Automatic transport system, information processing device, mobile body, information processing method, and program

Abstract

To provide an automatic transport system, an information processing device, a mobile body, an information processing method, and a program that can improve transport efficiency in a work area where transport work is performed by multiple automatic transport vehicles.SOLUTION: An automatic transport system according to an embodiment includes multiple automatic transport vehicles, a sensor, and a control unit. The sensor acquires information related to the position of an object in a work area. The control unit controls the multiple automatic transport vehicles. The control unit includes a route information acquisition unit, a congestion degree evaluation unit, and an automatic transport vehicle selection unit. The route information acquisition unit acquires, for each of the multiple automatic transport vehicles, route information indicating the route along which the automatic transport vehicle travels to the source of the object. The congestion degree evaluation unit evaluates a congestion degree distribution, which indicates the degree of congestion of objects in the work area, based on information related to the position of the object. The automatic transport vehicle selection unit selects an automatic transport vehicle from the multiple automatic transport vehicles to perform a transport task based on the route information and the congestion degree distribution.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] An embodiment of the present invention relates to an automated transport system, an information processing device, a mobile object, an information processing method, and a program. [Background technology]

[0002] BACKGROUND ART In order to solve the labor shortage in logistics and manufacturing sites, a technology in which an automatic conveying vehicle such as a movable robot conveys an object to be conveyed is known as one means for automating conveying operations in distribution warehouses, factory facilities, and the like. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-259337 Summary of the Invention [Problem to be solved by the invention]

[0004] The problem that the present invention aims to solve is to provide an automatic transport system, an information processing device, a mobile body, an information processing method, and a program that can improve transport efficiency in a work area where transport work is performed by multiple automatic transport bodies. [Means for solving the problem]

[0005] An embodiment of the automated transport system is an automated transport system for transporting objects within a work area. The automated transport system has multiple automated transport vehicles, a sensor, and a control unit. The multiple automated transport vehicles transport objects from a source to a destination. The sensor acquires information related to the position of the objects in the work area. The control unit controls the multiple automated transport vehicles. The control unit includes a route information acquisition unit, a congestion degree evaluation unit, and an automated transport vehicle selection unit. The route information acquisition unit acquires route information for each of the multiple automated transport vehicles indicating the route along which the automated transport vehicle will travel to the source of the object. The congestion degree evaluation unit evaluates a congestion degree distribution that indicates the degree of congestion of objects within the work area based on information related to the position of the object. The automated transport vehicle selection unit selects an automated transport vehicle from the multiple automated transport vehicles to perform a transport task based on the route information and the congestion degree distribution. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a schematic diagram showing an automatic transfer system according to a first embodiment. [Figure 2] FIG. 1 is a block diagram showing the system configuration of an automatic transport system according to a first embodiment. [Figure 3] 3 is a schematic diagram showing the congestion distribution in a work area and the routes set for each automated transport vehicle in the first embodiment. FIG. [Figure 4] 4 is a flowchart showing the flow of processing in the automatic transport system according to the first embodiment. [Figure 5] FIG. 10 is a schematic diagram showing an automatic transfer system according to a second embodiment. [Figure 6] FIG. 10 is a block diagram showing the system configuration of an automatic transport system according to a third embodiment. [Figure 7] FIG. 10 is a block diagram showing the system configuration of an automatic transport system according to a fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, an automatic conveying system, an information processing device, a moving body, an information processing method, and a program according to embodiments will be described with reference to the drawings. Note that the drawings are schematic or conceptual, and the relationship between the thickness and width of each part, the size ratio between parts, etc., are not necessarily the same as those in reality. Furthermore, even when the same part is shown, the dimensions and ratios may be different depending on the drawing.

[0008] In this specification, "based on XX" means "based on at least XX," and includes cases where it is based on other elements in addition to XX. Furthermore, "based on XX" is not limited to cases where XX is used directly, but also includes cases where it is based on XX that has been calculated or processed. "XX" is any element (for example, information).

[0009] (First embodiment) An automatic transfer system 1 according to a first embodiment will be described with reference to FIGS. First, the configuration of the automatic transfer system 1 will be described below with reference to FIGS.

[0010] Fig. 1 is a schematic diagram showing an automatic transfer system 1. Fig. 2 is a block diagram showing the system configuration of the automatic transfer system 1. As shown in FIGS. 1 and 2, the automated guided transport system 1 includes multiple automated guided transport vehicles 10 (10A-10C), one or more fixed cameras 20, a control unit 30, an input unit 40, and a notification unit 45. The automated guided transport system 1 transports an object O within a work area W using the automated guided transport vehicles 10. The work area W is not particularly limited, and may be, for example, a distribution warehouse, a factory facility, or a research laboratory. The object O may be, but is not particularly limited, a material, a product, equipment, cargo, a container, or the like. As shown in FIG. 1, the work area W contains automated guided transport vehicles 10 (three automated guided vehicles 10A, 10B, and 10C in FIG. 1) that are not performing transport work, as well as a worker B1, a temporary installation B2 (such as luggage), an environmental structure B3 (such as a pillar), and an automated guided transport vehicle B4 that is currently working.

[0011] In response to a transport instruction, the automated guided vehicle 10 first moves to the origin T0 to load the cargo, and then moves from the origin T0 to the destination T1 to unload the cargo. For example, the cargo, which is the object to be transported O, is sorted by destination at the origin T0 and loaded onto a cart. This loading onto the cart may be done manually or by using a separate automatic loading system. Once the cargo has been loaded onto the cart, a transport instruction to the destination T1 corresponding to the destination among multiple destinations is issued. The transport instruction includes position information of the origin T0 and the destination T1. The control unit 30 acquires this transport instruction and issues instructions to each automated guided vehicle 10, controlling the automated guided vehicles 10 so that the transport operation proceeds efficiently throughout the entire work area W. In response to an instruction from the control unit 30, the automated guided vehicle 10 first moves to the origin T0 where the cart loaded with the cargo is located, couples with the cart, and then moves to the specified destination T1 while transporting the cart.

