Operation plan generation device, learning device, operation plan generation method, learning method, and program

Abstract

To make it possible to automatically generate an operation plan that increases the number of autonomous mobile devices capable of being operated in the same time zone in a fixed space.SOLUTION: An operation plan generation device 1 includes: a control unit 1a for generating an operation plan including a plurality of passing points for an autonomous mobile device in response to an operation plan generation request including operation desire information; and a storage unit 1b. The storage unit 1b stores a relation between an execution operation plan and operation achievements at the time of execution. The control unit 1a generates an operation plan candidate including the plurality of passing points based on the operation desire information to predict a positional and temporal deviation that may occur when the operation is performed according to the operation plan candidate based on the relation with respect to the operation plan candidate. The control unit 1a sets an exclusive area for prohibiting competition of another autonomous mobile device based on predicted results with respect to the operation plan candidate. The control unit 1a determines the operation plan candidate that does not compete with the operation plan that has been generated and has not been completed with respect to the other autonomous mobile device to be the operation plan to be responded.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an operation plan generation device, a learning device, an operation plan generation method, a learning method, and a program. [Background technology]

[0002] In recent years, autonomously moving devices such as drones and self-driving cars have been actively researched and are being used in some areas. In addition, autonomous moving devices are expected to become more widespread in the future due to the decline in the working population, cost reduction, and business efficiency. For example, autonomous moving devices are expected to be used in situations closely related to people's lives as tasks such as transporting and delivering luggage are replaced by autonomous moving devices from people and cars driven by people. In that case, autonomous moving devices will be operated in people's living spaces, so they are required to operate safely and stably, such as by operating reliably and adhering to arrival times.

[0003] Patent Document 1 describes a traffic control support system having a memory unit, an instruction responsiveness estimation unit, a geographic E-map generation unit, and a movement prediction unit, with the aim of improving the accuracy of ship route control while suppressing the amount of calculation. The memory unit has behavior data related to the behavior of a moving object, geographic data in which geographic attribute information, which is information related to the movement standard of the moving object, is added to each section obtained by dividing a map into meshes, and responsiveness data related to the responsiveness of the moving object to an instruction. The instruction responsiveness estimation unit estimates an ideal behavior of the moving object based on the responsiveness data, calculates a difference between the estimated behavior and the behavior of the behavior data, and updates the responsiveness data based on the calculation result. The geographic E-map generation unit estimates the likelihood that the moving object exists at a certain coordinate at each time based on the behavior data, the geographic data, and the responsiveness data, and generates a geographic E-map. The movement prediction unit predicts future coordinates of the moving object based on the geographic E-map. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-36958 A Summary of the Invention [Problem to be solved by the invention]

[0005] The present inventors have considered how to operate an autonomous mobile device safely and stably. First, some autonomous mobile devices are operated by moving along a given route and time, that is, autonomously controlling the route to move according to the operation plan. Even if an autonomous mobile device encounters a disturbance during its movement, it autonomously corrects its trajectory to move along the route and time, that is, the operation plan. For example, for a device that operates in the air, such as a drone, or a device such as a ship that moves on the ocean, a typical example of a disturbance that may cause it to deviate from its operation route is wind. Even if an autonomous mobile device encounters a disturbance such as wind, it autonomously controls the vehicle to move according to the operation plan as much as possible.

[0006] A typical example of an autonomous mobile device is a drone that flies autonomously outside of visual line of sight. An autonomously flying drone does not receive sequential instructions from an operator, but can move while making its own decisions to follow a given route. However, although an autonomous mobile device has a certain degree of autonomous navigation function, in order to respond to unforeseen circumstances and ensure safe operation, it is desirable for the device to be operated while being monitored remotely from a traffic management device, such as by grasping its current location via communication. It is desirable for the traffic management device to also have monitoring of the route.

[0007] In addition, some autonomous mobile devices are equipped with a function to avoid collisions with each other using sensors, etc., but since they are used in cities, it is necessary to avoid collisions between autonomous mobile devices in the unlikely event that they occur. Therefore, even if multiple autonomous mobile devices are operated at the same time, it is desirable to adjust and plan the routes and times in advance so that the autonomous mobile devices do not collide with each other and contact each other. The adjusted and planned operation routes and passing times can be called an operation plan, and the formulation of such an operation plan is one of the functions of the operation management device.

[0008] After considering methods for formulating operation plans for autonomous mobile devices, we found that there are two main problems. The first problem is that there is no way to automatically carry out the series of procedures of designing a route and generating an operation plan that is coordinated with the routes of other autonomous mobile devices by simply setting a start point and an end point, and the procedure is carried out manually via the web. When there are few drones in operation and the operation density in a certain airspace is not high, it is possible to adjust the operation plan even through manual procedures, but this method breaks down as the number of drones in operation increases.

[0009] The second problem is that if operation plans are made by manual procedures, the efficiency of spatial and temporal utilization will be poor, and multiple autonomous mobile devices will not be able to operate efficiently in the same space. For example, it is possible to handle route design and collision detection using a uniform, fixed-width route (protected airspace), but even when such a uniform protected airspace is taken into account, it is difficult to say that efficient operation is possible.

[0010] Specifically, as described above, even if an autonomous mobile device determines a route for operation, it may deviate from the planned route temporarily due to disturbances. Therefore, it is usually considered to design the route so that a large margin is provided so as not to collide with other autonomous mobile devices. For example, in the case of an autonomous mobile device flying in the air, such as a drone, it is considered to provide a distance margin so that the device will not protrude into other operation sections even if it is blown away by the wind under the maximum wind speed at which operation is permitted. In addition, this large margin must also be secured in terms of time. For example, in the case of an autonomous mobile device flying in the air, such as a drone, if multiple drones enter the same spatial section at the same time, a collision will occur, so the time period during which a drone can occupy a certain space must be long in consideration of delays. However, if these margins are provided too much, the efficiency of space utilization will decrease, and the number of autonomous mobile devices that can operate simultaneously in the same time period within a certain space will be reduced.

[0011] Because of these problems, when formulating operation plans for autonomous mobile devices, it is necessary to increase the number of autonomous mobile devices that can operate simultaneously in the same time period within a certain space, and to automate the formulation of such plans.

[0012] On the other hand, the technology described in Patent Document 1 is a technology that takes into consideration ships manned by humans, and allows multiple ships to enter one large mesh section. This is because, since humans are on board, even if a collision is about to occur within the section, it can be avoided by human judgment. Therefore, it is desirable to automatically generate an operation plan that increases the number of autonomous mobile devices that can operate simultaneously in the same time period within a certain space without requiring human judgment to avoid collisions.

[0013] An object of the present disclosure is to provide an operation plan generation device, a learning device, and methods and programs thereof capable of automatically generating an operation plan that increases the number of autonomous mobile devices that can operate within a certain space during the same time period, in order to solve the above-mentioned problems. [Means for solving the problem]

[0014] An operation plan generation device according to a first aspect of the present disclosure includes a control unit that generates an operation plan including multiple pass points for an autonomous mobile device included as an operation target in an operation plan generation request including operation desire information, and a storage unit that stores a relationship between an execution operation plan, which is an executed operation plan, and an operation record at the time of execution of the execution operation plan. The control unit generates operation plan candidates including multiple pass points based on the operation desire information, predicts positional and time deviations that may occur when operating according to the operation plan candidates based on the relationship, sets an exclusive area as an area in which other autonomous mobile devices are prohibited from competing with each other to operate based on the predicted result, and determines, among the operation plan candidates, a candidate that does not compete with operation plans that have been generated but are not completed for other autonomous mobile devices as an operation plan to be responded to the operation plan generation request.

[0015] A learning device according to a second aspect of the present disclosure includes an acquisition unit that acquires, in response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include multiple pass points for an autonomous mobile device included as an operation target in the operation request information, and an operation record at the time of execution of the executed operation plan, and a control unit that generates a trained model that predicts, based on the information acquired by the acquisition unit, positional and time deviations that may occur when operating according to an operation plan candidate generated to include multiple pass points based on another operation request information. The operation plan is a plan in which, for each section route connecting adjacent pass points among multiple pass points included in the operation plan, an exclusive area is set as an area in which other autonomous mobile devices are prohibited from operating in competition so as to surround the section route.

[0016] An operation plan generation method according to a third aspect of the present disclosure includes a process of generating an operation plan including a plurality of pass points for an autonomous mobile device included as an operation target in an operation plan generation request including operation request information, based on the operation request information, generating operation plan candidates including a plurality of pass points, predicting positional and time deviations that may occur when operating according to the operation plan candidates based on a relationship between an actual operation plan, which is an operation plan that has been executed, and an operation record at the time of execution of the actual operation plan, setting an exclusive area as an area in which other autonomous mobile devices are prohibited from competing with each other to operate, based on the predicted result, and determining, among the operation plan candidates, a candidate that does not compete with operation plans that have been generated but are not completed for other autonomous mobile devices as an operation plan to be responded to the operation plan generation request.

[0017] A learning method according to a fourth aspect of the present disclosure is a method for generating an operation plan including operation request information, which includes an executed operation plan among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and an operation record at the time of executing the executed operation plan, and generates a trained model that predicts positional and time deviations that may occur when operating according to an operation plan candidate generated to include a plurality of pass points based on another operation request information, based on the acquired information. The operation plan is a plan in which an exclusive area is set as an area in which other autonomous mobile devices are prohibited from operating in competition with each other in each section route connecting adjacent pass points among a plurality of pass points included in the operation plan, so as to surround the section route.

[0018] A program according to a fifth aspect of the present disclosure is a program that causes a computer to execute a process of generating an operation plan including a plurality of pass points for an autonomous mobile device included as an operation target in an operation plan generation request including operation desire information, in response to the operation plan generation request including the operation desire information, generating operation plan candidates including a plurality of pass points based on the operation desire information, predicting positional and time deviations that may occur when operating according to the operation plan candidate based on a relationship between an executed operation plan, which is an executed operation plan, and operation performance at the time of execution of the executed operation plan, setting an exclusive area for the operation plan candidate as an area in which other autonomous mobile devices are prohibited from competing with each other to operate, based on the predicted result, and determining, from among the operation plan candidates, a candidate that does not compete with operation plans that have been generated but are not completed for other autonomous mobile devices, as the operation plan to be responded to the operation plan generation request.

[0019] A program according to a sixth aspect of the present disclosure causes a computer to execute a process of acquiring information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and an operation record at the time of execution of the executed operation plan, in response to a request for generating an operation plan including operation request information, and generating a trained model that predicts, based on the acquired information, positional and time deviations that may occur when operating according to an operation plan candidate generated to include a plurality of pass points based on another operation request information. The operation plan is a plan in which, for each section route connecting adjacent pass points among a plurality of pass points included in the operation plan, an exclusive area is set as an area in which other autonomous mobile devices are prohibited from operating in competition with each other so as to surround the section route. Effect of the Invention

[0020] The present disclosure makes it possible to provide an operation plan generation device, a learning device, and methods and programs thereof that are capable of automatically generating an operation plan that increases the number of autonomous mobile devices that can operate within a certain space during the same time period. [Brief description of the drawings]

