Autonomous mobile robot control system, autonomous mobile robot control method, and autonomous mobile robot control program

Abstract

To provide an autonomous mobile robot control system that can suppress the increase of detection error of a sensor device equipped with the autonomous mobile robot due to sunlight conditions within a moving range of the autonomous mobile robot.SOLUTION: An autonomous mobile robot control system 1A comprising a host management device 10 and an autonomous mobile robot 20, where the host management device comprises a data collection part 16 that collects sunshine condition data corresponding to a sunshine condition within a moving range of the autonomous mobile robot, and a parameter calculation part 17 that calculates an optimal parameter that reduces the influence of the sunshine condition corresponding to the sunshine condition data, and the autonomous mobile robot comprises a parameter setting part 40 that sets the optimal parameter and performs a predetermined operation based on the optimal parameters set by the parameter setting part.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to an autonomous mobile robot control system, an autonomous mobile robot control method, and an autonomous mobile robot control program. [Background Art]

[0002] Autonomous mobile robots that autonomously move to a destination while avoiding obstacles within a predetermined facility have been proposed (see, for example, Patent Document 1). This autonomous mobile robot is provided with a sensor device (for example, a laser sensor for detecting obstacles, and a camera serving as a recognition sensor). [Prior Art Literature] [Patent Literature]

[0003] [Patent Document 1] Japanese Unexamined Patent Application Publication No. 2018-156243 [Summary of the Invention] [Problem to be Solved by the Invention]

[0004] However, in Patent Document 1, there is a problem that depending on the sunlight conditions within the movement range of the autonomous mobile robot, the detection error of the sensor device increases, and as a result, it may become difficult for the robot to travel autonomously.

[0005] The present invention has been made to solve such problems, and provides an autonomous mobile robot control system, an autonomous mobile robot control method, and an autonomous mobile robot control program capable of suppressing an increase in the detection error of a sensor device included in the autonomous mobile robot caused by sunlight conditions within the movement range of the autonomous mobile robot. [Means for Solving the Problem]

[0006] The autonomous mobile robot control system according to the present disclosure includes: a host management device, Equipped with an autonomous mobile robot, The aforementioned higher-level management device is A data collection unit collects sunlight condition data corresponding to the sunlight conditions within the movement range of the autonomous mobile robot, A parameter calculation unit calculates optimal parameters that minimize the influence of the sunlight conditions corresponding to the sunlight conditions, based on the aforementioned sunlight condition data. The system includes a communication unit that transmits the aforementioned optimal parameters to the autonomous mobile robot, The aforementioned autonomous mobile robot A communication unit that receives the aforementioned optimal parameters, The system includes a parameter setting unit for setting the aforementioned optimal parameters, Based on the optimal parameters set by the parameter setting unit, a predetermined operation is performed.

[0007] This configuration makes it possible to suppress the increase in detection errors of the sensor devices (e.g., visible light camera, depth camera, laser sensor) equipped with the autonomous mobile robot, which can be caused by sunlight conditions within the robot's movement range.

[0008] This system includes a parameter calculation unit (learning model) that calculates optimal parameters based on sunlight condition data to minimize the influence of the corresponding sunlight conditions. The autonomous mobile robot then performs predetermined actions based on these optimal parameters.

[0009] Furthermore, in the above-mentioned autonomous mobile robot control system, The system further includes multiple environmental cameras that capture images of the movement range of the autonomous mobile robot and transmit the captured images to the higher-level management device. The aforementioned sunlight conditions data may include the aforementioned image.

[0010] Furthermore, in the above-mentioned autonomous mobile robot control system, The aforementioned sunshine condition data may further include date and time, time of day, weather, and temperature.

[0011] Furthermore, in the above-mentioned autonomous mobile robot control system, The aforementioned autonomous mobile robot is equipped with a visible camera that takes pictures of its surroundings. The aforementioned optimal parameter is at least one of the exposure time and the shutter interval. The predetermined operation may be an operation in which the visible camera takes pictures of the surroundings based on the optimal parameters set by the parameter setting unit.

[0012] Furthermore, in the above-mentioned autonomous mobile robot control system, The aforementioned autonomous mobile robot is equipped with a distance sensor, The aforementioned optimal parameters are parameters of a filter that performs noise cancellation processing on sensor data, which is the output of the distance sensor. The predetermined operation may be an operation in which noise cancellation processing is performed on the sensor data, which is the output of the distance sensor, based on the optimal parameters set by the parameter setting unit.

[0013] Furthermore, in the above-mentioned autonomous mobile robot control system, The distance sensor may be a depth camera or a laser sensor.

[0014] Furthermore, in the above-mentioned autonomous mobile robot control system, The parameter calculation unit calculates the optimal parameters for each of the multiple paths along which the autonomous mobile robot travels. The parameter setting unit may set the optimal parameters corresponding to the route when the autonomous mobile robot approaches one of the multiple routes.

[0015] Furthermore, in the above-mentioned autonomous mobile robot control system, The parameter calculation unit may be a learning model generated by a learning engine. [Effects of the Invention]

[0016] According to the present disclosure, there can be provided an autonomous mobile robot control system, an autonomous mobile robot control method, and an autonomous mobile robot control program capable of suppressing an increase in detection errors of a sensor device included in an autonomous mobile robot caused by sunlight conditions within the movement range of the autonomous mobile robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] [Figure 1] FIG. 1 is a block diagram of the autonomous mobile robot control system according to the first embodiment. [Figure 2] FIG. 2 is a schematic diagram of the autonomous mobile robot according to the first embodiment. [Figure 3] FIG. 3 is a diagram for explaining an example of a problematic situation that occurs during operation of the autonomous mobile robot according to the first embodiment and an example of a workaround therefor. [Figure 4] FIG. 4 is a flowchart explaining the operation of the autonomous mobile robot control system according to the first embodiment. [Figure 5] FIG. 5 is a flowchart explaining the detailed operation of security processing in the autonomous mobile robot control system according to the first embodiment. [Figure 6] FIG. 6 is a flowchart explaining the detailed operation of operation efficiency improvement processing in the autonomous mobile robot control system according to the first embodiment. [Figure 7] FIG. 7 is a schematic configuration diagram of an autonomous mobile robot control system 1A. [Figure 8] FIG. 8 is a schematic diagram for explaining an example of the operation of a learning engine 50. [Figure 9] FIG. 9 is an example of a route along which an autonomous mobile robot 20 travels. [Figure 10] FIG. 10 is an example of optimal parameters for each route. [Figure 11] FIG. 11 is a flowchart of an example of the operation of the autonomous mobile robot control system 1A. MODE FOR CARRYING OUT THE INVENTION

[0018] For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. Furthermore, each element shown in the drawings as a functional block performing various processes can be composed of a CPU (Central Processing Unit), memory, and other circuits in hardware terms, and implemented in software terms by programs loaded into memory. Therefore, it will be understood by those skilled in the art that these functional blocks can be implemented in various ways using hardware alone, software alone, or a combination thereof, and are not limited to any one of these. In each drawing, the same elements are denoted by the same reference numeral, and redundant explanations have been omitted where necessary.

