Patent Publication Number: US-2020277139-A1

Title: Warehouse system

Description:
TECHNICAL FIELD 
     The present invention relates to a warehouse system. 
     BACKGROUND ART 
     Robots that perform a transfer operation of transferring cargoes from one location to another location are referred to as unmanned vehicles or AGVs (Automatic Guided Vehicles). The AGVs have been widely used in facilities such as warehouses, factories, and harbors. Most operations for physical distribution in facilities may be automated by combining the cargo delivery operation occurring between storage sites and the AGVs, that is, the cargo handling operation with cargo handling devices for automatically performing the cargo handling operation. 
     With the recent diversification of consumers&#39; needs, warehouses that handle low-volume and high-variety objects, for example, objects for mail-order sales have increased. In terms of characteristics of objects to be managed, it takes much time and labor costs to search objects and load/unload cargoes. For this reason, it is further demanded that the operations for physical distribution in facilities are automated for the warehouse for mail-order sales as compared with conventional warehouses that handle a large amount of one item. 
     Patent literature 1 discloses a system that is suitable for transferring objects in warehouses for mail-order sales that handle various types of objects, and for transferring parts in factories that produce high-variety and low-volume parts. In the system, movable storage shelves are disposed in a space of the warehouse, and a transfer robot is coupled to the shelf that stores requested objects or parts. Then, the transfer robot transfers the storage shelf together with the objects to a work area where the objects are packed, products are assembled, or so on. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP2009-539727A 
     SUMMARY OF THE INVENTION 
     Technical Problem 
     The transfer robot in Patent literature 1 enters into a space below an inventory holder (shelf) having a plurality of inventory trays that directly store respective inventory items, lifts the inventory holder, and transfers the inventory holder in this state. Patent literature 1 describes in detail the technique of correcting displacement of an actual destination from a theoretical destination of the inventory holder due to a positional mismatch between the moving transfer robot and the inventory. However, the literature fails to focus on efficient and individual management of various types of objects. Accordingly, it is required to provide another means of loading target objects into a correct movable shelf, and unloading target objects from a correct movable shelf. 
     The present invention is made in light of the above-mentioned circumstances, and its object is to provide a warehouse system capable of correctly managing the inventory state of individual objects. 
     Solution to Problem 
     A warehouse system of the present invention for solving the above-described problems includes: 
     a storage shelf configured to store an object; 
     an arm robot including a mono-articulated or multi-articulated robot arm, a robot body supporting the robot arm, and a robot hand that is attached to the robot arm and grasps the object, the arm robot being configured to take the object out of the storage shelf; 
     a transfer robot configured to transfer the storage shelf together with the object to an operation range of the arm robot; 
     a robot teaching database configured to store raw teaching data that are teaching data for the arm robot based on a storage shelf coordinates model value that is a three-dimensional coordinates model value of the storage shelf and a robot hand coordinates model value that is a three-dimensional coordinates model value of the robot hand; and 
     a robot data generation unit configured to correct the raw teaching data based on a detection result of a sensor detecting a relative position relationship between the storage shelf and the robot hand, and to generate robot teaching data to be supplied to the arm robot. 
     In addition, a warehouse system of the present invention for solving the above-described problems includes: 
     a plurality of storage shelves each assigned to any of a plurality of zones divided on a floor surface and each configured to store a plurality of objects; 
     an arm robot including a mono-articulated or multi-articulated robot arm, a robot body supporting the robot arm, and a robot hand that is attached to the robot arm and grasps the object, the arm robot being configured to take the object out of the storage shelf; 
     transfer robots each assigned to any of the zones, each transfer robot being configured to transfer the storage shelf together with the objects from the assigned zone to an operation range of the arm robot; and 
     a controller configured to perform simulation of loading the object for each of the zones when the object to be unloaded is designated, and to determine the zone subjected to unloading processing of the object based on a result of the simulation. 
     In addition, a warehouse system of the present invention for solving the above-described problems includes: 
     a plurality of transfer lines each configured to transfer a transfer target; and 
     an analysis processor configured to, when a sensor detecting a state of one of the transfer lines determines that the one transfer line is crowded, instruct an operator to transfer the transfer target to another one of the transfer lines. 
     In addition, a warehouse system of the present invention for solving the above-described problems includes: 
     a dining table-shaped receiving base having an upper plate; 
     a transfer robot configured to enter below the receiving base and push the upper plate upwards, thereby supporting and moving the receiving base; and 
     a controller configured to horizontally rotate the transfer robot supporting the receiving base, provided that an inspection target placed on the upper plate is present in an inspectable range. 
     In addition, a warehouse system of the present invention for solving the above-described problems includes: 
     a plurality of storage shelves arranged in respective predetermined arrangement places on a floor surface, the storage shelves each being configured to store a plurality of unloadable objects; 
     a transfer robot configured to, when any of the plurality of objects is designated to be unloaded, transfer the storage shelf storing the designated object to an unloading gate provided at a predetermined position; and 
     a controller configured to predict frequencies with which the plurality of storage shelves are transferred to the unloading gate based on past unloading records of the plurality of objects, and when the frequency of a second storage shelf is higher than the frequency of a first storage shelf among the plurality of storage shelves and an arrangement place of the second storage shelf is further from the unloading gate than an arrangement place of the first storage shelf is, to change the arrangement place of the first storage shelf or the second storage shelf such that the arrangement place of the second storage shelf is closer to the unloading gate than the arrangement place of the first storage shelf is. 
     In addition, a warehouse system of the present invention for solving the above-described problems includes: 
     a bucket configured to store an object; 
     a plurality of storage shelves arranged in respective predetermined arrangement places on a floor surface, the storage shelves each being configured to store the plurality of unloadable objects in a state of being stored in the bucket; 
     a transfer robot configured to, when any of the plurality of objects is designated to be unloaded, transfer the storage shelf storing the designated object to an unloading gate provided at a predetermined position; 
     a stacker crane provided at the unloading gate, the stacker crane being configured to take the bucket storing the designated object out of the storage shelf; and 
     an arm robot configured to take the designated object out of the bucket taken by the stacker crane. 
     In addition, a warehouse system of the present invention for solving the above-described problems includes: 
     a storage shelf configured to store an object to be unloaded; 
     a sort shelf configured to sort the object for each destination; 
     an arm robot configured to take the object out of the storage shelf and store the taken object in a designated place in the sort shelf; and 
     a transfer device configured to move the arm robot or the sort shelf so as to reduce a distance between the arm robot and the designated place. 
     In addition, a warehouse system of the present invention for solving the above-described problems includes: a controller configured to perform such a control as to reduce a speed of the transfer robot as the transfer robot comes closer to an obstacle based on a detection result of a sensor detecting the transfer robot and the obstacle to the transfer robot. 
     Advantageous Effect of Invention 
     According to the present invention, the inventory state of individual objects may be correctly managed. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic configuration view showing a warehouse system in accordance with an embodiment of the present invention; 
         FIG. 2  is a plan view showing a warehouse; 
         FIG. 3  is a view showing the form of an object to be stored in a storage shelf; 
         FIG. 4  is an example of a perspective view showing a transfer robot; 
         FIG. 5  is a block diagram showing a central controller; 
         FIG. 6  is a block diagram showing a configuration of off-line teaching and robot operation track correction; 
         FIG. 7  is a block diagram showing detailed configuration of a first robot data generation unit and a second robot data generation unit; 
         FIG. 8  is a view showing a control configuration of the off-line teaching and the robot operation track correction; 
         FIG. 9  is a schematic view showing absolute coordinates obtained by a coordinate calculation unit; 
         FIG. 10  is a block diagram showing a configuration in which off-line teaching for an arm robot is performed in a collection and inspection area; 
         FIG. 11  is a block diagram showing another configuration in which off-line teaching for the arm robot is performed in the collection and inspection area; 
         FIG. 12  is a flow chart of simulation performed in each zone by a central controller; 
         FIG. 13  is an explanatory view showing a transfer robot operation sequence; 
         FIG. 14  is an explanatory view showing operations of off-line teaching for the arm robot; 
         FIG. 15  is a block diagram showing another configuration of off-line teaching and robot operation track correction; 
         FIG. 16  is a block diagram showing a detailed configuration of a second robot data generation unit in  FIG. 15 ; 
         FIG. 17  is a flow chart of processing executed by the second robot data generation unit; 
         FIG. 18  is a block diagram showing an analysis processor in the present embodiment; 
         FIG. 19  is a schematic view showing operations of the analysis processor in the present embodiment; 
         FIG. 20  is a schematic view showing a method of inspecting objects loaded using the transfer robot in the warehouse system; 
         FIG. 21  is a block diagram showing an inspection system applied to an inspection operation; 
         FIG. 22  is a flow chart of inspection processing; 
         FIG. 23  is a plan view showing a zone; 
         FIG. 24  is a block diagram showing a storage shelf interchange system applied to interchange processing of storage shelves; 
         FIG. 25  is a flow chart of a shelf arrangement routine; 
         FIG. 26  is a schematic view showing a configuration in which a bucket is taken out of the storage shelf; 
         FIG. 27  is a schematic view showing another configuration in which the bucket is taken out of the storage shelf; 
         FIG. 28  is a flow chart of processing applied to the configuration shown in  FIG. 27  by a central controller; 
         FIG. 29  is a schematic view showing a configuration in which the target object is taken out of the storage shelf and stored in a sort shelf at an unloading gate; 
         FIG. 30  is a flow chart of processing applied to the configuration shown in  FIG. 29  by the central controller; 
         FIG. 31  is a schematic view showing a configuration in which the target object is taken out of the storage shelf and sorted to another storage shelf at the unloading gate; 
         FIG. 32  is a schematic view showing another configuration in which the target object is taken out of the storage shelf and stored in another storage shelf at the unloading gate; 
         FIG. 33  is a flow chart of processing applied to the configuration shown in  FIGS. 31 and 32  by the central controller; 
         FIG. 34  is an explanatory view showing operations in the case where the transfer robot detects an obstacle; 
         FIG. 35  is a schematic view in the case where a plurality of transfer robots move along different paths; and 
         FIG. 36  is a flow chart showing processing performed to avoid a collision of the operator with the obstacle by the central controller. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     [Overall Configuration of Warehouse System] 
     &lt;Schematic Configuration&gt; 
       FIG. 1  is a schematic configuration view showing a warehouse system in accordance with an embodiment of the present invention. 
     A warehouse system  300  includes a central controller  800  (controller) that controls the overall system, a warehouse  100  that stores objects as inventory, a buffer device  104  that temporarily stores objects to be sent, a collection and inspection area  106  that collects and inspects the objects to be sent, a packing area  107  that packs the inspected objects, and a casting machine  108  that conveys the packed objects to delivery trucks and the like. 
