JP7483551B2 - Information processing device - Google Patents

Description

An embodiment of the present invention relates to an information processing device.

In recent years, a reading system has been provided that uses an autonomous mobile robot equipped with an antenna to read wireless tags such as RFID (radio frequency identifier) tags. The reading system reads the wireless tags by passing the autonomous mobile robot in front of a display such as a shelf on which multiple items with wireless tags are displayed.

Prior to reading the wireless tag, the reading system creates an environmental map for estimating the autonomous mobile robot's self-position while it moves autonomously. For example, the reading system creates an environmental map for estimating the autonomous mobile robot's self-position while scanning the surrounding environment using a laser range finder (LRF) fixed at a specified height on the autonomous mobile robot. The reading system also creates an environmental map for setting a movement path in which no-entry areas for the autonomous mobile robot are set for the environmental map for estimating the autonomous mobile robot's self-position.

JP 2019-46372 A

However, fixtures such as shelves have an uneven structure, with multiple shelves extending horizontally. The shape of the shelf detected by the LRF fixed at a specified height of the autonomous mobile robot may differ from the shape obtained by projecting the actual shelf vertically onto a horizontal plane. For this reason, it is difficult for the reading system to correctly set the no-entry area for the autonomous mobile robot to match the shape of the fixture. It is possible to manually input the position of the fixture, but it is difficult to input the position accurately.

The problem that the embodiment of the present invention aims to solve is to provide an information processing device that can accurately place fixtures on an environmental map.

The information processing device of the embodiment includes an input unit, a first acquisition unit, a second acquisition unit, and a processing unit. The input unit receives input of information indicating the type of fixture. The first acquisition unit acquires shape data corresponding to the type. The second acquisition unit acquires distance point data indicating the position of an object. The processing unit determines the position of the fixture of the type using the distance point data and a predetermined algorithm.

FIG. 1 is a schematic diagram showing an example of the configuration of a reading system according to first to third embodiments. FIG. 1 is a schematic diagram showing an example of the configuration of a reading system according to first to third embodiments. FIG. 1 is a block diagram showing an example of the arrangement of a reading system according to first to third embodiments. FIG. 1 is a diagram showing an example of an environment map. 4 is a flowchart showing an example of processing according to the first embodiment performed by a processor of the server in FIG. 3 . FIG. 4 is a diagram showing an example of a range selection screen displayed on the display device in FIG. 3 . FIG. 4 is a diagram showing an example of a fixture selection screen displayed on the display device in FIG. 3 . FIG. 4 is a diagram showing an example of a furniture table stored in the NVM of the server in FIG. 3 . 4 is a flowchart showing an example of processing related to an algorithm by a processor of the server in FIG. 3 . FIG. 11 is a diagram for explaining an example of a method for creating feature amount data. FIG. 11 is a diagram for explaining a comparison between feature amount data and detection data. FIG. 1 is a diagram showing an example of an environment map. FIG. 1 is a diagram showing an example of an environment map. FIG. 13 is a diagram showing an example of setting a travel route using an environmental map. 4 is a flowchart showing an example of processing according to the second and third embodiments by a processor of the server in FIG. 3 . 4 is a flowchart showing an example of processing according to the second and third embodiments by a processor of a system controller in FIG. 3 .

Below, the reading system according to several embodiments will be described with reference to the drawings. Note that the scale of each part in each drawing used in the description of the following embodiments may be changed as appropriate. Also, for the purpose of explanation, each drawing used in the description of the following embodiments may be shown with the configuration omitted. Also, in each drawing and in this specification, the same reference numerals indicate similar elements.

First Embodiment
1 and 2 are schematic diagrams showing an example of the configuration of a reading system 1 according to the first embodiment. Fig. 1 is a diagram showing an example of the configuration of the reading system 1 as viewed from a direction perpendicular to the traveling direction of an autonomous mobile robot 20. Fig. 2 is a diagram showing an example of the configuration of the reading system 1 as viewed along the traveling direction of the autonomous mobile robot 20.

The reading system 1 is a system that reads multiple wireless tags in an area where multiple wireless tags exist. For example, the reading system 1 is used for inventorying items in a store that has multiple shelves.

Here, the reading system 1 executes reading of wireless tags in a specified area A. Area A is an area in which multiple wireless tags are to be read by the reading system 1. For example, area A is a store surrounded by walls or the like. Area A contains multiple fixtures such as a cash register and shelves. Each shelf displays multiple items with wireless tags attached that are to be read by the reading system 1. The walls, cash register and shelves are each an example of a tangible object. Note that area A may contain objects other than walls and fixtures. Objects may be called obstacles since they may impede the movement of the autonomous mobile robot 20.

The reading system 1 includes a system controller 10, an autonomous mobile robot 20, and a server 30. The system controller 10, the autonomous mobile robot 20, and the server 30 are electrically connected to each other.

The system controller 10 is a device that controls the autonomous mobile robot 20. For example, the system controller 10 controls the autonomous movement of the autonomous mobile robot 20 using an environmental map Ma. For example, the system controller 10 controls the reading of multiple wireless tags by the autonomous mobile robot 20 when the autonomous mobile robot 20 moves autonomously.

The environmental map Ma is an environmental map for estimating the self-position of the autonomous mobile robot 20 when it moves autonomously in the area A. The environmental map Ma is a map that includes information indicating the positions of objects that exist in the area A in which the autonomous mobile robot 20 moves autonomously. The environmental map Ma will be described later.
The configuration of the control system of the system controller 10 will be described later.

The autonomous mobile robot 20 is a device that moves within the area A under the control of the system controller 10 .
The autonomous mobile robot 20 includes a cart 201 and wheels 202 .
The dolly 201 forms the outer shell of the autonomous mobile robot 20. The dolly 201 is provided with a support extending in the vertical direction to which each element of the autonomous mobile robot 20 is attached. The vertical direction refers to the same direction as the height direction. The vertical direction refers to the same direction as the perpendicular direction to a horizontal plane.

The wheels 202 are attached to the bottom of the cart 201. The wheels 202 are driven by a motor 262 (described later) to move the cart 201. The wheels 202 also change the direction of movement of the cart 201. Note that the cart 201 may be moved by something other than wheels. For example, the cart 201 moves using caterpillars or legs, etc.