[0019] Furthermore, the programs described above can be stored and supplied to a computer using various types of non-temporary computer-readable media. Non-temporary computer-readable media include various types of tangible recording media. Examples of non-temporary computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memory (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). Programs may also be supplied to a computer by various types of temporary computer-readable media. Examples of temporary computer-readable media include electrical signals, optical signals, and electromagnetic waves. Temporary computer-readable media can be supplied to a computer via wired communication channels such as electric wires and optical fibers, or via wireless communication channels.

[0020] Furthermore, while the following examples assume a hospital as an example of a facility to which an autonomous mobile robot control system can be applied, autonomous mobile robot control systems can be used in a variety of facilities, not just hospitals.

[0021] Embodiment 1 First, Figure 1 shows a block diagram of the autonomous mobile robot control system 1 according to Embodiment 1. As shown in Figure 1, the autonomous mobile robot control system 1 according to Embodiment 1 includes a higher-level management device 10, an autonomous mobile robot (for example, an autonomous mobile robot 20), environmental cameras 301 to 30n, and an alarm device 31. In Figure 1, one autonomous mobile robot 20 is shown, but multiple autonomous mobile robots 20 are provided. This autonomous mobile robot control system 1 efficiently controls multiple autonomous mobile robots 20 while autonomously moving them within a predetermined facility. Therefore, the autonomous mobile robot control system 1 installs multiple environmental cameras 301 to 30n within the facility to acquire images of the area in which the autonomous mobile robots 20 move. In the autonomous mobile robot control system 1, the images acquired by the multiple environmental cameras 301 to 30n are collected by the higher-level management device 10. Furthermore, the autonomous mobile robot control system 1 is equipped with an alarm device 31 to notify facility users who cannot directly control the actions of the autonomous mobile robot 20 of the messages necessary for its operation.

[0022] In the autonomous mobile robot control system 1 according to Embodiment 1, the higher-level management device 10 creates a route to the destination of the autonomous mobile robot 20 based on route planning information and instructs the autonomous mobile robot 20 to go to the destination according to the route plan. The autonomous mobile robot 20 then autonomously moves toward the destination specified by the higher-level management device 10. At this time, in the autonomous mobile robot control system 1 according to Embodiment 1, the autonomous mobile robot 20 autonomously moves toward the destination using sensors, floor maps, position information, etc., installed on the robot itself. Furthermore, the higher-level management device 10 uses environmental cameras 301-30n to prevent the operation of the autonomous mobile robot 20 from interfering with the actions of facility users, preventing a decrease in operational efficiency caused by the robots facing each other or crossing paths in the relationships between facility users and the autonomous mobile robot 20, the autonomous mobile robot 20 and the transport cart, and the autonomous mobile robot 20 and the autonomous mobile robot 20. In addition, the autonomous mobile robot control system 1 also has a function to prevent unauthorized persons from entering security areas where entry is restricted (for example, the dispensing room, centralized data room, and staff waiting area if the facility is a hospital).

[0023] The higher-level management device 10 includes an arithmetic processing unit 11, a storage unit 12, a buffer memory 13, and a communication unit 14. The arithmetic processing unit 11 performs calculations for controlling and managing the autonomous mobile robot 20. The arithmetic processing unit 11 can be implemented as a program-executing device such as a computer's central processing unit (CPU). Various functions can also be realized by programs. Figure 1 shows only the robot control unit 111, equipment control unit 112, mobile object detection unit 113, mobile object path estimation unit 114, and avoidance procedure generation unit 115, which are characteristic of the arithmetic processing unit 11, but other processing blocks are also included.

[0024] The robot control unit 111 performs calculations to remotely operate the autonomous mobile robot 20 and generates specific operation instructions for the autonomous mobile robot 20. The equipment control unit 112 controls whether to allow or deny the opening and closing of the alarm device 31 or a door (not shown) based on the avoidance procedure information generated by the avoidance procedure generation unit 115. Here, the alarm device 31 is a device that is installed in multiple locations within the facility. The alarm device 31 notifies facility users of alarms such as the passage of the autonomous mobile robot 20 using voice or text information.

[0025] The moving object detection unit 113 detects moving objects from image information acquired using environmental cameras 301 to 30n. The moving objects detected by the moving object detection unit 113 include, for example, autonomous mobile robots 20, transport carts for carrying objects, priority transport objects designated for priority movement (e.g., stretchers), and people and objects moving within a facility.

[0026] The mobile object path estimation unit 114 estimates the movement routes of multiple mobile objects from the current point in time based on the characteristics of each mobile object detected by the mobile object detection unit 113. More specifically, the mobile object path estimation unit 114 refers to the mobile object database 124 in the memory unit 12 to identify the type of mobile object, such as whether it is a person or an autonomous mobile robot 20. Then, for the autonomous mobile robot 20, the mobile object path estimation unit 114 estimates the movement route by referring to the route planning information 125. For mobile objects other than the autonomous mobile robot 20, the mobile object path estimation unit 114 estimates the movement route according to the past behavior history and the type of mobile object.

[0027] The avoidance procedure generation unit 115 sets multiple moving objects whose movement routes overlap as avoidance target objects, based on the movement routes estimated by the moving object path estimation unit 114. The avoidance procedure generation unit 115 also generates avoidance procedures for the avoidance target objects that do not interfere with each other's movements. Specific examples of these avoidance procedures and details of the processing performed by the calculation processing unit 11 will be described later.

[0028] The memory unit 12 is a memory unit that stores information necessary for the management and control of the robot. In the example in Figure 1, the floor map 121, robot information 122, robot control parameters 123, mobile body database 124, and route planning information 125 are shown, but other information may be stored in the memory unit 12. The arithmetic processing unit 11 performs calculations using the information stored in the memory unit 12 when performing various processing tasks.

[0029] The floor map 121 is map information of the facility in which the autonomous mobile robot 20 moves. This floor map 121 may be created in advance, generated from information obtained from the autonomous mobile robot 20, or it may be a pre-created base map with map correction information generated from information obtained from the autonomous mobile robot 20 added to it.

[0030] The robot information 122 describes the model number, specifications, etc., of the autonomous mobile robot 20 managed by the higher-level management device 10. The robot control parameters 123 describe the control parameters, such as distance threshold information to obstacles, for each of the autonomous mobile robots 20 managed by the higher-level management device 10. The robot control unit 111 uses the robot information 122, robot control parameters 123, and route planning information 125 to give specific operation instructions to the autonomous mobile robot 20.