     The warehouse  100  is an area where a below-mentioned transfer robot (AGV, Automatic Guided Vehicle) operates, and includes a storage shelf that stores objects, a transfer robot (not shown), an arm robot  200 , and a sensor  206 . Here, the sensor  206  has a camera that retrieves images of the entire warehouse including the transfer robot and the arm robot  200  as data. 
     As shown in a right end in  FIG. 1 , the arm robot  200  includes a robot body  201 , a robot arm  208 , and a robot hand  202 . The robot arm  208  is a mono-articulated or multi-articulated robot arm, and the robot hand  202  is attached to one end of the robot arm. The robot hand  202  is multi-fingered and grasps various objects. The robot body  201  is installed at each part in the warehouse system  300 , and holds the other end of the robot arm  208 . 
     The operation of grasping and conveying various objects with the robot arm  208  and the robot hand  202  is referred to as “picking”. 
     Although details will be described later, in the present embodiment, the arm robot  200  executes learning through off-line teaching to achieve accurate and high-speed picking. 
     By switching an object processing line between daytime and nighttime, the process of transferring objects through the casting machine  108  may be made efficient. 
     For example, at daytime, objects unloaded from the warehouse  100  are temporarily stored in the buffer device  104  via a transfer line  120  such as a conveyor. Objects picked from other warehouses are also temporarily stored in the buffer device via a transfer line  130 . 
     The central controller  800  determines whether or not the objects in the buffer device  104  are to be sent based on a detection result of the sensor  206  provided in the downstream collection and inspection area  106 . When the determination result is “Yes”, the objects stored in the buffer device  104  are taken out of the buffer device  104  and transferred to a transfer line  124 . 
     In the collection and inspection area  106 , the sensor  206  detects and determines the type and state of the transferred objects. When it is determined that the objects need to be inspected by an operator  310 , the objects are transferred to a line where the operator  310  is present. On the contrary, when it is determined that the objects do not need to be inspected by the operator  310 , the objects are transferred to a line where only the arm robot  200  is present, and then, inspected. Since the lot of operators  310  are ensured at daytime, the sensor  206  determines hard-to-handle objects, and the objects are transferred to the line where the operator  310  is present at daytime, thereby efficiently inspecting the objects. 
     Easy-to-handle objects are inspected in the line where only the arm robot  200  is present, thereby reducing the number of the operators  310  to efficiently inspecting the objects as a whole. 
     Then, the objects are sent to the downstream packing area  107 . Also, in the packing area  107 , the sensor  206  determines the state of the transferred objects. According to the state, the objects are classified and transferred to a corresponding line, for example, a line for small-sized objects, a line for medium-sized objects, a line for large-sized objects, a line for extra large-sized objects, or a line for objects of various size and states. In each of the lines, the operator  310  packs the objects, and the packed objects are transferred to the casting machine  108  and waits for shipping. 
     Since a lot of operator  310  may be ensured at daytime, the sensor  206  may determine the hard-to-handle objects, and the objects may be transferred to the line where the operator  310  is present at daytime, thereby efficiently inspecting the objects. The easy-to-handle objects may be inspected in the line where only the arm robot  200  is present, thereby efficiently inspecting the objects as a whole. 
     Next, at nighttime, the objects unloaded from the warehouse  100  are transferred to an image inspection step  114  via a nighttime transfer line  122 . The sensor  206  is used to measure the productivity of the arm robot  200  or the operator  310  both at daytime and nighttime. In the image inspection step  114 , in place of the collection and inspection area  106 , the sensor  206  determines whether or not the target objects are correctly transferred from the warehouse  100  one by one. 
     Thereby, the operator  310  may take the target objects from a storage shelf  702  in the warehouse  100  (see  FIG. 2 ) substantially reliably using the transfer robot. This makes it possible to achieve omission and replacement of the operator&#39;s inspection operation with only inspection of the sensor  206 . Based on a measurement result of the sensor  206 , the central controller  800  determines whether or not the target objects can be picked by the arm robot  200 , that is, whether or not the packing operation of the operator  310  is required. 
     When it is determined that the packing operation of the operator  310  is required, the objects are transferred to the line where the operator  310  is present in the packing area via a transfer line  126 . On the contrary, when it is determined that the arm robot  200  can pack the objects, the objects are transferred to the line where the particular arm robot  200  is arranged according to the shape of the objects, such as small, medium, large, and extra-large. The objects packed by the operator  310  and the arm robot  200  are transferred to the casting machine  108 , and waits for final shipping. 
     As described above, in the warehouse system  300  in the present embodiment, at daytime when man power of the operator is ensured, the hard-to-handle objects of complicated shape are unloaded from the warehouse, and the operator, with the operator&#39;s decision, casts the objects from the collection and inspection area via the packing area. On the contrary, at nighttime when manpower of the operator is less ensured, the easy-to-handle objects of simple shape are mainly transferred to the packing area  107  without passing through the collection and inspection area  106 . Such configuration makes it possible for the warehouse system  300  to achieve efficient shipping of the objects on a 24-hour basis. 
     &lt;Summary of Warehouse&gt; 
       FIG. 2  is a plan view showing the warehouse  100 . 
     A floor surface  152  of the warehouse  100  is divided into a plurality of virtual grids  612 . A bar code  614  indicating the absolute position of the grid  612  is adhered to each grid  612 . However,  FIG. 2  shows only one bar code  614 . 
     In the warehouse system  300 , the entire floor surface  152  of the warehouse is divided into a plurality of zones  11 ,  12 ,  13  . . . . A transfer robot  602  and the storage shelf  702  that move in the zone are assigned to each zone. 
     The warehouse  100  is provided with a wire netting wall  380 . The wall  380  separates areas where the transfer robot  602  and the storage shelf  702  move (that is, the zones  11 ,  12 ,  13  . . . ) from a work area  154  where the operator  310  or the arm robot  200  (see  FIG. 1 ) operates. 
     The wall  380  is provided with a loading gate  320  and an unloading gate  330 . Here, the loading gate  320  is a gate for loading objects into the target storage shelf  702  and the like. The unloading gate  330  is a gate for unloading objects from the target storage shelf  702  and the like. A “shelf island” consisting of, for example, the storage shelves  702  are provided on the floor surface  152 , and in this example, two “shelf islands” each consisting of 2 columns×3 rows of storage shelves. However, any shape and any number of “shelf islands” may be used. The transfer robots  602  may take a target storage shelf from the “shelf island” and move the target storage shelf. 
     At loading of the objects, the transfer robot  602  moves the target storage shelf to the front of the loading gate  320 . When the operator  310  receives the target objects, the transfer robot  602  moves the storage shelf to a next target grid. Further, at unloading of the objects, the transfer robot  602  extracts a target storage shelf from, for example, the “shelf island”, and moves the target shelf to the front of the unloading gate  330 . The operator  310  takes the target objects out of the storage shelf. 
     As represented by a storage shelf  712  in  FIG. 2 , a square containing a cross line indicates the shelf, and a square containing a circle indicates the transfer robot  602 . As represented by the storage shelf  702  in front of the unloading gate  330 , the storage shelf in which with a circle and a cross line overlap indicates the storage shelf supported by the transfer robot. Although details will be described later, the transfer robot  602  enters below the storage shelf and the upper side of the transfer robot  602  pushes the bottom of the shelf upwards to support the storage shelf. The storage shelf  702  shown in  FIG. 2  is in this state. 
     The area of the floor surface  152  of the warehouse  100 , in which the transfer robot  602  and the storage shelf  702  are disposed, may have any dimension. 
     &lt;Form of Object&gt; 
       FIG. 3  is a view showing the form of the object to be stored in the storage shelf. 
     In the example shown in  FIG. 3 , one object  203  is stored in one object bag  510 . An ID tag  402  using RFID is attached to the object  203 . 
     Although one object is stored in one object bag in this example, a plurality of objects may be stored in one object bag, and an RFID may be attached to each object. An RFID reader  322  reads the ID tag  402  to read a unique ID of each object. In place of the ID tags using the RFID, bar codes and a bar code scanner may be used to manage objects. The RFID reader  322  may be a handy-type or a fixed-type. 
     &lt;Transfer Robot&gt; 
       FIG. 4  is an example of a perspective view showing the transfer robot  602 . 
     The transfer robot  602  is an unmanned automated travelling vehicle driven by the rotation of a wheel (not shown) on its bottom. A collision detection unit  637  of the transfer robot  602  detects a surrounding obstacle prior to collision with an optical signal (infrared laser or the like) sent being blocked by the obstacle. The transfer robot  602  includes a communication device (not shown). The communication device includes a wireless communication device for the communication with the central controller  800  (see  FIG. 1 ) and an infrared communication unit  639  for the infrared communication with surrounding facilities such as a charge station. 
     As described above, the transfer robot  602  enters below the storage shelf, and the upper side of the transfer robot  602  pushes the bottom of the shelf upwards to support the storage shelf. Thereby, instead of that the operator walks to the vicinity of the shelf, the transfer robot  602  that transfers the shelf gets close to the surroundings of the operator  310 , achieving efficient picking of the cargo on the shelf. 
     The transfer robot  602  includes a camera on its bottom (not shown), and the camera reads the bar code  614  (see  FIG. 2 ), such that the transfer robot  602  recognizes the grid  612  on the floor surface  152 , in which the transfer robot  602  lies. The transfer robot  602  informs the result to the central controller  800  via the wireless communication device (not shown). 
     The transfer robot  602  may include a LiDAR sensor that measures the distance to a surrounding obstacle by laser in place of the bar code  614  (see  FIG. 2 ). 
     &lt;Central Controller  800 &gt; 
       FIG. 5  is a block diagram showing the central controller  800 . 
     The central controller  800  includes a central processing unit  802 , a database  804 , an input/output unit  808 , and a communication unit  810 . The central processing unit  802  performs various operations. The database  804  stores data on the storage shelf  702 , an object  404 , and so on. The input/output unit  808  inputs/outputs information to/from external equipment. The communication unit  810  performs wireless communication according to a communication mode such as Wi-Fi via an antenna  812  to input/output information to/from the transfer robot  602  or the like. 
     [Arm Robot Operational Track Correction by Off-Line Teaching] 
     &lt;Summary of Off-Line Teaching&gt; 
     Operations of picking objects from the storage shelf  702  that moves together with the transfer robot  602  (see  FIG. 2 ) using the arm robot  200  in the warehouse  100  (see  FIG. 1 ) will be described in detail below. When the object is picked from the storage shelf using the arm robot  200 , in order to process all operations in real time, arithmetic processing takes relatively long time. 
     Thus, it is suggested to set control parameters off-line in the time period when arm robot  200  is not operating. However, in this case, control parameters need to be set in advance using a teaching pendant, robot-specific off-line teaching software, or the like, for each type of the arm robot  200 , each type of the storage shelf  702 , each type of a container containing the objects, and each shape of the object, which results in enormous volume of work. 