The autonomous mobile robot 20 further includes a reader 21, a 3D (three-dimensional) distance sensor 22, a horizontal LRF 23, a vertical LRF 24, a camera 25, and a moving mechanism 26. The reader 21, the 3D distance sensor 22, the horizontal LRF 23, the vertical LRF 24, and the camera 25 are attached to the support of the cart 201.

The reader 21 is a device that wirelessly communicates with a wireless tag attached to an item. The readers 21 are aligned vertically and attached to the support of the cart 201 so that they face a direction perpendicular to the traveling direction of the autonomous mobile robot 20. As an example, the autonomous mobile robot is equipped with six readers 21, reader 211 to reader 216.

The reader 21 is equipped with a communication antenna and a communication control unit (not shown). The reader 21 is equipped with a directional antenna. The detection range of the reader 21 is designed based on the characteristics of the communication antenna and the installation direction of the communication antenna. The detection range corresponds to the communication range. The reader 21 transmits radio waves to the wireless tag. The reader 21 also receives radio waves from the wireless tag. The reader 21 reads tag information such as the identification information of the wireless tag based on the radio waves received from the wireless tag. The reader 21 transmits the tag information read from the wireless tag to the system controller 10.

The combined detection range of readers 211 to 216 is set to include the top to bottom of the highest shelf in area A. The number and positions of readers provided in the autonomous mobile robot 20 are not limited to a specific configuration. For example, the autonomous mobile robot 20 may be provided with one reader whose detection range is set to include the top to bottom of the highest shelf in area A.

The 3D distance sensor 22 is a sensor that measures the distance to an object present within the measurement range of the 3D distance sensor 22 in three dimensions. The autonomous mobile robot 20 is equipped with two 3D distance sensors 22, 3D distance sensor 221 and 3D distance sensor 222, as an example. For example, the 3D distance sensor 22 is composed of a camera such as an RGB-D camera, but is not limited to this. The 3D distance sensor 22 measures short distances. The 3D distance sensor 22 is used to capture object information around the autonomous mobile robot 20 in three dimensions. The 3D distance sensor 22 obtains three-dimensional distance point cloud data composed of multiple distance point data related to the measured position of the object for each measurement position. The 3D distance sensor 22 transmits distance information obtained by measurement to the system controller 10 for each measurement position. The distance information includes three-dimensional distance point cloud data.

The 3D distance sensors 22 are attached to the support of the cart 201 and aligned vertically so that they face in a direction perpendicular to the traveling direction of the autonomous mobile robot 20.

The upper end of the combined measurement ranges of the 3D distance sensor 221 and the 3D distance sensor 222 exceeds the height of the autonomous mobile robot 20, and is at a height at which the shape of the furniture can be seen. As an example, the combined measurement ranges of the 3D distance sensor 221 and the 3D distance sensor 222 are set to include from the floor surface to the ceiling surface of area A. Alternatively, the combined measurement ranges of the 3D distance sensor 221 and the 3D distance sensor 222 are set to include from the floor surface of area A to a height that exceeds the height of the autonomous mobile robot 20 and the height of the furniture. Note that the number and positions of the 3D distance sensors provided on the autonomous mobile robot 20 are not limited to a specific configuration. For example, the autonomous mobile robot 20 may be provided with one 3D distance sensor whose measurement range extends from the floor surface to the ceiling surface of area A.

The horizontal LRF23 is a sensor that uses a laser to measure the distance to an object present in the measurement range of the horizontal LRF23 in two dimensions. The horizontal LRF23 measures long distances. The horizontal LRF23 is attached to an arbitrary position of the dolly 201. For each measurement position, the horizontal LRF23 horizontally scans the surrounding environment of the horizontal LRF23 using a laser to measure the distance to an object present in area A. For each measurement position, the horizontal LRF23 obtains horizontal distance point cloud data consisting of multiple distance point data related to the position of the object. For each measurement position, the horizontal LRF23 transmits distance information based on the measurement to the system controller 10. The distance information includes horizontal distance point cloud data. Since the horizontal LRF23 can measure long distances, the distance information from the horizontal LRF23 can be used to create a stable environmental map Ma by SLAM (simultaneous localization and mapping). In addition, distance information from the horizontal LRF 23 can be used for robust self-position estimation when the autonomous mobile robot 20 is moving autonomously.

The vertical LRF24 is a sensor that uses a laser to measure the distance to an object present within the measurement range of the vertical LRF24 in two dimensions. The vertical LRF24 measures short distances. The vertical LRF24 is used to capture object information around the autonomous mobile robot 20 in three dimensions. The vertical LRF24 is attached to an arbitrary position on the cart 201. For each measurement position, the vertical LRF24 scans the surrounding environment of the vertical LRF24 in the vertical direction using a laser, and measures the distance to an object present in area A. For each measurement position, the vertical LRF24 obtains vertical distance point cloud data consisting of multiple distance point data related to the measured object position. For each measurement position, the vertical LRF24 transmits distance information based on the measurement to the system controller 10. The distance information includes vertical distance point cloud data.

The camera 25 is a device that captures an image. The camera 25 outputs the captured image. The camera 25 is typically a video camera that captures moving images. However, the camera 25 may be a camera that captures still images.
The configuration of the control system of the autonomous mobile robot 20 will be described later.

The server 30 is a device that processes information from the system controller 10. For example, the server 30 creates an environmental map Ma of the area A prior to the autonomous mobile robot 20 reading multiple wireless tags in the area A. The area A is also a target area for creating the environmental map Ma by the reading system 1. For example, the server 30 creates an environmental map Mb. The server 30 is an example of an information processing device. The configuration of the control system of the server 30 will be described later.

FIG. 3 is a block diagram showing an example of the configuration of the reading system 1. As shown in FIG.
The system controller 10 includes a processor 11, a read-only memory (ROM) 12, a random-access memory (RAM) 13, a non-volatile memory (NVM) 14, and a communication interface (I/F) 15. The processor 11, the ROM 12, the RAM 13, the NVM 14, and the communication interface 15 are connected to each other via a data bus or the like.

The processor 11 controls the operation of the entire system controller 10. For example, the processor 11 is a CPU (central processing unit). The processor 11 is an example of a control unit. The processor 11 may include an internal memory and various interfaces. The processor 11 realizes various processes by executing programs stored in advance in the internal memory, the ROM 12, the NVM 14, or the like.