[0031] The buffer memory 13 is a memory that stores intermediate information generated during processing in the arithmetic processing unit 11. The communication unit 14 is a communication interface for communicating with multiple environmental cameras 301-30n, an alarm device 31, and at least one autonomous mobile robot 20 installed in the facility where the autonomous mobile robot control system 1 is used. The communication unit 14 can perform both wired and wireless communication.

[0032] The autonomous mobile robot 20 includes a processing unit 21, a memory unit 22, a communication unit 23, proximity sensors (e.g., a group of distance sensors 24), a camera 25 (visible-view camera), a drive unit 26, a display unit 27, and an operation reception unit 28. Note that Figure 1 shows only representative processing blocks included in the autonomous mobile robot 20; however, the autonomous mobile robot 20 also includes many other processing blocks not shown.

[0033] The communication unit 23 is a communication interface for communicating with the communication unit 14 of the higher-level management device 10. The communication unit 23 communicates with the communication unit 14, for example, using wireless signals. The distance sensor group 24 is, for example, a proximity sensor that outputs proximity distance information indicating the distance to objects or people present around the autonomous mobile robot 20. The camera 25 takes images, for example, to understand the surrounding environment of the autonomous mobile robot 20. The camera 25 can also take pictures of position markers, for example, installed on the ceiling of a facility. In the autonomous mobile robot control system 1 according to Embodiment 1, the autonomous mobile robot 20 is made to understand its own position using these position markers. The drive unit 26 drives the drive wheels attached to the autonomous mobile robot 20. The display unit 27 displays a user interface screen that serves as the operation reception unit 28. The display unit 27 may also display information indicating the destination or status of the autonomous mobile robot 20. The operation reception unit 28 includes a user interface screen displayed on the display unit 27, as well as various switches provided on the autonomous mobile robot 20. These switches include, for example, an emergency stop button.

[0034] The arithmetic processing unit 21 performs calculations used to control the autonomous mobile robot 20. More specifically, the arithmetic processing unit 21 includes a movement command extraction unit 211, a drive control unit 212, and an ambient abnormality detection unit 213. Note that Figure 1 shows only representative processing blocks of the arithmetic processing unit 21, but it also includes processing blocks that are not shown.

[0035] The movement command extraction unit 211 extracts movement commands from control signals provided by the higher-level management device 10 and provides them to the drive control unit 212. The drive control unit 212 controls the drive unit 26 to move the autonomous mobile robot 20 at the speed and direction indicated by the movement command provided by the movement command extraction unit 211. Furthermore, if the drive control unit 212 receives an emergency stop signal from the emergency stop button included in the operation reception unit 28, it stops the operation of the autonomous mobile robot 20 and instructs the drive unit 26 not to generate driving force. The surrounding abnormality detection unit 213 detects an abnormality occurring around the autonomous mobile robot 20 based on information obtained from the distance sensor group 24, etc., and provides a stop signal to the drive control unit 212 to stop the autonomous mobile robot 20. Upon receiving the stop signal, the drive control unit 212 instructs the drive unit 26 not to generate driving force.

[0036] The memory unit 22 stores the floor map 221 and the robot control parameters 222. Figure 1 shows only a portion of the information stored in the memory unit 22, and includes information other than the floor map 221 and robot control parameters 222 shown in Figure 1. The floor map 221 is map information of the facility in which the autonomous mobile robot 20 moves. This floor map 221 is, for example, a downloaded version of the floor map 121 of the higher-level management device 10. Note that the floor map 221 may be a pre-created one. The robot control parameters 222 are parameters for operating the autonomous mobile robot 20, and include, for example, operation limit thresholds for stopping or limiting the operation of the autonomous mobile robot 20 from the distance to obstacles or people.

[0037] The drive control unit 212 refers to the robot control parameters 222 and stops the operation or limits the operating speed when the distance indicated by the distance information obtained from the distance sensor group 24 falls below the operation limit threshold.

[0038] Here, we will describe the appearance of the autonomous mobile robot 20. Figure 2 shows a schematic diagram of the autonomous mobile robot 20 according to Embodiment 1. The autonomous mobile robot 20 shown in Figure 2 is one form of the autonomous mobile robot 20, and other forms may also be used.

[0039] The example shown in Figure 2 is an autonomous mobile robot 20 having a storage compartment 291 and a door 292 that seals the storage compartment 291. The autonomous mobile robot 20 autonomously transports the stored items in the storage compartment 291 to a destination instructed by the higher-level management device 10. In Figure 2, the x-direction shown in Figure 2 is the forward and backward direction of the autonomous mobile robot 20, the y-direction is the left-right direction of the autonomous mobile robot 20, and the z-direction is the height direction of the autonomous mobile robot 20.

[0040] As shown in Figure 2, the exterior of the autonomous mobile robot 20 according to Embodiment 1 is equipped with a distance sensor group 24, which includes a front-to-back distance sensor 241 and a left-to-right distance sensor 242. The autonomous mobile robot 20 according to Embodiment 1 measures the distance to objects or people in the front-to-back direction using the front-to-back distance sensor 241. The autonomous mobile robot 20 according to Embodiment 1 also measures the distance to objects or people in the left-to-right direction using the left-to-right distance sensor 242.

[0041] In the autonomous mobile robot 20 according to Embodiment 1, a drive unit 26 is provided at the bottom of the storage compartment 291. The drive unit 26 is equipped with drive wheels 261 and casters 262. The drive wheels 261 are wheels for moving the autonomous mobile robot 20 forward, backward, left, and right. The casters 262 are driven wheels that are not driven by force and roll in accordance with the drive wheels 261.

[0042] In addition, the autonomous mobile robot 20 is equipped with a display unit 27, an operation interface 281, and a camera 25 on the top surface of the storage compartment 291. The display unit 27 also serves as an operation reception unit 28 and displays the operation interface 281. An emergency stop button 282 is also provided on the top surface of the display unit 27.

[0043] Next, the operation of the autonomous mobile robot control system 1 according to Embodiment 1 will be described. In the autonomous mobile robot control system 1 according to Embodiment 1, the movement of people and other moving objects such as the autonomous mobile robot 20 within the facility where the autonomous mobile robot 20 is operated is estimated, and the autonomous mobile robot 20 is controlled from the estimated movement route to avoid situations that would cause a decrease in the efficiency of the autonomous mobile robot 20's operation. In addition, the autonomous mobile robot control system 1 has a function to improve the operational efficiency of the autonomous mobile robot 20 and to prevent unauthorized persons from entering the security area within the facility. With reference to Figure 3, the situations in which problems occur in the autonomous mobile robot control system 1 and methods for avoiding them will be described. Figure 3 is a diagram illustrating examples of problematic situations that occur when operating the autonomous mobile robot according to Embodiment 1 and countermeasures for avoiding them.