[0012] The form of the automated transport vehicle 10 is not particularly limited and may be any mobile body. For example, the automated transport vehicle 10 may be in the form of a vehicle, a movable robot having a moving means such as wheels, caterpillar tracks, or walking legs, or a mobile body guided by a guide unit such as a rail. The automated transport vehicle 10 may also be a flying vehicle, but the following description will mainly focus on an automated transport vehicle 10 that moves on a floor surface.

[0013] As shown in FIG. 2, the automated guided vehicle 10 includes a vehicle body 12, a vehicle control unit 14, a position information acquisition unit 16, and an obstacle detection unit 18. The vehicle body 12 can travel by any means. The vehicle control unit 14 controls the movement of the vehicle body 12. The vehicle control unit 14 can communicate with the control unit 30 wirelessly or via a wired connection via a communication unit (not shown) mounted on the automated guided vehicle 10. The position information acquisition unit 16 identifies the position coordinates of the automated guided vehicle 10 by any known means. The obstacle detection unit 18 detects obstacles present around the automated guided vehicle 10. The specific means of the obstacle detection unit 18 is not particularly limited, and any known sensor such as a sonar sensor, ultrasonic sensor, or infrared sensor can be used.

[0014] The conveyance body control unit 14 can control the conveyance body 12 to stop or slow down when the obstacle detection unit 18 detects an obstacle. For example, when the obstacle detection unit 18 detects an obstacle ahead on the travel route, the conveyance body control unit 14 can stop the conveyance body 12 so that the conveyance body 12 does not collide with the obstacle.

[0015] One or more fixed cameras 20 (an example of a "sensor") are provided as optical sensors in the work area W. The positions of the fixed cameras 20 are not particularly limited, but it is preferable that the multiple fixed cameras 20 are installed so that they can capture as wide an area as possible within the work area W as a whole.

[0016] The fixed camera 20 acquires image data including images or videos of the work area W and transmits the image data to the control unit 30 wirelessly or via a wired connection. The image data may be transmitted from the fixed camera 20 to the control unit 30 via another information processing device. The control unit 30 can associate pixel positions in the image data with physical locations within the work area W based on the installation position and viewing angle of the fixed camera 20 and the image data of the fixed camera 20. When multiple fixed cameras 20 are installed, the control unit 30 may aggregate the image data captured by each fixed camera 20 to acquire image data of the entire work area W. The image data captured by the fixed camera 20 is an example of information regarding the positions of objects in the work area W (here, "objects" includes people such as a worker B1, objects temporarily installed in the work area W such as a temporary installation B2, objects regularly installed in the work area W such as an environmental structure B3, an automated transport vehicle B4 in operation, an automated transport vehicle 10 not performing transport work, etc.). The information about the position of the object can be used to recognize the position of the object in the work area W. The information about the position of the object is, for example, optically acquired information. An example of optically acquired information is an image (including video) of the work area W.

[0017] The control unit 30 (not shown in FIG. 1) may be composed of one or more information processing devices. The control unit 30 is connected to the automated transport vehicle 10 wirelessly or via a wire and is provided in a position where it can control the automated transport vehicle 10. The configuration of the control unit 30 will be described later.

[0018] The input unit 40 receives instruction information including a transport instruction. For example, the input unit 40 receives an input operation of a transport instruction by a user. There is no particular limitation on the method of inputting the instruction to the input unit 40. The input unit 40 may be implemented as a part of the control unit 30.

[0019] In response to a signal from the control unit 30, the notification unit 45 outputs a sound, an image, or the like to the worker B1 in the work area W to notify the worker B1.

[0020] As shown in Fig. 2, the control unit 30 includes, as its functional units, an acquisition unit 32, a communication unit 34, a storage unit 36, and a processing unit 38. The acquisition unit 32 acquires information from the outside. The communication unit 34 communicates with the outside, receiving signals from the outside and transmitting signals to the outside. The storage unit 36 ​​stores various data including programs. The processing unit 38 executes various arithmetic processes, which will be described later.

[0021] The acquisition unit 32 can acquire the photographic data received from the fixed camera 20 via the communication unit 34. The acquisition unit 32 can also acquire instruction information including a transport instruction received by the input unit 40.

[0022] The processing unit 38 includes, as its functional units, a congestion evaluation unit 50, a route setting unit 52, a travel time calculation unit 54, an automated transport unit selection unit 56, and an instruction generation unit 58. By having these functional units, the control unit 30 can select the automated transport unit 10 that is optimal for the transport task from among multiple automated transport units 10, taking into account the congestion level in the work area W. Below, the control processing in the automated transport system 1 will be explained through an explanation of each functional unit.

[0023] The congestion evaluation unit 50 evaluates the congestion level within the work area W based on the photographic data acquired by the acquisition unit 32 from the fixed camera 20. Here, "congestion level" refers to a quantity representing the degree of congestion of potential obstacles, such as objects, people, and automated guided vehicles, at a certain position within the work area W, which may impede the movement of the target automated guided vehicle 10A-10C. Furthermore, an "obstacle" here refers to a physical object present within the work area W that may impede the movement of the target automated guided vehicle at a certain time. If the movement path of the target automated guided vehicle is preset within the work area W, the degree of congestion of obstacles present in the area including the movement path is recognized as the congestion level. The congestion evaluation unit 50 can evaluate a congestion level distribution, which maps the congestion level for each position within the work area W.

[0024] 3 is a schematic diagram showing the congestion degree distribution within the work area W and the routes R0 and R1 set for each of the automated guided vehicles 10A-10C. At position P1, there are many obstacles (any of B1-B4), so the congestion degree value is high. At position P2, there are relatively few obstacles (any of B1-B4), so the congestion degree value is low. The congestion degree at position P3 is approximately intermediate between positions P1 and P2.