[0021] [Figure 1] 1 is a block diagram showing a configuration example of an operation plan generating device according to a first embodiment. [Diagram 2] 2 is a block diagram showing an example of the configuration of an autonomous mobile device that is to be operated in an operation plan generated by the operation plan generating device of FIG. 1. [Diagram 3] 4 is a flowchart illustrating a processing example in the operation plan generating device according to the first embodiment. FIG. [Figure 4] FIG. 1 is a block diagram showing an example configuration of a learning device that generates a trained model that can be used in the operation plan generation device according to embodiment 1. [Diagram 5] 5 is a flow diagram for explaining an example of processing in the learning device of FIG. 4. [Figure 6] FIG. 11 is a schematic diagram showing a configuration example of a traffic management system according to a second embodiment. [Figure 7] FIG. 11 is a block diagram showing a configuration example of a traffic management device according to a second embodiment. [Figure 8] FIG. 11 is a schematic diagram for explaining section management according to a comparative example. [Figure 9] FIG. 8 is a schematic diagram for explaining an example of management in the traffic management device of FIG. 7. [Figure 10] 1 is a conceptual diagram for explaining the relationship between waypoints, edges, and route deviations, as well as exclusive regions. [Figure 11] 11 is a diagram illustrating an example of a data format of an operation plan issuance request. FIG. [Figure 12] 12 is a diagram illustrating an example of a format of data stored in an operation plan issuance request storage unit in response to the request in FIG. 11 . [Figure 13] 11 is a diagram illustrating an example of a data format of an operation plan candidate created during the development of an operation plan. FIG. [Figure 14] 14 is a diagram illustrating an example of a data format of an operation plan issued for the operation plan candidate in FIG. 13. [Figure 15] 11 is a diagram illustrating an example of a data format of an issued operation plan. FIG. [Figure 16] 16 is a diagram showing an example of a data format in which a plurality of issued operation plans in FIG. 15 are arranged. FIG. [Figure 17] FIG. 11 is a diagram showing an example of a data format of coordinate information of a waypoint. [Figure 18] FIG. 11 is a diagram illustrating an example of a data format of edge information. [Figure 19] 8 is a sequence diagram showing an example of operation in the traffic management device of FIG. 7. [Figure 20] This is a sequence diagram continuing from Figure 19. [Figure 21] FIG. 11 is a flow diagram illustrating an example of a registration process of an operation plan issuance request. [Figure 22] 13 is a diagram illustrating an example of a data format of parameters of an operation plan request. [Figure 23] FIG. 11 is a flow diagram illustrating an example of a process for determining a spatial information acquisition range. [Figure 24] FIG. 13 is a schematic diagram showing an example of a spatial information acquisition range. [Diagram 25] 13 is a diagram illustrating an example of a data format of a spatial information acquisition request parameter. [Figure 26] FIG. 11 is a flow diagram illustrating an example of a process for determining a spatial information acquisition range. [Figure 27] 10 is a diagram illustrating an example of a data format of acquisition range space information. FIG. [Figure 28] FIG. 11 is a schematic diagram showing an example of acquisition range space information. [Figure 29] FIG. 11 is a flow diagram illustrating an example of a process for formulating an operation plan candidate. [Diagram 30] This is a flow chart continuing from Figure 29. [Diagram 31] FIG. 13 is a diagram illustrating an example of a data format of a predicted value. [Diagram 32] FIG. 13 is a schematic diagram for explaining a prediction result. [Diagram 33] FIG. 13 is a diagram illustrating an example of a data format of a calculated exclusive area. [Diagram 34] 13 is a diagram illustrating an example of a data format of an issued operation plan acquisition range. FIG. [Diagram 35] FIG. 35 is a schematic diagram showing an example of the spatial information acquisition range determined in FIG. 34. [Diagram 36] 13 is a diagram illustrating an example of a data format of the operation plan candidate to which the exclusive area calculation result of the operation plan candidate is added. FIG. [Figure 37] FIG. 13 is a schematic diagram illustrating an example of a conflict state. [Figure 38] FIG. 13 is a schematic diagram showing another example of a conflict state. [Figure 39] FIG. 13 is a schematic diagram showing another example of a conflict state. [Diagram 40] FIG. 11 is a diagram for explaining area contention. [Diagram 41] FIG. 11 is a diagram illustrating an example of a data format of an operation plan. [Diagram 42] FIG. 11 is a diagram illustrating an example of a data format of an operation plan. [Diagram 43] FIG. 11 is a diagram illustrating an example of a data format of an operation plan. [Diagram 44] FIG. 2 illustrates an example of a hardware configuration of the apparatus. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] Hereinafter, an embodiment will be described with reference to the drawings. In the embodiments, the same or equivalent elements may be denoted by the same reference numerals, and duplicated descriptions will be omitted as appropriate.

[0023] <Embodiment 1> An operation plan generating device according to the first embodiment will be described with reference to Fig. 1 and Fig. 2. Fig. 1 is a block diagram showing a configuration example of the operation plan generating device according to the first embodiment, and Fig. 2 is a block diagram showing a configuration example of an autonomous mobile device to be operated in an operation plan generated by the operation plan generating device of Fig. 1.

[0024] 1, an operation plan generation device 1 according to this embodiment includes a control unit 1a that controls the entire device, and a storage unit 1b, and generates operation plans for autonomous mobile devices. Operation plans are usually generated for multiple autonomous mobile devices, but it is sufficient that an operation plan generation request for a certain autonomous mobile device is received and the operation plan is generated in response to the operation plan generation request, and operation plans for all autonomous mobile devices under management do not necessarily need to be generated at the same time.

[0025] Before describing the configuration of the operation plan generating device 1, a configuration example of an autonomous mobile device to be operated in the generated operation plan will be described. As shown in Fig. 2, an autonomous mobile device (autonomous mobile equipment) 2 to be operated in the operation plan can include a plurality of sensors (sensor group) 2a, a communication unit 2b, a movement control unit 2c, and a driving unit 2d.

[0026] The autonomous mobile device 2 has information processing capabilities, controls actuators and the like, and moves autonomously, and can be any device responsible for transportation or movement.

[0027] Examples of the autonomous mobile device 2 include an autonomous automobile, an autonomous train, an autonomous ship, an autonomous flying aircraft, a drone, an AGV (Automated Guided Vehicle) used in a factory or a warehouse, a robot with a running function or a leg-based moving function, a robot electric wheelchair, a motorcycle, and the like. Here, the drone is not limited to an aircraft such as an unmanned aerial vehicle flying in the sky, but may be operated in any environment such as on the ground, in the sky, on the water, underwater, or in space. In addition, the robot may be moved by any method such as running or walking. Furthermore, examples of the autonomous mobile device 2 include construction vehicles such as forklifts, construction machines, and heavy machinery, vehicles used in logistics such as railroad vehicles, taxis, and trucks, police vehicles, and fire engines. Here, the railroad vehicles may be moved by any method such as light rail, iron wheel type, new transportation system, monorail, and magnetic levitation type. The autonomous mobile device 2 also includes devices with mode switching that sometimes operate autonomously and sometimes operate according to instructions or operations from a human, and devices that move part of their autonomous operation in cooperation with human operations or instructions. In other words, the autonomous mobile device 2 only needs to be capable of moving autonomously, and can also have a function of moving according to control from an external controller or according to the operation of the passenger.

[0028] The sensor group 2a is a plurality of sensors provided at various locations of the autonomous mobile device 2, and the results detected by each sensor are passed to the mobile control unit 2c and can be used to control the movement. In other words, the autonomous mobile device 2 is a device capable of moving autonomously, and can detect a moving (operating) state (current state) based on the detection result (sensor data) output from the sensor group 2a. Here, the current state can refer to the sensor value of the result of measuring the detection target at the installation location of each sensor included in the sensor group 2a, or the state indicated by the sensor value, and mainly includes the moving state (operating state). The moving state can include the inclination and position of the autonomous mobile device 2. Note that the installation location and the detection target generally differ depending on the type and shape of the autonomous mobile device 2. In addition, the sensor group 2a can be composed of a plurality of sensors necessary for controlling the autonomous mobile device 2, and can include not only a sensor group that collects the state of the autonomous mobile device 2 itself, but also a sensor group that collects the state of the surroundings (surrounding environment).

[0029] An example of the sensor group 2a will be described using a drone, which is a type of autonomous mobile device, as an example. The sensor group 2a can include a speed sensor that observes the speed of the aircraft, an acceleration sensor that observes the acceleration of the aircraft, a gyro sensor that observes the roll, pitch, and yaw of the aircraft, and a rotation sensor that observes the rotation speed of each rotor used for flight. The sensor group 2a can also include a pressure sensor and an altitude sensor that observe the altitude of the aircraft, a geomagnetic sensor that observes the direction from the aircraft, and a temperature sensor that detects heat generation of the circuit and rotor. Of course, the sensor group 2a does not need to include all of these examples, and can include only a part of them, and can also include other types of sensors without being limited to these examples.

[0030] For example, another sensor included in the sensor group 2a may be a sensor that acquires position information in a global navigation satellite system (GNSS). The GNSS is a general term for satellite positioning systems such as a global positioning system (GPS), a global navigation satellite system (GLONASS), Galileo, and a quasi-zenith satellite system. Here, the quasi-zenith satellite system is a quasi-zenith satellite system (QZSS). With this sensor, the autonomous mobile device 2 can receive a GNSS signal, measure its own position in space, and obtain position information. The sensor included in the sensor group 2a may be an optical camera or a stereo camera, and the position and attitude of the autonomous mobile device may be obtained by processing the image acquired by the camera. The sensor included in the sensor group 2a may be an acoustic wave sensor, an ultrasonic sensor, a 2D-LiDAR, a 3D-LiDAR, or the like. In this case, the autonomous mobile device 2 can process waveform data and point cloud data obtained from a sensing device using sound waves or lasers to obtain its own position, attitude, surrounding conditions, etc., and the information obtained in this way can also be considered sensor information. Therefore, such sensors that require information processing can also be included in the sensor group 2a.

[0031] In addition, the operating mode of the autonomous mobile device 2 (mode On / Off, current mode, etc.) and the status of other mounted devices (e.g., the rotor, etc.) and log data of a control device used for autonomous control can also be considered sensor information, so devices that provide such information can also be included in the sensor group 2a. Here, examples of the rotor status include valid / invalid, normal / abnormal, rotation speed, output, temperature, etc.

[0032] The communication unit 2b is a part that communicates with external devices such as the operation plan generation device 1, receives data of the operation plan generated by the operation plan generation device 1, and passes it to the movement control unit 2c. The communication unit 2b can be at least one of a wireless communication unit and a wired communication unit depending on the type of the autonomous moving device 2, the timing of communication of data with the outside, and the like.

[0033] The movement control unit 2c is a control unit that controls the movement of the autonomous moving device 2, and can also be called a drive control unit. The movement control unit 2c is the brain that controls the drive unit 2d (for example, an actuator such as a rotor) mounted on the autonomous moving device 2 based on sensing information provided by the sensor group 2a in accordance with the operation plan data received by the communication unit 2b, and is the part that performs autonomous control. The autonomous moving device 2 performs autonomous control so as to move along the route, coordinates, attitude, and speed provided as an operation plan at the start of operation. Therefore, even if the autonomous moving device 2 shifts its position slightly due to control errors or external disturbances such as wind, it can control the actuators based on the sensing information and maintain its original position, attitude, and speed by itself.

[0034] In this way, the movement control unit 2c controls the driving unit 2d according to the detection results of the sensor group 2a, and moves the autonomous moving device 2. Alternatively, the movement control unit 2c transmits some or all of the detection results of the sensor group 2a to an external server device (which may be the operation plan generating device 1 as long as it has a function of performing movement control) via the communication unit 2b. Then, the movement control unit 2c receives information for movement control from the server device via the communication unit 2b, and controls the driving unit 2d according to this information. The driving unit 2d drives the autonomous moving device 2 according to the control from the movement control unit 2c, and moves the autonomous moving device 2.

[0035] The movement control unit 2c can be configured as a control unit (not shown) that controls the entire autonomous moving device 2. This control unit can be realized, for example, by a CPU (Central Processing Unit), a working memory, and a non-volatile storage device that stores a program. This program can be a program that executes processing related to the autonomous movement control. In addition, this control unit can also be realized, for example, by an integrated circuit.

[0036] Next, each component of the operation plan generation device 1 that generates an operation plan for an autonomous mobile device such as the autonomous mobile device 2 will be described.

[0037] The operation plan generating device 1 functions as a server device that provides an operation plan, and can be implemented as a server computer. The control unit 1a controls the entire operation plan generating device 1. Therefore, the control unit 1a can be realized by, for example, a CPU, a working memory, and a non-volatile storage device that stores a program. This program can be a program that executes processing related to the generation of the operation plan. In addition, the control unit can also be realized by, for example, an integrated circuit.

[0038] In response to an operation plan generation request including operation request information received from the outside, the control unit 1a generates an operation plan including a plurality of passing points for the autonomous mobile device included as an operation target in the operation request information. The operation request information may include, for example, a departure point, a destination, and a departure (possible or desired) time, and may also include a desired arrival time.

[0039] The storage unit 1b stores the relationship between an executed operation plan, which is an executed operation plan, and the operation record at the time of executing the executed operation plan. Here, the executed operation plan is basically a plan generated by the control unit 1a, but may be a plan generated by another device, and may include both of these plans. The above relationship may be stored in a state in which a deviation described later can be predicted (for example, a calculation formula showing the relationship), but it is preferable to store it as a learned model as described later.

[0040] If the actual operation plan is a plan generated by the control unit 1a, it can be stored in the storage unit 1b when it is generated, and if not, it can be obtained from the outside via communication or a portable recording medium. The operation record can be obtained by direct communication from the autonomous mobile device 2, but the timing does not matter, and it can also be received via another device or obtained via a portable recording medium. However, it is sufficient that the storage unit 1b stores the above-mentioned relationship obtained from them, and it is not necessary that either the actual operation plan or the operation record is stored.

[0041] The control unit 1a first generates a candidate operation plan including multiple passing points based on the above-mentioned operation desire information, and then predicts the positional and time deviations that may occur when operating according to this candidate operation plan based on the above-mentioned relationships.

[0042] For this prediction, for example, a trained model can be used. In other words, the above relationship is a trained model generated by machine learning based on the actual operation plan and operation record, and can be a trained model that predicts positional and time deviations that may occur when operating according to the operation plan candidate. In this case, the trained model is stored in the memory unit 1b, and the control unit 1a uses this trained model at the time of prediction to perform the above prediction. This trained model is a model that has learned the deviation in the arrival location and the arrival time between past operation plans and actual results. The arrival location referred to here can include not only the destination but also multiple set passing points.

[0043] Based on the prediction result for the operation plan candidate, the control unit 1a sets an exclusive area as an exclusive area in which other autonomous mobile devices are prohibited from competing with each other to operate. This exclusive area can be set as an area in which other autonomous mobile devices for which operation plans have not been generated or autonomous mobile devices for which operation plans have been generated but not completed are prohibited from operating. Therefore, this exclusive area corresponds to a margin range of the operation route of the autonomous mobile device determined based on the deviation prediction result.