[0044] Figure 3 shows six examples of situations in which the problem occurs. The first example occurs when the movement routes of two autonomous mobile robots 20 overlap. In this first example, two autonomous mobile robots 20 face each other in a single passage, or their movement routes intersect at a corner or intersection of a passage. When a situation like this first example occurs, the autonomous mobile robots 20 will stop moving at a safe distance from each other using sensors installed on their units. However, this stopped state will not be resolved unless some means of avoidance action is given, and a deadlock state occurs where the movement of the autonomous mobile robots 20 stops unless an avoidance action is prepared separately.

[0045] To prevent such a deadlock situation from occurring, the autonomous mobile robot control system 1 instructs the autonomous mobile robots 20 to take a deadlock avoidance action, which involves putting one autonomous mobile robot 20 into a waiting state until the other autonomous mobile robot 20 has passed, based on the priority assigned to each autonomous mobile robot 20.

[0046] Priority is set higher, for example, if the cargo carried by the autonomous mobile robot 20 is urgent, and also higher when the autonomous mobile robot 20 is on its outward journey. The method of determining priority is not limited to this, and can be set arbitrarily, taking into account the circumstances of the facility to which the autonomous mobile robot control system 1 is applied.

[0047] The second example is when the movement routes of the autonomous mobile robot 20 and the transport cart or priority transport object face each other or intersect on a facility passageway. The transport cart or priority transport object is pushed by a person or carried by the autonomous mobile robot. In addition, the transport cart or priority transport object may be left stationary in the facility passageway. When such a transport cart or priority transport object passes, the autonomous mobile robot 20 may enter an emergency stop state by the operation of a button by a facility employee or other person, and since human operation is required to release the emergency stop state, the autonomous mobile robot 20 may enter a deadlock state. Furthermore, the transport cart or priority transport object is often considered to have a higher priority than the autonomous mobile robot 20, and situations in which the autonomous mobile robot 20 obstructs their passage should be avoided.

[0048] Therefore, in the autonomous mobile robot control system 1, if a situation like the second example occurs, the autonomous mobile robot 20 is instructed to wait until the transport cart or priority transport object has passed, or to take a detour by changing its travel route. This prevents a decrease in the operational efficiency of the autonomous mobile robot 20 when the problem in the second example occurs.

[0049] A third example is when a person and the autonomous mobile robot 20 face each other or cross paths on the autonomous mobile robot 20's travel route. The autonomous mobile robot 20 is programmed to stop if it cannot maintain a certain distance (e.g., a safety distance) from a person using sensors installed on the robot. Therefore, for example, if the autonomous mobile robot 20 is passing through a crowded area, this safety distance cannot be maintained, and the autonomous mobile robot 20 will stop in the middle of the crowd, resulting in a deadlock situation where the autonomous mobile robot 20 cannot move until the crowd clears.

[0050] To resolve such deadlocks, the autonomous mobile robot control system 1 instructs the autonomous mobile robot 20 to wait before entering areas with high human congestion, or to take a route that avoids areas with high human congestion. Furthermore, when human congestion is low, the autonomous mobile robot control system 1 instructs the autonomous mobile robot 20 to pass through areas with low human congestion while notifying people of the robot's presence via voice or text. This notification may be made using the alarm device 31, or using a notification device (not shown in Figure 2) provided on the autonomous mobile robot 20.

[0051] A fourth example is when another autonomous mobile robot 20, a transport cart, a priority transport object, or a person is already inside the elevator car that a passenger is scheduled to board. In such a case, if the path taken by the person or autonomous mobile robot 20 to exit the elevator coincides with the path taken by the autonomous mobile robot 20 waiting in the elevator hall to enter the elevator, a situation will occur where there is no space to wait inside the elevator car, or no space to exit the elevator. When such a situation occurs, not only will the autonomous mobile robot 20 be locked into a deadlock, but the elevator passenger will also be unable to exit the elevator.

[0052] Therefore, in the fourth example, the autonomous mobile robot control system 1 instructs the autonomous mobile robot 20 in the elevator hall to wait in a space outside the movement route (pathway) of people getting off the elevator or the autonomous mobile robot 20.

[0053] The fifth example is when an autonomous mobile robot 20 is inside an elevator car and tries to get out of the car, but there is a person in the elevator hall and the autonomous mobile robot 20 is prevented from getting out of the car by the person in the elevator hall.

[0054] In this fifth example, the autonomous mobile robot control system 1 notifies people near the elevator hall in advance that the autonomous mobile robot 20 will be disembarking via an alarm device 31 installed near the elevator hall.

[0055] The sixth example is one in which a security risk arises when an unauthorized person, who is prohibited from entering the security area, enters accompanied by the autonomous mobile robot 20. In this sixth example, if the autonomous mobile robot control system 1 detects the person accompanying the autonomous mobile robot 20 as a moving object, it checks the detected person against security information, issues an alarm notification via the alarm device 31, and prevents the unlocking of the door to the security area. Furthermore, if the security risk in the sixth example occurs, the autonomous mobile robot control system 1 will have the autonomous mobile robot 20 wait outside the security area.

[0056] The situations in which the above problems occur are just one example of events that reduce the operational efficiency of the autonomous mobile robot 20 within the facility. In the autonomous mobile robot control system 1 according to Embodiment 1, even in situations in which problems other than those described above occur, procedures are generated to avoid problems according to the characteristics of the mobile object, such as the detected mobile object and the location where the mobile object was detected. Based on the generated avoidance procedures, the autonomous mobile robot control system 1 instructs the autonomous mobile robot 20 to take avoidance actions such as waiting, detouring, or issuing an alarm notification.

[0057] Here, the operation of the autonomous mobile robot control system 1 according to Embodiment 1 will be described in detail. In the following description, the process related to the generation of avoidance procedures in the autonomous mobile robot control system 1 according to Embodiment 1 will be described in particular, but the autonomous mobile robot control system 1 according to Embodiment 1 also performs other necessary processes. Furthermore, the avoidance procedures generated by the autonomous mobile robot control system 1 according to Embodiment 1 will be appropriately modified depending on the situation in which the problem occurs, not limited to the procedure shown in Figure 3.

[0058] Figure 4 shows a flowchart illustrating the operation of the autonomous mobile robot control system according to Embodiment 1. As shown in Figure 4, the autonomous mobile robot control system 1 according to Embodiment 1 operates the autonomous mobile robot 20 according to route planning information 125 (step S1). Next, the autonomous mobile robot control system 1 acquires image information within the facility using environmental cameras 301-30n, and the mobile object detection unit 113 detects mobile objects within the facility based on the acquired image information (step S2). Subsequently, the autonomous mobile robot control system 1 uses the mobile object path estimation unit 114 to estimate the movement routes of multiple mobile objects based on the characteristics of each mobile object detected by the mobile object detection unit 113 (step S3). After that, the autonomous mobile robot control system 1 performs security processing (step S4) and operation efficiency processing (step S5). The order in which this security processing and operation efficiency processing are performed may vary.