[0025] Here, three types of methods will be specifically described as the method by which the congestion degree evaluation unit 50 evaluates the congestion degree, but the method is not limited to these. (1) A method for assessing congestion based on floor area (2) A method for assessing crowding levels based on object recognition on the floor (3) A method for assessing congestion using a trained model based on machine learning

[0026] In method (1), the congestion degree evaluation unit 50 recognizes the floor surface of the work area W from the image data acquired from the fixed cameras 20 and evaluates the size of the floor surface. For example, the congestion degree evaluation unit 50 can combine the image data acquired by each fixed camera 20 to generate image data of the entire work area W and recognize the floor surface within the work area W. Next, the congestion degree evaluation unit 50 compares the normal floor size stored in the memory unit 36 ​​with the floor size evaluated from the image data to calculate the floor concealment rate. Here, the "floor concealment rate" refers to the proportion of the unexposed floor area per unit floor area. The congestion degree evaluation unit 50 can determine the floor concealment rate at each position in the work area W as the congestion degree at that position by assuming that an obstacle exists in the unexposed portion of the floor. The congestion degree evaluation unit 50 can obtain a distribution of the floor concealment rate mapped to each position on the floor as a congestion degree distribution. The congestion degree evaluation unit 50 may determine the congestion degree as a value obtained by performing any calculation process on the floor surface concealment rate.

[0027] In method (2), the congestion assessment unit 50 recognizes non-stationary objects (e.g., workers B1, temporary installations B2, and automated guided vehicles B4) temporarily present within the work area W from the image data acquired from the fixed cameras 20. Here, a "non-stationary object" refers to an object (e.g., a person) that can change its position within the work area W, is temporarily located at a certain position, and is movable from there. For example, the congestion assessment unit 50 can combine the image data captured by each fixed camera 20 to generate image data of the entire work area W and recognize non-stationary objects within the work area W. The congestion assessment unit 50 calculates the occupancy rate of non-stationary objects by comparing the normal floor size stored in the memory unit 36 ​​with the area occupied by non-stationary objects. Here, the "occupancy rate of non-stationary objects" refers to the ratio of the area occupied by non-stationary objects per unit area of ​​floor. Ideally, the floor obscuration rate matches the occupancy rate of non-stationary objects. The congestion degree evaluation unit 50 can regard non-stationary objects as obstacles and determine the occupancy rate of non-stationary objects at each position in the work area W as the congestion degree at that position. The congestion degree evaluation unit 50 can obtain a distribution in which the occupancy rates of non-stationary objects are mapped to each position on the floor surface as a congestion degree distribution. Note that the congestion degree evaluation unit 50 may determine the congestion degree as a value obtained by performing any arithmetic processing on the occupancy rate of non-stationary objects. Note that the congestion degree evaluation unit 50 may evaluate the congestion degree based on the occupancy rate of not only non-stationary objects but also objects including stationary objects that cannot change their position within the work area W, such as the environmental structure B3.

[0028] In method (3), the congestion evaluation unit 50 uses a trained model that has learned the relationship between the photographic data of the work area W and the congestion distribution, and outputs the congestion distribution within the work area W in response to input of photographic data (or data processed from the photographic data) acquired from the fixed camera 20. The training method for the trained model is not particularly limited, and any known method such as deep learning can be used. The trained model can be trained in advance and stored in the storage unit 36.

[0029] The congestion degree evaluation unit 50 may not only evaluate the congestion degree distribution at a certain point in time, but may also acquire the congestion degree distribution that changes over time in real time. Furthermore, the congestion degree evaluation unit 50 may predict the congestion degree distribution after a predetermined time based on the change in the congestion degree distribution over time. For example, the congestion degree evaluation unit 50 may recognize the movement (e.g., movement direction and movement speed) of each unsteady object within the work area W and predict the position of the object after a predetermined time. The congestion degree evaluation unit 50 may calculate the position of each object after a predetermined time by assuming that the movement direction and movement speed of each object will be maintained, or may predict the movement of the object using any known algorithm or machine learning. The congestion degree evaluation unit 50 can predict the congestion degree distribution after a predetermined time by predicting the position of each object after a predetermined time for all unsteady objects.

[0030] In this way, the congestion degree evaluation unit 50 can automatically evaluate the congestion degree distribution from the photographed data without requiring input from outside (for example, a user or another information processing device) regarding the position or degree of congestion of non-stationary objects outside the system management.

[0031] The route setting unit 52 (an example of a "route information acquisition unit") sets routes R0 and R1 for transporting an object O within the work area W for each automated guided vehicle 10. Specifically, the route setting unit 52 acquires the current position of each automated guided vehicle 10, the current position of the object O (i.e., the position of the source T0), and the position of the destination T1 of the object O, and sets, for each automated guided vehicle 10, a route R0 from the current position to the source T0 and a route R1 from the source T0 to the destination T1. The current position of each automated guided vehicle 10 can be acquired by the position information acquisition unit 16 of the automated guided vehicle 10 and transmitted from the vehicle control unit 14 to the control unit 30. The position information of the source T0 and the destination T1 can be included in the transport instruction acquired by the acquisition unit 32.

[0032] The route setting unit 52 can set a different route R0 for each automatic transport vehicle 10 for the route R0 from the current position to the transport source T0, while setting a common route R1 for all automatic transport vehicles 10 for the route R1 from the transport source T0 to the transport destination T1. However, the route setting unit 52 may set a different route R1 for each automatic transport vehicle 10.

[0033] The path setting unit 52 may arbitrarily set a path on the floor of the work area W, or may set a path along a predetermined reference path within the work area W. For example, the reference path may be a path that passes through a travel path provided for the automated guided vehicle 10 on the floor, or a path that follows a rail on the floor. When a reference path is used, the path setting unit 52 can set a path according to a predetermined reference path without having to calculate the path. For example, the path setting unit 52 can set a path that allows the automated guided vehicle 10 to travel from a starting position to a destination position in the shortest distance. When there are multiple shortest paths, the path setting unit 52 may select and set one of the shortest paths based on any criterion (for example, so that the integrated value of the congestion degree at the positions through which the path passes is minimized), or may set all of the multiple shortest paths as candidate paths.

[0034] The travel time calculation unit 54 calculates the time required for each automated guided vehicle 10 to travel from its current position to its origin T0 along the route R0. The travel time calculation unit 54 also calculates the time required for each automated guided vehicle 10 to travel from its origin T0 to its destination T1 along the route R1. Hereinafter, the time required for an automated guided vehicle 10 to travel from one position to another will be referred to as the "travel time." For example, the travel time calculation unit 54 can calculate the travel time for each route of the automated guided vehicle 10 using basic information such as the travel speed profile of each automated guided vehicle 10 stored in the memory unit 36.