[0044] Then, the control unit 1a determines, from among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous moving devices, as an operation plan to be responded to the operation plan generation request. The operation plan generation device 1 can return the operation plan determined in this manner as a response to the operation plan generation request. Note that the determined operation plan can also be transmitted to the autonomous moving device 2, for example, sequentially during operation of the autonomous moving device 2, as much as is necessary for control at that time.

[0045] Furthermore, as described above, this embodiment can be constructed as an autonomous mobile system including the operation plan generation device 1 and the autonomous mobile device 2 that is equipped with a group of sensors that collect information to be included in the operation record and can communicate with the operation plan generation device 1. Furthermore, the operation plan generation device 1 can be configured as a single device, but can also be configured as a system by distributing functions among multiple devices.

[0046] Next, an operation plan generating method executed in the operation plan generating device 1 will be described with reference to Fig. 3. Fig. 3 is a flow diagram for explaining an example of processing in the operation plan generating device according to the first embodiment.

[0047] As described above, in response to an operation plan generation request including operation request information, the operation plan generation device 1 performs processing to generate an operation plan including a plurality of pass points for the autonomous mobile devices 2 included as operation targets in the operation request information.

[0048] In this process, first, the operation plan generation device 1 generates an operation plan candidate including a plurality of passing points based on the operation request information (step S1). Next, the operation plan generation device 1 predicts positional and time deviations that may occur when operating according to the operation plan candidate based on the relationship between an actual operation plan and the operation performance at the time of executing the actual operation plan (step S2).

[0049] Next, the operation plan generation device 1 sets an exclusive area for the operation plan candidates based on the predicted result (step S3). Then, the operation plan generation device 1 determines, among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous moving devices, as an operation plan to respond to the operation plan generation request (step S4), and ends the process. The operation plan determined here is set in the autonomous moving device 2 that is set as the device to be operated in the operation plan generation request. Also, as described above, the operation plan generation device 1 can be configured by a computer, and the program in this case can be a program that causes the computer to execute the above-mentioned processing.

[0050] As described above, in this embodiment, an exclusive area is set based on a predicted deviation based on past performance in a candidate operation plan, so that the set exclusive area is variable and based on performance. Therefore, according to this embodiment, an operation plan that increases the number of autonomous mobile devices that can operate in the same time period within a certain space can be automatically generated, and the accommodation density of autonomous mobile devices in the space and the number of simultaneous operations can be increased while avoiding collisions between the autonomous mobile devices. In other words, in this embodiment, it is possible to prevent collisions and improve the efficiency of space utilization at the same time. In addition, in this embodiment, not only two dimensions such as the ground can be assumed as the exclusive area, but also three dimensions such as the airspace can be assumed.

[0051] In addition, in this embodiment, as described as an exclusive area where other operations are prohibited, the autonomous mobile device 2 can be operated based on the concept of operation zones where only one vehicle can enter one zone. From this viewpoint, this embodiment can be said to be safe and useful as a concept of route design in areas where collision avoidance is difficult for the autonomous mobile device itself or where there is a limit to the avoidance ability including judgment and detection, such as a small drone flying beyond visual line of sight without a human on board. In addition, according to this embodiment, an operation plan can be automatically generated by simply specifying operation request information.

[0052] Next, a learning device that generates a trained model that can be used in the operation plan generating device 1 will be described with reference to Fig. 4. Fig. 4 is a block diagram showing an example of a configuration of a learning device that generates a trained model that can be used in the operation plan generating device 1.

[0053] 4, the learning device 4 may include an acquisition unit 4a and a control unit 4b. Here, the control unit 4b may include a storage unit 4c.

[0054] The acquisition unit 4a acquires information including an execution operation plan and operation results of operation based on the execution operation plan. If the execution operation plan is a plan generated by the control unit 1a, it can be acquired from the operation plan generation device 1 after generation or from the autonomous mobile device 2 that has acquired the generated plan via communication or a portable recording medium. The operation results can also be acquired, for example, via the operation plan generation device 1 or directly from the autonomous mobile device 2 by communication or via a portable recording medium. In addition, the learning can also include various environmental data acquired from the sensor group 2a, a weather information providing site, etc. as other learning data.

[0055] The control unit 4b generates a trained model as described above based on the information acquired by the acquisition unit 4a. Specifically, the control unit 4b inputs the information acquired by the acquisition unit 4a as training data into the untrained model 4d stored in the storage unit 4c, executes machine learning, obtains the trained model 4e, and stores it in the storage unit 4c. Alternatively, the untrained model 4d is updated to become the trained model 4e. The algorithm in the untrained model 4d does not matter, as long as the trained model 4e generated can perform the prediction as described above. Note that the learning algorithm (learning device) may be arbitrarily determined by the analyst. Then, the trained model resulting from the training can be operated and an appropriate one can be selected by the analyst, or it can be automatically selected to meet predetermined conditions that are deemed appropriate.

[0056] The learning device 4 can be configured with a computer. Therefore, the control unit 4b can be realized by, for example, a CPU, a working memory, and a non-volatile storage device storing a program. This program can be a trained model generation program.

[0057] In addition, the learning device 4 can be configured as a single device, but can also be configured as a system by distributing functions among multiple devices. In addition, the learning device 4 can be provided in the operation plan generation device 1 or connected to the operation plan generation device 1. However, the learning device 4 can also be configured as an independent device that is not connected to the operation plan generation device 1, and in that case, for example, the trained model generated by the learning device 4 may be copied or moved to the operation plan generation device 1 by using a portable recording medium or the like.

[0058] Next, a learning method executed in the learning device 4 will be described with reference to Fig. 5. Fig. 5 is a flow diagram for explaining an example of processing in the learning device 4 of Fig. 4. First, the learning device 4 acquires information including an actual operation plan and operation results of operations based on the actual operation plan (step S11). Then, the learning device 4 generates a trained model based on the acquired information (step S12).

[0059] Such a learning device 4 can generate a learned model that can be used as the above relationship in the operation plan generation device 1, and the operation plan generation device 1 can obtain appropriate prediction results based on machine learning, and can determine an exclusive area that corresponds to the actual deviation based on the prediction results.

[0060] <Embodiment 2> The second embodiment will be described with reference to Figs. 6 to 43, focusing on the differences from the first embodiment, but the various examples described in the first embodiment can be applied. The following description will be given with an example in mind in which the operation plan generated by the operation plan generation device according to this embodiment is a plan generated for a drone, but the present invention will not essentially change even if the drone is replaced with another type of autonomous mobile device.

[0061] This embodiment will be described with reference to FIG. 6, taking as an example a case where a delivery company (transportation business operator) Tr uses a drone for home delivery transport. FIG. 6 is a schematic diagram showing an example of the configuration of an operation management system according to this embodiment. However, this can be applied to autonomous mobile devices other than drones, and can also be applied to purposes other than home delivery transport. For example, the use of drones can be applied to business purposes such as mediating, managing, and issuing operation routes, etc., as performed by the Ministry of Land, Infrastructure, Transport and Tourism, for businesses that fly beyond visual line of sight for business such as transporting, photographing, and monitoring luggage, other than home delivery transport. In addition, this embodiment is not limited to drone operation management and business using the drone, but can be applied to operation management of autonomous mobile devices in general, such as robots, self-driving cars, factory automatic guided vehicles, ships and aircraft equipped with automatic navigation functions, and business using these. In addition, this embodiment can be applied to the transportation industry, manufacturing industry, infrastructure inspection and maintenance, construction and civil engineering, public (flood control, dam management, disaster prevention, disaster response, police, firefighting), security, etc.

[0062] As shown in Fig. 6, the operation management system according to this embodiment includes an operation management device 10 that performs operation management, and a delivery company Tr that receives an order from a shipper sh becomes an operation plan issue requester, requests the operation management device 10 to issue an operation plan, and obtains an operation plan. The operation management device 10 functions as a server device that provides the operation plan.

[0063] The traffic management device 10 is an example of the traffic plan management device 1 according to the first embodiment, and in this example, has a mechanism for managing the traffic of the drone 3 and a control function for the aircraft. The traffic management device 10 has a function of issuing a traffic plan in response to a request from a traffic management issue requester Tr, and a function of collecting and managing the traffic history when the drone 3 is operating, either continuously via communication or online or offline after the operation is completed.

[0064] A shipper sh requests the transport of luggage (operation of drone 3) from an operation plan issue requester Tr. The shipper sh provides the operation plan issue requester Tr with requests regarding operation conditions (operation start time (time or time zone when luggage is loaded), operation end time (desired delivery time or time zone), operation start location (location where luggage is loaded), operation end location (destination of luggage)), etc.). Although the shipper sh is depicted as a person icon in FIG. 6, the work of this component may be replaced with a computer system or the like and automated. For example, the shipper sh can use a terminal device such as a PC (Personal Computer), smartphone, or dedicated terminal to place an order to a server device or the like used by the delivery company Tr.

[0065] The operation plan issuance requester Tr requests the operation management device 10 to issue an operation plan based on an order from the shipper sh, and has the operation plan issued. The requester Tr has the role of registering the received issued plan in the drone 3 to be operated, and operating the drone 3 according to the operation plan.

[0066] The operation plan issue requester Tr is shown as a person icon in FIG. 6, but it only mediates the exchange of information between the shipper sh, the drone 3, and the operation management device 10, and the work of this component may be replaced with a computer system or the like to be automated. For example, the delivery company Tr uses a terminal device such as a PC, smartphone, or dedicated terminal to access a server device used by the delivery company Tr, transmits an operation plan issue request to the operation management device 10, and receives the operation plan as a response. This operation plan is generated and issued by the operation management device 10 based on the operation plan issue request from the delivery company Tr.

[0067] The drone 3 transports the cargo of the shipper sh according to the registered operation plan. Note that, although the present embodiment will be described in terms of a transportation use case, the present invention can be similarly applied to passenger transportation, photography, and other cases in which the drone 3 is operated.

[0068] Next, a configuration example of the traffic management device 10 according to the present embodiment will be described in outline with reference to Fig. 7 to Fig. 9. Fig. 7 is a block diagram showing a configuration example of the traffic management device according to the second embodiment, Fig. 8 is a schematic diagram for explaining section management according to a comparative example, and Fig. 9 is a schematic diagram for explaining an example of management in the traffic management device of Fig. 7.

[0069] As shown in FIG. 7, the operation management device 10 according to this embodiment can include a performance learning unit 100, a performance management unit 200, a plan formulation unit 300, a plan management unit 400, and a space management unit 500. Each component shown in FIG. 7 may be configured as a program on a computer, may be configured as an electric / electronic circuit, or may be a combination of a program and an electric / electronic circuit. Information can be exchanged between the components connected by lines in FIG. 7. If this is a program, information between elements can be exchanged via a shared memory or storage, or if the units are separate in implementation, information can be exchanged via communication.

[0070] In general, the operation management device 10 according to this embodiment can include an operation plan formulation unit 304 that sets an exclusive area based on deviation prediction and formulates an operation plan in the plan formulation unit 300. The deviation prediction can refer to learning the route deviation between the actual operation plan and the operation record, and predicting the route and passage time deviation using the learned result. The operation plan formulation unit 304 can automatically generate an operation plan based on the prediction result. In addition, in the case of the drone 3, the operation plan can be called a flight plan.

[0071] Furthermore, the traffic management device 10 according to this embodiment manages the travel route of an autonomous mobile device 2 such as a drone 3 as follows, instead of managing exclusivity by zone as in the comparative example shown in Fig. 8. That is, the traffic management device 10 manages exclusivity by using waypoints (passage points) and edges (section routes connecting the pass points) connecting them, as shown by circles in Fig. 9, and variable exclusive areas set around the edges.

[0072] To be more specific, in this embodiment, the deviations in destination locations and arrival times between past plans and actual results are learned, and based on the results of this learning, predictions of these deviations are used when formulating an operation plan to determine a margin of error (hereinafter, in this embodiment, referred to as a danger zone) for the operation route of the autonomous mobile device.

[0073] In this embodiment, when designing a route, a method is not adopted in which the marginal range of the entire space (all sections) is selected from a space in which rectangular parallelepipeds of fixed, uniform size are arranged in succession, and the exclusion time of the danger zone is not set to exclude the entire section from the start to the end of operation of an autonomous mobile device.

[0074] In the route design according to the comparative example, a rectangular parallelepiped (cube) larger than the danger zone is defined in advance to fill the space as shown in Fig. 8, and the cube is selected to set the route for the autonomous mobile device to follow as shown by the shaded area in Fig. 8, and the autonomous mobile device is made to move along the center of the cube as much as possible. In the comparative example, a route is set that will prevent collisions between the autonomous mobile devices using this route design.

[0075] In contrast, in this embodiment, as shown in Fig. 9, a plurality of points called waypoints are densely defined in space, and a route is set by selecting waypoints to be passed through. A waypoint is a passing point, and is a point that defines a place to be passed through and a time or time period to be passed through in an operation plan. Furthermore, in this embodiment, a danger zone in which routes for other autonomous mobile devices are not set (exclusion zone in which routes for other devices are not set) is set around a line connecting the waypoints, in a range according to a predicted value of route deviation.

[0076] More specifically, in locations where the predicted deviation from the route in the operation plan based on actual operation records is small, the exclusive space called the danger zone is made smaller, and conversely, if the deviation from the predicted value is large, the exclusive space of the danger zone is made larger.