Note that some of the various functions realized by the processor 11 executing the program may be realized by a hardware circuit. In this case, the processor 11 controls the functions executed by the hardware circuit.

The ROM 12 is a non-volatile memory that stores control programs, control data, and the like in advance. The ROM 12 is incorporated into the system controller 10 during the manufacturing stage with the control programs, control data, and the like stored therein. In other words, the control programs and control data stored in the ROM 12 are incorporated in advance according to the specifications of the system controller 10.

RAM 13 is a volatile memory. RAM 13 temporarily stores data being processed by processor 11. RAM 13 stores various application programs based on instructions from processor 11. RAM 13 may also store data required to execute application programs and execution results of application programs.

The NVM 14 is a non-volatile memory to which data can be written and rewritten. For example, the NVM 14 is composed of a hard disk drive (HDD), a solid state drive (SSD), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The NVM 14 stores control programs, applications, various data, and the like according to the operational use of the system controller 10. The NVM 14 is an example of a storage unit.

The NVM 14 stores a furniture database.
The furniture database is a database that stores and manages data on various furniture such as desks and shelves. There is no limitation on the types of furniture managed by the furniture database. For example, the types of furniture include store furniture, office furniture, and home furniture.
The furniture database also stores and manages furniture models as data on the furniture. The furniture models are data such as images that indicate the shape and dimensions of the furniture.

The communication I/F 15 is an interface for transmitting and receiving data in a wired or wireless manner. For example, the communication I/F 15 is an interface that supports a LAN (local area network) connection. The communication I/F 15 transmits and receives information to and from the autonomous mobile robot 20 in a wired or wireless manner. The communication I/F 15 transmits and receives information to and from the server 30 in a wired or wireless manner.

The movement mechanism 26 of the autonomous mobile robot 20 is a mechanism that moves the autonomous mobile robot 20. The movement mechanism 26 includes a travel controller 261, a motor 262, an encoder 263, and an IMU (inertial measurement unit) 264. The travel controller 261, the motor 262, the encoder 263, and the IMU 264 are electrically connected to each other.

The travel controller 261 moves the autonomous mobile robot 20 according to the control of the system controller 10. The travel controller 261 controls the motor 262 and the like to move the autonomous mobile robot 20. For example, the travel controller 261 supplies power or pulses to the motor 262.

The driving controller 261 is configured with a processor and the like. The driving controller 261 may be realized by the processor executing a program such as software. The driving controller 261 may also be configured with hardware such as an application specific integrated circuit (ASIC) as a processor.

The motor 262 is driven according to the control of the driving controller 261. The motor 262 is connected to the wheels 202 via gears, a belt, or the like. The motor 262 rotates the wheels 202 by its own driving force.

The encoder 263 is connected to the rotation shaft of the motor 262. The encoder 263 is an example of a sensor that measures the rotation angle of the motor 262. The encoder 263 transmits measurement information indicating the rotation angle to the system controller 10. The encoder 263 may be built into the motor 262.

The IMU 264 is a sensor that measures three-axis angles or angular velocities and three-axis acceleration. The IMU 264 transmits measurement information indicating the three-axis angles or angular velocities and three-axis acceleration to the system controller 10.

The autonomous mobile robot 20 may be equipped with a system controller 10. The autonomous mobile robot 20 may also realize the functions (or part of the functions) realized by the processor 11 of the system controller 10.

The server 30 includes a processor 31, a ROM 32, a RAM 33, an NVM 34, an input device 35, a display device 36, and a communication I/F 37. The processor 31, the ROM 32, the RAM 33, the NVM 34, the input device 35, the display device 36, and the communication I/F 37 are connected to each other via a data bus or the like.

The processor 31 controls the operation of the entire server 30. For example, the processor 31 is a CPU. The processor 31 is an example of a control unit. The processor 31 may include an internal memory and various interfaces. The processor 31 realizes various processes by executing programs stored in advance in the internal memory, the ROM 32, the NVM 34, or the like.

Note that some of the various functions realized by the processor 31 executing the program may be realized by a hardware circuit. In this case, the processor 31 controls the functions executed by the hardware circuit.

ROM 32 is a non-volatile memory that stores control programs, control data, and the like in advance. ROM 32 is incorporated into server 30 during the manufacturing stage with the control programs, control data, and the like stored therein. In other words, the control programs and control data stored in ROM 32 are incorporated in advance according to the specifications of server 30.

RAM 33 is a volatile memory. RAM 33 temporarily stores data being processed by processor 31. RAM 33 stores various application programs based on instructions from processor 31. RAM 33 may also store data necessary for executing application programs and execution results of application programs.

The NVM 34 is a non-volatile memory to which data can be written and rewritten. For example, the NVM 34 is composed of an HDD, an SSD, an EEPROM, a flash memory, etc. The NVM 34 stores control programs, applications, various data, etc. according to the operational purpose of the server 30.
The NVM 14 also stores a furniture table T, which will be described later.

The input device 35 is a device that accepts instructions based on operations by a user. For example, the input device 35 is a keyboard or a touchpad, but is not limited to these. The input device 35 may be an element included in the server 30, or may be an element independent of the server 30.

The display device 36 is a device that displays various images. For example, the display device 36 is a liquid crystal display, but is not limited to this. The display device 36 is an example of a display unit. The display device 36 may be an element included in the server 30, or an element independent of the server 30. The input device 35 and the display device 36 may be a touch panel.

The communication I/F 37 is an interface for transmitting and receiving data in a wired or wireless manner. For example, the communication I/F 37 is an interface that supports a LAN connection. The communication I/F 37 transmits and receives information to and from the system controller 10 in a wired or wireless manner.

For example, the communication I/F 37 receives distance information from the horizontal LRF 23, distance information from the vertical LRF 24, distance information from the 3D distance sensor 22, measurement information from the encoder 263, and measurement information from the IMU 264 from the system controller 10. The distance information from the horizontal LRF 23, distance information from the vertical LRF 24, distance information from the 3D distance sensor 22, measurement information from the encoder 263, and measurement information from the IMU 264 are also referred to as sensor information.