[0035] The travel time calculation unit 54 can calculate the travel time of the automated guided vehicle 10 taking into account the congestion level distribution calculated by the congestion level evaluation unit 50. For example, when calculating the travel time of the automated guided vehicle 10, the travel time calculation unit 54 can correct the calculation so that the travel time of the automated guided vehicle 10 is slower in sections on the route where the congestion level is high. The method of correcting the calculation is not particularly limited. For example, the travel time calculation unit 54 may add a stop time according to the congestion level to the travel time after a section where the congestion level is higher than a predetermined value, or may multiply the travel time by a coefficient that has an inverse correlation with the congestion level (for example, a coefficient that is inversely proportional to the congestion level).

[0036] When the congestion degree evaluation unit 50 predicts a time change in the congestion degree distribution, the travel time calculation unit 54 can calculate the travel time of the automated guided vehicle 10 taking the predicted congestion degree distribution into consideration. For example, the travel time calculation unit 54 predicts the position of the automated guided vehicle 10 at each time using information about the route set by the route setting unit 52 and basic information, such as the travel speed profile of each automated guided vehicle 10, stored in the memory unit 36. To take the predicted congestion degree into consideration, the travel time calculation unit 54 can compare information about the predicted position of the automated guided vehicle 10 at each time with the predicted congestion degree distribution at each time. For example, when the congestion degree at the predicted position of the automated guided vehicle 10 is high (e.g., greater than a predetermined threshold), the travel time calculation unit 54 predicts a time change in the position of the automated guided vehicle 10, assuming that the automated guided vehicle 10 will stop at the predicted position until the congestion is resolved and will resume movement when the congestion degree at the predicted position becomes sufficiently small (e.g., smaller than a predetermined threshold). In this way, the travel time calculation unit 54 can calculate the travel time taking into account future changes in the congestion level. Note that the calculation method is not limited to the above example.

[0037] The automated transport body selection unit 56 selects the automated transport body 10 that is most suitable for transporting the object O. For example, the automated transport body selection unit 56 can compare the travel times of each automated transport body 10 calculated by the travel time calculation unit 54 and select the automated transport body 10 that will transport the object O. Specifically, the automated transport body selection unit 56 can select the automated transport body 10 with the shortest travel time. If the travel time calculation unit 54 calculates the travel times for multiple routes, the automated transport body selection unit 56 can select the route with the shortest travel time. Once the route is selected, the automated transport body 10 is automatically selected. However, it is not necessary to select the automated transport body 10 with the shortest travel time; the automated transport body selection unit 56 may select the automated transport body 10 that will transport the object O by taking into consideration other conditions as well as the travel time. Here, the travel time used as the basis for selecting the automatic transport body 10 may be the travel time from the current position of the automatic transport body 10 to the origin T0, or the total travel time from the current position of the automatic transport body 10 to the origin T0 and then from the origin T0 to the destination T1, or the travel time for any other section.

[0038] The automated transport vehicle 10 selected based on the congestion level as described above does not necessarily have to have the shortest route R0 from the current location to the origin T0. In other words, the automated transport vehicle selection unit 56 can select the automated transport vehicle 10 most suitable for transporting the object O without identifying the automated transport vehicle 10 closest to the object O or the shortest route to the object O. Furthermore, the automated transport vehicle selection unit 56 can automatically select the optimal automated transport vehicle 10 without requiring human judgment. For example, as shown in FIG. 3, among the waiting automated transport vehicles 10A, 10B, and 10C, the automated transport vehicle 10B has the shortest distance from the current location to the origin T0, followed by the automated transport vehicle 10A, and the automated transport vehicle 10C, and the automated transport vehicle 10C has the longest distance. However, considering the congestion level distribution shown in FIG. 3, the automated transport vehicle 10C, which has no congestion between the current location and the origin T0, is expected to be the most efficient. Therefore, the automatic transport body selection unit 56 can select the automatic transport body 10C as the automatic transport body 10 that transports the object O.

[0039] However, the method for selecting an automated transporting body 10 is not limited to the above example. For example, the automated transporting body selection unit 56 may select an automated transporting body 10 to transport the object O based on information about the route of each automated transporting body 10 and the congestion level on the route, without calculating the travel time. Specifically, the automated transporting body selection unit 56 may add up the congestion levels on the route set by the route setting unit 52 for each automated transporting body 10 and select the automated transporting body 10 with the smallest sum of the congestion levels to transport the object O. When adding up the congestion levels on the route, the automated transporting body selection unit 56 may calculate the congestion level at each position on the route at the predicted time the automated transporting body 10 will pass, taking into account changes in the congestion level over time, and then add these up. The automated transporting body selection unit 56 may select an automated transporting body 10 to transport the object O by taking into account other conditions, such as the length of the route of each automated transporting body 10, in addition to the congestion level.

[0040] The instruction generation unit 58 generates a movement instruction that indicates a movement route for the automated transport body 10. For example, the movement instruction may include information on a route R0 from the current position of the automated transport body 10 selected by the automated transport body selection unit 56 to the origin T0 and a route R1 from the origin T0 to the destination T1. The control unit 30 transmits the movement instruction to the selected automated transport body 10 via the communication unit 34. The automated transport body control unit 14 of the automated transport body 10 controls the automated transport body main body 12 to move within the work area W in accordance with the received movement instruction.

[0041] The instruction generation unit 58 also generates a notification instruction for causing the notification unit 45 to issue a predetermined alert. Specifically, when the congestion degree distribution or the state of the automated transported vehicle 10 satisfies a predetermined condition, the instruction generation unit 58 can generate a notification instruction for causing the notification unit 45 to issue an alert to relieve the congestion. For example, when there is a position on the route with a high degree of congestion, the instruction generation unit 58 generates a notification instruction for causing the notification unit 45 to issue an alert to relieve the congestion at that position. Alternatively, when the instruction generation unit 58 determines that there is an automated transported vehicle 10 that has been waiting for a long time due to congestion in the work area W, it generates an instruction for causing the notification unit 45 to issue an alert to relieve the congestion on the movement route of the automated transported vehicle 10. For example, if the number of times that the automated transporting body 10 closest to the origin T0 has not been selected by the automated transporting body selection unit 56 exceeds a predetermined number of times, or if the standby time of the automated transporting body 10 exceeds a predetermined time, the instruction generation unit 58 determines that there is an automated transporting body 10 that has been waiting for a long time, and can generate an instruction to have the notification unit 45 issue an alert to relieve congestion on the movement path of the automated transporting body 10. Alternatively, if the degree of congestion on the path of each automated transporting body 10 is greater than a predetermined threshold, the instruction generation unit 58 can generate an instruction to have the notification unit 45 issue an alert to relieve congestion on the path. There are no particular limitations on the specific conditions for issuing an alert. The alert to relieve congestion may be, for example, an alert instructing a worker B1 on the path to move away, or an alert instructing a worker B1 to remove a temporary installation B2 on the path.