[0077] Furthermore, in the traffic management device 10, when the discrepancy between the planned arrival time and the actual arrival time at the waypoint is large and an advance or delay in the arrival time is predicted, the device processes as follows: That is, in the traffic management device 10, depending on the magnitude of the discrepancy, the device lengthens the time for which the section is excluded as a danger zone (lifespan of the danger zone), and conversely, if the discrepancy is small, the device shortens the exclusion time (lifespan of the danger zone).

[0078] In this manner, in this embodiment, a route can be designed so as to pass through smaller partitioned sections (or conversely, larger partitions) rather than through large partitions of a fixed size.

[0079] In this way, the traffic management device 10 can set routes for the autonomous mobile devices more flexibly. In particular, the traffic management device 10 can reduce the area and time that the danger zone occupies in space compared to the comparative example, making it possible to set more traffic routes and increase the number of autonomous mobile devices that can be accommodated in a space.

[0080] In this manner, in this embodiment, points called high-density waypoints are defined in space instead of partitions, and a travel route is set to pass through these waypoints, and the travel route is designed. In particular, in this embodiment, not only is a danger zone (a zone that excludes other autonomous mobile devices) set to surround the travel route, but it can also be set as follows. That is, the size (the size of the surface perpendicular to the edge of the exclusive zone) and the exclusion time (exclusion period) can be determined and set based on the predicted deviation value predicted from learning past route deviation information. Here, the danger zone can be set to a different size for each edge between waypoints on the design route, based on the prediction.

[0081] In other words, the size of the plane perpendicular to the travel direction in the exclusive area (plane perpendicular to the route) is set to be different for at least two edges connecting adjacent waypoints. In this manner, in this embodiment, the exclusive area is set according to the predicted deviation, and the set size is allowed to be different, so that it is possible to divide the area into non-uniform sections with different sizes.

[0082] Furthermore, the exclusion time (exclusion period) can also be set to be different for at least two edges, which makes it possible to realize a non-uniform partitioning in which the partitions have different temporal sizes.

[0083] Next, a description will be given of each component in Fig. 7. Note that the cooperation between the components will be described later as the operation of the traffic management device 10 with reference to Figs. 19 and 20 and the like.

[0084] As described with reference to Fig. 6, the traffic management device 10 has a mechanism for managing the operation of the drone 3 and a control function for the aircraft. Specifically, the traffic management device 10 has a function for issuing a traffic plan in response to a request from a traffic management issue requester Tr, and a function for collecting and managing the operation history when the drone 3 operates, either continuously via communication or online or offline after the operation is completed. As a configuration for this purpose, as described above, the traffic management device 10 has a track record learning unit 100, a track record management unit 200, a plan formulation unit 300, a plan management unit 400, and a space management unit 500.

[0085] (Performance Learning Section 100) When the drone 3 is actually operated, a deviation occurs between the operation plan and the actual operation route. Therefore, the track record learning unit 100 learns the operation track record information collected from the drone 3 by the track record management unit 200. This makes it possible to obtain a trained model that can be used to predict the deviation of the route of the drone 3 from the operation plan. To achieve this, the track record learning unit 100 includes a trained model management unit 101, an operation track record learning unit 102, and a trained model holding unit 103.

[0086] The trained model management unit 101 manages the trained model data stored in the trained model holding unit 103 (such as inputting, outputting, and searching for data).

[0087] The operation record learning unit 102 mainly learns the differences (positional differences, time differences) between the operation plan and operation records of the drone 3. The trained model obtained by learning is stored in the trained model holding unit 103 via the trained model management unit 101.

[0088] The trained model holding unit 103 holds the trained model trained by the operation record learning unit 102. This trained model is used by the plan formulation unit 300 (described later) when formulating an operation plan, in order to predict a generated operation plan and positional and time deviations that may occur when operating according to that operation plan.

[0089] The operation record learning unit 102 reads out information on the operation record of the drone stored in the operation record storage unit 202 of the record management unit 200 via the operation record management unit 201. The information on the operation record of the drone can be information indicating the history of how the drone actually traveled between waypoints, such as the deviation of the route (dashed line) in the left diagram of Fig. 10. Fig. 10 is a conceptual diagram for explaining the relationship between the waypoint, the edge, and the route deviation, as well as the exclusive area.

[0090] The operation record learning unit 102 compares this information with the issued operation plan stored in the issued operation plan storage unit 402 read via the issued operation plan management unit 401 of the plan management unit 400. Then, the operation record learning unit 102 learns the route deviation and the deviation of the arrival time at the waypoint between the plan and the actual result obtained by the comparison, the time period in which the deviation occurred, and the edge (and the waypoints at both ends) in which the deviation occurred, and constructs a learned model. The route deviation may be the maximum value or the most frequent value of the deviation in the section. In addition, the time period in which the deviation occurred may be, for example, a tag representing a month, a tag representing each week in which one year is divided into one week, a tag representing a time divided into six parts of a day, or a combination of these, since the deviation is greatly affected by the season, temperature, climate, and weather. In other words, the time period in which the deviation occurred means a time period divided into units in which the same tendency for the deviation can be obtained.

[0091] The operation record learning unit 102 stores the constructed trained model in the trained model holding unit 103 via the trained model management unit 101. When a location (edge ​​(or waypoints at both ends)) where a deviation is to be predicted and a planned time of operation at that location are given to the trained model stored in the trained model holding unit 103, a predicted deviation value is obtained. The obtained predicted value is a predicted deviation value for the route deviation and the end point passing time (the time of passing the end of the edge) that will occur at that time and place. However, in this embodiment, it is assumed that the trained model is transferred to the plan formulation unit 300 and used.

[0092] (Performance Management Department 200) When the drone 3 is operated, the record management unit 200 collects, stores, and manages the operation history via communication at all times or online or offline after the operation is completed. To achieve this, the record management unit 200 includes an operation record storage unit 202 and an operation record management unit 201.

[0093] The operation record storage unit 202 stores the operation history collected and accumulated by the drone 3 during operation and the information collected from the operation history. The operation record management unit 201 manages the operation history stored in the operation record storage unit 202. In addition, the operation record management unit 201 searches for and extracts operation records from the operation record storage unit 202 in response to a request from the record learning unit 100, and passes them to the record learning unit 100.

[0094] (Planning Department 300) The plan formulation unit 300 formulates an operation plan in cooperation with other functional units in response to a request from an operation plan issuance requester Tr. To achieve this, the plan formulation unit 300 includes a trained model holding unit 103, an exclusive region calculation unit 301, an operation plan issuance request management unit 302, an operation plan issuance request holding unit 303, an operation plan formulation unit 304, and an operation plan candidate temporary holding unit 305.

[0095] The trained model holding unit 103 of the plan formulation unit 300 holds the trained model generated by the performance learning unit 100. The trained model held here may be the trained model itself generated by the performance learning unit 100, or a copy acquired via the trained model management unit 101. It is used to predict an operation plan and an expected deviation from the plan when formulating an operation plan.

[0096] The exclusive area calculation unit 301 calculates an exclusive area to be set around the operation plan. The exclusive area calculation unit 301 predicts positional and time deviations that will occur on the designed operation route using the trained model managed by the trained model holding unit 103, and calculates an exclusive area by adding a safety margin range (danger range) to the predicted range.

[0097] An exclusive area calculation unit 301 is provided to design a flight route so that other drones 3 do not enter this exclusive area. The plan formulation unit 300 designs a flight route so that the exclusive area of ​​a drone 3 does not overlap with the exclusive area of ​​another drone 3, since there is a risk of collision if the exclusive area of ​​the drone 3 overlaps with the exclusive area of ​​another drone 3.

[0098] An operation plan issuance request management unit 302 accepts a request from an operation plan issuance requester Tr, registers the request in an operation plan issuance request holding unit 303, and requests an operation plan formulation unit 304 to formulate an operation plan.

[0099] The operation plan issuance request holding unit 303 holds the contents of the request from the operation plan issuance requester Tr, and also holds the status of whether or not an operation plan has been issued in response to the request. The operation plan issuance request holding unit 303 can hold this information in a format that allows searching by any item, like a relational database.

[0100] Here, an example of the data format (information items) held in the operation plan issuance request holding unit 303 will be described with reference to Fig. 11 and Fig. 12. Fig. 11 is a diagram showing an example of the data format of an operation plan issuance request (an operation plan generation request including desired operation information), and Fig. 12 is a diagram showing an example of the data format held in the operation plan issuance request holding unit 303 for the request in Fig. 11.

[0101] 11, a request from an operation plan requester Tr can include the departure point (latitude, longitude, altitude), arrival point (latitude, longitude, altitude), possible departure time (year, month, day, hour, minute, second), and desired arrival time (year, month, day, hour, minute, second) as desired operation information. Note that the operation plan requester Tr can also be configured to access the operation management device 10 from a PC or the like and make a request by, for example, specifying the departure point and arrival point by waypoint ID.

[0102] Then, when the operation plan issuance request management unit 302 receives a request from an operation plan issuance requester Tr, as shown in Fig. 12, it registers the request management number and the operation route issue number in addition to the information included in the request in the operation system threat issuance request holding unit 303. That is, when a request is received, the operation plan issuance request holding unit 303 registers the request management number, departure point (latitude, longitude, altitude), arrival point (latitude, longitude, altitude), possible departure time (year / month / date / hour / minute / second), desired arrival time (year / month / date / hour / minute / second), and operation route issue number.

[0103] In addition, when the operation plan issuance request management unit 302 completes the generation of the operation plan and returns a response to the operation plan issuance requester Tr, it updates the operation plan issuance request storage unit 303 by adding an issued flag and an operation route issuance number to the row that matches the request management number.

[0104] As described above, the plan formulation unit 300 formulates an operation plan in cooperation with other functional units in response to a request from the operation plan issuance requester Tr. The formulation is executed by the operation plan formulation unit 304. The operation plan formulation unit 304 designs an operation route so that other drones 3 do not enter the exclusive area calculated by the exclusive area calculation unit 301. If the exclusive area of ​​a certain drone 3 overlaps with the exclusive area of ​​another drone 3, there is a risk of collision, so the operation plan formulation unit 304 designs an operation route so that they do not overlap.

[0105] The operation plan candidate temporary storage unit 305 temporarily stores candidates for operation plans created during the formulation of an operation plan. The operation plan candidate temporary storage unit 305 can store information indicating candidates for operation plans in a format that allows searching by any item, such as a relational database.

[0106] Here, an example of the data format (information items) stored in the operation plan candidate temporary storage unit 305 will be described with reference to Fig. 13 and Fig. 14. Fig. 13 is a diagram showing an example of the data format of an operation plan candidate created during the formulation of an operation plan, and Fig. 14 is a diagram showing an example of the data format of an operation plan issued for the operation plan candidate of Fig. 13.

[0107] 13, the operation plan candidate temporary storage unit 305 temporarily stores a list of the waypoint ID of the departure point, the possible departure time (year / month / date / hour / minute / second), the waypoint ID of the arrival point, the arrival deadline time (year / month / date / hour / minute / second), and the operation route. Here, the list of operation routes has columns of the section sequence number, the starting point waypoint ID, the end point waypoint ID, the target end point passing time, and the edge ID.

[0108] Furthermore, the operation plan candidate temporary storage unit 305 also temporarily stores a conflict flag indicating whether or not this candidate conflicts with others. The conflict flag item can be used when determining whether the operation plan candidate conflicts with others, that is, whether or not there is a problem with adopting it as an operation plan. Then, if there is no problem with adopting it as an operation plan, the operation plan formulation unit 304 decides on it as an operation plan by removing the conflict flag item stored in the operation plan candidate temporary storage unit 305, as shown in FIG. 14.

[0109] In this way, when formulating an operation plan, the operation plan formulation unit 304 first generates a proposal (candidate) of an operation plan and temporarily stores it in the operation plan candidate temporary storage unit 305. Then, the operation plan formulation unit 304 obtains an exclusive area for the stored operation plan, calculates whether the exclusive area overlaps with an already issued operation plan, and issues it as a formal operation plan if there is no overlap. On the other hand, if there is an overlap, the operation plan formulation unit 304 performs an operation of generating another operation plan proposal again, and therefore includes a mechanism for temporarily storing the operation plan proposal such as the operation plan candidate temporary storage unit 305.

[0110] (Planning and Management Department 400) The plan management unit 400 holds and manages issued operation plans. The issued operation plans managed here are used to learn the discrepancy between the operation plan and the operation results, and when formulating an operation plan, to check whether the exclusive area of ​​the formulated operation plan overlaps with the exclusive area of ​​an operation plan that has already been issued. To achieve this, the plan management unit 400 includes an issued operation plan management unit 401 and an issued operation plan holding unit 402.

[0111] The issued operation plan management unit 401 manages, that is, inserts and removes and searches, the issued operation plans in the issued operation plan holding unit 402. The issued operation plan management unit 401 provides the issued operation plans to the performance learning unit 100 and exchanges the issued operation plans with the plan formulation unit 300.

