Control device, mobile body system, control method, and program

Abstract

A control device that switches between a delivery mode in which a mobile body moves to a destination and a remote operation mode in which the mobile body is operated remotely by a user, and causes the mobile body to act, wherein the control device includes: a determination section that determines whether the mobile body reaches the destination; a mode switching section that switches an act mode of the mobile body from the delivery mode to the remote operation mode when a condition including a case where the mobile body reaches the destination is satisfied during the mobile body is acting in the delivery mode; a generation section that generates a target track of the mobile body based on the act mode after the switching; and a control section that controls the mobile body based on the target track.

Description

Technical Field

[0001] This invention relates to control devices, mobile systems, control methods, and procedures. Background Technology

[0002] Previously, guide robots, which are autonomous mobile entities that guide users in supermarkets, shopping malls, and other similar locations, have been proposed (Patent Document 1). These guide robots generate a path based on the surrounding congestion.

[0003] Prior technology literature

[0004] [Patent Documents]

[0005] Patent Document 1: International Publication No. 2003 / 189105 Summary of the Invention

[0006] [The problem the invention aims to solve]

[0007] However, while conventional mobile devices such as navigation robots have a navigation mode for guiding users, they lack a delivery mode for moving to the destination and a remote operation mode for users to remotely control the mobile device. Therefore, conventional mobile devices sometimes cannot switch between delivery and remote operation modes.

[0008] The present invention was made in consideration of such circumstances, and one of its objectives is to provide a control device, mobile body system, control method, and program that enables a mobile body to switch between delivery mode and remote operation mode for operation.

[0009] [Methods used to solve problems]

[0010] The control device, moving body system, control method and program involved in this invention adopt the following structure.

[0011] (1): A control device according to one aspect of the present invention switches between a delivery mode for moving to a destination and a remote operation mode for remote operation of the mobile body by a user, wherein the control device comprises: a determination unit that determines whether the mobile body has arrived at the destination; a mode switching unit that switches the operation mode of the mobile body from the delivery mode to the remote operation mode when a condition including the mobile body arriving at the destination is met during the operation of the mobile body in the delivery mode; a generation unit that generates a target trajectory for the mobile body based on the switched operation mode; and a control unit that controls the mobile body based on the target trajectory.

[0012] (2): In the above-mentioned (1) solution, the control device further includes: a work data acquisition unit that acquires work data from the work equipment mounted on the mobile body; and a work data transmission unit that uses a communication device mounted on the mobile body to transmit the work data to an external information processing device.

[0013] (3): In the above (2) scheme, the control device also has a data anomaly determination unit, which determines whether the operation data is abnormal. When the conditions of the mobile body arriving at the destination and the operation data being abnormal are met, the mode switching unit switches from the delivery mode to the remote operation mode.

[0014] (4): In the above (1) scheme, the control device further includes a call detection unit that detects a call to the mobile body. When a call to the mobile body is detected while the mobile body is operating in the delivery mode, the mode switching unit determines whether to switch to the remote operation mode based on the content of the call.

[0015] (5): In the above (4) scheme, the control device further includes a response output unit. When it is determined that the remote operation mode will not be switched, the response output unit generates a response content to the call and outputs the response content.

[0016] (6): The mobile system involved in other embodiments of the present invention includes a plurality of mobile bodies and a terminal device capable of communicating with the plurality of mobile bodies, wherein each of the plurality of mobile bodies includes a control device as described in any one of (1) to (5) above, and the terminal device selects one of the plurality of mobile bodies based on at least one of the type of operating equipment mounted on each of the plurality of mobile bodies, the location of each of the plurality of mobile bodies, and the charging state of each of the plurality of mobile bodies.

[0017] (7): In other embodiments of the present invention, the control method is performed by a control device, wherein the control device switches between a delivery mode for moving to a destination and a remote operation mode for remote operation of the mobile body by the user to make the mobile body move, and the control method causes the control device to perform the following processes: determining whether the mobile body has arrived at the destination; when a condition including the mobile body arriving at the destination is met during the operation of the mobile body in the delivery mode, switching the operation mode of the mobile body from the delivery mode to the remote operation mode; generating a target trajectory of the mobile body based on the switched operation mode; and controlling the mobile body based on the target trajectory.

[0018] (8): In other embodiments of the present invention, the program is executed by the processor of a control device, wherein the control device switches between a delivery mode for moving to a destination and a remote operation mode for remote operation of the mobile body to make the mobile body move, and the program is used to make the processor of the control device perform the following processes: determining whether the mobile body has arrived at the destination; when a condition including the mobile body arriving at the destination is met during the operation of the mobile body in the delivery mode, switching the operation mode of the mobile body from the delivery mode to the remote operation mode; generating a target trajectory of the mobile body based on the switched operation mode; and controlling the mobile body based on the target trajectory.

[0019] [Invention Effects]

[0020] According to the schemes (1) to (8), the mobile body can switch between delivery mode and remote operation mode to travel. Attached Figure Description

[0021] Figure 1 This is a diagram illustrating an example of the structure of a mobile body system 1, which includes a mobile body 100.

[0022] Figure 2 This is a three-dimensional diagram representing the moving body 100.