[0042] Next, the flow of control of the automatic transport body 10 by the automatic transport system 1 will be described with reference to FIG. FIG. 4 is a flowchart showing the flow of processing in the automatic transport system 1.

[0043] In step S100, the position information acquisition unit 16 of each automatic conveying body 10 acquires the position information of the automatic conveying body 10. In step S101, the conveying body control unit 14 of each automatic conveying body 10 transmits the acquired position information to the control unit 30. Note that while FIG. 4 shows that the automatic conveying body 10 acquires and transmits the position information only once, these operations can be performed continuously.

[0044] In step S200, the fixed camera 20 captures an image of the work area W. In step S201, the fixed camera 20 transmits the captured image data to the control unit 30. Note that although FIG. 4 shows that the fixed camera 20 captures and transmits the image only once, these operations may be performed continuously.

[0045] In step S300, the acquisition unit 32 acquires a transportation instruction from the input unit 40. In step S301, the congestion evaluation unit 50 evaluates the congestion distribution of the work area W based on the photographic data acquired from the fixed camera 20. In step S302, the route setting unit 52 sets the positions of the movement targets (origin T0 and destination T1) based on the acquired transportation instruction. In step S303, the route setting unit 52 sets a movement route R0 for each automated guided vehicle 10 to reach the origin T0 based on the position information of the automated guided vehicle 10. The route setting unit 52 can also set a movement route R1 from the origin T0 to the destination T1. In step S304, the travel time calculation unit 54 calculates the travel time of each automated guided vehicle 10 based on the congestion distribution evaluated in step S301 and the movement route set in step S303. In step S305, the automated transport body selection unit 56 selects the automated transport body 10 with the shortest travel time calculated in step S304 as the automated transport body 10 that will transport the target object O. In step S306, the instruction generation unit 58 generates a travel instruction for the automated transport body 10 selected in step S305 to travel to the origin T0. The instruction generation unit 58 can also generate a travel instruction for the selected automated transport body 10 to travel from the origin T0 to the destination T1. In step S307, the control unit 30 transmits the travel instruction generated in step S306 to the automated transport body 10 selected in step S305. In step S102, the selected automated transport body 10 travels to the origin T0, which is the travel destination, in accordance with the received travel instruction. If the automatic transport body 10 also receives a movement instruction from the transport source T0 to the transport destination T1, the automatic transport body 10 loads the object O at the transport source T0, and then moves to the next movement target, the transport destination T1, in accordance with the movement instruction to the transport destination T1.

[0046] According to the automatic transport system 1 of the first embodiment, when automating transport using an automatic transport body 10 such as a mobile robot at a logistics site or manufacturing site, the automatic transport body 10 can be selected so that it can move while avoiding congestion as much as possible.

[0047] To explain the advantages of the automated transport system 1 in detail, we will first provide an overview of conventional automated transport systems. In conventional automated transport technologies, the allocation of a mobile robot to perform the next transport task is often based on the mobile robot's transport task execution status, the estimated time until the current task is completed, the distance to the source of the transport, and other factors. However, since it is difficult to secure a dedicated space for mobile robots at logistics and manufacturing sites, mobile robots often move and transport in shared spaces that are also used by workers. For this reason, if workers are present in the space along the mobile robot's planned route, causing congestion, the mobile robot may have to wait, reducing overall transport efficiency.

[0048] In contrast, the automated transport system 1 according to the first embodiment can consider the degree of congestion on the transport route as a criterion for selecting an automated transport vehicle to carry out the transport in response to a transport request. This allows for preferential selection of an automated transport vehicle 10 that is less likely to cause a long wait due to congestion, thereby shortening the waiting time of the automated transport vehicle 10. By evaluating the degree of congestion in the work area W based on photographic data of non-stationary objects such as workers B1 and temporary installations B2, which are factors outside the control of the transport system, the optimal automated transport vehicle 10 can be selected according to the actual congestion. In this way, the efficiency of automated transport can be improved.

[0049] (Second embodiment) An automatic transport system 1 according to a second embodiment will be described with reference to Fig. 5. The second embodiment differs from the first embodiment in that the automatic transport system 10 is selected based on data acquired by a transport system camera 120 and / or an optical scanner 220 mounted on the automatic transport system 10. The following mainly describes the differences from the above embodiment, and does not repeat the description of the points in common with the above embodiment.

[0050] FIG. 5 is a schematic diagram showing an automatic transfer system 1 according to the second embodiment. In the second embodiment, as shown in FIG. 5, each automated transport vehicle 10 is equipped with a transport vehicle camera 120 and / or an optical scanner 220 as an optical sensor.

[0051] The vehicle camera 120 (an example of a "sensor") is a camera mounted on the vehicle body 12. Each automated vehicle 10 can capture images of its surroundings using the vehicle camera 120 while traveling or stopped. The captured image data (an example of "information relating to the position of an object") captured by the vehicle camera 120 is transmitted from the vehicle control unit 14 to the control unit 30.