[0112] The issued operation plan storage unit 402 stores an issued operation plan and an exclusive area for the operation plan. The issued operation plan storage unit 402 can store information about an issued operation plan and an exclusive area for the operation plan in a format that allows searching by any item, such as a relational database.

[0113] Here, an example of the data format (information items) stored in the issued operation plan storage unit 402 will be described with reference to Fig. 15 and Fig. 16. Fig. 15 is a diagram showing an example of the data format of an issued operation plan, and Fig. 16 is a diagram showing an example of the data format in which a plurality of issued operation plans of Fig. 15 are arranged.

[0114] The issued operation plan storage unit 402 can store information as shown in Fig. 15 as an issued operation plan. That is, an issued operation plan can include waypoint IDs of departure and arrival points, possible departure times (year / month / date / hour / minute / second), arrival deadline times (year / month / date / hour / minute / second), request management numbers, operation route issue numbers, operation routes, and exclusive areas (exclusive area radius and exclusive end time). Then, these pieces of information can be grouped together and added to each issued operation plan, thereby making it possible to have a data format as shown in Fig. 16.

[0115] (Space Management Department 500) The space management unit 500 manages information about the space in which the drone 3 is operated. This information (space information) can be composed of coordinate information of points in space called waypoints, which are reference routes for the drone 3 to travel and waypoints during operation, and edge information, which are lines (vectors) connecting the points. As a configuration for managing the space information, the space management unit 500 includes a space information management unit 501 and a waypoint (WP) information storage unit 502.

[0116] The spatial information management unit 501 manages and searches waypoints and edge information. The waypoint information storage unit 502 stores coordinate information of spatial points called waypoints, which are rough guides for routes when the drone 3 is operated and intermediate points during operation. The waypoint information storage unit 502 can store coordinate information of waypoints in a format that allows searching by any item, like a relational database.

[0117] Here, an example of the data format (information items) stored in the waypoint information storage unit 502 will be described with reference to FIG. 17. FIG. 17 is a diagram showing an example of the data format of coordinate information of a waypoint. As shown in FIG. 17, the waypoint storage unit 502 stores a list of waypoint IDs and waypoint coordinates (latitude, longitude, altitude) as coordinate information of a waypoint. For example, it stores coordinates of points in space as shown in FIG. 9. Note that in the example of FIG. 9, the waypoints are arranged evenly vertically, horizontally and vertically, but the points do not necessarily need to be arranged evenly, and it is acceptable if they are arranged irregularly.

[0118] The edge information storage unit 503 stores information on edges, which are lines (vectors) connecting waypoints, and flags indicating whether the edges can be used as part of a travel route (unusable flags). The edge information storage unit 503 can store edge information in a format that allows searching by any item, like a relational database.

[0119] Here, an example of the data format (information items) stored in the edge information storage unit 503 will be described with reference to Fig. 18. Fig. 18 is a diagram showing an example of the data format of edge information.

[0120] 18, the waypoint storage unit 502 can store information about edges connecting waypoints, such as edge IDs, starting waypoint IDs that are the starting points of the edges, end waypoint IDs that are the end points, and a list of unusable flags. The unusable flags are set to indicate that a section that should not be used as part of a travel route, for example, because there is an obstacle on the line connecting the waypoints, cannot be used.

[0121] (Example of operation of the traffic management device 10) An example of the internal operation of the traffic management device 10 will be described with reference to FIGS. First, the operation around the performance learning unit 100 will be described. As described above, the operation record learning unit 102 of the record learning unit 100 reads out information on the operation record of the drone in the operation record holding unit 202 of the record management unit 200 via the operation record management unit 201. The information on the operation record of the drone can be information indicating the history of how the drone actually operated between waypoints, such as the deviation of the route (dashed line) in the left diagram of FIG. 10. In addition, the operation record learning unit 102 compares this information with the issued operation plan held in the issued operation plan holding unit 402 read in via the issued operation plan management unit 401 of the plan management unit 400.

[0122] The operation record learning unit 102 then learns the route deviation between the plan and the actual results obtained by the comparison, the deviation in arrival time at the waypoint, the time period in which the deviation occurred, and the edge (and the waypoints at both ends) in which the deviation occurred, and constructs a learned model. The route deviation may be the maximum value or most frequent value of the deviation in the section. Also, the time period in which the deviation occurred means a time period divided into units that can obtain the same tendency for the deviation, as described above.

[0123] Next, the operation record learning unit 102 stores the constructed trained model in the trained model holding unit 103 via the trained model management unit 101. When the location (edge ​​(or both end waypoints)) where deviation is to be predicted and the planned time of operation at that location are given to the trained model stored in the trained model holding unit 103, a predicted deviation value is obtained. The obtained predicted value is the predicted deviation value for the route deviation and the end point passing time (the time of passing the end of the edge) that will occur at that time and place.

[0124] Next, the operation of the planning unit 300 and the related components will be described. The cooperation between the elements is shown in the sequence diagrams of Figures 19 and 20, and the processing within the functional units will be explained with reference to individual flow diagrams. Figures 19 and 20 show an example of sequential execution of the operation plan formulation process. Figures 19 and 20 are sequence diagrams showing an example of a series of operations in the operation management device 10. Here, the entire sequence is expressed in the notation of UML (Unified Modeling Language) 2.0.

[0125] First, an operation plan issuance requester requests the operation plan issuance request management unit 302 to issue an operation plan by specifying information of the items in Fig. 11 (step S21). As shown in Fig. 11, the items of the specified information (desired operation information) can be the departure point (latitude, longitude, altitude), arrival point (latitude, longitude, altitude), possible departure time (year, month, day, hour, minute, second), and desired arrival time (year, month, day, hour, minute, second).

[0126] The operation plan issuance request management unit 302 registers the received operation plan issuance request in the operation plan issuance request storage unit 303 (step S22). The processing of step S22 will be described with reference to Fig. 21. Fig. 21 is a flow diagram for explaining an example of a registration processing of an operation plan issuance request.

[0127] The operation plan issuance request management unit 302 receives an operation plan issuance request including the information shown in Fig. 11 from an operation plan issuance requester (step S51). The operation plan issuance request management unit 302 adds a request management number and an issued flag (False) to the contents of the operation plan issuance request as shown in Fig. 12, and registers and stores the request in the operation plan issuance request storage unit 303 (step S52). Here, a request management number is added to the information of Fig. 11 specified in step S21, and the information is added to the operation plan issuance request storage unit 303 as shown in Fig. 12. At this point, the issued flag is added as False, and the operation route issue number is added as a blank.

[0128] Next, the operation plan issuance request management unit 302 makes an operation plan formulation request to the operation plan formulation unit 304 using the parameters of the operation plan formulation request as shown in Fig. 22 as parameters (step S53). This step S53 corresponds to step S23 in Fig. 19. Here, Fig. 22 is a diagram showing an example of a data format of the parameters of the operation plan request.

[0129] In this way, in step S23, the operation plan issuance request management unit 302 issues an operation plan formulation request to the operation plan formulation unit 304 by specifying parameters. As parameters of this operation plan request, as shown in Fig. 22, a request management number, a departure point (latitude, longitude, altitude), an arrival point (latitude, longitude, altitude), a possible departure time (year / month / date / hour / minute / second), and a desired arrival time (year / month / date / hour / minute / second) are specified. The request management number is added when responding with the result, so that the operation plan issuance request held in the operation plan issuance request holding unit 303 can be associated with the operation plan that was responded to.

[0130] Following step S23, an operation plan formulation request is received, and the operation plan formulation unit 304 determines a spatial information acquisition range (step S24). An example of the processing in step S24 will be described with reference to Fig. 23. Fig. 23 is a flow diagram for describing an example of the processing for determining a spatial information acquisition range.

[0131] The operation plan formulation unit 304 receives the operation plan formulation request transmitted in step S23 from the operation plan issuance request management unit 302 using the parameters of the operation plan formulation request in Fig. 22 as parameters (step S61). Next, as shown in Fig. 24, the operation plan formulation unit 304 calculates a requested range start point and a requested range end point based on the departure point coordinates and arrival point coordinates of the parameters of the operation plan formulation request in Fig. 22 (step S62). Fig. 24 is a schematic diagram showing an example of a spatial information acquisition range.

[0132] Next, the operation plan formulation unit 304 sends a spatial information acquisition request in step S25 to the spatial information management unit 501 using the parameters of the spatial information acquisition request shown in Fig. 25 as parameters (step S63). Fig. 25 is a diagram showing an example of a data format of the spatial information acquisition request parameters.

[0133] As shown in FIG. 24, the operation plan formulation unit 304 sets the spatial information acquisition range to a range that can be taken as a route of the operation plan. Here, as shown in FIG. 24, the spatial information acquisition range is calculated to be a range wider than the range of a rectangular parallelepiped with the departure point and the arrival point as diagonals. This is because, when an operation route conflicts with an already issued operation route, a route that makes a large detour to avoid the conflict may be created. In this way, a detour can be secured by setting the spatial information acquisition range to a range that is wider by a predetermined distance than a rectangle with the coordinates on the horizontal plane of the departure point and the arrival point as diagonals. In addition, since a drone is used as an example here, the upper limit altitude and the lower limit altitude as shown in FIG. 24 are not specified because they are legally determined, but when applied to other autonomous mobile devices, the spatial information acquisition range may be determined by specifying the altitude.

[0134] In the spatial information acquisition request in step S25, the operation plan formulation unit 304 requests acquisition of spatial information by providing search parameters to the spatial information management unit 501. As shown in Fig. 25, the search parameters include information on items such as a starting point (latitude, longitude, altitude), an arrival point (latitude, longitude, altitude), a requested range start point (latitude, longitude), and a requested range end point (latitude, longitude).

[0135] The spatial information management unit 501 receives the spatial information acquisition request in step S25 and searches for spatial information (step S26). An example of the processing in step S26 will be described with reference to Fig. 26. Fig. 26 is a flow diagram for describing an example of processing for determining a spatial information acquisition range.

[0136] In step S26, the spatial information management unit 501 receives a spatial information acquisition request with the requested range as a parameter (search parameter) from the operation plan formulation unit 304 (step S71). Next, the spatial information management unit 501 reads out spatial information on the requested range (FIG. 25), which is a parameter of the spatial information acquisition request in step S25, from the waypoint information in FIG. 17 and the edge information in FIG. 18 (step S72). The spatial information read out here includes the requested range start point, the requested range apex end point, the upper limit of the permitted operation, the waypoint included in the range determined by the lower limit of the permitted operation, and the edge, as shown in FIG. 27, and this becomes the spatial information of the requested acquisition range. FIG. 27 is a diagram showing an example of a data format of the acquisition range spatial information. The acquisition range spatial information acquired in this way is an image as shown in FIG. 28. FIG. 28 is a schematic diagram showing an example of the acquisition range spatial information.

[0137] Then, the spatial information management unit 501 responds to the operation plan formulation unit 304 with the spatial information of the acquisition range shown in FIG. 27 as the spatial information of the acquisition request range (steps S73 and S27).

[0138] The operation plan formulation unit 304 receives spatial information of the requested range from the spatial information management unit 501 (step S27), formulates an operation plan candidate based on the spatial information (step S28), and sends an exclusive area calculation request to the exclusive area calculation unit 301 (step S29). In the process of step S28, an operation plan candidate is generated, not an operation plan. In the subsequent process, it is checked whether there is a conflict of operation routes with operation plans that have already been issued, and then the operation plan is generated, so what is generated at this point is an operation plan candidate.

[0139] A processing example of steps S27 to S29 will be described with reference to Figs. 29 and 30. Figs. 29 and 30 are flow diagrams for explaining an example of a process of formulating an operation plan candidate. First, the operation plan formulation unit 304 receives spatial information within the acquisition request range (Fig. 27) from the spatial information management unit 501 as the spatial information of the acquisition request range (step S81). If the formulation of step S28 is the first execution, the operation plan formulation unit 304 registers the information shown in Fig. 13 in the operation plan candidate temporary storage unit 305. That is, the waypoint ID of the departure point, the possible departure time, the waypoint ID of the arrival point, and the arrival deadline time are registered, and an empty table of the operation route and the conflict flag is registered. Every time a candidate is formulated, an empty table of the operation route and the conflict flag is generated.

[0140] Next, the operation plan developer 304 performs a loop process between steps S82a and S82b. The loop condition in this loop process is that the process is repeated until the waypoint ID of the arrival point and the end point waypoint ID match.

[0141] In this loop process, first, the operation plan formulation unit 304 determines the starting waypoint as follows (step S83): The first time, it searches for and reads out the waypoint ID of the starting point of the spatial information (FIG. 27) within the acquisition request range received in step S81, and from the second time onwards, it searches for and reads out an edge where the end waypoint ID and the starting waypoint ID from the previous process match and the unusable flag is false.

[0142] Next, the operation plan formulation unit 304 determines the target end point passing time for each selected edge (step S84) as follows: Using the start point waypoint IDs and end point waypoint IDs of the multiple edges searched and read in step S83 as keys, the waypoint coordinates of the waypoints whose waypoint IDs in the spatial information within the acquisition request range (FIG. 27) match are acquired. Then, the time required to travel that edge (route section) is calculated from the distance between the two acquired waypoints and the standard speed of the drone to be used. Calculating the required time means that the target end point passing time for the edge is calculated.