[0059] Security processing, for example, is the process of preventing unauthorized access to the security area, as explained in Example 6 of Figure 3. Operational efficiency processing, on the other hand, is the process of preventing a decrease in operational efficiency, such as deadlock avoidance, as explained in Examples 1 to 5 of Figure 3. The following sections will explain security processing and operational efficiency processing in detail.

[0060] Figure 5 shows a flowchart illustrating the detailed operation of the security processing of the autonomous mobile robot control system according to Embodiment 1. The security processing is mainly performed using the avoidance procedure generation unit 115, the robot control unit 111, and the equipment control unit 112.

[0061] In the security processing, the avoidance procedure generation unit 115 performs the person detection processing in steps S11 to S16. In step S11, it is determined whether or not there is a security area at the end of the mobile robot's movement route. If the mobile robot's movement route does not include a security area in step S11, the autonomous mobile robot control system 1 terminates the security processing. On the other hand, if it is determined in step S11 that the mobile robot's movement route includes a security area, the avoidance procedure generation unit 115 sets the mobile robot that includes the security area in its movement route as the mobile robot to be avoided (step S12).

[0062] Subsequently, the avoidance procedure generation unit 115 determines whether or not a person is included in the object to be avoided (step S13). In step S13, if no person is included in the object to be avoided, the autonomous mobile robot control system 1 terminates the security process. On the other hand, if a person is included in the object to be avoided in step S13, the unit determines whether or not the distance between the autonomous mobile robot 20, which is set as the object to be avoided, and the person is less than or equal to the security distance, which is set in advance as a distance at which safety is ensured (step S14). In step S14, if the distance between the autonomous mobile robot 20 and the person is greater than the security distance, the autonomous mobile robot control system 1 terminates the security process, as the safety of the security area is ensured. On the other hand, if it is determined in step S14 that the distance between the autonomous mobile robot 20 and the person is less than or equal to the security distance, the avoidance procedure generation unit 115 refers to security information, which is not shown in Figure 1, and determines whether the person near the autonomous mobile robot 20 is a person who can enter the security area (steps S15, S16).

[0063] Then, in step S16, the bypass procedure generation unit 115 generates a bypass procedure (step S17) that prohibits entry into the security area if the person is determined to be an unauthorized person. The bypass procedure generated in step S17 may include, for example, the autonomous mobile robot 20 waiting outside the security area, a measure to prohibit unlocking the door to the security area, and a measure to notify the alarm device 31 that an unauthorized person is nearby.

[0064] Subsequently, the autonomous mobile robot control system 1, based on the avoidance procedure generated in step S17, has the robot control unit 111 give specific operation instructions to the autonomous mobile robot 20, and the equipment control unit 112 controls the alarm device 31 and the door (step S18).

[0065] Next, the operational efficiency improvement process will be explained in detail. Figure 6 shows a flowchart illustrating the detailed operation of the operational efficiency improvement process of the autonomous mobile robot control system according to Embodiment 1. This is mainly performed using the avoidance procedure generation unit 115, the robot control unit 111, and the equipment control unit 112.

[0066] As shown in Figure 6, in the operational efficiency improvement process, the avoidance procedure generation unit 115 determines whether or not there are any moving bodies whose movement routes intersect (overlap or cross) with each other (step S21). If there are no moving bodies whose movement routes intersect in step S21, the operational efficiency improvement process ends. On the other hand, if there are moving bodies whose movement routes intersect in step S21, the avoidance procedure generation unit 115 sets each of the moving bodies whose movement routes intersect as a moving body to be avoided (step S22). After that, the avoidance procedure generation unit 115 determines whether or not a person is included as at least one of the moving bodies to be avoided (step S23). Here, a moving body including a person includes a person pushing a transport cart and a priority transport body.

[0067] In step S23, if the moving object to be avoided includes a person, the avoidance procedure generation unit 115 generates an avoidance procedure for the autonomous mobile robot 20, and the robot control unit 111 gives the autonomous mobile robot 20 an avoidance action instruction in accordance with the avoidance procedure (step S24). As a result, the autonomous mobile robot 20, having received the instruction to take an avoidance action, performs the avoidance action (step S25). Furthermore, if the avoidance procedure generated in step S24 includes an instruction for alarm notification using the alarm device 31 (the YES branch of step S26), the equipment control unit 112 performs an alarm notification using the alarm device 31 in accordance with the avoidance procedure (step S27). Furthermore, if the avoidance procedure in step S25 does not include an alarm notification using the alarm device 31, the alarm notification process in step S27 is not performed and the process ends.

[0068] On the other hand, if no people are included in the objects to be avoided in step S23, the avoidance procedure generation unit 115 generates an avoidance procedure for the objects with the lowest priority among the objects included in the objects to be avoided, and the robot control unit 111 gives the autonomous mobile robot 20 an avoidance action instruction in accordance with the avoidance procedure (step S28). As a result, the autonomous mobile robot 20, having received the instruction to take an avoidance action, performs the avoidance action (step S29).

[0069] As described above, the autonomous mobile robot control system 1 according to Embodiment 1 detects in advance any problematic situations in the operation of the autonomous mobile robot 20 based on image information within the facility that constitutes the autonomous mobile robot 20's movement range, and generates an avoidance procedure that shows the steps for avoidance action based on the detection results. Then, by controlling the autonomous mobile robot 20 or the alarm device 31 according to the avoidance procedure, the operational efficiency of the autonomous mobile robot 20 can be improved.

[0070] Furthermore, the autonomous mobile robot control system 1 according to Embodiment 1 can improve the security of the security area by preventing unauthorized persons from entering the security area through the security processing described in Figure 5.

[0071] Furthermore, by acquiring images including light reflections as image information from the environmental cameras 301-30n used in the above-mentioned autonomous mobile robot control system 1, it is possible to understand, for example, the status of trays being returned to a transport cart used as a tray return rack.

[0072] Embodiment 2 Next, the autonomous mobile robot control system 1A of Embodiment 2 will be described.

[0073] Figure 7 is a schematic diagram of the autonomous mobile robot control system 1A.

[0074] As shown in Figure 7, the autonomous mobile robot control system 1A of Embodiment 2 differs from the autonomous mobile robot control system 1 of Embodiment 1 mainly in that the higher-level management device 10 (for example, an information processing device such as a server) further includes a data acquisition unit 16 and a learning model 17 (an example of a parameter calculation unit in this disclosure), and the communication unit 14 of the higher-level management device 10 transmits the optimal parameters calculated by the learning model 17 to the autonomous mobile robot 20. Furthermore, the communication unit 23 of the autonomous mobile robot 20 receives the optimal parameters, and the autonomous mobile robot 20 further includes a parameter setting unit 40 that sets these received optimal parameters, and executes predetermined operations based on the optimal parameters set by this parameter setting unit 40. The following description will focus on the differences from Embodiment 1, and components similar to those in Embodiment 1 will be denoted by the same reference numerals, with explanations omitted as appropriate.

