or that the distance between the predetermined position of the POD 33 and the actual position of the POD 33 is equal to or greater than a predetermined distance, the processor 231 determines that an abnormality has occurred. In this case, the processor 231 may acquire the same information as that included in the abnormality analysis request.

[0127] The other steps included in steps ST308 to ST313 are the same as steps ST107 to ST112 described with reference to FIG. 8, and therefore a duplicated description will be omitted here.

[0128] (Operation and effect of the second modification of the first embodiment) According to the second modified example of the first embodiment described above, when the POD 33 deviates from a state that can be acquired by the transport AMR 27, the anomaly detection system 23 uses the drone 25 to determine the extent to which the POD 33 has deviated, and transmits the deviated distance X to the automated guided vehicle control system 22 via the warehouse operation system 21. This allows the automated guided vehicle control system 22 to move the transport AMR 27 by the distance X to acquire the POD 33. This eliminates the need for the transport AMR 27 to have high-level sensing capabilities, thereby reducing the cost of the entire system.

[0129] [Second embodiment] (composition) The configurations of the transport management system S and transport control system 2 in the second embodiment are the same as those in the first embodiment, so a duplicated description will be omitted here.

[0130] (operation) FIG. 17 is a sequence diagram showing an example of an item transport operation according to the second embodiment. The operation of this sequence is realized by processor 211 of warehouse operation system 21, processor 221 of automatic guided vehicle control system 22, and processor 231 of anomaly detection system 23 reading and executing programs stored in memory 212, memory 222, and memory 232, respectively.

[0131] This sequence begins, for example, when the warehouse operation system 21 receives an order list, the processor 211 creates a work plan, and transmits the work plan to the automated guided vehicle control system 22.

[0132] In step ST401, the processor 211 performs a transport operation in a normal manner. Step ST401 is similar to the operation in step ST101 described with reference to Fig. 8, and therefore a duplicated description will be omitted here.

[0133] In step ST402, the processor 231 transmits an anomaly detection instruction to the drone 25. The processor 231 generates an anomaly detection instruction including shelf unit identification information, and transmits the anomaly detection instruction to the drone 25. Upon receiving the instruction, the drone 25 moves to the shelf position information included in the shelf unit identification information, and photographs the cases 32 stored on the shelf unit 31. The processor 251 of the drone 25 then transmits the photographed image to the anomaly detection system 23. The processor 231 may also control the drone 25 so that it preferentially photographs the cases 32 that are scheduled to be transported by the automated guided vehicle 24.

[0134] In step ST403, the processor 231 detects an abnormality. The processor 231 detects an abnormality in the case 32 placed on the storage shelf 3 based on the captured image. For example, the processor 231 may perform the same processing as the analysis of the abnormality content described in detail using FIG. 9 on the captured image of the shelf section to determine whether an abnormality has occurred. If the result of the determination is that the case 32 is not in a predetermined position on the shelf section 31, that the case 32 does not exist, or that the distance between the shelf section label 311 and the case label 321 is equal to or greater than a predetermined distance, the processor 231 determines that an abnormality has occurred. At this time, the processor 231 may acquire the same information as that included in the abnormality analysis request.

[0135] In step ST404, the processor 231 transmits a movement instruction to move the drone 25 to the location where the abnormality has been detected. As a result, the drone 25 moves to the location where the abnormality has occurred.

[0136] Steps ST405 and ST406 are the same as steps ST106 and ST107 described with reference to Fig. 8, and therefore will not be described again here. If the analysis of the abnormality is completed in step ST403, the processes of steps ST404 and ST405 may be skipped.

[0137] In step ST408, the processor 231 transmits an abnormality detection instruction to the drone 25. As described in step ST402, the processor 231 transmits the abnormality detection instruction to the drone 25. That is, the drone 25 will perform an operation to detect whether there is an abnormality in the case 32, as in normal operation.

[0138] Steps ST408 to ST411 are the same as steps ST109 to ST112 described with reference to FIG. 8, and therefore a duplicated description will be omitted here.

[0139] (Effects of the second embodiment) According to the second embodiment described above, it is possible to use the drone 25 to detect an abnormality before the automated guided vehicle 24 transports the case 32. This allows the abnormality to be communicated to the warehouse operation system 21 at an early stage, and the automated guided vehicle 27 can smoothly acquire the case 32 in which the abnormality has occurred. Furthermore, there is no need to equip the automated guided vehicle 24 with advanced sensing capabilities, which reduces the cost of the entire system.