[0143] Next, the operation plan formulation unit 304 selects a route section to proceed to the next waypoint from among the multiple edges searched and read in step S84 (step S85). At this time, edges whose edge ID matches the table (there may be multiple) of operation routes and conflict flags of the operation route of the operation plan candidate temporary storage unit 305 shown in Fig. 13, whose target end time is close, and whose conflict flag is true are excluded from selection. After such exclusion, an edge is selected according to a route search algorithm such as a greedy algorithm or an A-star algorithm so that the route of the operation plan that is finally created is as short as possible. As a result, the edge ID for one section and the end waypoint (= next start waypoint ID) are determined.

[0144] Next, the operation plan formulation unit 304 adds a record to the table of operation routes and conflict flags in the operation plan candidate temporary storage unit 305 shown in Fig. 13 (step S86). Here, the assigned section sequence number (serial number starting from 0), the start waypoint ID, the end waypoint ID, and the conflict flag value "false" are added as one record.

[0145] In step S82b, when the above-mentioned loop condition is satisfied, the loop processing is terminated, and at the time of termination of the loop processing, one table of operation routes and conflict flags is completed.

[0146] Next, the operation plan formulation unit 304 requests the exclusive area calculation unit 301 to calculate the exclusive area of ​​the operation plan candidate using the operation plan candidate last registered as the information of Figure 13 in the operation plan candidate temporary storage unit 305 as a parameter (steps S87 and S29).

[0147] As a result of this processing, the items shown in FIG. 13 are formulated as candidates for the operation plan. In addition, the operation plan candidate generated and added in the operation plan candidate temporary storage unit 305 in this step goes through the loop in FIG. 20 until the operation plan is determined, and is stored until the registration and issuance of the operation plan is completed, and then it is deleted.

[0148] The exclusive area calculation request for the operation plan candidate in step S29 is made to the exclusive area calculation unit 301 by specifying the items in Fig. 14. The items shown in Fig. 14 are the waypoint ID of the departure point, the possible departure time (year / month / date / hour / minute / second), the waypoint ID of the arrival point, the arrival deadline time (year / month / date / hour / minute / second), and the operation route. The operation route includes the section sequence number, the starting point waypoint ID, the end point waypoint ID, the target end point passing time, and the edge ID.

[0149] The exclusive area calculation unit 301, which has received this exclusive area calculation request, calculates the exclusive area of ​​the operation plan (step S30). Prior to the processing of step S30, it is assumed that the trained model described above has been stored in the trained model holding unit 103 of the plan formulation unit 300. In other words, when the location (edge ​​(or both end waypoints)) where deviation is to be predicted and the planned time of operation at that location are given to this trained model, a predicted value of the deviation of the route that will occur at that time and location and the deviation for the end point passing time can be obtained. This end point passing time refers to the time of passing the end of the edge.

[0150] Step S30 will now be described in detail. 13 one record at a time, and obtains predicted values ​​(predicted route deviation value, predicted end point passing time deviation value) using the trained model in the trained model storage unit 103. Then, the exclusive zone calculation unit 301 adds the predicted values ​​(predicted route deviation value, predicted end point passing time deviation value) to each record.

[0151] This provides information as shown in Fig. 31. As shown in Fig. 32, the route deviation prediction value is obtained as a prediction value indicating the maximum deviation in the vertical direction on the line connecting the waypoints (section operation route), and the end point passing time deviation prediction value is obtained as a prediction value indicating the deviation from the target end point passing time. The idea of ​​deviation is as explained with reference to Fig. 10. Here, Fig. 31 is a diagram showing an example of the data format of the prediction value, and Fig. 32 is a schematic diagram for explaining the prediction result.

[0152] For each record of the predicted values ​​(predicted route deviation value, predicted end point passing time deviation value) calculated in this way, the safety distance and safety time are added to calculate the exclusive area (exclusion area radius, exclusive end time). The result of calculating the exclusive area is shown in Fig. 33. Fig. 33 is a diagram showing an example of the data format of the calculated exclusive area.

[0153] Next, the exclusive region calculation unit 301 responds to the operation plan formulation unit 304 with the processing result in step S30 shown in Fig. 33 (step S31). The operation plan formulation unit 304 receives this response and determines the range for acquiring an issued operation plan (issued operation plan acquisition range) using the information shown in Fig. 34 as a parameter (step S32). Fig. 34 is a diagram showing an example of the data format of the issued operation plan acquisition range. The parameters include a time that is earlier than the reachable time by a safety time, a time that is later than the arrival deadline by a safety time, a point (latitude, longitude) that is wider than the requested range start point by a safety range, and a point (latitude, longitude) that is wider than the requested range end point by a safety range.

[0154] The acquisition range (spatial information acquisition range) determined in this manner is the sum of the safety range at the start point of the requested range and the safety range at the end point of the requested range, as shown in Fig. 35, and this is determined as the range of the exclusive area acquisition request. Fig. 35 is a schematic diagram showing an example of the spatial information acquisition range determined in Fig. 34. Any past operation plan whose start and end times overlap with the above-mentioned time period and whose exclusive area overlaps even partially within this section will be read out.

[0155] Next, the operation plan formulation unit 304 requests the issued operation plan management unit 401 to acquire an exclusive area of ​​the issued operation plan by using the exclusive area acquisition request range determined in step S32 as a parameter (step S33).

[0156] The issued operation plan management unit 401 receives this request, and searches for and reads out issued operation plans from the operation plans stored in the issued operation plan storage unit 402 and whose data format is shown in Fig. 16, thereby calculating the exclusive area of ​​the issued operation plan (step S34). Here, from the operation plans, it searches for and reads out issued operation plans that are included in the requested range shown in Fig. 35 and that fall within the time between the time when the departure time of the current operation plan candidate is advanced by a safety time and the time when the arrival deadline time is delayed by a safety time. Note that there may be multiple applicable operation plans.

[0157] Here, the route issue number, departure waypoint ID, possible departure time, arrival waypoint ID, arrival deadline time, request management number, and the corresponding route and exclusive area list for the operation plan that meets the conditions are read.

[0158] Then, the issued operation plan management unit 401 returns the exclusive areas of the issued operation plans in the acquisition request range as a response to the operation plan formulation unit 304 (step S35). Here, the response includes the operation route issue number, the waypoint ID of the departure point, the possible departure time, the waypoint ID of the arrival point, the arrival deadline time, the request management number, and a list of the corresponding operation routes and exclusive areas of the operation plans that meet the conditions read in step S34. An example of this list is shown in Fig. 36. Fig. 36 is a diagram showing an example of the data format of the operation plan candidate to which the exclusive area calculation result of the operation plan candidate is added.

[0159] The information shown in Fig. 36 is information on the operation plan candidate to which the exclusive area calculation result of the operation plan candidate is added, and includes a list of operation sections including the section sequence number, start point waypoint ID, end point waypoint ID, target end point passing time, and edge ID as the operation route. In addition, the information shown in Fig. 36 includes a route deviation prediction value and an end point passing time deviation prediction value as exclusive area information.

[0160] The operation plan formulation unit 304 receives this response and checks whether there is a conflict between the exclusive area of ​​the operation plan candidate and the exclusive area of ​​the issued operation plan (Step S36). There are two cases in which a conflict is determined.

[0161] The first case is when the combination of the start waypoint and the end waypoint matches and the exclusive time overlaps between the records of the operation route included in the operation plan candidate and the operation route included in the issued operation plan obtained in step S31. Such a case is as shown in FIG. 37. FIG. 37 is a schematic diagram showing an example of a conflict state. Here, the combination of the start waypoint and the end waypoint matches even if the start point and the end point are reversed, and it does not matter whether it is uphill or downhill. In addition, the above-mentioned exclusive time refers to the period from the exclusive end time of the record having the section sequence number one section sequence number before the target operation route record to the exclusive end time of the target operation route record.

[0162] The second case is when the space calculated from the exclusive area radius and the exclusive time overlap at least partially between the operation route included in the operation plan candidate and the operation route included in the issued operation plan obtained in step S31. Such a case is as shown in Figures 38 and 39. Figures 38 and 39 are schematic diagrams showing other examples of a conflict state.

[0163] With reference to Fig. 40, a supplementary explanation of area contention will be given. Fig. 40 is a diagram for explaining area contention. The space obtained from the above-mentioned exclusive area radius refers to a cylindrical space with both ends hemispherical and with a radius equal to the exclusive area radius, which surrounds the edges and waypoints of one section of the route, and is a space in which both ends of the cylindrical space in Fig. 32 are attached with hemispheres of the same radius as the cylinder (see Fig. 10). The above-mentioned exclusive time refers to the period from the exclusive end time of the record having the section sequence number one before the section sequence number of the target operation route record to the exclusive end time of the target operation route record.

[0164] As shown in Fig. 40, the operation plan candidate to which the exclusive area calculation result of the operation plan candidate is added is compared with the exclusive area of ​​the issued operation plan in the acquisition request range to determine whether the areas conflict. Here, when determining the conflict, the section occupancy time is calculated from the time obtained by adding the predicted time deviation value to the target end point addition time before and after, and then the safety time is added, and the safety range is added to the predicted route deviation value.

[0165] If any of the above conflict conditions are met, the operation plan formulation unit 304 determines that there is a route conflict or an exclusive area conflict, and sets the conflict flag of the corresponding operation route record of the operation plan candidate stored in the operation plan candidate temporary storage unit 305 to true.

[0166] On the other hand, if an operation plan in which no conflict occurs is obtained, the operation plan formulation unit 304 exits the loop processing in FIG. 20 and proceeds to the next step S37.

[0167] In step S37, the operation plan formulation unit 304 registers, as an operation plan, an operation plan candidate whose exclusive area does not conflict with the issued operation plan in the issued operation plan management unit 401. As described above, when an operation plan candidate that does not conflict with the issued operation plan is obtained, the operation plan formulation unit 304 exits from the loop processing shown in FIG. 20 and enters the processing of step S37.

[0168] In step S37, the operation plan formulation unit 304 requests the issued operation plan management unit 401 to register the operation plan using the information of the items shown in FIG. 15 (information of the operation plan that was last generated at this point in time among the operation plan candidates stored in the operation plan candidate temporary storage unit 305) as a parameter.

[0169] The issued operation plan management unit 401 registers the operation plan by adding the information of the operation plan (FIG. 15) given in step 37 to the issued operation plan holding unit 402 (step S38). When adding, an operation route issue number is assigned and added to the operation plan. As a result, the information of the items shown in FIG. 16 is held in the issued operation plan holding unit 402.

[0170] The operation plan formulation unit 304 adds information of the request management number, the departure point, and the arrival point received in the operation plan formulation request in step S23, as shown in Fig. 41. The operation plan formulation unit 304 also specifies a waypoint ID and inquires of the spatial information management unit 501, thereby adding corresponding coordinates to the start waypoint ID and the end waypoint ID. Then, the operation plan formulation unit 304 responds to the operation plan issuance request management unit 302 with the data of the items shown in Fig. 41 as an operation plan (step S39). Fig. 41 is a diagram showing an example of the data format of an operation plan.

[0171] Upon receiving this response, the operation plan issuance request management unit 302 sets an issued flag in the operation plan issuance request in the operation plan issuance request storage unit 303 (step S40). Here, the operation route issuance number is added to the record (see FIG. 12) whose request management number matches the received operation plan, and the issued flag is set to true. As a result, data of the items shown in FIG. 42 is generated as an operation plan. FIG. 42 is a diagram showing an example of the data format of an operation plan.

[0172] Next, the operation plan issuance request management unit 302 issues the operation plan by responding with the operation plan to the operation plan issuance requester (step S41). The issued operation plan can remove the issued flag and can include, for example, information of the items shown in Fig. 43. Fig. 43 is a diagram showing an example of the data format of the operation plan.

[0173] As described above, the traffic management device 10 according to this embodiment has a function of learning route deviations, a function of predicting route and passage time deviations (spatial and temporal deviations), and a function of automatically generating a traffic plan.

[0174] Then, based on such a prediction result for the operation plan candidate, the operation management device 10 sets an exclusive region so as to surround each edge connecting adjacent waypoints included in the operation plan candidate. This improves spatial utilization efficiency compared to a case where settings based on the prediction result are not performed, and therefore improves time utilization efficiency as well.

[0175] In this case, a trained model equivalent to the trained model 4e described in the first embodiment can be generated as follows. Here, an example of training using the learning device 4 in FIG. 4 will be described.

[0176] In response to an operation plan generation request including operation request information, the acquisition unit 4a acquires information including an executed operation plan that has been executed among operation plans generated to include a plurality of waypoints for an autonomous mobile device included as an operation target in the operation request information, and operation results. The operation results are operation results at the time of execution of the executed operation plan. Here, the operation plan is a plan in which an exclusive area is set to surround each edge connecting adjacent waypoints among a plurality of waypoints included in the operation plan. Here, too, the exclusive area is an area in which other autonomous mobile devices are prohibited from competing with each other to operate.

[0177] The control unit 4b generates a learned model that predicts, based on the acquired information, positional and time deviations that may occur when operating according to a candidate operation plan generated to include multiple waypoints based on new operation request information (i.e., different operation request information).