[0075] First, let's explain the configuration of the higher-level management device 10 (data acquisition unit 16, learning model 17).

[0076] The data acquisition unit 16 collects sunlight condition data that corresponds to (is related to) the sunlight conditions within the movement range of the autonomous mobile robot 20 (for example, the first path R1, second path R2, and third path R3 described later).

[0077] The sunlight condition data includes images (features described later) taken within the movement range of the autonomous mobile robot 20, such as images taken along the first path R1, the second path R2, and the third path R3, as described later. These images are taken by environmental cameras 301-30n. Hereinafter, these images will be referred to as environmental camera images.

[0078] Environmental camera images are collected at predetermined intervals. For example, one environmental camera image is collected every minute. The higher-level management device 10 extracts one or more features from the collected environmental camera images by performing predetermined image processing on them.

[0079] Furthermore, the sunlight conditions data includes the date and time, time of day, weather, and temperature. The date and time are, for example, internet time collected from the internet (e.g., an internet time server). The internet time is collected at predetermined intervals. For example, it is collected every minute in conjunction with the collection timing of environmental camera images.

[0080] The weather data is that of the region where the facility (in this case, a hospital) where the autonomous mobile robot 20 is used is located. The weather data is collected, for example, from a specific website using web scraping. The weather data is collected at predetermined intervals, for example, every 30 minutes.

[0081] The temperature is the temperature within the range of movement of the autonomous mobile robot 20. The temperature is collected, for example, from IoT devices (including temperature sensors) installed within the range of movement of the autonomous mobile robot 20. The temperature is collected at predetermined intervals. For example, it is collected every minute in conjunction with the collection timing of environmental camera images.

[0082] As described above, the sunlight condition data (for example, environmental camera images (feature quantities), date and time, time of day, weather, temperature) collected by the data collection unit 16 is stored in the storage unit 12 of the higher-level management device 10.

[0083] As described above, the sunlight condition data stored in the memory unit 12 is input to the learning engine (AI engine) as training data at regular intervals (for example, one week, one year).

[0084] Figure 8 is a schematic diagram illustrating an example of the operation of the learning engine 50.

[0085] As shown in Figure 8, the learning engine 50 takes training data D1 and training data D2 (ground truth data) as inputs and outputs a learning model 17. The learning engine 50 is, for example, scikit-learn or PyTorch. The training data D1 is sunlight condition data for a certain period of time stored in the memory unit 12 as described above. The training data D2 (ground truth data) is the optimal parameters corresponding to the sunlight condition data.

[0086] These optimal parameters are designed to minimize the influence of sunlight conditions, corresponding to the sunlight condition data.

[0087] For example, in the case of camera 25 (an example of a visible camera in this disclosure), this optimal parameter is at least one of the exposure time and the shutter interval. In the case of a depth camera, which is one of the distance sensor group 24, this optimal parameter is the parameter of the filter (a filter that performs noise cancellation processing on the sensor data, which is the output of the depth camera). In the case of a laser sensor, which is another of the distance sensor group 24, this optimal parameter is the parameter of the filter (a filter that performs noise cancellation processing on the sensor data, which is the output of the laser sensor).

[0088] These optimal parameters may be determined (set) by a person based on experience, etc., to minimize the influence of sunlight conditions corresponding to the sunlight condition data, or they may be determined (set) automatically by a predetermined program based on a predetermined algorithm.

[0089] For example, if sunlight may affect the output of a sensor device (e.g., camera 25, depth camera, laser sensor) (e.g., if reflected light is too strong), the noise is likely to be greater than usual, so it may be considered to shorten the exposure time or adjust (set) the parameters in a way that removes noise. On the other hand, if sunlight is unlikely to affect the sensor device (e.g., camera 25, depth camera, laser sensor) (e.g., if reflected light is weak), it may be considered to lengthen the exposure time or adjust (set) the parameters in a way that does not remove noise (in a way that uses raw data as much as possible).

[0090] The learning model 17 is the learning result generated by the learning engine 50 (e.g., machine learning). The learning model 17 takes the data to be predicted as input and the prediction result as output. The data to be predicted is, for example, sunlight condition data. The prediction result is, for example, the optimal parameters corresponding to the sunlight condition data.

[0091] When sunlight condition data is input, the learning model 17 calculates (outputs) optimal parameters that minimize the influence of the sunlight conditions corresponding to the sunlight condition data, based on the sunlight condition data and the learning results. The learning model 17 is an example of the parameter calculation unit of this disclosure.

[0092] The timing for calculating these optimal parameters is, for example, after the path that the autonomous mobile robot 20 should take has been determined. At that time, the learning model 17 calculates the optimal parameters for each of the multiple paths that the autonomous mobile robot 20 will take.

[0093] For example, as shown in Figure 9, the paths that the autonomous mobile robot 20 will travel on are determined as follows: a first path R1 from position CP1 in room 401 of facility 40 (in this case, a hospital) to position CP2 in corridor 402; a second path R2 from position CP2 in corridor 402 to position CP3; and a third path R3 from position CP3 in corridor 402 to position CP4 in elevator hall 403 in front of elevator EV1. Figure 9 is an example of a path that the autonomous mobile robot 20 will travel on.

[0094] In this case, the learning model 17 calculates the optimal parameters for each of the paths R1, R2, and R3. Figure 10 shows an example of the optimal parameters for each path. These optimal parameters are transmitted to the autonomous mobile robot 20 in association with the paths. These optimal parameters are received by the autonomous mobile robot 20 and stored in the storage unit 22 of the autonomous mobile robot 20.

[0095] Next, we will explain the configuration of the autonomous mobile robot 20 (parameter setting unit 40).

[0096] The parameter setting unit 40 sets the optimal parameters transmitted from the higher-level management device 10.

[0097] If the optimal parameters set by the parameter setting unit 40 are exposure time and shutter interval, the camera 25 will photograph the surroundings based on these optimal parameters (exposure time and shutter interval).

[0098] On the other hand, if the optimal parameters set by the parameter setting unit 40 are parameters for a filter (a filter that performs noise cancellation processing on sensor data which is the output of the depth camera), the autonomous mobile robot 20 performs noise cancellation processing on the sensor data which is the output of the depth camera based on those optimal parameters (filter parameters). Similarly, if the optimal parameters set by the parameter setting unit 40 are parameters for a filter (a filter that performs noise cancellation processing on sensor data which is the output of the laser sensor), the autonomous mobile robot 20 performs noise cancellation processing on the sensor data which is the output of the laser sensor based on those optimal parameters (filter parameters).

[0099] Next, an example of the operation of the autonomous mobile robot control system 1A with the above configuration will be described.