Next, the functions and operations realized by the processor 31 will be described.
The processor 31 executes programs such as software stored in the ROM 32 or the NVM 34 to realize the functions exemplified below.

The processor 31 has a function of creating the environmental map Ma. The processor 31 acquires detection data for creating the environmental map Ma. The detection data is data related to a predetermined height range in the area A in which the autonomous mobile robot 20 moves. The predetermined height range is an arbitrary range. The predetermined height range may be a predetermined height that does not have a width in the height direction, or may have a width in the height direction. Therefore, the detection data may be two-dimensional distance point cloud data or three-dimensional distance point cloud data. The predetermined height corresponds to the height position of the distance point cloud data obtained by the horizontal LRF 24. Note that the predetermined height range is related to the creation of the environmental map Ma. Therefore, for example, from the viewpoint of data processing, it is preferable that the predetermined height range is a predetermined height or a relatively narrow range.
As described above, the processor 31 functions as a second acquisition unit that acquires distance point data by acquiring detection data.

For example, in order to create an environmental map Ma, the processor 31 controls the autonomous mobile robot 20 to patrol within area A via the system controller 10. As the autonomous mobile robot 20 patrols within area A, the processor 31 acquires sensor information from the autonomous mobile robot 20 via the system controller 10. The processor 31 collects sensor information until the autonomous mobile robot 20 finishes patrolling within area A. The processor 31 stores the sensor information in the NVM 34.

An example of acquiring detection data when the specified height range is a specified height will be described. In one example, the processor 31 refers to the horizontal distance point cloud data included in the distance information from the horizontal LRF 23. The processor 31 acquires the horizontal distance point cloud data as two-dimensional distance point cloud data at the specified height. In another example, the processor 31 refers to the three-dimensional distance point cloud data included in the distance information from the 3D distance sensor 22. The processor 31 acquires two-dimensional distance point cloud data at the specified height from the three-dimensional distance point cloud data.

An example of acquiring detection data when the specified height range has a width in the height direction will be described. In one example, the processor 31 refers to horizontal distance point cloud data included in the distance information from the horizontal direction LRF 23 and vertical distance point cloud data included in the distance information from the vertical direction LRF 24. In another example, the processor 31 refers to three-dimensional distance point cloud data included in the distance information from the 3D distance sensor 22. The processor 31 acquires three-dimensional distance point cloud data in the specified height range from the three-dimensional distance point cloud data. The processor 31 combines the horizontal distance point cloud data and the vertical distance point cloud data to construct three-dimensional distance point cloud data. The processor 31 acquires three-dimensional distance point cloud data in the specified height range from the three-dimensional distance point cloud data.

The processor 31 creates the environmental map Ma based on the detection data. For example, the processor 31 creates the environmental map Ma by SLAM based on the detection data. When the specified height range is a specified height, the processor 31 creates the environmental map Ma by SLAM based on the distance information as seen from the horizontal direction LRF23. In this case, the environmental map Ma is a two-dimensional environmental map. When the specified height range has a width in the height direction, the processor 31 creates the environmental map Ma by SLAM based on the three-dimensional distance point cloud data. This determines the position information of the object measured in three dimensions. In this case, the environmental map Ma is a three-dimensional environmental map. In addition to the detection data, the processor 11 may use measurement information based on the measurement of the encoder 263 and measurement information based on the measurement of the IMU 264 included in the sensor information. The processor 11 stores data indicating the environmental map Ma in the NVM 34. When the autonomous mobile robot 20 moves autonomously, the system controller 10 can estimate the self-position of the autonomous mobile robot 20 by matching distance information from the horizontal direction LRF 23 with the environmental map Ma.

Fig. 4 is a diagram showing an example of the environmental map Ma in the case where the predetermined height range is a predetermined height. Therefore, the environmental map Ma shown in Fig. 4 is a two-dimensional environmental map.
The environmental map Ma is shown with three attributes: unknown area, free area, and obstacle area. The unknown area is an area that cannot be measured due to the presence of an object due to the horizontal direction LRF23 (the object acts as a barrier). In FIG. 4, the unknown area is shown with dots. The free area is an area where the absence of an object is guaranteed as a result of measurement using the horizontal direction LRF23. In FIG. 4, the free area is shown in white (solid color). The obstacle area is an area where the presence of an object is recognized as a result of measurement using the horizontal direction LRF23. In FIG. 4, the obstacle area is shown in black (solid color).

The processor 31 may set the predetermined height range to have a width in the height direction and create a three-dimensional environmental map Ma that is expanded in the height direction. In this case, the system controller 10 can use the three-dimensional environmental map Ma to estimate the self-position of the autonomous mobile robot 20 more robustly against environmental changes.

The processor 31 has a function of placing a fixture model showing the fixtures installed in the area A on the environmental map Ma. The processor 31 receives input of the type of fixture and the range in which the fixture is installed. The processor 31 then identifies where within the range the fixture is located. To this end, the operator of the server 30 inputs the type of fixture and the range in which the fixture is installed to the server 30 using the input device 35 or the like. The function of placing the fixture model on the environmental map Ma will be described in detail below with reference to the figures.

Figure 5 is a flow chart showing an example of processing related to the function of placing a furniture model on the environmental map Ma by the processor 31 of the server 30. Note that the content of the processing in the following operational description is an example, and various processing that can obtain similar results can be used as appropriate. The processor 31 executes the processing of Figure 5 based on a program stored in, for example, the ROM 32 or the NVM 34.

The processor 31 starts the process shown in FIG. 5, for example, in response to receiving an input instructing the processor 31 to start executing a function of placing a fixture model on the environmental map Ma.
5, the processor 31 of the server 30 generates an image corresponding to the range selection screen SCa. Then, the processor 31 instructs the display device 36 to display the generated image. In response to the display instruction, the display device 36 displays the range selection screen SCa.

Figure 6 is a diagram showing an example of the range selection screen SCa displayed on the display device 36. The range selection screen SCa includes, as an example, an environmental map Ma, a confirm button Ba, and an end button Bb. The confirm button Ba is a button that the operator of the server 30 operates to confirm the selected range when the range selection described below is completed. The end button Bb is a button that the operator of the server 30 operates to instruct the server 30 to end the placement of the furniture model on the environmental map Ma.