[0178] Moreover, this learning device 4 can execute the learning method as described above. Furthermore, as described above, the learning device 4 can be configured as a computer, and in this case, the program can be a program that causes the computer to execute the above-mentioned processing.

[0179] Furthermore, the operation management device 10 can set an exclusive period as a period during which operation of other autonomous mobile devices for which operation plans have not been generated is prohibited in the exclusive area, together with the exclusive area, based on the above-mentioned predicted result for the operation plan candidate. Therefore, time utilization efficiency can be further improved compared to the case where only the exclusive area is set. In particular, the length of the exclusive period for the operation plan candidate can be set to be long according to the length of the time difference between the predicted passing times at each waypoint, thereby further improving time utilization efficiency.

[0180] Furthermore, in the traffic management device 10 according to this embodiment, waypoints are defined at high density instead of partitions in space, and a traffic route is set to pass through the waypoints, and a traffic route is designed. In this embodiment, a danger zone (a zone that excludes other autonomous mobile devices) is particularly set to surround the traffic route, and its size and exclusion period are determined based on a predicted deviation value predicted from learning of past route deviation information. As a result, the danger zone can be set to a different size based on the prediction result for each section between waypoints on the designed route.

[0181] In addition, in the operation management device 10 according to the present embodiment, non-conflicting candidates are determined as candidates that operate in areas excluding the set exclusive areas that are set to surround each edge included in the generated but incomplete operation plan among the operation plan candidates. Therefore, the set exclusive areas can also be determined based on the prediction results, and the time and space utilization efficiency can be further improved.

[0182] In addition, regarding the operation plan formulation process, an example has been given in which conflicts are determined each time an operation plan candidate is generated, but the process is not limited to this example. It is also possible to generate multiple operation plan candidates and then determine the candidate with the lowest degree of conflict as the operation plan.

[0183] In addition, in this embodiment, the non-conflicting candidates are determined as candidates that operate in an area excluding the set exclusive area during the set exclusive period set for the set exclusive area related to the generated but incomplete operation plan among the operation plan candidates. Therefore, this exclusive period can also be determined based on the prediction result, and the time and space utilization efficiency can be further improved.

[0184] Furthermore, when a non-conflicting candidate is determined to be a candidate that operates in an area excluding the set exclusive area related to an operation plan that has been generated but not yet completed among the operation plan candidates, a trained model corresponding to the trained model 4e described in the first embodiment can be generated as follows. Here, an example of training by the learning device 4 in FIG. 4 will be described.

[0185] In response to an operation plan generation request including operation desire information, the acquisition unit 4a acquires information including an executed operation plan that has been executed among operation plans generated to include a plurality of waypoints for an autonomous mobile device included as an operation target in the operation desire information, and operation results. The operation results are operation results at the time of execution of the executed operation plan. Here, the operation plan may be a plan that can operate in an area excluding the following exclusive area among operation plan candidates including a plurality of waypoints generated based on the operation desire information. The exclusive area here refers to a set exclusive area that is set to surround each edge connecting adjacent waypoints in a plurality of waypoints included in an operation plan that has been generated and is not yet completed for another autonomous mobile device.

[0186] The control unit 4b generates a trained model that predicts, based on the information acquired by the acquisition unit, positional and time deviations that may occur when operating according to a candidate operation plan that is generated to include multiple waypoints based on other desired operation information.

[0187] Moreover, the learning device 4 in this example can execute the learning method as described above. As described above, the learning device 4 can be configured as a computer, and in this case, the program can be a program that causes the computer to execute the above-mentioned processing.

[0188] As described above, according to this embodiment, not only can it be achieved to prevent collisions and improve the efficiency of space utilization, as in the first embodiment, but it also has the effect of each of the components provided individually. In this embodiment, the problem described as the first problem, that there is no method for automatically performing a series of procedures of designing a route and generating an operation plan adjusted with the route of other drones 3 by simply setting the start point and end point, can be solved, and it becomes possible to automatically execute the procedure. In addition, in this embodiment, the problem described as the second problem, that the efficiency of space utilization (both spatially and temporally) is poor in the existing operation route design method, and multiple drones cannot be operated efficiently, can also be solved. That is, according to this embodiment, in places where the deviation between the planned operation route and the actual result is small, the number of vehicles that can be accommodated per space can be increased, and the utilization efficiency is improved spatially and temporally.

[0189] <Embodiment 3> As the third embodiment, various application examples applicable to the first and second embodiments will be described, focusing on the differences from the second embodiment. In the third embodiment, the various examples described in the first and second embodiments can be appropriately used.

[0190] In the second embodiment, a drone is used as an example, but the present invention can also be applied to an autonomous mobile device that travels on the ground without flying. In that case, instead of the operation plan in the three-dimensional space exemplified in the second embodiment, an operation plan in a two-dimensional plane may be applied for the autonomous mobile device traveling on the ground. In the operation plan in the two-dimensional plane, it is not necessary to use information on the upper limit altitude, the lower limit altitude, the altitude of the target coordinates, and the altitude of the actual coordinates for the section. Even when applied to a traveling autonomous mobile device, when used in a situation with a hierarchical structure such as a multi-story parking lot, a large logistics center that vehicles can enter, or a factory, the information may be expressed by floor height information in addition to the altitude.

[0191] In addition, in Fig. 9, for the sake of simplicity, the waypoints are arranged at equal intervals, but they do not necessarily have to be arranged at equal intervals. For example, if it is clear that a space is impassable due to mountains or buildings, or if it is clear that a space cannot be navigated due to legal regulations, there may be places where no waypoints are set from the beginning. In other words, the multiple waypoints included in the operation plan or candidates may be selected and determined from pre-set spatial points.

[0192] Waypoints can also be placed as follows to represent cases where it is clear that a space is impassable due to mountains or buildings, or where travel is not permitted due to legal restrictions, for example. That is, as shown in Figure 9, waypoints can be set densely and at equal intervals regardless of obstacles, and only in places that cross obstacles can unnavigable spaces be represented by setting the unusable flags of edges that pass through those places to true. The data structure of edges in this case is as shown in Figure 27.

[0193] In other words, the multiple waypoints included in the operation plan or candidates may be selected and determined from spatial points that are preset as lattice points of an equally spaced grid, excluding spatial points where obstacles exist and spatial points included in areas where operation is not legally permitted.

[0194] As can be seen from these examples, multiple waypoints included in the operation plan or candidates can be determined so that the edge lengths between at least two edges are different. This allows the route to be designed to pass through smaller (or larger) segments rather than through larger segments of a fixed size.

[0195] The exclusion region can also be set by allowing the exclusion line to represent the edge itself.

[0196] In the second embodiment, the week number tag, the time zone tag, the month tag, and the season zone tag are used as explanatory variables for learning in the operation record learning unit 102 and for predicting the position shift and the time shift in the exclusive area calculation unit 301, but these items may be increased or replaced with other items. Also, as long as the information is expected to have a correlation with the objective variable, it is possible to use items from other information sources as well as obtaining learning data from the operation record alone.

[0197] For example, when applied to an autonomous mobile device that travels on land, the type of ground surface condition of each section may be added with a number (paved, unpaved, gravel, sand, etc.), and when applied to an autonomous mobile device that travels on the ocean, the wave height of each section may be added. Even in the case of an autonomous mobile device that travels in the air, weather information for each section may be added. The information obtained from the operation record described in the second embodiment may have more items. For example, a sensor may be installed in the autonomous mobile device, and information such as vibration information, temperature information, fluctuations in the output of the motor or engine used for propulsion, inclination, and fuel consumption status may be added to the explanatory variables. This improves the prediction accuracy and allows the construction of a learning model that is more in line with reality.

[0198] In the second embodiment, the operation record learning unit 102 learns the positional deviation and the time deviation are predicted by the exclusive area calculation unit 301. The operation record learning unit 102 learns the positional deviation and the time deviation by the exclusive area calculation unit 301. The operation record learning unit 10 learns the positional deviation and the time deviation by the exclusive area calculation unit 301. The operation record learning unit 10 learns the positional deviation and the time deviation by the exclusive area calculation unit 301. The operation record learning unit 10 learns the positional deviation and the time deviation by the exclusive area calculation unit 302 ...

[0199] In addition, in the second embodiment, no mention is made of a machine learning method used for learning in the operation record learning unit 102 and for predicting the position shift and time shift in the exclusive area calculation unit 301, but multiple regression analysis, for example, can be used. However, other machine learning means such as deep learning may be used as the learning method.

[0200] <Embodiment 4> The fourth embodiment will be described, focusing on the differences from the first embodiment, with reference again to Fig. 1. In the fourth embodiment, the various examples described in the first to third embodiments can be appropriately used.

[0201] The operation plan generating device 1 according to this embodiment includes a control unit 1a, and in response to an operation plan generation request including operation request information, the control unit 1a generates an operation plan including a plurality of passing points for the autonomous mobile devices included as operation targets in the operation request information. Note that in this embodiment, the storage unit 1b does not need to store data such as operation results, and can be used to temporarily store intermediate processing data and generated data, but it is sufficient that the control unit 1a includes a storage unit.

[0202] The control unit 1a generates an operation plan candidate including a plurality of pass points based on the operation request information. The control unit 1a sets an exclusive area for the operation plan candidate as an area in which other autonomous mobile devices are prohibited from competing with each other to operate. Here, as a feature of the present embodiment, the exclusive area is set not based on a prediction result but for each section route connecting adjacent pass points among the plurality of pass points included in the operation plan candidate so as to surround the section route. Then, the control unit 1a determines, among the operation plan candidates, a candidate that does not compete with operation plans that have been generated but are not completed for other autonomous mobile devices as an operation plan to be responded to the operation plan generation request.

[0203] In this manner, in this embodiment, unlike the first embodiment, the setting of the exclusive area is not performed using the prediction result, but an exclusive area can be set so as to surround each section route. Since the exclusive area can be set more finely than in the comparative example, according to this embodiment, it is possible to automatically generate an operation plan that increases the number of autonomous mobile devices that can operate in the same time period within a certain space, and it is possible to prevent collisions and improve the efficiency of space utilization at the same time. Although the description is omitted in this embodiment, such an operation plan generation method and processing program can also be adopted.

[0204] <Other embodiments> [a] In each embodiment, the functions of the operation plan generation device, the functions of the autonomous moving device, the functions of the learning device, etc. have been described, but each device is not limited to the exemplified configuration examples, and it is sufficient if each device can realize these functions.

[0205] [b] Each device according to each embodiment may have the following hardware configuration. Fig. 44 is a diagram showing an example of the hardware configuration of the device. The same applies to the other embodiment [a] described above.

[0206] The device 1000 shown in FIG. 44 may include a processor 1001, a memory 1002, and an interface 1003. The processor 1001 may be, for example, a microprocessor, a microprocessor unit (MPU), or a CPU. The processor 1001 may include a plurality of processors. The memory 1002 is, for example, a combination of a volatile memory and a non-volatile memory. The functions of each device described in each embodiment are realized by the processor 1001 reading and executing a program stored in the memory 1002. At this time, input and output of information can be performed via an interface 1003 such as a communication interface that communicates with other internal parts or other external devices. For example, when the device 1000 is an operation plan generating device or a learning device, the interface 1003 can include at least a communication interface and a user interface that accepts user operations. For example, when the device 1000 is an autonomous mobile device, the interface 1003 can include at least an interface with each sensor and a communication interface.

[0207] In the above examples, the program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the computer-readable medium or tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, the transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

[0208] The present disclosure is not limited to the above-described embodiment, and may be modified as appropriate without departing from the spirit and scope of the present disclosure. In addition, the present disclosure may be implemented by appropriately combining the respective embodiments.