     Accordingly, when the off-line teaching is merely introduced, static errors such as an installation error of the robot body  201  may be corrected, but dynamic errors that vary at different times, for example, a positional error of the storage shelf moved by the transfer robot may not be easily corrected. 
     The present embodiment solves these problems and achieves high-speed picking of objects. 
     In the present embodiment, the arm robot  200  is caused to learn a picking operation pattern off-line for each type of transfer robot, each type of storage shelf, each type of container containing objects, and each shape of object. At actual picking, the robot arm  208  is driven based on data in off-line, while the sensor  206  detects the position of the transfer robot, the position of the storage shelf moved to a picking station, and the actual position of the arm robot, and the positions are corrected in real time to perform operation track correction of the robot arm. In this manner, the objects are picked correctly and rapidly. 
       FIG. 6  is a block diagram showing a configuration of off-line teaching and robot operation track correction in the present embodiment. 
     As described above, the arm robot  200  includes the robot arm  208  and the robot hand  202 , which are driven to move the object  203 . On the floor surface  152 , the transfer robot  602  moves the storage shelf  702 . Before transfer, the transfer robot  602  mounts the storage shelf  702  and the like thereon at a shelf position  214  on the floor surface  152 . The transfer robot  602  moves to a transferred shelf position  216  along a transfer path  217 . Here, the shelf position  216  is a position adjacent to the work area  154 , that is, a position adjacent to the loading gate  320  or the unloading gate  330  (see  FIG. 2 ). 
     The shelf position and the object stocker position in the shelf, which vary due to behavior of the arm robot  200  and the transfer robot  602 , are monitored by the sensor  206  of the image camera. 
     An off-line robot teaching data generation step and an off-line robot teaching data generation step will be described below. 
     In  FIG. 6 , first input data  220  are data on system configuration, equipment specifications, robot dimension diagram, device dimension diagram, and layout diagram. For off-line robot teaching, the first input data  220  is input to a first robot data generation unit  224 . Thereby, the first robot data generation unit  224  generates raw teaching data (not shown) based on the first input data  220 . 
     A second robot data generation unit  230  (robot data generation unit) is used for off-line robot teaching. The raw teaching data output from the first robot data generation unit  224  and second input data  222  are input to the second robot data generation unit  230 . Here, the second input data  222  include priorities, operation order, limitations, information on obstacle, inter-robot work sharing rules, and so on. 
     On the contrary, information from the sensor  206  that images the arm robot  200  is input to a shelf position and object stocker position error calculation unit  225 . Based on the input information, the shelf position and object stocker position error calculation unit  225  calculates a positional error of the moving shelf and a positional error of the object stocker (container that stores a plurality of objects). The calculated positional errors are input to a robot position correction value calculation unit  226 . 
     The robot position correction value calculation unit  226  outputs a static correction value  228  indicating an initially-effective static correction installation error. The robot position correction value calculation unit  226  outputs a dynamic correction value  227  indicating dynamic correction AGV repeat accuracy in-shelf clearance. 
     The static correction value  228  is input to the second robot data generation unit  230 , and the dynamic correction value  227  is input to an on-line robot position control unit  240 . Data from a robot teaching database  229  are also input to the second robot data generation unit  230  and the on-line robot position control unit  240 . 
     The second robot data generation unit  230  generates robot teaching data based on the raw teaching data, the second input data  222 , and the static correction value  228  from the first robot data generation unit  224 , and data from the robot teaching database  229 . The generated robot teaching data are input to the on-line robot position control unit  240 . A signal from the on-line robot position control unit  240  is input to a robot controller  252 . The robot controller  252  controls the arm robot  200  according to the signal from the on-line robot position control unit  240  and a command input from a teaching pendant  250 . 
     &lt;Detailed Configuration of Robot Teaching Data&gt; 
       FIG. 7  is a block diagram showing a detailed configuration of the above-mentioned first robot data generation unit  224  and the second robot data generation unit  230 . 
     The first input data  220  includes robot dimension data  220   a , device dimension data  220   b , and layout data  220   c . In  FIG. 7 , the terms “data” in the robot dimension data  220   a , the device dimension data  220   b , and the layout data  220   c  is omitted. Here, the robot dimension data  220   a  identify dimensions of parts of n arm robots  200 - 1  to  200 - n . The device dimension data  220   b  identify dimensions various devices included in the n arm robots  200 - 1  to  200 - n . The layout data  220   c  identify layout of the warehouse  100  (see  FIG. 2 ). 
     The first robot data generation unit  224  includes a data retrieval and storage unit  261 , a data reading unit  262 , a three-dimensional model generation unit  263 , and a data generation unit  264  (robot data generation unit). The above-mentioned robot dimension data  220   a , the device dimension data  220   b , and the layout data  220   c  are supplied to the data retrieval and storage unit  261  in the first robot data generation unit  224 . 
     A signal from the data retrieval and storage unit  261  is input to the data reading unit  262  as well as a database  266  that stores robot dimension diagram, device dimension diagram, and layout diagram. A signal from the data reading unit  262  is input to the three-dimensional model generation unit  263 . 
     A signal from the three-dimensional model generation unit  263  is input to the data generation unit  264 , and a signal from a correction value retrieval unit  241  is also input to the data generation unit  264 . Raw teaching data output from the data generation unit  264  are stored in the robot teaching database  229 . 
     The second robot data generation unit  230  includes a data reading unit  231 , a teaching function  232 , a data copy function  233 , a work sharing function  234 , a robot coordination function  235 , a data generation unit  236  (in  FIG. 7 , described as “three-dimensional position (X, Y, Z) . . . ”), a robot data reading/storage unit  237 , robot controller links  238  corresponding to the n arm robots  200 - 1  to  200 - n . Parameter priority and limitation data  222   a  is a part of the second input data  222  (see  FIG. 6 ), and specifies various parameters, priorities, limitations, and so on. The parameter priority and limitation data  222   a  is input to the data reading unit  231 . 
     The data generation unit  236  calculates coordinates of three-dimensional position X, Y, Z for each of the n arm robots  200 - 1  to  200 - n , and generates robot teaching data θ 1  to θn that are raw teaching data. The data generation unit  236  calculates correction values Δθ 1  to Δθn of the robot teaching data, and calculates robot teaching data θ 1 ′ to θn′ supplied to the respective arm robots  200 - 1  to  200 - n  based on the robot teaching data θ 1  to θn that are raw teaching data and the correction values Δθ 1  to Δθn. 
     The robot data reading/storage unit  237  inputs/outputs data such as axial position data, operation modes, and tool control data about the n arm robots  200 - 1  to  200 - n  to/from the robot teaching database  229 . 
     The n arm robots  200 - 1  to  200 - n  each include a robot controller  252 , a robot mechanism  253 , and an actuator  254  for the robot hand  202  (see  FIG. 6 ). However,  FIG. 7  shows an internal configuration of only the arm robot  200 - 1 . The n robot controllers  252  are linked to the robot controller links  238  in the second robot data generation unit  230 , and exchanges various signals therebetween. In each of the arm robots  200 - 1  to  200 - n , the robot controllers  252  controls the respective robot mechanisms  253  and actuators  254 . 
     When an object is picked from the storage shelf in real time, the sensor  206  detects a relative position between the object  203  or a stocker  212  and the actuator  254 . The detected relative position is output as the above-mentioned static correction value  228 , and is also output to the robot position correction value calculation unit  226 . 
     &lt;Operational Configuration of Coordinate System Data&gt; 
       FIG. 8  is a view showing a control configuration of off-line teaching and robot operation track correction. 
     In the present embodiment, picking are related to five elements: the transfer robot  602 , the storage shelf  702 , the sensor  206 , the robot body  201 , and the robot hand  202 . Thus,  FIG. 8  shows these five elements. In  FIG. 8 , a coordinate system calculation unit  290  includes a modeling virtual environment unit  280 , a data retrieval unit  282 , coordinate calculation unit  284 , a position command unit  286 , and a control unit  288 . The coordinate system calculation unit  290  handles coordinates of the above-mentioned five elements in an absolute coordinate system. 
     The coordinates of the transfer robot  602  among the above-mentioned five elements are measured by a position sensor  207 . Here, a LiDAR sensor that measures the distance to a surrounding object (including the transfer robot  602 ) may be used as the position sensor  207 . The operation status and position of the transfer robot  602  are controlled by an AVG controller  276 . Position data on the robot body  201  of the arm robot  200  are retrieved in advance. The coordinates of the robot hand  202  during the operation of the arm robot  200  are measured by a sensor such as an encoder. When the coordinates of the robot hand  202  are measured, the information is supplied to the coordinate system calculation unit  290  in real time, and the position of the robot hand  202  is controlled via a robot controller  274 . 
     The camera included in the sensor  206  is controlled by a camera controller  272 . The position data on the stopped sensor  206  are retrieved into the coordinate system calculation unit  290  in advance. When the sensor  206  is scanning surroundings, the coordinates of the sensor  206  are supplied from the camera controller  272  to the coordinate system calculation unit  290  in real time. Shelf information  278  is supplied to the coordinate system calculation unit  290 . The shelf information  278  specifies the shape and dimensions of the storage shelf  702 . 
     The camera included in the sensor  206  takes an image of the storage shelf  702 . The modeling virtual environment unit  280  of the coordinate system calculation unit  290  models the storage shelf  702  based on the shelf information  278  and the image of the storage shelf  702 . The coordinate calculation unit  284  calculates the coordinates of the above-mentioned five elements based on data such as a modeling result of the modeling virtual environment unit  280 . The control unit  288  calculates a position command to each of the transfer robot  602 , the robot body  201 , the robot hand  202 , the sensor  206 , and the storage shelf  702  based on calculation results of the coordinate calculation unit  284 . 
       FIG. 9  is a schematic view showing absolute coordinates obtained by the coordinate calculation unit  284  (see  FIG. 8 ). In  FIG. 9 , transfer robot coordinates Q 602 , storage shelf coordinates Q 702 , sensor coordinates Q 206 , robot body coordinates Q 201 , and robot hand coordinates Q 202  indicate absolute coordinates of the transfer robot  602 , the storage shelf  702 , the sensor  206 , the robot body  201 , and the robot hand  202 , respectively. 
     Among them, the absolute coordinates of the storage shelf coordinates Q 702 , robot body coordinates Q 201 , and the robot hand coordinates Q 202  may be calculated by the above-mentioned off-line teaching, in consideration of various situations (for example, type of the storage shelf  702 , type of the robot body, and type of the robot hand). 