[0100] Figure 11 is a flowchart showing an example of the operation of the autonomous mobile robot control system 1A.

[0101] The following example describes a scenario where the autonomous mobile robot 20 is to travel along a path as shown in Figure 9, where the first path R1 is from position CP1 in room 401 of facility 40 (in this case, a hospital) to position CP2 in corridor 402, the second path R2 is from position CP2 in corridor 402 to position CP3, and the third path R3 is from position CP3 in corridor 402 to position CP4 in elevator hall 403 in front of elevator EV1.

[0102] Environmental cameras 301 to 30n each capture images (environmental camera images) of their respective areas (for example, the first path R1, the second path R2, and the third path R3) at predetermined timings (step S1). These captured environmental camera images are transmitted from environmental cameras 301 to 30n to the data acquisition unit 16 (step S2).

[0103] The data acquisition unit 16 collects (receives) environmental camera images transmitted from environmental cameras 301 to 30n.

[0104] Next, the higher-level management device 10 extracts one or more features from each of the environmental camera images collected by performing predetermined image processing on each of the environmental camera images (step S3).

[0105] Furthermore, the data collection unit 16 collects sunshine condition data (for example, the current date and time, time of day, weather, and temperature) from the internet or other sources (step S4).

[0106] The sunlight condition data collected as described above (environmental camera images (features), current date and time, time of day, weather, temperature) is input into the learning model 17 (step S5).

[0107] When sunlight condition data is input, the learning model 17 calculates the optimal parameters that minimize the influence of the sunlight conditions corresponding to the sunlight condition data, based on the sunlight condition data and the learning results (step S6). At that time, as shown in Figure 10, the learning model 17 calculates the optimal parameters (here, optimal parameter 1, optimal parameter 2, and optimal parameter 3) for each of the multiple paths (here, the first path R1, the second path R2, and the third path R3).

[0108] Next, the higher-level management device 10 (communication unit 14) transmits the optimal parameters calculated in step S6 (see Figure 10) to the autonomous mobile robot 20 (step S7).

[0109] The autonomous mobile robot 20 (communication unit 23) receives optimal parameters transmitted from the higher-level management device 10 (communication unit 14). These optimal parameters are stored in the storage unit 22 of the autonomous mobile robot 20. In Figure 7, the reference numeral 223 represents the optimal parameters stored in the storage unit 22 in this manner. Hereafter, this will be referred to as the optimal parameter 223.

[0110] Next, the parameter setting unit 40 reads from the storage unit 22 and sets the optimal parameter (in this case, optimal parameter 1) that corresponds to the path corresponding to the current location of the autonomous mobile robot 20 (in this case, the first path R1) from the optimal parameters 223 (step S8).

[0111] Next, the autonomous mobile robot 20 performs a predetermined operation based on the optimal parameter (in this case, optimal parameter 1) set by the parameter setting unit 40 (step S9).

[0112] These predetermined operations include, for example, the operation of capturing images of the surroundings with the camera 25 based on the optimal parameters set in step S9, the operation of performing noise cancellation processing on the sensor data which is the output of the depth camera based on the optimal parameters (filter parameters) set in step S9, and the operation of performing noise cancellation processing on the sensor data which is the output of the laser sensor based on the optimal parameters (filter parameters) set in step S9.

[0113] This makes it possible to suppress the detection error of the sensor devices (e.g., camera 25, depth camera, laser sensor) equipped with the autonomous mobile robot 20, which is caused by sunlight conditions within the movement range of the autonomous mobile robot 20 (in this case, the first path R1). As a result, it is possible to suppress a decrease in recognition rate or a decrease in self-position accuracy caused by sunlight conditions within the movement range of the autonomous mobile robot 20 (in this case, the first path R1).

[0114] Next, if the autonomous mobile robot 20 has not yet reached the next path (in this case, the second path R2) (step S10: NO), that is, if the distance to the next path exceeds a threshold, the process returns to step S1, and the processes from step S1 onward are repeatedly executed.

[0115] On the other hand, when the autonomous mobile robot 20 is autonomously moving and approaches the next path (in this case, the second path R2) (step S10: YES), that is, when the distance to the next path becomes less than or equal to a threshold, the parameter setting unit 40 reads out the optimal parameter (in this case, optimal parameter 2) from the storage unit 22 that is associated with the next path (in this case, the second path R2) from the optimal parameters 223 and sets it (step S8).

[0116] Next, the autonomous mobile robot 20 performs the predetermined operation based on the optimal parameter (in this case, optimal parameter 2) set by the parameter setting unit 40 (step S9).

[0117] This makes it possible to suppress the detection error of the sensor devices (e.g., camera 25, depth camera, laser sensor) equipped with the autonomous mobile robot 20, which is caused by the sunlight conditions within the movement range of the autonomous mobile robot 20 (here, the second path R2). As a result, it is possible to suppress a decrease in recognition rate or a decrease in self-position accuracy caused by the sunlight conditions within the movement range of the autonomous mobile robot 20 (here, the second path R2). In addition, the autonomous mobile robot 20 can automatically set optimal parameters suitable for the sunlight conditions of the next path (here, the second path R2) before it reaches (enters) that path.

[0118] Next, if the autonomous mobile robot 20 has not yet reached the next path (in this case, the third path R3) (step S10: NO), that is, if the distance to the next path exceeds a threshold, the process returns to step S1, and the processes from step S1 onward are repeatedly executed.

[0119] On the other hand, when the autonomous mobile robot 20 is autonomously moving and approaches the next path (in this case, the third path R3) (step S10: YES), that is, when the distance to the next path becomes less than or equal to a threshold, the parameter setting unit 40 reads out the optimal parameter (in this case, optimal parameter 3) from the storage unit 22 that is associated with the next path (in this case, the third path R3) from the optimal parameters 223 and sets it (step S8).

[0120] Next, the autonomous mobile robot 20 performs the predetermined operation based on the optimal parameters (in this case, optimal parameter 3) set by the parameter setting unit 40 (step S9).

[0121] This makes it possible to suppress the detection error of the sensor devices (e.g., camera 25, depth camera, laser sensor) equipped with the autonomous mobile robot 20, which is caused by the sunlight conditions within the movement range of the autonomous mobile robot 20 (in this case, the third path R3). As a result, it is possible to suppress a decrease in recognition rate or a decrease in self-position accuracy caused by the sunlight conditions within the movement range of the autonomous mobile robot 20 (in this case, the third path R3). In addition, the autonomous mobile robot 20 can automatically set optimal parameters suitable for the sunlight conditions of the next path (in this case, the third path R3) before it reaches (enters) that path.

[0122] As described above, according to Embodiment 2, it is possible to suppress the increase in detection errors of the sensor devices (e.g., camera 25, depth camera, laser sensor) equipped with the autonomous mobile robot 20, which can be caused by sunlight conditions within the movement range of the autonomous mobile robot 20.