[0209] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes. (Appendix 1) a control unit that generates, in response to an operation plan generation request including operation request information, an operation plan including a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information; A storage unit that stores a relationship between an actual operation plan, which is an operation plan that has been executed, and an operation record at the time of execution of the actual operation plan; Equipped with The control unit is Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, a prediction of positional and time deviations that may occur when operating according to the operation plan candidate is performed based on the relationship; Based on the predicted result, an exclusive area is set as an area in which other autonomous mobile devices are prohibited from competing with each other to operate the operation plan candidate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. Operation plan generation device. (Appendix 2) the control unit sets, for each of the section routes connecting adjacent passing points among a plurality of passing points included in the operation plan candidate, the exclusive region so as to surround the section route based on the prediction result. 2. An operation plan generating device according to claim 1. (Appendix 3) the control unit sets, for the operation plan candidate, the exclusive area and an exclusive period as a period during which other autonomous mobile devices are prohibited from competing with each other to operate in the exclusive area, based on the predicted result. 3. An operation plan generating device according to claim 2. (Appendix 4) the control unit determines, as the operation plan to be responded to, a candidate that operates in an area excluding a set exclusive area that is set to surround each of section routes connecting adjacent pass points among a plurality of pass points included in the generated but incomplete operation plan, as a candidate that does not conflict with the generated but incomplete operation plan, among the operation plan candidates; 4. An operation plan generating device according to claim 1, wherein the operation plan generating device is a device for generating an operation plan. (Appendix 5) the control unit determines, as the operation plan to be responded to, a candidate that operates in an area excluding the set exclusive area during a set exclusive period that is set for the set exclusive area, among the operation plan candidates, as a candidate that does not conflict with the generated but incomplete operation plan; 5. An operation plan generating device according to claim 4. (Appendix 6) a size of a surface perpendicular to a travel direction in the exclusive area is set to be different for at least two section routes connecting adjacent passing points among the plurality of passing points; 6. An operation plan generating device according to any one of claims 1 to 5. (Appendix 7) the exclusive area is set by allowing a section route connecting adjacent passing points among the plurality of passing points to be an exclusive line indicating the section route itself; 7. An operation plan generating device according to any one of claims 1 to 6. (Appendix 8) the plurality of passing points are determined such that, for a section route connecting adjacent passing points, lengths of the section routes are different between at least two of the section routes; An operation plan generating device according to any one of appendix 1 to 7. (Appendix 9) The plurality of passing points are selected from spatial points preset as lattice points of an equally spaced grid, excluding spatial points where an obstacle exists and spatial points included in an area where operation is not legally permitted. An operation plan generating device according to any one of appendix 1 to 8. (Appendix 10) The plurality of passing points are selected and determined from among preset spatial points. An operation plan generating device according to any one of appendix 1 to 8. (Appendix 11) an exclusive period as a period during which other autonomous mobile devices are prohibited from competing with each other to operate in the exclusive area is set so as to be different for at least two section routes connecting adjacent pass points among the plurality of pass points; 6. An operation plan generating device according to claim 3 or 5. (Appendix 12) The control unit sets a length of the exclusive period for the operation plan candidate to be long in accordance with a length of a time difference between predicted passing times at each passing point. 4. An operation plan generating device according to claim 3. (Appendix 13) The control unit performs prediction of a distance deviation and a direction deviation as a prediction of a positional deviation. 13. An operation plan generating device according to any one of claims 1 to 12. (Appendix 14) The relationship is stored in the storage unit as a trained model that predicts positional and time deviations that may occur when operating according to the operation plan candidate, the trained model being generated by machine learning based on the actual operation plan and the operation record, The control unit performs the prediction using the trained model. An operation plan generating device according to any one of Supplementary notes 1 to 13. (Appendix 15) a control unit that generates, in response to an operation plan generation request including operation request information, an operation plan including a plurality of passing points for an autonomous mobile device included as an operation target in the operation request information; The control unit is Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, an exclusive area is set so as to surround each section route connecting adjacent pass points among a plurality of pass points included in the operation plan candidate, as an area in which other autonomous mobile devices are prohibited from competing with each other to operate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. Operation plan generation device. (Appendix 16) an acquisition unit that acquires, in response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan; A control unit that generates a trained model that predicts positional and time deviations that may occur when operating according to a candidate operation plan that is generated to include a plurality of passing points based on another operation request information, based on the information acquired by the acquisition unit; and Equipped with The operation plan is a plan in which an exclusive area is set as an area in which other autonomous mobile devices are prohibited from competing with each other to operate in a manner surrounding each section route connecting adjacent pass points among a plurality of pass points included in the operation plan. Learning device. (Appendix 17) an acquisition unit that acquires, in response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan; A control unit that generates a trained model that predicts positional and time deviations that may occur when operating according to a candidate operation plan that is generated to include a plurality of passing points based on another operation request information, based on the information acquired by the acquisition unit; and Equipped with The operation plan is: Among the operation plan candidates including a plurality of passing points generated based on the operation request information, a plan that allows the autonomous vehicle to operate in an area excluding a set exclusive area that is set to surround each of a section route connecting adjacent pass points among a plurality of pass points included in an operation plan that has been generated but not completed for another autonomous vehicle, Learning device. (Appendix 18) An autonomous mobile device comprising: the operation plan generation device according to claim 14; a learning device; and a sensor group that collects information to be included in the operation record and is capable of communicating with the operation plan generation device; The learning device includes: An acquisition unit that acquires information including the actual operation plan and the operation record; A learning device-side control unit that generates the trained model based on the information acquired by the acquisition unit; Equipped with Autonomous mobility system. (Appendix 19) An autonomous mobile system comprising: an operation plan generating device according to any one of claims 1 to 14; and an autonomous mobile device equipped with a sensor group that collects information to be included in the operation record and capable of communicating with the operation plan generating device. (Appendix 20) In response to an operation plan generation request including operation request information, the operation plan includes a plurality of passing points for an autonomous mobile device included as an operation target in the operation request information; The process comprises: Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, a prediction of positional and time deviations that may occur when operating according to the operation plan candidate is performed based on a relationship between an actual operation plan, which is an operation plan that has already been executed, and an operation record at the time of execution of the actual operation plan; Based on the predicted result, an exclusive area is set as an area in which other autonomous mobile devices are prohibited from competing with each other to operate the operation plan candidate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. How to generate operation plans. (Appendix 21) In response to an operation plan generation request including operation request information, the operation plan includes a plurality of passing points for an autonomous mobile device included as an operation target in the operation request information; The process comprises: Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, an exclusive area is set so as to surround each section route connecting adjacent pass points among a plurality of pass points included in the operation plan candidate, as an area in which other autonomous mobile devices are prohibited from competing with each other to operate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. How to generate operation plans. (Appendix 22) In response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan is acquired; A trained model is generated based on the acquired information to predict positional and time deviations that may occur when operating according to a candidate operation plan that is generated based on another operation request information and includes multiple passing points; and The operation plan is a plan in which an exclusive area is set as an area in which other autonomous mobile devices are prohibited from competing with each other to operate in a manner surrounding each section route connecting adjacent pass points among a plurality of pass points included in the operation plan. How to learn. (Appendix 23) In response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan is acquired; A trained model is generated based on the acquired information to predict positional and time deviations that may occur when operating according to a candidate operation plan that is generated based on another operation request information and includes multiple passing points; and The operation plan is: Among the operation plan candidates including a plurality of passing points generated based on the operation request information, a plan that allows the autonomous vehicle to operate in an area excluding a set exclusive area that is set to surround each of a section route connecting adjacent pass points among a plurality of pass points included in an operation plan that has been generated but not completed for another autonomous vehicle, How to learn. (Appendix 24) On the computer, A process of generating an operation plan including a plurality of passing points for an autonomous mobile device included as an operation target in an operation request including operation request information, the process comprising: Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, a prediction of positional and time deviations that may occur when operating according to the operation plan candidate is performed based on a relationship between an actual operation plan, which is an operation plan that has already been executed, and an operation record at the time of execution of the actual operation plan; Based on the predicted result, an exclusive area is set as an area in which other autonomous mobile devices are prohibited from competing with each other to operate the operation plan candidate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. A program that executes a process. (Appendix 25) On the computer, A process of generating an operation plan including a plurality of passing points for an autonomous mobile device included as an operation target in an operation request including operation request information, the process comprising: Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, an exclusive area is set so as to surround each section route connecting adjacent pass points among a plurality of pass points included in the operation plan candidate, as an area in which other autonomous mobile devices are prohibited from competing with each other to operate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. A program that executes a process. (Appendix 26) On the computer, In response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan is acquired; Generate a trained model that predicts, based on the acquired information, positional and time deviations that may occur when operating according to a candidate operation plan that is generated based on other desired operation information and includes multiple transit points. Execute the process, The operation plan is a plan in which an exclusive area is set as an area in which other autonomous mobile devices are prohibited from competing with each other to operate in a manner surrounding each section route connecting adjacent pass points among a plurality of pass points included in the operation plan. program. (Appendix 27) On the computer, In response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan is acquired; Generate a trained model that predicts, based on the acquired information, positional and time deviations that may occur when operating according to a candidate operation plan that is generated based on other desired operation information and includes multiple transit points. Execute the process, The operation plan is: Among the operation plan candidates including a plurality of passing points generated based on the operation request information, a plan that allows the autonomous vehicle to operate in an area excluding a set exclusive area that is set to surround each of a section route connecting adjacent pass points among a plurality of pass points included in an operation plan that has been generated but not completed for another autonomous vehicle, program. [Explanation of symbols]

[0210] 1. Operation plan generation device 1a Control section 1b Storage section 2. Autonomous Mobile Device 2a Sensor group 2b Communication Department 2c Movement control section 2d Drive unit 3. Drone 4. Learning Device 4a Acquisition part 4b Control section 4c storage section 4d Untrained model 4e Pre-trained model 10 Traffic control device 101 Trained Model Management Unit 102 Operational Performance Learning Department 103 Trained model storage unit 200 Performance Management Department 201 Operation Record Management Department 202 Operation Records Department 300 Planning Department 301 Exclusive area calculation unit 302 Operation Plan Issuance Request Management Department 303 Operation plan issuance request storage unit 304 Operation Planning Department 305 Temporary storage unit for operation plan candidates 400 Planning and Management Department 401 Issued Operation Plan Management Department 402 Issued Operation Plan Storage Unit 500 Space Management Department 501 Spatial Information Management Department 502 Waypoint (WP) Holder 503 Edge information storage unit 1000 devices 1001 Processor 1002 Memory 1003 Interface

Claims

1. a control unit that generates, in response to an operation plan generation request including operation request information, an operation plan including a plurality of passing points for an autonomous mobile device included as an operation target in the operation request information; The control unit is Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, an exclusive area is set so as to surround each section route connecting adjacent pass points among a plurality of pass points included in the operation plan candidate, as an area in which other autonomous mobile devices are prohibited from competing with each other to operate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. Operation plan generation device.

2. the control unit determines, as the operation plan to be responded to, a candidate that operates in an area excluding a set exclusive area that is set to surround each of section routes connecting adjacent pass points among a plurality of pass points included in the generated but incomplete operation plan, as a candidate that does not conflict with the generated but incomplete operation plan, among the operation plan candidates; The operation plan generating device according to claim 1 .

3. the control unit determines, as the operation plan to be responded to, a candidate that operates in an area excluding the set exclusive area during a set exclusive period that is set for the set exclusive area, among the operation plan candidates, as a candidate that does not conflict with the generated but incomplete operation plan; The operation plan generating device according to claim 2 .

4. a size of a surface perpendicular to a travel direction in the exclusive area is set to be different for at least two section routes connecting adjacent passing points among the plurality of passing points; The operation plan generating device according to any one of claims 1 to 3.

5. the exclusive area is set by allowing a section route connecting adjacent passing points among the plurality of passing points to be an exclusive line indicating the section route itself; The operation plan generating device according to any one of claims 1 to 4.

6. an acquisition unit that acquires, in response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan; A control unit that generates a trained model that predicts positional and time deviations that may occur when operating according to a candidate operation plan that is generated to include a plurality of passing points based on another operation request information, based on the information acquired by the acquisition unit; and Equipped with The operation plan is: Among the operation plan candidates including a plurality of passing points generated based on the operation request information, a plan that allows the autonomous vehicle to operate in an area excluding a set exclusive area that is set to surround each of a section route connecting adjacent pass points among a plurality of pass points included in an operation plan that has been generated but not completed for another autonomous vehicle, Learning device.

7. In response to an operation plan generation request including operation request information, a process of generating an operation plan including a plurality of passing points for an autonomous mobile device included as an operation target in the operation request information is provided; The process comprises: Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, an exclusive area is set so as to surround each section route connecting adjacent pass points among a plurality of pass points included in the operation plan candidate, as an area in which other autonomous mobile devices are prohibited from competing with each other to operate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. How to generate operation plans.

8. In response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan is acquired; A trained model is generated based on the acquired information to predict positional and time deviations that may occur when operating according to a candidate operation plan that is generated based on another operation request information and includes multiple passing points; and The operation plan is: Among the operation plan candidates including a plurality of passing points generated based on the operation request information, a plan that allows the autonomous vehicle to operate in an area excluding a set exclusive area that is set to surround each of a section route connecting adjacent pass points among a plurality of pass points included in an operation plan that has been generated but not completed for another autonomous vehicle, How to learn.

9. On the computer, A process of generating an operation plan including a plurality of passing points for an autonomous mobile device included as an operation target in an operation request including operation request information, the process comprising: Generate a candidate operation plan including a plurality of passing points based on the operation request information; For the operation plan candidate, an exclusive area is set so as to surround each section route connecting adjacent pass points among a plurality of pass points included in the operation plan candidate, as an area in which other autonomous mobile devices are prohibited from competing with each other to operate; Among the operation plan candidates, a candidate that does not conflict with operation plans that have been generated but are not yet completed for other autonomous mobile devices is determined as an operation plan to be responded to the operation plan generation request. A program that executes a process.

10. On the computer, In response to an operation plan generation request including operation request information, information including an executed operation plan that has been executed among operation plans generated to include a plurality of pass points for an autonomous mobile device included as an operation target in the operation request information, and operation results at the time of execution of the executed operation plan is acquired; Generate a trained model that predicts, based on the acquired information, positional and time deviations that may occur when operating according to a candidate operation plan that is generated based on other operation request information and includes multiple transit points. Execute the process, The operation plan is: Among the operation plan candidates including a plurality of passing points generated based on the operation request information, a plan that allows the autonomous vehicle to operate in an area excluding a set exclusive area that is set to surround each of a section route connecting adjacent pass points among a plurality of pass points included in an operation plan that has been generated but not completed for another autonomous vehicle, program.

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