[0140] [Other embodiments] The first and second embodiments can be combined. For example, before the drone 25 detects an abnormality through an anomaly detection operation, the automated guided vehicle 24 or the transport AMR 27 may transmit detection information to the automated guided vehicle control system 22. In such a case, the same operation as in the first embodiment is performed.

[0141] The processing of the second embodiment can also be implemented in the first modified example of the first embodiment. For example, the processor 231 analyzes the image captured by the ceiling camera 26 to detect an abnormality.

[0142] Furthermore, the anomaly detection system 23 may be part of the warehouse operation system 21. That is, the operations performed by the processor 231 described above may be performed by the processor 211 of the warehouse operation system 21.

[0143] The program according to this embodiment may be transferred in a state where it is stored in an electronic device (computer) such as the anomaly detection system 23, or may be transferred in a state where it is not stored in an electronic device. In the latter case, the program may be transferred via a network, or may be transferred in a state where it is stored in a storage medium. The storage medium is a non-transitory, tangible medium. The storage medium is a medium that can be read by a computer such as the WES 21 (computer-readable medium). The storage medium may be in any form, such as an optical disk (e.g., a CD-ROM), a magnetic disk, or a semiconductor memory (e.g., a memory card), as long as it is capable of storing a program and is readable by a computer.

[0144] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0145] S...Transportation management system 1. Warehouse management system 2...Transport control system 21...Warehouse operation system 211...processor 2111…Acquisition Department 2112...Generation section 2113...Output section 212...Memory 213...Interface 22...Automatic guided vehicle control system 221...Processor 222...Memory 223...Interface 23...Anomaly detection system 231...Processor 232...Memory 233...Interface 24...Automated guided vehicle 2401...Main body 2402...Support part 2403...Shuttle Club 2404…Tray 2405...Shuttle part code reader 2406...Floor code reader 2407...Tire 241...Processor 242...ROM 243...RAM 244...Auxiliary storage device 245...Communication interface 246…Sensing devices 247...Drive unit 248...Battery 249…Charging mechanism 25...Drone 251...processor 252...ROM 253...RAM 254...Auxiliary storage device 255...Communication interface 256...Camera 257...Drive unit 2571…Propeller 258...Battery 259…Charging mechanism 26...Ceiling camera 27...Transport AMR 3...Storage shelf 31...shelf 311...Shelf label 32…Case 321...Case label 33...POD 331...POD label 4...Floor 411...Floor label 5. Picking station

Claims

1. A transport control system including a warehouse operation device and an abnormality detection device, The anomaly detection device a processor that acquires an abnormality analysis request indicating that an abnormality has occurred in a storage unit that stores items to be transported based on a work plan and that includes location information of the location where the abnormality has occurred, acquires a storage unit photographed image including the storage unit based on the location information, analyzes the content of the abnormality based on the storage unit photographed image, and generates an abnormality analysis result including an analysis result; an interface that transmits the abnormality analysis result to the warehouse operation device; Equipped with The warehouse operation device includes a processor that outputs a response instruction according to the abnormality analysis result. Conveyance control system.

2. The storage unit is a case stored on a shelf of a storage shelf, and the storage unit photographed image is a shelf unit photographed image including the shelf unit. The transport control system according to claim 1 .

3. The storage unit is a POD shelf, and the storage unit photographed image includes the POD shelf. The transport control system according to claim 1 .

4. Further comprising a drone controlled by the anomaly detection device, The processor of the anomaly detection device outputs a movement instruction to the drone to move to the location where the anomaly has occurred based on the position information, and acquires a photographed image of the shelf portion taken by a camera equipped in the drone. The transport control system according to claim 2 .

5. The warehouse further includes a plurality of ceiling cameras that are controlled by the anomaly detection device and are capable of photographing the cases stored on the shelf, and are arranged on the ceiling of the warehouse, the processor of the anomaly detection device acquires the shelf portion photographed image by a ceiling camera selected from the plurality of ceiling cameras according to the position information. The transport control system according to claim 2 .

6. The processor of the anomaly detection device performs a labeling process based on the photographed image of the shelf portion, and determines whether or not a shape of a connected region of the photographed image of the shelf portion after the labeling process matches the case. The transport control system according to claim 2 .

7. If it is determined that the case does not match, the processor of the anomaly detection device determines that the case has fallen to the floor or that the case is not present. The transport control system according to claim 6.