[0123] This is achieved by including a learning model 17 that calculates optimal parameters that minimize the influence of sunlight conditions corresponding to the sunlight conditions data, based on sunlight condition data and learning results, and the autonomous mobile robot 20 then performs the predetermined operation based on these optimal parameters.

[0124] Next, I will explain some variations.

[0125] In the above embodiment 2, an example of generating the learning model 17 by supervised learning was described, but it is not limited to this. For example, the learning model 17 may be generated by a method other than supervised learning, such as reinforcement learning. When using reinforcement learning, it is conceivable to set the reward high enough to reduce the time required for the autonomous mobile robot 20 to travel along a path (route travel) and to teach it a strategy for determining the parameters in each passage. This is based on the hypothesis that an autonomous mobile robot 20 with inappropriate parameters will pick up unnecessary information through sensing, or will not be able to obtain necessary information, resulting in a longer travel time.

[0126] The numerical values ​​shown in each of the above embodiments are all examples, and it goes without saying that other appropriate numerical values ​​can be used.

[0127] The embodiments described above are merely illustrative in all respects. The invention is not to be construed as being limited by the descriptions of the embodiments above. The invention can be carried out in various other ways without departing from its spirit or main features. [Explanation of symbols]

[0128] 1, 1A…Autonomous Mobile Robot Control System 10…Upper management device 11…Processing Unit 12...Storage section 13…Buffer memory 14… Communications Department 16…Data Collection Department 17…Parameter calculation unit (learning model) 20…Autonomous mobile robots 21… Processing Unit 22...Storage section 23… Communications Department 24… Distance sensor group 25...Camera (visible camera) 26…Drive unit 27…Display section 28... Operation reception section 31...Alarm device 40...Parameter setting section 50…Learning engine 111...Robot Control Unit 112... Equipment Control Unit 113...Moving object detection unit 114...Moving object path estimation unit 115... Avoidance procedure generation unit 121...Floor map 122…Robot Information 123... Robot control parameters 124…Mobile Database 125…Route planning information 211…Movement command extraction unit 212... Drive control unit 213...Ambient Anomaly Detection Unit 221... Floor map 222…Robot control parameters 223...Optimal parameters 241...Front and rear distance sensor 242...Left / Right Distance Sensor 261…Drive wheels 262... Caster 301-30n... Environmental camera 281…Operation Interface 282... Emergency stop button 291... Storage room 292... Door 401... Room 402… Corridor 403... Elevator Hall CP1~CP4…Position D1…Training data D2...Teacher data EV1…Elevator Routes R1 to R3...

Claims

1. Higher-level management device, Autonomous mobile robots and Multiple environmental cameras installed in the facility where the autonomous mobile robot is used capture images of the movement range of the autonomous mobile robot and transmit the captured images to the higher-level management device, Equipped with, The aforementioned higher-level management device is A data collection unit collects sunlight condition data corresponding to the sunlight conditions within the movement range of the autonomous mobile robot, A parameter calculation unit calculates optimal parameters that minimize the influence of the sunlight conditions corresponding to the sunlight conditions, based on the aforementioned sunlight condition data. The system includes a communication unit on the higher-level management device side that transmits the aforementioned optimal parameters to the autonomous mobile robot, The aforementioned autonomous mobile robot A robot-side communication unit that receives the aforementioned optimal parameters, The system includes a parameter setting unit for setting the aforementioned optimal parameters, An autonomous mobile robot control system that performs a predetermined operation based on the optimal parameters set by the parameter setting unit, The parameter calculation unit calculates the optimal parameters for each of the multiple paths that the autonomous mobile robot should travel along. The communication unit on the higher-level management device side transmits the optimal parameters for each of the multiple paths to the autonomous mobile robot in a manner associated with the multiple paths. The parameter setting unit sets the optimal parameters corresponding to the route when the autonomous mobile robot approaches one of the multiple routes. The aforementioned sunlight condition data includes the aforementioned image, and is part of an autonomous mobile robot control system.

2. The autonomous mobile robot control system according to claim 1, wherein the aforementioned sunlight condition data further includes date and time, time of day, weather, and temperature.

3. The aforementioned autonomous mobile robot is equipped with a visible camera that takes pictures of its surroundings. The aforementioned optimal parameter is at least one of the exposure time and the shutter interval. The autonomous mobile robot control system according to claim 1, wherein the predetermined operation is an operation in which the visible camera takes pictures of the surroundings based on the optimal parameters set by the parameter setting unit.

4. The aforementioned autonomous mobile robot is equipped with a distance sensor, The aforementioned optimal parameters are parameters of a filter that performs noise cancellation processing on sensor data, which is the output of the distance sensor. The autonomous mobile robot control system according to claim 1, wherein the predetermined operation is an operation in which noise cancellation processing is performed on sensor data which is the output of the distance sensor, based on the optimal parameters set by the parameter setting unit.

5. The autonomous mobile robot control system according to claim 4, wherein the distance sensor is a depth camera or a laser sensor.

6. The autonomous mobile robot control system according to any one of claims 1 to 5, wherein the parameter calculation unit is a learning model generated by a learning engine.

7. The higher-level management device collects sunlight condition data corresponding to the sunlight conditions within the movement range of the autonomous mobile robot in a data collection step, The above-level management device performs a parameter calculation step of calculating an optimal parameter that minimizes the influence of the sunlight conditions corresponding to the sunlight conditions data, based on the sunlight conditions data. The above-level management device includes a communication step of transmitting the optimal parameters to the autonomous mobile robot, The parameter calculation step involves calculating the optimal parameters for each of the multiple paths that the autonomous mobile robot should travel along. The communication step is an autonomous mobile robot control method that transmits the optimal parameters for each of the plurality of paths to the autonomous mobile robot in association with the plurality of paths, wherein the sunlight condition data includes images taken by a plurality of environmental cameras installed in a facility where the autonomous mobile robot is used, which photograph the range of movement of the autonomous mobile robot.

8. A data collection step to collect sunlight condition data corresponding to the sunlight conditions within the movement range of an autonomous mobile robot, A parameter calculation step, based on the aforementioned sunlight condition data, calculates the optimal parameter that minimizes the influence of the sunlight conditions corresponding to the aforementioned sunlight condition data. An autonomous mobile robot control program that causes a higher-level management device to execute a communication step of transmitting the aforementioned optimal parameters to the autonomous mobile robot, The parameter calculation step involves calculating the optimal parameters for each of the multiple paths that the autonomous mobile robot should travel along. The communication step is an autonomous mobile robot control program that transmits the optimal parameters for each of the plurality of paths to the autonomous mobile robot in association with the plurality of paths, wherein the sunlight condition data includes images taken by a plurality of environmental cameras installed in the facility where the autonomous mobile robot is used, which photograph the range of movement of the autonomous mobile robot.

Images