The operator of the server 30 selects a range on the environmental map Ma so as to include the locations where the fixtures are installed. In FIG. 6, the selection range SA is shown by a dashed line as an example. The selection range SA may be of any size as long as it includes the locations where the fixtures are installed. However, the smaller the selection range SA is, the shorter the processing time of the server 30 will be. Note that although the shape of the selection range SA shown in FIG. 6 is rectangular, the shape of the selection range SA may be other than rectangular. In addition, various known methods may be used for range selection.

In ACT 12, the processor 31 determines whether or not an operation to confirm the selection range has been performed. That is, the processor 31 determines whether or not a predetermined operation, such as operating the confirmation button Ba, has been performed. If an operation to confirm the selection range has not been performed, the processor 31 determines No in ACT 12 and proceeds to ACT 13.

In ACT 13, processor 31 determines whether an operation to end the placement of the fixture model has been performed. That is, processor 31 determines whether a predetermined operation, such as operating end button Bb, has been performed. If an operation to end the placement of the fixture model has not been performed, processor 31 determines No in ACT 13 and returns to ACT 12. Thus, processor 31 enters a standby state in which ACT 12 and ACT 13 are repeated until an operation to confirm the selection range or an operation to end the placement of the fixture model is performed.

If an operation to confirm the selection range is performed while in the standby state in ACT 12 and ACT 13, the processor 31 judges the result as Yes in ACT 12 and proceeds to ACT 14.
As described above, by performing the process of ACT 12, the processor 31 cooperates with the input device 35 to function as an input unit that receives input of the selection range SA.

In ACT 14, the processor 31 generates an image corresponding to the fixture selection screen SCb. The processor 31 then instructs the display device 36 to display the generated image. In response to the display instruction, the display device 36 displays the fixture selection screen SCb.

Figure 7 is a diagram showing an example of the fixture selection screen SCb displayed on the display device 36. The fixture selection screen SCb includes, for example, multiple areas AR showing information about fixtures. Each area AR is based on information stored in the fixture table T.

Figure 8 is a diagram showing an example of a fixture table T stored in NVM 34. As an example, the fixture table T stores and manages various pieces of information about the fixtures by associating various pieces of information about the fixtures with a fixture ID (identifier). Examples of the various pieces of information about the fixtures include the manufacturer name, product name, model data, dimensions (W (width)), dimensions (D (depth)), dimensions (H (height)), feature data, and algorithm used. The fixture ID is unique identification information for each fixture. The model data is data that indicates the two-dimensional or three-dimensional shape of the fixture. The model data that indicates the two-dimensional shape is, for example, a plan view. The feature data is data that indicates the feature amounts of the shape of the fixture. The feature data is an example of shape data. The algorithm used indicates which algorithm is used to determine the position of the fixture. The algorithm used may also include the values of parameters used in the algorithm.

Returning to the explanation of Fig. 7, the area AR is a button for selecting a furniture model displayed in the area AR. The area AR includes, for example, areas ARa to ARe.
Area ARa indicates the fixture ID of the fixture model.
Area ARb displays the manufacturer name and the like associated with the fixture ID.
Area ARc displays the product name of the fixture, etc. associated with the fixture ID.

The area ARd displays a fixture model U based on the model data associated with the fixture ID. The fixture model U shows the shape of the fixture and may be two-dimensional or three-dimensional. The fixture model U shown in FIG. 7 is a plan view of the fixture seen from above. The fixture model U includes a portion Ua. The fixture model U may further include a portion Ub. In FIG. 6, the portion Ua of the fixture model U is shown by a solid line, and the portion Ub is shown by a dashed line. The portion Ua indicates an area in which at least a portion of the fixture exists regardless of the state of the movable part of the fixture. The portion Ub indicates an area in which a portion of the fixture exists or does not exist depending on the state of the movable part of the fixture. In other words, the portion Ub indicates an area excluding the portion Ua from the area through which the movable part of the fixture passes. As an example, the portion Ub is an area through which a drawer provided in the fixture passes when it is pulled out. In this case, part Ub is usually the area that indicates the state in which the drawer is pulled out to its maximum. As another example, part Ub indicates the area through which a hinged door of the fixture passes when opening and closing. For this reason, a fixture model U of a fixture that does not have a movable part does not include part Ub. Even if the fixture model U of a fixture has a movable part, for example, if the movable part is a sliding door and there is no difference in the shape of the fixture viewed from above regardless of the state of the movable part (whether open or closed), the fixture model U does not include part Ub.

Area ARe displays the dimensions associated with the fixture ID.

The operator of the server 30 looks at the information displayed in areas ARb to ARe and selects the furniture model that he or she wishes to place within the selection range SA of the environmental map Ma. The furniture model in question is a model of the furniture installed in the range corresponding to the selection range SA of area A. The furniture model selected here will hereinafter be referred to as the "selected model."

In ACT 15, processor 31 waits for an operation to select a furniture model. That is, processor 31 waits for a predetermined operation, such as operating any of the areas AR, to be performed. If an operation to select a furniture model is performed, processor 31 determines Yes in ACT 15 and proceeds to ACT 16.
Therefore, the processor 31 cooperates with the input device 35 to process ACT 15, thereby functioning as an input section that receives input information indicating the type of fixture.

In ACT 16, the processor 31 acquires feature data associated with the selected model.
Therefore, by performing the process of ACT 15, the processor 31 functions as a first acquisition unit that acquires feature amount data.

In ACT 17, the processor 31 obtains the usage algorithm associated with the selected model.

In ACT 18, the processor 31 executes the algorithm acquired in ACT 17. An example of the algorithm is described below.

Figure 9 is a flowchart showing an example of processing related to the algorithm acquired in ACT 17 by the processor 31 of the server 30. The processor 31 executes the processing of Figure 9 based on a program stored in, for example, the ROM 32 or the NVM 34.

In ACT 31 of FIG. 9, the processor 31 of the server 30 identifies the position of the fixture by comparing the feature amount indicated by the feature amount data acquired in ACT 16 of FIG. 8 with the feature amount extracted from within the selection range SA of the detection data. The feature amount data used here will be explained with reference to FIG. 10.