8. Further comprising a drone controlled by the anomaly detection device, The processor of the anomaly detection device outputs an instruction to the drone to photograph the floor surface, and determines whether the case can be detected based on the photographed floor surface image. The transport control system according to claim 7.

9. When the processor of the anomaly detection device detects the case from the photographed image of the floor surface, it creates the anomaly analysis result indicating that the case has fallen from the shelf portion; The processor of the warehouse operation device notifies the operator who manages the warehouse operation device of the abnormality analysis result. The transport control system according to claim 8 .

10. If the processor of the anomaly detection device cannot detect the case from the photographed image of the floor surface, it creates the anomaly analysis result indicating that the case is not present; The processor of the warehouse operation device notifies the operator who manages the warehouse operation device of the abnormality analysis result. The transport control system according to claim 8 .

11. If it is determined that the case matches, the processor of the anomaly detection device determines that the case has shifted to the left or right from a predetermined position. The transport control system according to claim 6.

12. a processor of the anomaly detection device detects a shelf label attached to the shelf based on the shelf image, determines the position of the case based on the shelf image, detects a case label attached to the case based on the determined case position, calculates a distance between the shelf label and the case label based on the positions of the shelf label and the case label, and creates the anomaly analysis result including the distance; The transport control system according to claim 11.

13. an automated guided vehicle control device connected to the warehouse operation device and controlling the case transport vehicle; The warehouse operation device further includes an interface that transmits a response instruction according to the abnormality analysis result to the automatic guided vehicle control device, the automated guided vehicle control device includes a processor that controls the automated guided vehicle control device to move by the distance from a floor label that corresponds to the shelf label and is affixed to a floor surface; The transport control system according to claim 12.

14. the anomaly analysis request includes case identification information that identifies the case; The processor of the anomaly detection device reads case identification information from the barcode or two-dimensional code printed on the case label and determines whether it matches the case identification information included in the anomaly analysis request. The transport control system according to claim 12.

15. When the processor of the anomaly detection device cannot detect the case label attached to the case, it determines that the case label is not in its attached position. The transport control system according to claim 12.

16. The system further includes a drone controlled by the anomaly detection device. The processor of the anomaly detection device controls the drone to photograph the back side of the case. The transport control system according to claim 15.

17. Further comprising a drone controlled by the anomaly detection device, The processor of the anomaly detection device controls the drone to photograph the case stored in the storage shelf to acquire a photographed image of the case, and determines whether an abnormality has occurred in the case based on the photographed image of the case. The transport control system according to claim 1 .

18. the processor of the anomaly detection device generates the anomaly analysis request when the case cannot be detected from the case photographed image. The transport control system according to claim 17.

19. the processor of the anomaly detection device generates the anomaly analysis request when a distance between a case label attached to the case and a shelf label attached to the storage shelf, which is detected from the case photographed image, is equal to or greater than a predetermined distance. The transport control system according to claim 17.

20. a processor that acquires an abnormality analysis request indicating that an abnormality has occurred in a storage unit that stores items to be transported based on a work plan and that includes location information of the location where the abnormality has occurred, acquires a storage unit photographed image including the storage unit based on the location information, analyzes the content of the abnormality based on the storage unit photographed image, and generates an abnormality analysis result including an analysis result; an interface that transmits the abnormality analysis result to a warehouse operation device; An anomaly detection device comprising:

21. An anomaly detection method executed by a processor of an anomaly detection device, acquiring an abnormality analysis request indicating that an abnormality has occurred in a storage unit that stores an item to be transported based on the work plan, and including location information of the location where the abnormality has occurred; acquiring a storage unit photographed image including the storage unit based on the location information; Analyzing the abnormality based on the storage unit photographed image; generating an anomaly analysis result including the analysis result; transmitting the abnormality analysis result to a warehouse operation device; An anomaly detection method comprising:

22. An anomaly detection program comprising instructions to be executed by a processor of an anomaly detection device, acquiring an abnormality analysis request indicating that an abnormality has occurred in a storage unit that stores an item to be transported based on the work plan, and including location information of the location where the abnormality has occurred; acquiring a storage unit photographed image including the storage unit based on the location information; Analyzing the abnormality based on the storage unit photographed image; generating an anomaly analysis result including the analysis result; transmitting the abnormality analysis result to a warehouse operation device; An anomaly detection program.

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