     Each of the coordinates Q 201 , Q 202 , Q 206 , Q 602 , and Q 702  obtained by off-line teaching is referred to as coordinates “model value”. At operation of the transfer robot  602  and the arm robot  200 , position data are retrieved from the transfer robot  602 , the robot body  201 , the robot hand  202 , and the sensor  206 , and differences between the data and the model value are calculated. Based on the calculated differences, the raw teaching data (robot teaching data e 1  to en) are corrected in real time to obtain teaching data. 
     With such configuration, off-line teaching for various objects may be performed to increase the working efficiency (robot teaching and so on) and improve the working quality due to higher positional accuracy. 
     &lt;Operational Configuration of Collection and Inspection Area&gt; 
       FIG. 10  is block diagram showing the configuration in which off-line teaching for the arm robot  200  is performed in the collection and inspection area  106  (see  FIG. 1 ). The constituents having the same configuration and effect in  FIG. 10  as those in  FIGS. 1 to 9  are given the same reference numerals, and description thereof may be omitted. 
     In  FIG. 10 , an addition calculation unit  291  includes a complementation functional unit  292 , a coordination functional unit  294 , a group control unit  296 , and a copy function unit  298 . 
     The addition calculation unit  291  inputs/outputs data to/from the coordinate system calculation unit  290 . Layout installation error data  268  of individual robot are also input to the coordinate system calculation unit  290 . In this manner, teaching data for the arm robot  200  in the collection and inspection area  106  may be created offline. 
     With such configuration, off-line teaching for more variety of objects may be performed to increase the working efficiency (robot teaching and so on) and improve the working quality due to higher positional accuracy. 
     The configuration shown in  FIG. 10  may be applied to the arm robot  200  in the packing area  107 . 
       FIG. 11  is a block diagram showing another configuration in which off-line teaching for the arm robot  200  is performed in the collection and inspection area  106  (see  FIG. 1 ). 
     In the configuration shown in  FIG. 11 , in addition to the configuration shown in  FIG. 10 , a deep learning processing unit  269  is provided. The deep learning processing unit  269  exchanges data with the coordinate system calculation unit  290  and the addition calculation unit  291  to execute artificial intelligence processing by deep learning. 
     With such configuration, off-line teaching for more variety of objects may be performed to increase the working efficiency (robot teaching and so on) and improve the working quality due to higher positional accuracy. 
     Like the configuration shown in  FIG. 10 , the configuration shown in  FIG. 11  may be also applied to the arm robot  200  in the packing area  107 . 
     As described above, the configuration shown in  FIGS. 6 to 11  includes: the robot teaching database ( 229 ) that stores raw teaching data (robot teaching data θ 1  to θn) that is teaching data for the arm robot ( 200 ) based on the storage shelf coordinates model value (Q 702 ) that is the three-dimensional coordinates model value of the storage shelf ( 702 ) and the robot hand coordinates model value (Q 202 ) that is the three-dimensional coordinates model value of the robot hand ( 202 ); the sensor ( 206 ) that detects the relative positional relationship between the storage shelf ( 702 ) and the robot hand ( 202 ); and the robot data generation unit ( 264 ,  230 ) that corrects the raw teaching data based on a detection result of the sensor ( 206 ) to generate the robot teaching data ( 81 ′ to θn′) to be supplied to the arm robot ( 200 ). 
     With such configuration, the raw teaching data (robot teaching data θ 1  to θn) is the teaching data for the arm robot ( 200 ) based on the sensor coordinates model value (Q 206 ) that is the three-dimensional coordinates model value of the sensor ( 206 ), the transfer robot coordinates model value (Q 602 ) that is the three-dimensional coordinates model value of the transfer robot ( 602 ), and the robot body coordinates model value (Q 201 ) that is the three-dimensional coordinates model value of the robot body ( 201 ), in addition to the storage shelf coordinates model value (Q 702 ) and the robot hand coordinates model value (Q 202 ). 
     Thereby, off-line teaching for various objects may be performed to increase the working efficiency and improve the working quality due to higher positional accuracy. This may correctly manage the inventory state of the individual objects. 
     [Transfer in Zone/Autonomous Control of Arm Robot] 
     &lt;Summary of Autonomous Control&gt; 
     When operation control of the transfer robot is performed by simulation in the zone  12  or the like shown in  FIG. 2 , operation control of the arm robot  200  (see  FIG. 1 ) may be preferably performed. 
     Thus, in the present embodiment, simulation of the arm robot  200  in the zone is performed to reduce the picking time, thereby increasing shipments per unit time. 
     The number of times of picking and shipments per unit time may be increased by performing more minute control, that is, autonomous control in unit of zone in consideration of in-zone equipment characteristics (for example, singularities of the arm robot  200  and the operation sequence giving a high priority to workability). 
     Specifically, the warehouse system  300  may perform simulation of the transfer robot  602  and the arm robot  200  to execute the efficient operation sequence, thereby efficiently controlling the transfer robot and the arm robot in each zone. 
       FIG. 12  is a flow chart of simulation performed in each zone by the central controller  800  (see  FIG. 1 ). In the present embodiment, simulation is performed in the zone before bringing an actual picking system into operation. The simulation includes (1) establishment of the autonomous operation sequence for the transfer robot (Steps S 105  to S 107 ) and (2) in-shelf simulation of the arm robot (Steps S 108  to S 110 ). 
     When the processing starts in Step S 101  in  FIG. 12 , the processing proceeds to Step S 102 , and the central controller  800  simulates the plan of the whole warehouse system. Next, when the processing proceeds to Step S 103 , the central controller  800  receives data on the inventory volume in the shelf as parameters. Next, when the processing proceeds to Step S 104 , the central controller  800  starts in-zone simulation. Hereinafter, the processing in Steps S 105  to S 107  and the processing in Steps S 108  to S 110  are executed in parallel. 
     First, when the processing proceeds to Step S 105 , the central controller  800  determines the operation sequence for the transfer robot. That is, the operation sequence in the related zone is determined. Next, when the processing proceeds to Step S 106 , the central controller  800  performs coordinate calculation and coordinate control for the transfer robot. Next, when the processing proceeds to Step S 107 , the central controller  800  performs operation control for the transfer robot. 
     When the processing proceeds to Step S 108 , the central controller  800  performs in-shelf simulation of the arm robot. In other words, the operation sequence is determined. At this time, the central controller  800  uses the off-line teaching technique to perform in-shelf simulation. Next, when the processing proceeds to Step S 109 , the central controller  800  performs coordinate calculation and coordinate control for the arm robot. Next, when the processing proceeds to Step S 110 , the central controller  800  performs operation control for the arm robot. 
     Particular two-dimensional coordinates  111  are previously set to two-dimensional coordinates in the zone. As shelf information  113  on a certain object, a zone to which the storage shelf belongs, an address in the zone to which the storage shelf belongs, and a position of the object in the storage shelf are set. 
       FIG. 13  is an explanatory view of a transfer robot operation sequence as a result of autonomous control simulation in unit of zone. 
     It is assumed that the warehouse system  300  (see  FIG. 1 ) receives an order list data  458  as an order  452  of the object (object). In the state where shipment list data  460  is decided as shipment  454  shipped from the warehouse system, precondition and limitation data  468  of in-zone plans of the zones  11 ,  12 , and  13  are settled and considered. 
     As a result, in the present embodiment, autonomous control simulation of the transfer robot demonstrates that, when the storage shelf is moved and taken out of each zone by the transfer robot, the target object may be efficiently picked from the zone  11  surrounded with a dotted line if possible, in consideration of the moving distance and the number of times of movement of the transfer robot as objective functions. 
       FIG. 14  is an explanatory view showing operations of off-line teaching for the arm robot  200 . 
     For off-line teaching for the arm robot  200 , a control computer  474  that installs software dedicated to off-line teaching therein is provided. A database  476  stored in the control computer  474  contains (1) point, (2) path, (3) operation mode (interpolation type), (4) operation rate, (5) hand position, (6) operation conditions as teaching data. 
     The arm robot  200  is caused to perform learning using a dedicated controller  470  and a teaching pendant  472 . As an example of learning, for example, the arm robot learns off-line so as to increase the working efficiency by setting the moving distance and the number of times of movement of the robot arm  208  and the robot hand  202  as objective functions. In other words, in taking the object out of the storage shelf  702 , the robot arm  208  learns off-line the operation sequence of efficiently moving the robot hand  202  from any opening. 
       FIG. 15  is a block diagram showing another configuration of off-line teaching and robot operation track correction in the present embodiment. In  FIG. 15 , unless otherwise specified, the constituents having the same reference numerals as in the example of  FIG. 6  have similar configurations and effects. 
     As compared with the configuration in  FIG. 6 , the configuration in  FIG. 15  includes AGV controller  276 , and a second robot data generation unit  230 A (robot data generation unit) in place of the second robot data generation unit  230 . Third input data  223  are supplied to the second robot data generation unit  230 A. 
     Here, the third input data  223  contains (1) zone information, (2) shelf information, and (3) operation sequence determination conditions. The AGV controller  276  decides (1) the autonomous operation sequence of the transfer robot  602  and (2) the operation sequence obtained by in-shelf simulation of the arm robot  200  to control operations of the transfer robot  602  in real time. 
       FIG. 16  is a block diagram showing a detailed configuration of the second robot data generation unit  230 A in  FIG. 15 . 
     In  FIG. 16 , unless otherwise specified, the constituents having the same reference numerals as in  FIG. 7  have similar configurations and effects. 
     As described above, the second input data  222  and the third input data  223  are input to the second robot data generation unit  230 A. Operation record data  354  are also input to the second robot data generation unit  230 A. Here, the operation record data  354  are data indicating loading/unloading records of various objects. 
     The second input data  222 , the third input data  223 , and the operation record data  354  are read by the second robot data generation unit  230 A via the data reading unit  231 ,  356 ,  358 , respectively. The second robot data generation unit  230 A includes an overall system simulation unit  360  and an in-zone simulation and in-shelf simulation unit  362 . The overall system simulation unit  360  and the in-zone simulation and in-shelf simulation unit  362  input/output data to/from a simulation database  366  and finally, the operation sequence determination unit  364  determines the overall control sequence including the transfer robot  602  and the arm robot  200 . 
     With such configuration, (1) the autonomous operation sequence of the transfer robot  602  and (2) the operation sequence obtained by in-shelf simulation of the arm robot  200  are determined to achieve high-speed and high-accuracy control. 
       FIG. 17  is a flow chart of processing executed by the second robot data generation unit  230 A. 
     In  FIG. 17 , when the processing proceeds to Step S 201 , the second robot data generation unit  230 A creates a model of the warehouse system  300 . Next, when the processing proceeds to Step S 203 , the second robot data generation unit  230 A performs simulation of the overall warehouse system  300  based on the model created in Step S 201  and the second input data  222  (priorities, operation order, limitations, information on obstacle, inter-robot work sharing rules, and so on). 