[0052] The congestion evaluation unit 50 can evaluate the congestion distribution of the work area W using the image data captured by the vehicle camera 120 acquired from the automated guided vehicle 10. Specifically, the congestion evaluation unit 50 can aggregate the image data captured by the vehicle camera 120 of each automated guided vehicle 10 to recognize the position of objects in the work area W. The congestion evaluation unit 50 may evaluate the congestion level of the work area W based solely on the image data captured by the vehicle camera 120. However, since the field of view of the vehicle camera 120 of each automated guided vehicle 10 alone may be limited, it is preferable to use both the image data captured by the fixed camera 20 and the image data captured by the vehicle camera 120. The fixed camera 20 captures the entire work area W, but blind spots of the fixed camera 20 may be created by non-stationary objects, environmental structures B3, and the like. The vehicle camera 120 is mounted on the self-propelled automated guided vehicle 10, and can therefore compensate for such blind spots of the fixed camera 20. Therefore, by using the image data captured by the stationary camera 20 and the image data captured by the vehicle camera 120 in combination, the congestion degree evaluation unit 50 can reduce blind spots in the image recognition of the work area W for the congestion degree evaluation.

[0053] The optical scanner 220 (an example of a "sensor") is an optical measurement sensor mounted on the carrier body 12. The optical scanner 220 can acquire point cloud data of the work area W by irradiating light onto surrounding objects and detecting the reflected light. Any known optical scanner can be used as the optical scanner 220. The point cloud data acquired by the optical scanner 220 (an example of "information related to the position of the object") is transmitted from the carrier control unit 14 to the control unit 30.

[0054] The congestion evaluation unit 50 can evaluate the congestion distribution of the work area W using point cloud data of the optical scanner 220 acquired from the automated transport vehicle 10. Specifically, the congestion evaluation unit 50 can aggregate the point cloud data of the optical scanner 220 of each automated transport vehicle 10 to recognize the positions of objects, including people, in the work area W. The congestion evaluation unit 50 may evaluate the congestion of the work area W using only the point cloud data of the optical scanner 220. However, as with the vehicle camera 120, the optical scanner 220 of each automated transport vehicle 10 may have limited field of view. Therefore, it is preferable to use both the photographic data of the fixed camera 20 and the point cloud data of the optical scanner 220. Like the vehicle camera 120, the optical scanner 220 is mounted on the self-propelled automated transport vehicle 10, and can therefore compensate for blind spots of the fixed camera 20. Note that the optical scanner 220 may be mounted in the work area W, similar to the fixed camera 20, in addition to (or instead of) being mounted on the automated transport vehicle 10.

[0055] Only one of the vehicle camera 120 and the optical scanner 220 may be mounted on the automated transport vehicle 10, or both may be mounted on the automated transport vehicle 10. The congestion degree evaluation unit 50 may evaluate the congestion degree distribution of the work area W based on the photographic data of the vehicle camera 120 and the point cloud data of the optical scanner 220, without the fixed camera 20.

[0056] According to the second embodiment, information on parts of the work area W that cannot be sufficiently photographed by the fixed camera 20 alone can be acquired by the vehicle camera 120 and / or the optical scanner 220 mounted on the self-propelled automatic transport vehicle 10. This can improve the accuracy of the evaluation of the congestion degree distribution by the congestion degree evaluation unit 50.

[0057] (Third embodiment) An automatic conveying system 1 according to a third embodiment will be described with reference to Fig. 6. The third embodiment differs from the first embodiment in that the processing unit 38 includes a floor surface condition recognition unit 60, and the travel time calculation unit 54 calculates the travel time taking into consideration the recognition result of the floor surface condition recognition unit 60. Below, differences from the above embodiments will be mainly described, and explanations of points in common with the above embodiments will not be repeated.

[0058] FIG. 6 is a block diagram showing the system configuration of the automatic transport system 1 according to the third embodiment. In the third embodiment, the processing unit 38 of the control unit 30 includes a floor surface condition recognition unit 60, as shown in FIG.

[0059] The floor condition recognition unit 60 can recognize the condition of the floor of the work area W based on information about the positions of objects, such as the photographic data of the fixed camera 20, the photographic data of the carrier camera 120, and the point cloud data of the optical scanner 220. For example, the floor condition recognition unit 60 detects abnormalities such as wetness or soiling of the floor from images of the floor included in the various photographic data.

[0060] Information about the abnormality detected by the floor surface condition recognition unit 60 can be used by one or more of the route setting unit 52, the travel time calculation unit 54, and the automated transport vehicle selection unit 56. For example, the route setting unit 52 can set a route so as to avoid the floor surface abnormality. When the route passes through an abnormal part of the floor surface, the travel time calculation unit 54 can correct the travel time through the abnormal part (for example, so that it requires a longer travel time than usual). The automated transport vehicle selection unit 56 can exclude the automated transport vehicle 10 that passes through the floor surface abnormality from selection candidates.

[0061] According to the third embodiment, information about the positions of objects acquired by various sensors such as the fixed camera 20 can be used not only to evaluate the congestion distribution but also to recognize the condition of the route along which the automated transport vehicle 10 travels. This makes it possible to set the route of the automated transport vehicle 10 and select the automated transport vehicle 10 to transport goods, taking into account abnormalities on the floor surface.

[0062] (Fourth embodiment) An automatic transport system 1 according to a fourth embodiment will be described with reference to Fig. 7. The fourth embodiment differs from the first embodiment in that a transport body control unit 14 of the automatic transport body 10 performs the functions of the control unit 30 instead of the control unit 30. Below, differences from the above embodiments will be mainly described, and explanations of points in common with the above embodiments will not be repeated.

[0063] FIG. 7 is a block diagram showing the system configuration of the automatic transport system 1 according to the fourth embodiment. As shown in FIG. 7, the automated guided vehicle 10 (an example of a "mobile vehicle") according to the fourth embodiment includes, similarly to the first embodiment, a guided vehicle main body 12 (an example of a "mobile vehicle main body"), a guided vehicle control unit 14 (an example of a "mobile vehicle control unit"), a position information acquisition unit 16, and an obstacle detection unit 18. The guided vehicle control unit 14 is connected directly or indirectly to a stationary camera 20 and an input unit 40 by wireless or wired means. Furthermore, the guided vehicle control units 14 of the automated guided vehicles 10 are connected to each other directly or indirectly by wireless or wired means.