Figure 10 is a diagram for explaining an example of a method for creating feature amount data. As shown in Figure 10, the processor 31 extracts the foreground surface from the fixture model U as a plane, and detects feature points Pa of the plane. The processor 31 then generates template data TE indicating the feature points Pa detected in this manner, and stores the template data TE in the NVM 34 or the like. The feature amount data includes the template data TE.

FIG. 11 is a diagram for explaining the comparison of feature amount data and detection data. Processor 31 extracts a plane from within the selection range SA of the detection data. Processor 31 then detects feature points Pb of the plane. Processor 31 further associates feature points Pa of template data TE with feature points Pb of the plane. Processor 31 then considers a portion where the degree of match between feature points Pa and Pb is equal to or greater than a threshold as a match between the plane indicated by template data TE and the plane extracted from the detection data. In other words, processor 31 considers the portion where the degree of match between feature points Pa and Pb is equal to or greater than a threshold as the foreground of the fixture corresponding to the selected model. Processor 31 may extract multiple planes from within the selection range SA of the detection data and perform a similar comparison.

In ACT 32, the processor 31 determines whether or not there is a portion in the processing of ACT 31 where the degree of match is equal to or greater than the threshold. If there is a portion in which the degree of match is equal to or greater than the threshold, the processor 31 determines Yes in ACT 32 and proceeds to ACT 33.

In ACT 33, the processor 31 places the selected model at each position on the environmental map Ma that corresponds to the portion where the degree of matching is equal to or greater than the threshold. That is, the processor 31 places the selected model on the environmental map Ma so that the foreground of the selected model matches the plane where the degree of matching is equal to or greater than the threshold. After the processing of ACT 33, the processor 31 ends the processing shown in FIG. 9.

On the other hand, if there is no part where the degree of matching is equal to or greater than the threshold in the processing of ACT 31, the processor 31 judges No in ACT 32 and proceeds to ACT 34. If there is no part within the selection range SA where the degree of matching is equal to or greater than the threshold, it can be assumed that no fixture corresponding to the selected model is installed within the selection range SA. Note that if the processor 31 is unable to extract a plane from the detection data, it also judges No in ACT 32 as there is no part where the degree of matching is equal to or greater than the threshold.

In ACT 34, the processor 31 controls the display device 36 to display an image including an error message indicating that no fixture corresponding to the selected model is installed within the selection range SA. After processing in ACT 34, the processor 31 ends the processing shown in FIG. 9.

9, the processor 31 ends the process of ACT 18 and returns to ACT 11. In this way, the processor 31 places the furniture model U on the environmental map Ma by repeating the processes of ACT 11 to ACT 18. As a result, the processor 31 obtains an environmental map Mb in which the furniture model U is placed on the environmental map Ma as shown in FIG. 12. FIG. 12 is a diagram showing an example of the environmental map Mb. The processor 31 stores data indicating the environmental map Mb in the NVM 34.
As described above, by performing the processing of ACT18, the processor 31 functions as a processing unit that determines the position of the fixture using distance point data and a predetermined algorithm.

When the operator of the server 30 wishes to finish arranging the fixture model U, he or she operates the end button Bb.
When the processor 31 is in the standby state of ACT 12 and ACT 13, if an operation is performed to end the arrangement of the fixture model, such as operating the end button Bb, the processor 31 judges Yes in ACT 13 and proceeds to ACT 19.

In ACT 19, the processor 31 creates an environmental map Mc for setting a movement path for the autonomous mobile robot 20. After processing in ACT 19, the processor 31 ends the processing shown in FIG. 5.

Figure 13 is a diagram showing an example of the environmental map Mc. The environmental map Mc is an environmental map in which no-entry areas K for the autonomous mobile robot 20 are set for the environmental map Ma or the environmental map Mb. Note that in Figure 13, the no-entry areas K are shown with thick lines for ease of understanding. The no-entry areas K are areas into which the autonomous mobile robot 20 is prohibited from entering. The no-entry areas are treated as virtual objects. For example, the processor 31 sets an area that includes a furniture model U arranged on the environmental map Mb as the no-entry area K. The processor 31 stores data indicative of the environmental map Mc in the NVM 34.

The processor 31 has a function of setting a movement path for the autonomous mobile robot 20 using the environmental map Mc, as will be described below.
FIG. 14 is a diagram showing an example of setting a travel route using the environmental map Mc.
The processor 31 accepts an input from the user using the input device 35 to start a reading operation. Next, the processor 31 acquires data showing the environmental map Mc from the NVM 34. Then, the processor 31 uses the environmental map Mc to set a movement route from the current position Qa to the work start position Qb. The processor 31 sets a movement route that avoids the entry-prohibited area K so that the autonomous mobile robot 20 does not enter the entry-prohibited area K. It is preferable that the processor 31 sets a movement route that passes through a position that is away from the entry-prohibited area K by at least an amount that takes into account the size of the autonomous mobile robot 20. This is because if the entry-prohibited area K exactly abuts an object and the movement route exactly abuts the entry-prohibited area K, the autonomous mobile robot 20 is highly likely to come into contact with the object. Similarly, the processor 31 uses the environmental map Mc to set a movement route from the work start position Qb to the target position.

According to the reading system 1 of the first embodiment, the server 30 receives an input to select the type of fixture to be placed on the environmental map Ma. The server 30 then places a fixture model of the fixture on the environmental map Ma using a predetermined algorithm. Therefore, the server 30 of the first embodiment can place the fixture model in a more accurate position than if a person were to place the fixture model on the environmental map Ma.

Furthermore, according to the reading system 1 of the first embodiment, the server 30 places the furniture model at a position where the feature points Pa and Pb coincide. Therefore, the server 30 of the first embodiment can place the furniture model at an accurate position on the environmental map Ma.

Furthermore, according to the reading system 1 of the first embodiment, the server 30 sets the no-entry area K using the furniture model that is accurately positioned as described above. Therefore, the server 30 of the first embodiment can set the no-entry area K in an accurate position.

Furthermore, according to the reading system 1 of the first embodiment, the server 30 determines the position of the fixture using an algorithm associated with the selection model. Therefore, the server 30 of the first embodiment can use an algorithm suitable for each fixture.

Second Embodiment
The reading system 1 of the second embodiment identifies the type of fixture by reading information stored in a marker attached to the fixture.
The configuration of the reading system 1 of the second embodiment is similar to that of the first embodiment, and therefore a description thereof will be omitted.