     Next, when the processing proceeds to Step S 205 , the second robot data generation unit  230 A performs in-zone simulation based on a result of the simulation in Step S 203  and the third input data  223  (zone information, shelf information, operation sequence determination conditions, and so on). Next, when the processing proceeds to Step S 206 , the second robot data generation unit  230 A performs in-shelf simulation. 
     Next, when the processing proceeds to Step S 208 , the second robot data generation unit  230 A determines an operation sequence based on the in-shelf simulation result in Step S 206  and the operation record data  354  (loading/unloading records of various objects). Next, when the processing proceeds to Step S 208 , the second robot data generation unit  230 A performs coordinate calculation and various type of control based on the processing results in Steps S 201  to S 208 . 
     Thereby, the second robot data generation unit  230 A performs simulation of the transfer robot  602  and the arm robot  200  in the warehouse system  300  to achieve the efficient operation sequence. This can efficiently control the transfer robot  602  and the arm robot  200  in each zone. 
     As described above, the configuration shown in  FIGS. 12  to  17  includes: transfer robots ( 602 ) that each are assigned to any zone ( 11 ,  12 ,  13 ) and transfers the storage shelf ( 702 ) together with the object ( 203 ) from the assigned zone ( 11 ,  12 ,  13 ) to the operation range of the arm robot ( 200 ); and the controller ( 800 ) that performs simulation of loading the object ( 203 ) for each of the zones ( 11 ,  12 ,  13 ) (S 104 ) when the object to be unloaded is designated, and determines the zone ( 11 ,  12 ,  13 ) subjected to the unloading processing of the object ( 203 ) based on the result of the simulation. 
     With such configuration, the controller ( 800 ) determines the zone in which the moving distance or the number of times of movement of the transfer robot ( 602 ) is smallest among the plurality of zones ( 11 ,  12 ,  13 ) as the zone ( 11 ,  12 ,  13 ) subjected to the unloading processing of the object ( 203 ), based on the result of the simulation. 
     Thereby, in each zone ( 11 ,  12 ,  13 ), the transfer robots ( 602 ) and the arm robot ( 200 ) may be efficiently controlled. 
     [Box Pile-Up Sign Detection] 
     Next, a technique of predicting box pile-up in the line in the collection and inspection area  106  or the packing area  107  of the warehouse system  300  (see  FIG. 1 ) will be described. 
     In the warehouse system  300  in the present embodiment, the sensors  206  are strategically installed in the conveyor line, and measure the pile-up status of the flowing containers. When detecting a congestion sign of the conveyor, the central controller  800  informs the sign to the information terminal (smart phone, smart watch, and so on) of the operator  310  in real time before actual pile-up to promote some action. Details will be described below. 
       FIG. 18  is a block diagram showing an analysis processor  410  in the present embodiment. The analysis processor  410  may be separated from the central controller  800 , or may be integrated with the central controller  800 . 
     The analysis processor  410  includes a feature amount extraction unit  412 , a feature amount storage unit  414 , a difference comparison unit  416 , a threshold setting unit  418 , an abnormality determination processing unit  420 , an abnormality activation processing unit  422 , an analysis unit  428 , a feedback unit  430 , and an abnormality occurrence prediction unit  432 . 
     Image data from the sensor  206  are sent to the feature amount extraction unit  412  of the analysis processor  410 . The image data are sent to the feature amount storage unit  414  and then, are compared with a below-mentioned reference image by the difference comparison unit  416 . Then, data are sent to the threshold setting unit  418 , and the abnormality determination processing unit  420  determines a deviation from a threshold. The determination result of the abnormality determination processing unit  420  is supplied to the abnormality activation processing unit  422 , and an abnormality occurrence display device  424  displays the supplied information. 
     To set a threshold and the like, other information  426  is supplied from the outside to the analysis unit  428 . The other information  426  is information on, for example, day&#39;s order volume, day&#39;s handled object category, the number of operators, camera position, conveyor position. Data from the analysis unit  428  are supplied to the feedback unit  430 . The threshold setting unit  418  sets a threshold based on the information supplied to the feedback unit  430 . 
     The data from the feature amount storage unit  414  are also supplied to the analysis unit  428 . A determination result of the abnormality determination processing unit  420  is also input to the analysis unit  428 . Analysis data from the analysis unit  428  are sent to the abnormality occurrence prediction unit  432  as well as an external other plan system and controller  436 . As a result, when an abnormality occurs, the abnormality occurrence may be informed to the abnormality occurrence display device  424 . Here, the abnormality occurrence display device  424  to which the abnormality occurrence is informed may be, for example, an alarm light (not shown) in the warehouse system, the smart phone, smart watch, or the like of the operator  310 , or so on. 
     When the abnormality occurrence is predicted, the abnormality occurrence prediction unit  432  supplies data indicating the predication to a prediction information display device  434 . Thereby, the prediction information display device  434  may display, for example, the prediction status “pile-up will occur within X minutes”. Here, like the abnormality occurrence display device  424 , the prediction information display device  434  that displays the prediction status may be the smart phone, smart watch, or the like of the operator  310 . 
       FIG. 19  is a schematic view showing operations of the analysis processor  410  in the present embodiment. 
     In the shown example shown in  FIG. 19 , a box-shaped container  560  (transfer target) is used as an example of transfer target. To detect and predict the pile-up of the containers  560 , for example, an image of the transfer line  124  on which nothing is placed (no operation) is captured by the sensor  206 . This image is referred to as a reference image  562 . The feature amount of the reference image  562  is stored in the difference comparison unit  416  (see  FIG. 18 ). An image of the transfer line  124  acquired during the operation of the warehouse system  300  is captured by the sensor  206 . This image is referred to as an acquired image  564 . The feature amount extraction unit  412  extracts the feature amount of the acquired image  564 , and the extracted feature amount is supplied to the feature amount storage unit  414  and then, supplied to the analysis unit  428 . 
     Next, after an elapse of n seconds, an image of the transfer line  124  is captured by the sensor  206 . The image data at this time is also sent to the analysis unit  428 , to find threshold values th 1 , th 2  (not shown) for determining the abnormality occurrence. Here, the threshold value th 1  is a threshold for determining the presence/absence of the possibility that the transfer line  124  begins to be crowded, and the threshold value th 2  is a threshold for determining whether or not an abnormality has occurred. Accordingly, a relation of “th 1 &lt;th 2 ” holds. 
     Here, it is assumed that the threshold value th 1  is “1” and the threshold value th 2  is “3”. For example, since the number of container images is equal to or larger than the threshold value th 1  in an acquired image  566  having the number of container images of “0”, the analysis processor  410  determines that “no abnormality occurs”. Although the number of container images is “1” in the above-mentioned acquired image  564 , also in this case, the number of container images is equal to or smaller than the threshold value th 1  and thus, the analysis processor  410  determines that “no abnormality occurs” 
     When the number of the number of container images exceeds the threshold value th 1  and is less than threshold value th 2 , the analysis processor  410  determines that “it is likely to begin to be crowded”. For example, since the number of container images exceeds the threshold value th 1  (=1) and is equal to or smaller than the threshold value th 2  (=3) in an acquired image  568  having the number of container images of “2”, the analysis processor  410  determines that “it is likely to begin to be crowded”. 
     In this case, as described above, the analysis processor  410  informs that “it is likely to begin to be crowded” to the smart phone, smart watch, or the like of the operator  310 . 
     When the number of container images exceeds the threshold value th 2  (=3) as in an acquired image  570  shown in  FIG. 19 , the analysis processor  410  determines that “abnormality has occurred (containers  560  pile up)”. 
     In this case, as described above, the analysis processor  410  flashes an alarm light (not shown) in the warehouse system  300  and further, informs pile-up abnormality occurrence to the smart phone, smart watch, or the like of the operator  310 . In this case, the transfer line  124  may be forcibly stopped. 
     Then, to avoid pile-up, for example, in the collection and inspection area  106 , the operator  310  may reduce the number of containers  560  flowing in the line of the robot body  201  so as to pass a lot of containers  560  to the line where the operator  310  is present. 
     To avoid pile-up, the processing of passing the container  560  to another transfer line may be instructed by the central controller  800  without waiting for an instruction from the operator  310  or the like. 
     As described above, the configuration shown in  FIGS. 18 and 19  includes: the plurality of transfer lines ( 120 ,  122 ,  124 ,  126 ,  130 ) that each transfer the transfer target ( 560 ); the sensor ( 206 ) that detects the state of one transfer line; and the analysis processor ( 410 ) that instructs the operator to transfer the transfer target ( 560 ) to another transfer line when the sensor ( 206 ) determines that the one transfer line is crowded. 
     In this configuration, when the number of transfer targets ( 560 ) exceeds the first threshold (th 1 ), the analysis processor ( 410 ) informs the operator of that effect, and when the number of transfer targets ( 560 ) exceeds the second threshold (th 2 ) that is larger than the first threshold (th 1 ), the analysis processor ( 410 ) stops the related transfer line ( 124 ). 
     Thereby, the operator may reliably detect pile-up of the transfer targets ( 560 ), rapidly performing a proper action such as a line change. 
     [Inspection Using Image] 
       FIG. 20  is a schematic view showing a method of inspecting the loaded objects using the transfer robot  602  in the warehouse system  300 . As shown in  FIG. 2 , the storage shelf  702  and so on are arranged in each of the zones  11 ,  12 , and  13  in the warehouse  100 . However, to store boxes that pack objects (for example, corrugated cardboard box) as they are, the space efficiency of the warehouse  100  may be increased by stacking these boxes rather than storing the boxed in the shelf. Thus, in the present embodiment, in place of some or all storage shelves  702 , the dining table-shaped receiving base  852  as shown in  FIG. 20  may be used. The receiving base  852  may be a palette. 
     Since an upper plate  852   a  of the receiving base  852  is a rectangular flat plate, a receiving object  854  (inspection target) such as a corrugated cardboard box may be placed on the upper plate. As in the case of the storage shelf  702 , the transfer robot  602  enters below the receiving base  852  and pushes the upper plate  852   a  of the receiving base  852 , thereby supporting and moving the receiving base  852 . 
       FIG. 21  is a block diagram showing an inspection system  270  applied to an inspection operation in the warehouse system  300 . 
     In  FIG. 21 , the inspection system  270  includes an AGV controller  276 , the transfer robot  602 , a controller  860 , an illuminator  858 , a sensor  206 , and a laser device  856 . The controller  860  may be separated from the central controller  800 , or may be integrated with the central controller  800 . In response of a command from the AGV controller  276 , the transfer robot  602  moves or rotates the receiving base  852  on which the receiving object  854  (see  FIG. 20 ) is placed. 