[0064] The conveyance body control unit 14 includes a congestion degree evaluation unit 150, a route setting unit 152, a travel time calculation unit 154, an automated conveyance body selection unit 156, and an instruction generation unit 158. These functional units perform the same processes as the congestion degree evaluation unit 50, the route setting unit 52, the travel time calculation unit 54, the automated conveyance body selection unit 56, and the instruction generation unit 58 included in the control unit 30 in the above embodiment. Specifically, the conveyance body control unit 14 acquires information about the position of an object in the work area W (e.g., photographic data of the work area W) from the fixed camera 20, and acquires information about a transport instruction or a transport target from the input unit 40. The congestion degree evaluation unit 150 evaluates the congestion degree distribution in the work area W based on the information about the position of the object. The route setting unit 152 sets a route for transporting the object O (specifically, a route R0 from the current position of the automated transport vehicle 10 to the transport source T0 and a route R1 from the transport source T0 to the transport destination T1) based on the transport instruction or transport target information acquired from the input unit 40 and the position information of the automated transport vehicle 10 itself acquired by the position information acquisition unit 16. The automated transport vehicle selection unit 156 selects an automated transport vehicle 10 from the multiple automated transport vehicles 10 that will transport the object O based on the set route and congestion distribution. In this embodiment, the automated transport vehicle selection unit 156 is provided for each automated transport vehicle 10, so it can be said that the automated transport vehicle selection unit 156 determines whether the automated transport vehicle 10 to which it is provided will transport the object O. The instruction generation unit 158 ​​generates an instruction for the selected automated transport vehicle 10 to transport the object O along the route based on the processing result of the automated transport vehicle selection unit 156.

[0065] In the above example, the conveyance body control unit 14 of the automated conveyance body 10 executes a series of processes. Therefore, although the control unit 30 is not shown in FIG. 7, it goes without saying that a separate control unit 30 may be provided. The overall control method for the multiple automated conveyance bodies 10 is not particularly limited. For example, each of the multiple automated conveyance bodies 10 may be comprehensively controlled by the control unit 30, or the automated conveyance bodies 10 may cooperate autonomously through communication between each other without the intervention of the control unit 30, or one or more specific automated conveyance bodies 10 among the multiple automated conveyance bodies 10 may control the other automated conveyance bodies 10.

[0066] A modification of the above embodiment will be described below. The control unit 30 may be implemented using a single information processing device (such as a personal computer) or may be implemented by distributed processing among multiple information processing devices. For example, some or all of the functional units of the control unit 30 may be implemented by a cloud server or the like. In the first to third embodiments, the control unit 30 is described as performing all of the congestion evaluation, route setting, automated transport body selection, and instruction generation, while in the fourth embodiment, the transport body control unit 14 is described as performing all of the above processing. However, the control unit 30 and the transport body control unit 14 may perform distributed processing. For example, the control unit 30 may perform congestion evaluation, automated transport body selection, and instruction generation, and the transport body control unit 14 of each automated transport body 10 may perform its own route setting. However, the division of processing in distributed processing is not limited to the above example. Distributed processing may also be performed among multiple automated transport bodies 10.

[0067] In the above embodiment, it has been described that the congestion degree evaluation unit 50 evaluates the congestion degree distribution and then the route setting unit 52 sets the route, but this order is not particularly limited. The congestion degree evaluation unit 50 may evaluate the congestion degree distribution after the route setting unit 52 sets the route, or the processing of the congestion degree evaluation unit 50 and the processing of the route setting unit 52 may be performed in parallel.

[0068] In each of the above embodiments, the processing in the control unit 30 is assumed to be realized by program software stored in an external storage device such as a memory using one or more processors such as a CPU (Central Processing Unit), but may also be realized by hardware (e.g., circuitry) that does not use a CPU. Also, the processing may be executed via a cloud server.

[0069] The instructions shown in the processing procedures described in each embodiment can be executed based on a software program. A general-purpose computer system can also obtain effects similar to those of the above-described processing procedures by loading a pre-stored program. The instructions described in each embodiment are recorded as a program that can be executed by an information processing device such as a computer on a non-transitory computer-readable recording medium such as a magnetic disk (flexible disk, hard disk, etc.), an optical disk (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD±R, DVD±RW, Blu-ray (registered trademark) Disc, etc.), semiconductor memory, or similar. The storage format of the recording medium may be any format as long as it is readable by a computer or embedded system. A computer can realize operations similar to those of the above-described processing procedures by loading the program from the recording medium and executing the instructions described in the program on a CPU based on the program. A computer may also acquire or load the program via a network.

[0070] According to at least one of the embodiments described above, the congestion distribution within the work area W can be evaluated based on information regarding the position of objects, such as photographic data and point cloud data of the work area W, and an automatic transport body 10 can be selected based on the congestion distribution, thereby improving transport efficiency.

[0071] The hardware configuration for realizing each of the above software functional units will be described below. The above-mentioned components such as the acquisition unit 32, communication unit 34, memory unit 36, processing unit 38, and input unit 40 are functional units realized by the cooperation of a hardware configuration including a processor, memory, storage, input / output IF, communication IF, and buses that interconnect these. A processor is hardware that processes data and instructions written in a program. A processor is composed of, for example, a control unit, an arithmetic unit, and registers. Memory is hardware that temporarily stores programs and data. For example, memory is volatile memory such as SRAM (Static Random Access Memory) or DRAM (Dynamic Random Access Memory). Storage is hardware that stores programs and data, such as non-volatile memory such as flash memory, HDD (Hard Disc Drive), and ferroelectric memory. The input / output interface functions as an interface between an input device that accepts input operations from users and an output device that presents information to users. Examples of input devices include pointing devices such as a mouse or touch panel, and keyboards. Examples of output devices include displays and speakers. The communication IF is an interface for inputting and outputting signals for communicating with external devices.

[0072] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These 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 within the scope and spirit of the invention, as well as within the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0073] 1...automatic transport system, 10 (10A to 10C)...automatic transport body (moving body), 12...transport body main body (moving body main body), 14...transport body control unit (moving body control unit), 16...position information acquisition unit, 18...obstacle detection unit, 20...fixed camera (sensor), 30...control unit, 32...acquisition unit, 34...communication unit, 36...memory unit, 38...processing unit, 40...input unit, 45...alarm unit, 50, 150...congestion degree evaluation unit, 52, 152...route setting unit (route information acquisition unit), 54, 154...travel time calculation unit, 56, 156...automatic transport body selection unit, 58, 158...instruction generation unit, 60...floor condition recognition unit, 120...transport body camera (sensor), 220...optical scanner (sensor), O...object, T0...source of transport, T1...destination of transport, W...work area.