In the second embodiment, as shown in FIG. 11, a marker MK is attached to each fixture installed in area A. The marker MK is a barcode or a two-dimensional code. The marker MK attached to the fixture stores the fixture ID of the fixture model corresponding to the fixture.

The operation of the reading system 1 according to the second embodiment will be described below with reference to Figs. 15, 16, 9, etc. Note that the contents of the process in the following operation description are merely examples, and various processes capable of obtaining similar results can be used as appropriate. Fig. 15 is a flowchart showing an example of the process by the processor 31 of the server 30. The processor 31 executes the process of Fig. 15 based on a program stored in, for example, the ROM 32 or the NVM 34. Fig. 16 is a flowchart showing an example of the process by the processor 11 of the system controller 10. The processor 11 executes the process of Fig. 16 based on a program stored in, for example, the ROM 12 or the NVM 14.

In ACT 41 of FIG. 15, the processor 31 of the server 30 instructs the communication I/F 37 to send a start instruction to the system controller 10. The start instruction is information that instructs the system controller 10 to start detecting markers using the autonomous mobile robot 20. Upon receiving the instruction to send, the communication I/F 37 sends the start instruction to the system controller 10. The sent start instruction is received by the communication I/F 15 of the system controller 10.

Meanwhile, in ACT 51 of FIG. 16, the processor 11 of the system controller 10 waits for a start instruction to be received by the communication I/F 15. If a start instruction is received, the processor 11 judges Yes in ACT 51 and proceeds to ACT 52.

In ACT 52, the processor 11 controls the autonomous mobile robot 20 to start moving within area A. Based on this control, the autonomous mobile robot 20 moves while taking images with the camera 25. The images taken by the camera 25 are input to the system controller 10.

In ACT 53, processor 11 determines whether or not a marker attached to a fixture has been detected. Processor 11 performs image recognition on the image captured by camera 25. If a marker is present in the image captured by camera 25, processor 11 detects the presence of the marker. If processor 11 has not detected a marker, processor 11 determines No in ACT 53 and proceeds to ACT 54.

In ACT 54, the processor 11 determines whether or not to end the movement. For example, the processor 11 determines to end the movement when it determines that the autonomous mobile robot 20 has sufficiently gone around area A. If the processor 11 does not determine to end the movement, it determines No in ACT 54 and returns to ACT 53. Thus, the processor 11 enters a standby state in which ACT 53 and ACT 54 are repeated until the processor 11 detects a marker or determines to end the movement.

If the processor 11 detects a marker while in the standby state of ACT 53 and ACT 54, it judges Yes in ACT 53 and proceeds to ACT 55. The marker detected here is hereinafter referred to as the "detected marker."

In ACT 55, the processor 11 reads the fixture ID stored in the detection marker by image recognition or the like. The processor 11 also measures the position of the detection marker on the environmental map Ma. The processor 11 may use various known methods to measure the position of the marker.

In ACT 56, the processor 11 generates detection information. The detection information is information that notifies that a marker has been detected. The detection information includes the fixture ID and detection position information acquired in ACT 55. The detection position information is, for example, information that can identify the position of the detection marker on the environmental map Ma. After generating the detection information, the processor 11 instructs the communication I/F 15 to transmit the detection information to the server 30. Upon receiving this transmission instruction, the communication I/F 15 transmits the detection information to the server 30. The transmitted detection information is received by the communication I/F 37 of the server 30. After processing in ACT 56, the processor 31 returns to ACT 53.

On the other hand, in ACT 42 of FIG. 15, the processor 31 of the server 30 determines whether or not detection information has been received by the communication I/F 37. If detection information has not been received, the processor 31 determines No in ACT 42 and proceeds to ACT 43.

In ACT 43, the processor 31 determines whether or not termination information has been received by the communication I/F 37. If termination information has not been received, the processor 31 determines No in ACT 43 and returns to ACT 42. Thus, the processor 31 enters a standby state in which ACT 42 and ACT 43 are repeated until detection information or termination information is received. The termination information will be described later.

If the processor 31 receives detection information while in the standby state in ACT 42 and ACT 43, the processor 31 judges that the result is Yes in ACT 42 and proceeds to ACT 44. The detection information received here is hereinafter referred to as "received detection information".
The received detection information includes a fixture ID indicating the type of the fixture. Therefore, the processor 31 cooperates with the communication I/F 37 to perform the process of ACT42, thereby functioning as an input unit that receives input of information indicating the type of the fixture.

In ACT 44, the processor 31 determines a range including the position on the environmental map Ma indicated by the detection position information included in the received detection information as the selection range SA. For example, the processor 31 determines a range within a predetermined distance from the position on the environmental map Ma indicated by the detection position information as the selection range SA.
Therefore, by performing the process of ACT42, the processor 31 cooperates with the communication I/F 37 to function as an input unit that receives input of the selection range SA.

In ACT45, the processor 31 refers to the fixture table T and acquires feature data associated with the fixture ID included in the received detection information.
Therefore, by performing the process of ACT45, the processor 31 functions as a first acquisition unit that acquires feature amount data.

In ACT 46, the processor 31 refers to the fixture table T and obtains the usage algorithm associated with the fixture ID included in the received detection information.

In ACT 47, the processor 31 executes the algorithm acquired in ACT 46. The algorithm is the same as that in the first embodiment. That is, an example of the process of the algorithm is the process shown in FIG. 9. However, in the second embodiment, in ACT 31 of FIG. 9, the processor 31 uses the feature amount data acquired in ACT 45 of FIG. 15. With the end of the process of FIG. 9, the processor 31 ends the process of ACT 47 and returns to ACT 42. As described above, the processor 31 of the server 30 repeats ACT 42 to ACT 47 of FIG. 15, and the processor 11 of the system controller 10 repeats the processes of ACT 53 to ACT 56 of FIG. 16. As a result, the processor 31 obtains an environmental map Mb in which the furniture model U is arranged on the environmental map Ma as shown in FIG. 12.
As described above, by performing the processing of ACT47, the processor 31 functions as a processing unit that determines the position of the fixture using distance point data and a predetermined algorithm.