     The command from the AGV controller  276  is also supplied to the controller  860  and in response to the command, the sensor  206  such as a camera operates to take an image of the receiving object  854 . The controller  860  irradiates the receiving object  854  with strobe light using the illuminator  858 , and irradiates the receiving object  854  with red lattice light (red lattice laser light) using the laser device  856 . When the receiving object  854  is, for example, a cubic object such as a corrugated cardboard box, a red lattice image is projected onto the receiving object  854  using red lattice light. 
     Here, in a case where an abnormality such as“crushing” has occurred in the receiving object  854 , since such an abnormality generates a strain in a lattice-shaped image, the abnormality of the receiving object  854  may be detected by taking the image with the sensor  206 . When the illuminator  858  emits strobe light to generate a shadow on the receiving object  854 , the abnormality of the receiving object  854  may be detected by the shape of the shadow as well. The inspection system  270  may automatically inspect the receiving object  854  in the middle of the transfer line where the transfer robot  602  transfers the receiving object  854 . Accordingly, since it is no need to fix the inspection site at a particular place, the portability of the inspection site in the warehouse system  300  may be increased. In the example shown in  FIG. 21 , the inspection system  270  includes both the laser device  856  and the illuminator  858 , but may include one of them. 
     When the sensor  206  is a camera, the sensor  206  may take an image of the receiving object  854 , and reads product name, product code, the number of objects, expiration data, and lot No that are described on the receiving object  854 , a bar code or two-dimensional code associated with related information, and product label and loading label that describe such information. Based on the read information, the controller  860  may perform the inspection operation of the inspection system  270 . The sensor  206  is not limited to the camera, and may be an RFID reader, and read information on an RFID tag attached to the receiving object  854 , thereby inspecting objects to be shipped. 
       FIG. 22  is a flow chart of inspection processing executed by the controller  860 . 
     When the processing starts in Step S 300  in  FIG. 22 , the processing proceeds to Step S 301 , and the receiving object  854  is mounted on the receiving base  852 . That is, the receiving object  854  transferred from the outside by a truck or the like is placed on a conveyor  304  and then, is sent to the upper side of the receiving base  852 . Generally, the plurality of receiving objects  854  are mounted on the receiving base  852 . 
     Next, when the processing proceeds to Step S 302 , under control of the controller  860 , the transfer robot  602  moves the receiving base  852  to the front of the sensor  206 . That is, the transfer robot  602  enters below the receiving base  852 , and lifts the receiving object  854  including the receiving base  852 . With placed on the receiving base  852 , the receiving object  854  is transferred to a place where it may be photographed using the camera of the sensor  206 . 
     Next, when the processing proceeds to Step S 303 , in response to a command from the controller  860 , the transfer robot  602  rotates in front of the sensor  206  by 360 degrees. The sensor  206  captures an image of the receiving object  854  at this time, and transmits the captured image to the controller  860 . 
     Next, when the processing proceeds to Step S 304 , based on the captured image, the controller  860  determines whether or not an abnormality (scratch, discoloring, deformation, and so on) occurs in the receiving object  854 . 
     When the determination result in Step S 304  is “No”, the processing proceeds to Step S 305 . Here, under control of the controller  860 , the transfer robot  602  moves together with receiving base  852  to the loading gate  320  (see  FIG. 2 ). On the contrary, when the determination result in Step S 304  is “Yes”, the processing proceeds to Step S 306 . Here, the controller  860  turns on an alarm light (not shown) in the warehouse system  300 . The controller  860  informs the abnormality occurrence to the information terminal (smart phone, smart watch, or the like) of the operator  310 , and moves the receiving base  852  and the receiving object  854  to a place other than the loading gate  320 . 
     As described above, the configuration shown in  FIGS. 20 to 22  includes: the dining table-shaped receiving base ( 852 ) having the upper plate ( 852   a ), the sensor ( 206 ) that detects the state of the inspection target ( 854 ) placed on the upper plate ( 852   a ); the transfer robot ( 602 ) that enters below the receiving base ( 852 ) and pushes the upper plate ( 852   a ) upwards to support and move the receiving base ( 852 ); and a controller ( 860 ) that horizontally rotates the transfer robot ( 602 ) supporting the receiving base ( 852 ), provided that the inspection target ( 854 ) is located to be inspected by the sensor ( 206 ). 
     The configuration further includes an irradiation device ( 858 ,  856 ) that irradiates the inspection target ( 854 ) with light, and the controller ( 860 ) determines the state of the inspection target ( 854 ) based on a result of irradiation of the inspection target ( 854 ) with light. 
     Thereby, the presence/absence of abnormality of the inspection target ( 854 ) may be detected with high accuracy. 
     [Efficient Shelf Arrangement] 
       FIG. 23  is a plan view of the zone  12  and an explanatory view showing of efficient arrangement of the storage shelves. 
     In  FIG. 23 , an island  750  is formed in the zone  12 , and contains a storage shelf  720 . The other configuration of the zone  12  is similar to the configuration shown in  FIG. 2 . However, an island having six storage shelves including storage shelves  732 ,  742  is referred to as “an island  751 ”, and an island having six storage shelves including storage shelves  712 ,  714  is referred to as “an island  752 ”. 
       FIG. 24  is a block diagram showing a storage shelf interchange system  370  applied to interchange processing of the storage shelves in the warehouse system  300 . 
     In  FIG. 24 , the storage shelf interchange system  370  includes a controller  820 , the AGV controller  276 , the transfer robot  602 , and an object and shelf database  367 . The controller  820  may be separated from the central controller  800 , or may be integrated with the central controller  800 . 
     The object and shelf database  367  stores object unloading probability data on the unloading probability of the various objects  203 , and storage shelf unloading probability data on the unloading probability of each storage shelf. 
     Referring to the object and shelf database  367 , the controller  820  determines a pair of interchanged storage shelves. In the example shown in  FIG. 22 , the determined storage shelves are a storage shelf  716  (first storage shelf) and a storage shelf  720  (second storage shelf). The controller  820  specifies the pair of determined storage shelves to the AGV controller  276 , and causes the AGV controller to interchange the storage shelves. 
       FIG. 25  is a flow chart of shelf arrangement routine performed by the controller  820 . 
     When the processing starts in Step S 400  in  FIG. 25 , the processing proceeds to Step S 401 . In Step S 401 , the controller  820  stores statistical data of the unloading status of the objects  203  (see  FIG. 3 ) in a particular zone ( FIG. 23  in the example shown in zone  12 ) in the warehouse  100  for a predetermined sample period. 
     Next, when the processing proceeds to Step S 402 , the controller  820  executes statistical processing on the statistical data, and selects the object  203  having a high unloading frequency based on the processing result. Next, when the processing proceeds to Step S 403 , the controller  820  selects the storage shelf having a high unloading frequency (hereinafter referred to as the high-frequency storage shelf) that stores the selected object  203 . In the example shown in  FIG. 23 , the storage shelf  720  is the high-frequency storage shelf. 
     In the processing in Step S 403 , it is preferable to select the object  203  having a high unloading probability predicted for a future period, in addition to a high unloading frequency for a past certain sample period. Specifically, the unloading frequency predicted in future may be obtained in consideration of future season, weather, temperature, time, and trend, and the object  203  having a high unloading probability may be selected based on the prediction and further, the high-frequency storage shelf that stores the selected object  203  may be selected. 
     Next, when the processing proceeds to Step S 404 , the object having a low unloading frequency is selected from the objects  203  stored in the island near the unloading gate  330  (the island located nearest to the unloading gate  330  or within a predetermined distance from the unloading gate  330 ). In Step S 404 , the storage shelf that stores the object having a low unloading frequency (hereinafter referred to as low-frequency storage shelf) is selected. In the example shown in  FIG. 23 , the low-frequency storage shelf is the storage shelf  716 . 
     Next, when the processing proceeds to Step S 405 , the controller  820  instructs the transfer robot  602  to take the low-frequency storage shelf out of the current island, and move the low-frequency storage shelf to an island located away from the unloading gate  330 . In the example shown in  FIG. 23 , the storage shelf  716  that is the low-frequency storage shelf is taken from the island  752 , and is moved to the island  750  located away from the unloading gate  330 . Next, when the processing proceeds to Step S 406 , the controller  820  instructs the transfer robot  602  to take the high-frequency storage shelf out of the current island, and move the high-frequency storage shelf to an island near the unloading gate  330 . In the example shown in  FIG. 23 , the storage shelf  720  that is the high-frequency storage shelf is taken from the island  750 , and is moved to the island  752  near the unloading gate  330 . 
     Through the above-mentioned processing, the storage shelf storing the object that is likely to be taken may be located near the unloading gate  330 . This may reduce the distance of the storage shelf moved by the transfer robot  602  to shorten the picking time of the object  203 . 
     In the above-mentioned example, the storage shelves are interchanged in the particular zone, but the transfer robot  602  may be operated across the all zones to interchange the storage shelves. 
     As described above, the configuration shown in  FIGS. 23 to 25  includes: the plurality of storage shelves ( 716 ,  720 ) that are arranged in respective predetermined arrangement places on the floor surface ( 152 ) and each store the plurality of unloadable objects ( 203 ); the transfer robot ( 602 ) that, when unloading of any of the plurality of objects ( 203 ) is designated, transfers any storage shelf ( 716 ,  720 ) storing the designated object ( 203 ) to the unloading gate ( 330 ) provided at the predetermined position; and the controller ( 800 ) that predicts the frequencies with which the plurality of storage shelves ( 716 ,  720 ) are transferred to the unloading gate ( 330 ) based on records of past shipment of the plurality of objects ( 203 ), and when the frequency of a second storage shelf ( 720 ) is higher than the frequency of a first storage shelf ( 716 ) among the plurality of storage shelves ( 716 ,  720 ) and the arrangement place of the second storage shelf ( 716 ) is further from the unloading gate ( 330 ) than the arrangement place of the first storage shelf ( 720 ) is, to change the arrangement place of the first storage shelf ( 716 ) or the second storage shelf ( 720 ) such that the arrangement place of the second storage shelf ( 720 ) is closer to the unloading gate ( 330 ) than the arrangement place of the first storage shelf ( 716 ) is. 
     With such configuration, when the arrangement place of the first storage shelf ( 716 ) or the second storage shelf ( 720 ) is to be changed, the controller ( 800 ) interchanges the arrangement places of the first storage shelf ( 716 ) and the second storage shelf ( 720 ). 
     Thereby, the storage shelf storing the object that is likely to be taken may be located near the unloading gate. This may reduce the distance of the storage shelf moved by the transfer robot ( 602 ) to shorten the picking time of the object. 
     [Cooperation with Stacker Crane] 
       FIG. 26  is a schematic view showing a configuration in which a bucket  480  (bucket) is taken out of the storage shelf in the warehouse system  300 . 
     The bucket  480  is a substantially cubic box placed on each storage shelf, with the upper surface opened. The bucket  480  generally stores a plurality of objects  203  of the same type (see  FIG. 3 ). 