Claims

1. 1. An automated transport system for transporting objects within a work area, comprising: a plurality of automated transport vehicles that transport the objects from a source to a destination; a sensor for obtaining information regarding the position of an object in the work area; a control unit that controls the plurality of automated transport bodies; Equipped with The control unit a route information acquisition unit that acquires, for each of the plurality of automated transport vehicles, route information indicating a route that the automated transport vehicle will take to the source of the object; a congestion degree evaluation unit that evaluates a congestion degree distribution that indicates a degree of congestion of objects in the work area based on information about the positions of the objects; an automatic transport body selection unit that selects an automatic transport body to perform a transport task from among the plurality of automatic transport bodies based on the route information and the congestion degree distribution; Equipped with Automatic transport system.

2. the sensor includes a stationary camera provided in the work area; The information about the position of the object includes image data of the work area acquired by the fixed camera. The automatic transport system according to claim 1 .

3. the sensor includes a vehicle camera mounted on each of the plurality of automated transport vehicles; the information about the position of the object includes image data of the work area acquired by the vehicle camera; 3. The automatic transport system according to claim 1 or 2.

4. the sensor includes an optical scanner mounted on each of the plurality of automated transport vehicles; the information about the position of the object includes point cloud data of the work area acquired by the optical scanner; 3. The automatic transport system according to claim 1 or 2.

5. the congestion degree evaluation unit recognizes a floor surface of the work area from information about the position of the object, and evaluates the congestion degree distribution based on an exposed area of ​​the floor surface.

3. The automatic transport system according to claim 1 or 2.

6. the congestion degree evaluation unit recognizes objects in the work area from information about the positions of the objects, and evaluates the congestion degree distribution based on the degree of space occupation by the objects.

3. The automatic transport system according to claim 1 or 2.

7. the congestion degree evaluation unit uses a trained model that has learned a relationship between information about the object's position and a congestion degree distribution to output the congestion degree distribution from information about the object's position acquired by the sensor.

3. The automatic transport system according to claim 1 or 2.

8. The path is a predetermined reference path within the work area.

3. The automatic transport system according to claim 1 or 2.

9. the control unit further includes a travel time calculation unit that calculates, for each of the plurality of automated transport vehicles, a required time for the automated transport vehicle to travel to the source of the object based on the route information and the congestion degree distribution; the automatic transport body selection unit selects an automatic transport body having the shortest required time from among the plurality of automatic transport bodies as an automatic transport body to perform the transport work.

3. The automatic transport system according to claim 1 or 2.

10. the automated transport body selection unit selects, from among the plurality of automated transport bodies, an automated transport body with a minimum degree of congestion on the route based on the route information and the congestion degree distribution, as an automated transport body to perform the transport work.

3. The automatic transport system according to claim 1 or 2.

11. the congestion degree evaluation unit predicts a time change in the congestion degree distribution, the automated transport vehicle selection unit selects an automated transport vehicle to perform a transport task from among the plurality of automated transport vehicles based on the route information and the predicted change in congestion degree distribution over time.

3. The automatic transport system according to claim 1 or 2.

12. the control unit further includes a floor condition recognition unit that detects an abnormality on the floor by recognizing a condition of the floor of the work area based on information about the position of the object, the automatic transport body selection unit selects an automatic transport body to perform a transport task from among the plurality of automatic transport bodies based on the congestion degree distribution, the route information, and the recognition result of the floor surface condition recognition unit.

3. The automatic transport system according to claim 1 or 2.

13. a notification unit that issues an alert to alleviate congestion on the route; the control unit instructs the notification unit to issue the alert when the congestion degree distribution or the state of the automated transport vehicle satisfies a predetermined condition.

3. The automatic transport system according to claim 1 or 2.

14. the object includes at least one of a person, an object temporarily installed in the work area, an object permanently installed in the work area, and the plurality of automated transport vehicles; 3. The automatic transport system according to claim 1 or 2.

15. An information processing device for controlling a plurality of automatic conveyance bodies that convey objects from a source to a destination in an automatic conveyance system for conveying objects within a work area, comprising: a route information acquisition unit that acquires, for each of the plurality of automated transport vehicles, route information indicating a route that the automated transport vehicle will take to the source of the object; a congestion degree evaluation unit that evaluates a congestion degree distribution that represents a degree of congestion of objects in the work area based on information about positions of objects in the work area acquired by a sensor installed in the work area; an automatic transport body selection unit that selects an automatic transport body to perform a transport task from among the plurality of automatic transport bodies based on the route information and the congestion degree distribution; Equipped with Information processing device.

16. In an automatic transport system including a plurality of automatic transport vehicles that transport objects from a source to a destination within a work area, a moving body configured as one of the plurality of automatic transport vehicles, A mobile body; A mobile object control unit; Equipped with The moving body control unit a route information acquisition unit that acquires route information indicating the route that the automated transport vehicle will take to the transport source of the object; a congestion degree evaluation unit that evaluates a congestion degree distribution that represents a degree of congestion of objects in the work area based on information about positions of objects in the work area acquired by a sensor installed in the work area; an automatic transport body selection unit that selects an automatic transport body to perform a transport task from among the plurality of automatic transport bodies based on the route information and the congestion degree distribution; Equipped with Mobile object.

17. 1. An information processing method for controlling a plurality of automated transport vehicles that transport objects from a source to a destination in an automated transport system for transporting objects within a work area, comprising: acquiring, for each of the plurality of automated transport vehicles, route information indicating a route that the automated transport vehicle will take to the source of the object; a step of evaluating a congestion degree distribution representing a degree of congestion of objects in the work area based on information about positions of objects in the work area acquired by a sensor provided in the work area; selecting an automated transport vehicle to perform a transport task from among the plurality of automated transport vehicles based on the route information and the congestion degree distribution; Including, Information processing methods.

18. A program causing an information processing device to execute the method according to claim 17.

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