On the other hand, if the processor 11 determines to end the movement when in the standby state of ACT 53 and ACT 54 in FIG. 16, it judges Yes in ACT 54 and proceeds to ACT 57.

In ACT 57, the processor 11 controls the autonomous mobile robot 20 to end the movement.

In ACT 58, the processor 11 instructs the communication I/F 15 to send end information to the server 30. The end information notifies that detection of markers in area A has ended and that the movement of the autonomous mobile robot 20 has ended. In response to this instruction to send, the communication I/F 15 sends the end information to the server 30. The sent end information is received by the communication I/F 37 of the server 30. After processing ACT 58, the processor 11 returns to ACT 51.

On the other hand, if the processor 31 of the server 30 receives termination information while in the standby state of ACT 42 and ACT 43 of FIG. 15, it judges Yes in ACT 43 and proceeds to ACT 19.

According to the reading system 1 of the second embodiment, the server 30 can obtain the same effects as those of the first embodiment.
Furthermore, according to the reading system 1 of the second embodiment, the server 30 identifies the type of fixture by using the fixture ID stored in the marker. Therefore, the server 30 of the second embodiment does not require a person to take the trouble of inputting the type of fixture.

Furthermore, according to the reading system 1 of the second embodiment, the server 30 reads the information stored in the marker by optical means.

Third Embodiment
In the reading system 1 of the third embodiment, the marker attached to the fixture is a wireless tag or the like.
The configuration of the reading system 1 of the third embodiment is similar to that of the first and second embodiments, and therefore a description thereof will be omitted.

In the third embodiment, as in the second embodiment, as shown in FIG. 11, a marker MK is attached to each fixture installed in area A. However, the marker MK in the third embodiment is a wireless tag such as an RFID tag, or other device capable of communication and storage. The marker MK attached to the fixture stores the fixture ID of the fixture model corresponding to that fixture.

The reading system 1 of the third embodiment performs the processes shown in Figures 15, 16, and 9, similar to the second embodiment. However, the reading system 1 of the third embodiment differs from the second embodiment in the following respects.

In the third embodiment, the autonomous mobile robot 20 moves while detecting the marker MK using the reader 21.

In the third embodiment, in ACT 53 of FIG. 16, the processor 11 of the system controller 10 determines whether or not the marker MK is detected by the reader 21.
In the third embodiment, in ACT 55, the processor 11 communicates with the detection marker using the reader 21 to read the fixture ID stored in the detection marker.

According to the reading system 1 of the third embodiment, the server 30 can obtain the same effect as the second embodiment.

The above embodiment may be modified as follows.
In the above embodiment, the processor 31 of the server 30 detects the feature points Pa from the furniture model U. However, the feature points Pa may be detected from an actual furniture.

The reading system 1 may perform detection of the detection data and the marker simultaneously or in parallel. In this case, the autonomous mobile robot 20 does not need to move to the same location multiple times to obtain the detection data and detect the marker.

Processor 11 or processor 31 may implement some or all of the processing implemented by the program in the above embodiments through a hardware circuit configuration.

Each device in the above embodiment is transferred to the administrator of the device, for example, with the program for executing each of the above processes stored therein. Alternatively, each device is transferred to the administrator without the program stored therein. The program is then transferred separately to the administrator, and stored in each device based on an operation by the administrator or a serviceman. The program can be transferred in this case using a removable storage medium such as a disk medium or semiconductor memory, or by downloading via the Internet or a LAN, for example.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims.
The invention as originally claimed in the present application is set forth below.
[C1]
An input unit that receives input of information indicating the type of fixture;
A first acquisition unit that acquires shape data corresponding to the type;
a second acquisition unit that acquires distance point data indicative of a position of an object;
An information processing device comprising: a processing unit that determines the position of the fixture of the type using the distance point data and a predetermined algorithm.
[C2]
The information processing device described in [C1], wherein the processing unit uses the algorithm to determine that the fixture is located at a position where the features indicated by the shape data and the features indicated by the distance point data match.
[C3]
The information processing device described in [C1] or [C2], wherein the input unit receives input of the information indicating the type of the fixture stored in a marker attached to the fixture.
[C4]
The input unit receives an input of an area in which the fixtures are installed,
The processing unit determines the position of the fixture within the range.
The information processing device according to any one of [C1] to [C3].
[C5]
The processing unit uses the algorithm corresponding to the type.
The information processing device according to any one of [C1] to [C4].

1...Reading system, 10...System controller, 11, 31...Processor, 12, 32...ROM, 13, 33...RAM, 14, 34...NVM, 15, 37...Communication I/F, 20...Autonomous mobile robot, 21, 211, 212, 213, 214, 215, 216...Reader, 22, 221, 222...3D distance sensor, 23...Horizontal LRF, 24...Vertical LRF, 25...Camera, 26...Moving mechanism, 3 0: server, 35: input device, 36: display device, 201: trolley, 202: wheels, 261: travel controller, 262: motor, 263: encoder, 264: IMU, K: no-entry area, Ma, Mb, Mc: environmental map, MK: marker, Pa, Pb: feature point, Qa: current position, Qb: work start position, T: furniture table, TE: template data, U: furniture model, Ua, Ub: part

Claims (5)

an input unit that receives an input of an area in which fixtures selected by an operator on an environmental map are installed and receives an input of information indicating the type of the fixtures;
A first acquisition unit that acquires shape data corresponding to the type;
a second acquisition unit that acquires distance point data indicative of a position of an object;
An information processing device comprising: a processing unit that determines the position of the fixture of the type within the range using the distance point data and a predetermined algorithm. An input unit that receives input of information indicating the type of the fixture stored in a marker attached to the fixture ;
A first acquisition unit that acquires shape data corresponding to the type;
a second acquisition unit that acquires distance point data indicative of a position of an object;
An information processing device comprising: a processing unit that determines the position of the fixture of the type using the distance point data and a predetermined algorithm. The information processing device according to claim 1 or 2 , wherein the processing unit uses the algorithm that determines that the fixture is located at a position where a feature amount indicated by the shape data and a feature amount indicated by the distance point data match. The processing unit uses the algorithm corresponding to the type.
The information processing device according to claim 1 . The information processing device according to claim 1 , wherein the input unit receives an input of the information indicating the type of the fixture, the information being stored in a marker attached to the fixture.