     In taking the bucket  480  out of the storage shelf  702 , the bucket  480  may be picked and drawn using the robot hand  202  of the arm robot  200 . 
     In  FIG. 26 , the arm robot  200  includes one robot arm  208  and one robot hand  202 . In contrast, the arm robot may include two robot arms  208  and two robot hands  202 . That is, one robot arm  208  may draw the bucket  480 , and the other robot arm  208  may take the object  203  out of the bucket  480 . 
     However, since control of the robot arm  208  takes much time, according to any of the above-mentioned techniques, it is difficult to speed up take-out of the object  203 . 
     Thus, in the present embodiment, a stacker crane  482  for taking the bucket  480  out of the storage shelf  702  is provided. Here, the stacker crane  482  includes a drawing arm  486  that carries the bucket  480  into/out of the storage shelf  702 , and has a function of moving the drawing arm  486  in horizontal direction with respect to the opposed surface of the storage shelf  702  and a function of vertically moving the drawing arm  486 . The stacker crane  482  is provided at the unloading gate  330  (see  FIG. 2 ). 
     The transfer robot  602  moves the storage shelf  702  that stores the target object to the front of the unloading gate  330 . The buckets  480  stored in the storage shelf  702  are systematically classified according to type. Accordingly, in response to an instruction from the central controller  800 , the stacker crane  482  may identify the bucket to be drawn. Thus, as compared to the case of driving the robot arm  208 , the bucket  480  may be drawn from the storage shelf  702  rapidly and correctly. 
       FIG. 27  is a schematic view showing another configuration in which the bucket  480  is taken out of the storage shelf in the warehouse system  300 . 
     In the example shown in  FIG. 27 , a buffer shelf  484  that temporarily stores the bucket  480  taken by the stacker crane  482  is provided. That is, the buckets  480  taken by the stacker crane  482  is once stored in the buffer shelf  484 . The arm robot  200  picks the object  203  from the bucket  480  placed on the buffer shelf  484 . 
     In the example shown in  FIG. 27 , unlike the example in  FIG. 26 , (for example, a plurality of) buckets  480  for picking may be stored in the buffer shelf  484  and then, the arm robot  200  may perform picking. Although the picking time of the arm robot  200  varies according to the type and status of the target object  203 , the picking time of the robot arm  208  may be made uniform by once holding the bucket  480  in the buffer shelf  484 . 
       FIG. 28  is a flow chart of processing applied to the configuration shown in  FIG. 27  by the central controller  800  (see  FIG. 1 ). 
     When the processing starts in Step S 500  in  FIG. 28 , the processing proceeds to Step S 501 . Here, the central controller  800  searches for the object  203  to be unloaded based on object data on the object stored in the warehouse  100 , and identifies the storage shelf  702  that stores the target object, and the position of the object  203  in the storage shelf. Next, when the processing proceeds to Step S 502 , the central controller  800  causes the transfer robot  602  to move the storage shelf  702  that stores the object  203  to the unloading gate  330 . 
     Next, when the processing proceeds to Step S 503 , the central controller  800  controls the stacker crane  482  to move the drawing arm  486  to the bucket  480  that stores the target object  203  and draws the target bucket  480 . Next, when the processing proceeds to Step S 504 , under control of the central controller  800 , the stacker crane  482  moves the target bucket  480  to the buffer shelf  484 . Next, when the processing proceeds to Step S 505 , in response to a command from the central controller  800 , the arm robot  200  takes the target object  203  out of the bucket  480  of the buffer shelf  484  using the robot arm  208  and the robot hand  202 , and unloads the target object. 
       FIG. 28  is the flow chart applied to the configuration in  FIG. 27 , and in the configuration shown in  FIG. 26 , Step S 504  may be skipped, and the other processing is the same as the above-mentioned processing. As described above, in the example shown in  FIGS. 26 to 28 , since the stacker crane  482  rather than the robot arm  208  takes the bucket  480  out of the storage shelf  702 , picking may be performed more rapidly as compared to the case of using the robot arm  208 . 
     As described above, the configuration shown in  FIGS. 26 to 28  includes: the bucket ( 480 ) that stores the objects ( 203 ); the plurality of storage shelves ( 702 ) that are arranged in respective predetermined arrangement places on the floor surface ( 152 ) and store the plurality of unloadable objects ( 203 ) ina state of being stored in the bucket ( 480 ); the transfer robot ( 602 ) that, when unloading of any of the plurality of objects ( 203 ) is designated, transfers the storage shelf ( 702 ) storing the designated object ( 203 ) to the unloading gate ( 330 ) located at the predetermined position; the stacker crane ( 482 ) that is provided at the unloading gate ( 330 ) and takes the bucket ( 480 ) storing the designated object ( 203 ) out of the storage shelf ( 702 ); and the arm robot ( 200 ) that takes the designated object ( 203 ) out of the bucket ( 480 ) taken by the stacker crane ( 482 ). 
     The configuration in  FIG. 27  further includes the buffer shelf ( 484 ) that holds the bucket ( 480 ) taken by the stacker crane ( 482 ), and the arm robot ( 200 ) takes the object ( 203 ) out of the bucket ( 480 ) held in the buffer shelf ( 484 ). 
     In this manner, the stacker crane ( 482 ) may take the object ( 203 ) out of the storage shelf ( 702 ), thereby achieving high-speed picking. 
     [Movement of Sort Shelf by AGV] 
       FIG. 29  is a schematic view showing a configuration in which the target object is taken from the storage shelf  702  and stored in a sort shelf  902  at the unloading gate  330  (see  FIG. 2 ). The sort shelf  902  sorts objects according to destination. 
     In the example shown in  FIG. 29 , two parallel rails  492  are lied on the floor surface. The robot body  201  includes wheels placed on the rails  492  and a motor for driving the wheels (not shown). Thus, the robot body  201  is movable along the rails  492 . The bucket  480  storing the target object  203  is stored in the storage shelf  702 . The arm robot  200  moves the robot arm  208  to the position opposed to the bucket  480 . 
     Thereby, the arm robot  200  may pick the object with high working efficiency to move the target object to the sort shelf  902 . 
       FIG. 30  is a flow chart of processing applied to the configuration shown in  FIG. 29  by the central controller  800 . 
     When the processing starts in Step S 600  in  FIG. 30 , the processing proceeds to Step S 601 . Here, the central controller  800  searches for the object  203  to be unloaded based on object data on the objects stored in the warehouse  100 , and identifies the storage shelf  702  that stores the target object and the position of the object  203  in the storage shelf. Next, when the processing proceeds to Step S 602 , the central controller  800  moves the identified storage shelf  702  to the unloading gate  330  using the transfer robot  602 . 
     Next, when the processing proceeds to Step S 603 , under control of the central controller  800 , the robot body  201  moves on the rails  492  to the position where the robot arm  208  and the robot hand  202  easily take out the target object  203 . Next, when the processing proceeds to Step S 604 , under control of the central controller  800 , the arm robot  200  draws the bucket  480  using the robot arm  208  and the robot hand  202  to take out the target object  203 . Next, when the processing proceeds to Step S 605 , the central controller  800  moves the robot body  201  on the rails  492  such that the taken object is stored at a designated position in the sort shelf  902 . 
     Next, when the processing proceeds to Step S 606 , under control of the central controller  800 , the arm robot  200  stores the taken object at the designated position in the sort shelf  902 . 
     In the example shown in  FIG. 29 , the arm robot  200  draws the bucket  480 , but as shown in  FIGS. 26 and 27 , the stacker crane  482  may be provided and draw the bucket  480  storing the target object. 
       FIG. 31  is a schematic view showing a configuration in which the target object is taken out of the storage shelf  702  and sorted to the other storage shelves  722 ,  724  (sort shelves) at the unloading gate  330  (see  FIG. 2 ). 
     In the example shown in  FIG. 29 , the robot body  201  moves on the two rails  492 . In contrast, in the example shown in  FIG. 31 , in place of the sort shelf  902 , the storage shelves  722 ,  724  are used. That is, as necessary, the transfer robot  602  moves the storage shelves  722 ,  724  to the operation range of the arm robot  200 . 
     Thereby, the object  203  (see  FIG. 3 ) taken from the bucket  480  in the storage shelf  702  may be moved to the buckets  480  in the storage shelves  722 ,  724  by operating the robot arm  208  and the robot hand  202  without moving the robot body  201  of the arm robot  200 . That is, in the storage shelves  722 ,  724 , an opened space of the bucket  480  placed as opposed to the arm robot  200  may store the object  203 . 
     When no space is present in the bucket  480  on the surfaces of the storage shelves  722 ,  724  opposed to the arm robot  200 , the transfer robot  602  rotates the storage shelves  722 ,  724  such that the bucket  480  on the opposite side may store the object. When no space is present in all the buckets  480  of storage shelves  722 ,  724 , the transfer robot  602  moves another new storage shelf (not shown) to the operation range of the arm robot  200 . Thus, the object may be stored in the new storage shelf in the same manner. As described above, in the example shown in  FIG. 31 , the storage shelves  722 ,  724  each function as the sort shelf. 
       FIG. 32  is a schematic view showing another configuration in which the target object is taken out of the storage shelf  702  and stored in the other storage shelves  722 ,  724  at the unloading gate  330  (see  FIG. 2 ). 
     A difference between the example shown in  FIG. 32  and the example shown in  FIG. 31  is that the transfer robot  602  minutely drives the storage shelves  722 ,  724  each functioning as the sort shelf. That is, the transfer robot  602  minutely moves the storage shelves  722 ,  724  in unit of width of the bucket  480  according to the place of the bucket  480  that stores the target object. 
     In the example shown in  FIG. 32 , when the object to be picked is put into the storage shelves  722 ,  724 , the central controller  800  determines which bucket  480  in the storage shelves  722 ,  724 , the target object is to be stored. The transfer robot  602  laterally moves the storage shelves  722 ,  724  in the unit of width of the bucket  480  so as to coincide the position of the bucket  480  with the moving position of the robot hand  202 . This may reduce the moving distance of the robot arm  208  and the robot hand  202  and makes it possible to rapidly perform the step of storing the object picked from the storage shelf  702  into the storage shelves  722 ,  724 . 
       FIG. 33  is a flow chart of the processing applied to the configuration shown in  FIGS. 31 and 32  by the central controller  800 . 
     When the processing starts in Step S 700  in  FIG. 33 , the processing proceeds to Step S 701 . Here, the central controller  800  searches for the object  203  to be unloaded based on object data on the objects stored in the warehouse  100 , and identifies the storage shelf  702  that stores the target object and the position of the object  203  in the storage shelf. Next, when the processing proceeds to Step S 702 , the central controller  800  moves the identified storage shelf  702  to the unloading gate  330  using the transfer robot  602 . 
     Next, when the processing proceeds to Step S 703 , under control of the central controller  800 , the arm robot  200  draws the bucket  480  from the storage shelf  702  using the robot arm  208  and the robot hand  202  to take out the target object  203 . Next, when the processing proceeds to Step S 704 , under control of the central controller  800 , the transfer robot  602  moves the sort storage shelves  722 ,  724  to the sort position of the unloading gate  330 . Describing in more detail, the transfer robot  602  moves the storage shelves  722 ,  724  in unit of width of the bucket  480  such that the robot arm  208  and the robot hand  202  may easily store the target object at the designated position in the sort storage shelves  722 ,  724 . 
     Next, when the processing proceeds to Step S 705 , under control of the central controller  800 , the arm robot  200  stores the object in the bucket  480  at the designated position of the sort storage shelves  722 ,  724 . Next, when the processing proceeds to Step S 706 , the central controller  800  determines whether or not an additional target object is to be put into the sort storage shelves  722 ,  724 . When the determination result is affirmative (addition), the processing returns to Step S 701 , the same processing as the above-mentioned processing is repeated. On the contrary, when the determination result is negative (no addition), the storage shelf  702  is moved from the sort position. 
     In the example described with reference to  FIG. 31  to  FIG. 33 , the arm robot  200  draws the bucket  480 , but as shown in  FIGS. 26 and 27 , the stacker crane  482  may be provided and draw the bucket  480  storing the target object. After the taken bucket  480  is moved to the buffer shelf  484  (see  FIG. 27 ), the arm robot  200  may be take the object out of the bucket  480 . 
     In Step S 704 , the sort storage shelves  722 ,  724  are moved in unit of width of the bucket using the transfer robot  602 , but as shown in  FIG. 31 , the object may be stored in the storage shelves  722 ,  724 , with the sort storage shelves  722 ,  724  fixed, by using the rapidly-operating arm robot  200 . 
     As described above, the configuration shown in  FIGS. 29 to 33  includes: the storage shelf ( 702 ) that stores the object to be unloaded ( 203 ); the sort shelf ( 902 ,  722 ,  724 ) that sorts the object ( 203 ) for each destination; the arm robot ( 200 ) that takes the object ( 203 ) out of the storage shelf ( 702 ) and stores the object at the designated place in the sort shelf ( 902 ,  722 ,  724 ); and the transfer device ( 201 ,  602 ) that moves the arm robot ( 200 ) or the sort shelf ( 722 ,  724 ) so as to reduce the distance between the arm robot ( 200 ) and the designated place. 
     Thereby, the step of storing the object ( 203 ) taken from the storage shelf ( 702 ) in the sort shelves ( 902 ,  722 ,  724 ) may be rapidly performed. 
     With the configuration shown in  FIGS. 31 and 32 , the transfer device ( 602 ) is the transfer robot ( 602 ) that enters below the sort shelf ( 722 ,  724 ) and pushes the sort shelf ( 722 ,  724 ) upwards to support and move the sort shelf ( 722 ,  724 ). The sort shelf ( 722 ,  724 ) and the transfer robot ( 602 ) are used in each zone ( 11 ,  12 ,  13 ), thereby standardizing various members in the warehouse ( 100 ). 
     [Detection of Closeness of Obstacle] 
     Generally, when the transfer robot  602  is operated in the warehouse system, the operation area of the transfer robot  602  and the work area of the operator are set so as not overlap each other. This is due to that the operator and a cargo carried by the operator may become an obstacle in operating the transfer robot  602 . However, the combination of the operator and the transfer robot  602  may achieve the efficient loading operation. To enable such operation, it is demanded to properly operate the transfer robot  602  with the obstacle. 
       FIG. 34  is an explanatory view showing operations in the case where the transfer robot  602  detects an obstacle.  FIG. 34  shows an example in which the operator  310  is the obstacle. In  FIG. 34 , unless otherwise specified, members having the same reference numerals as in  FIGS. 1 to 33  have similar configurations and effects. 
     In the present embodiment, the sensor  206  such as a camera is arranged on a ceiling in the area where the transfer robot  602  operates, and monitors the transfer robot  602  and the surrounding state. 
     In the present embodiment, to avoid a collision with the obstacle (operator  310  and the like), following virtual areas  862 ,  864 , and  866  are set ahead in the moving direction of the transfer robot  602 . 
     (1) the area  866  in front of the transfer robot  602  by 5 m to 3 m 
     (2) the area  864  in front of the transfer robot  602  by 3 m to 1 m 
     (3) the area  862  in front of the transfer robot  602  by 1 m or less 
       FIG. 35  is a schematic view showing the case where the plurality of transfer robots  602  move along different paths  882 ,  884 . 
     In the example shown in  FIG. 35 , the two transfer robots  602  move along the different paths  882 ,  884 . The paths  882 ,  884  are virtual paths on the floor surface, and are not physically formed on the floor surface. 
     The central controller  800  sets virtual areas  872 ,  874  for the transfer robots  602  to control the operation state of each transfer robot  602  to avoid a collision with an obstacle (operator  310  or the like). 
     In the example shown in  FIG. 35 , two transfer robots  602  are used, but the number of the transfer robots  602  may be three. 
       FIG. 36  is a flow chart of the processing executed to avoid a collision of the operator  310  or the like with the obstacle by the central controller  800 . 
     When the processing starts in Step S 700  in  FIG. 36 , the processing proceeds to Step S 701 . Here, to avoid the collision of the operator  310  or the like with the obstacle, the central controller  800  sets following three virtual areas with respect to the moving direction of the transfer robot  602 . 
     (1) the area  866  in front of the transfer robot  602  by 5 m to 3 m 
     (2) the area  864  in front of the transfer robot  602  by 3 m to 1 m 
     (3) the area  862  in front of the transfer robot  602  by 1 m or less 
     Next, when the processing proceeds to Step S 702 , the transfer robot  602  sends own position data to the central controller  800 . However, irrespective of the execution timing of Step S 702 , the transfer robot  602  sends own position data to the central controller  800  at all times. Next, when the processing proceeds to Step S 703 , the sensor  206  detects whether or not an obstacle is present around the transfer robot  602 . However, irrespective of the execution timing of Step S 703 , the sensor  206  detects whether or not an obstacle is present around the transfer robot  602 . 
     Next, when the processing proceeds to Step S 704 , the central controller  800  calculates a relative distance between the obstacle detected by the sensor  206  and the transfer robot  602 , and branches the processing according to the calculation result. First, when the relative distance is equal to or smaller than 1 m, the processing proceeds to Step S 705 , and the central controller  800  urgently stops the transfer robot  602 . Next, when the processing proceeds to Step S 706 , the central controller  800  issues an alarm to an information terminal (smart phone, smart watch, or the like) of the operator  310 . 
     On the contrary, when the calculated relative distance is equal to or larger than 1 m and less than 3 m, the processing proceeds from Steps S 704  to S 707 . In Step S 707 , the central controller  800  reduces the speed of the transfer robot  602  to 30% of normal speed. On the contrary, when the calculated relative distance is equal to or larger than 3 m and less than 5 m, the processing proceeds from Steps S 704  to S 708 . In Step S 708 , the central controller  800  reduces the speed of the transfer robot  602  to 50% of the normal speed. 
     When Step S 707  or S 708  is executed, the processing returns to Step S 702 . When the calculated relative distance is 5 m or more, the processing returns to Step S 702  without reducing the speed of the transfer robot  602 . In this manner, unless urgent stop (Step S 705 ) occurs, the same processing as the above-mentioned processing is repeated. 
     Through the above-mentioned processing, the transfer robot  602  may be safely operated while enabling movement of the operator  310 . That is, the work area of the operator  310  and the work area of the transfer robot  602  may overlap each other, achieving an efficient loading operation. 
     As described above, the configuration shown in  FIGS. 34 to 36  includes: the transfer robot ( 602 ) that travels in the warehouse ( 100 ); the sensor ( 206 ) that detects the obstacle ( 310 ) to the transfer robot ( 602 ) and the transfer robot ( 602 ); and the controller ( 800 ) that performs such a control as to reduce the speed of the transfer robot ( 602 ) as the transfer robot ( 602 ) comes closer to the obstacle ( 310 ) based on the detection result of the sensor ( 206 ). 
     When the distance between the transfer robot ( 602 ) and the obstacle ( 310 ) is a predetermined value or less, the controller ( 800 ) stops the transfer robot ( 602 ). 
     Thereby, even when the obstacle ( 310 ) such as the operator is present, the transfer robot ( 602 ) may be operated to achieve the efficient loading operation. 
     [Modifications] 
     The present invention is not limited to the above-mentioned embodiment, and may be modified in various manners. The above-mentioned embodiment is shown for describing the present invention in an easily understandable manner, and is not limited to include all of the described constituents. Any other configuration may be added to the above-mentioned configuration, and a part of the configuration may be replaced with another configuration. Control lines and information lines in the figures are drawn for explanation, and do not necessarily indicate all required control lines and information lines. Actually, almost all constituents may be interconnected. 
     REFERENCE SIGNS LIST 
     
         
           11 ,  12 ,  13  Zone 
           100  Warehouse 
           120 ,  122 ,  124 ,  126 ,  130  Transfer line 
           152  Floor surface 
           200 ,  200 - 1  to  200 - n  Arm robot 
           201  Robot body 
           202  Robot hand 
           203  Object 
           206  Sensor 
           207  Position sensor 
           208  Robot arm 
           229  Robot teaching database 
           230 ,  230 A Second robot data generation unit (robot data generation unit) 
           264  Data generation unit (robot data generation unit) 
           300  Warehouse system 
           310  Operator (obstacle) 
           330  Unloading gate 
           410  Analysis processor 
           480  Bucket 
           482  Stacker crane 
           484  Buffer shelf 
           560  Container (transfer target) 
           602  Transfer robot 
           702 ,  704 ,  706 ,  708 ,  710 ,  712 ,  714 ,  732 ,  742  Storage shelf 
           716  Storage shelf (first storage shelf) 
           720  Storage shelf (second storage shelf) 
           722 ,  724  Storage shelf (sort shelf) 
           800  Central controller (controller) 
           852  Receiving base 
           852   a  Upper plate 
           854  Receiving object (inspection target) 
           860  Controller 
           902  Sort shelf 
           81 ′ to θn′ Robot teaching data 
         Q 201  Robot body coordinates (robot body coordinates model value) 
         Q 202  Robot hand coordinates (robot hand coordinates model value) 
         Q 206  Sensor coordinates (sensor coordinates model value) 
         Q 602  Transfer robot coordinates (transfer robot coordinates model value) 
         Q 702  Storage shelf coordinates (storage shelf coordinates model value) 
         th 1  Threshold (first threshold) 
         th 2  Threshold (second threshold)