[0023] Figure 3 This is a diagram showing the structure of the moving body 100 equipped with the control device 200.

[0024] Figure 4 This is a diagram showing an example of the structure of the control device 200.

[0025] Figure 5 This is a diagram showing an example of a target trajectory generated by the generation unit 260.

[0026] Figure 6 It is a graph used to illustrate the risk function of environmental object targets and the benefit function based on environmental object targets.

[0027] Figure 7 It represents X e_l A diagram showing an example of the risk function for environmental objects along the axis and the benefit function based on environmental objects.

[0028] Figure 8 It represents Y e_l A diagram of an example of the potential field function along the axis.

[0029] Figure 9 This is a schematic diagram of the revenue function based on environmental object targets when environmental object targets are detected at multiple detection locations.

[0030] Figure 10 This is a graph used to illustrate the traffic participant-obstacle risk function.

[0031] Figure 11 This is a graph used to illustrate the traffic participant-obstacle risk function obtained based on traffic participant U2.

[0032] Figure 12 It represents X tp_l A diagram of an example of the traffic participant-obstacle risk function along the axis.

[0033] Figure 13 It represents Y tp_l A diagram of an example of the traffic participant-obstacle risk function along the axis.

[0034] Figure 14 This diagram illustrates the situation where the mobile body 100 is automatically moving in delivery mode.

[0035] Figure 15 This diagram illustrates a scenario where user U1 instructs the moving body 100 to move to the left.

[0036] Figure 16 This is a graph representing an example of a remote operation revenue function.

[0037] Figure 17 This diagram illustrates the change in the direction of travel of the moving body 100 when a remote operation benefit function is set.

[0038] Figure 18 This diagram depicts a mobile vehicle 100 traveling to a ward where a patient is present.

[0039] Figure 19 This is a flowchart illustrating an example of a process performed by the control device 200. Detailed Implementation

[0040] Hereinafter, embodiments of the control device, mobile body system, control method, and program of the present invention will be described with reference to the accompanying drawings. The control device of the present invention controls the moving mechanism of the mobile body to move the mobile body. The mobile body of the present invention moves autonomously within a pedestrian walking area. A pedestrian walking area refers to sidewalks, open spaces, building floors, etc., and may also include driveways. In the following description, it is assumed that a person is not riding on the mobile body, but it is also possible that a person is riding on the mobile body. The mobile body operates in a delivery mode to its destination and a remote operation mode where the user remotely operates the mobile body.

[0041] [Mobile System]

[0042] Figure 1This diagram illustrates an example of the structure of a mobile system 1 including a mobile body 100. The mobile system 1 includes, for example, a mobile body 100, a user terminal device 300, and a nurse station 400. They communicate, for example, via a network NW. The network NW can be any network such as a LAN, WAN, or Internet connection. It should be noted that the mobile body 100, user terminal device 300, and nurse station 400 may also communicate directly via short-range wireless communication without using a network NW. In the following description, it is assumed that the mobile body 100 is traveling within a hospital, and the nurse station 400 is located within the hospital. The nurse station 400 is equipped with a computer capable of communicating with the mobile body 100.

[0043] [User terminal device]

[0044] User terminal device 300, for example, is a computer that remotely operates mobile body 100 or inputs the destination in delivery mode based on the operation of user U1. User terminal device 300 may have input units such as a keyboard, mouse, or touch panel for the user to input the destination in delivery mode. User terminal device 300 is located at a remote location other than nurse station 400.

[0045] The user terminal device 300 may include an input device such as a joystick. For example, the user U1 can also operate the movement direction of the moving body 100 by operating the joystick. Specifically, the user U1 can instruct the moving body 100 to move to the left relative to the movement direction by tilting the joystick to the left. In addition, the user U1 can instruct the moving body 100 to move to the right relative to the movement direction by tilting the joystick to the right.

[0046] The user terminal device 300 sends the operation information obtained based on the user U1's operation to the mobile body 100. It should be noted that the joystick may also be equipped with a start button for indicative of starting the movement of the mobile body 100 and a stop button for indicative of stopping the movement of the mobile body 100.

[0047] User terminal device 300 sends operation information output from input devices such as joysticks to mobile device 100 via network NW. Additionally, user terminal device 300 sends destination information indicating the destination in the delivery mode input by the user to mobile device 100 via network NW.

[0048] Additionally, the user terminal device 300 receives image information transmitted from the mobile body 100. The image information is an image captured by a camera installed on the mobile body 100. The user terminal device 300 may include a display unit such as a liquid crystal display (LCD) or an organic EL display (OLED). The user terminal device 300 can display the image information received from the mobile body 100 on the display unit. The user U1 operates input devices such as a joystick based on the image of the periphery of the mobile body 100 displayed on the display unit 330.

[0049] User U1 can also control the camera's viewing angle on the mobile body 100 via the user terminal device 300. For example, user U1 can also control the pan, tilt, and zoom of the camera mounted on the mobile body 100 by operating the user terminal device 300. Alternatively, the user terminal device 300 can also be a wearable device such as VR glasses. In this case, the user can also configure the VR glasses' movements to control the camera.

[0050] It should be noted that the user terminal device 300 may also be a mobile terminal device such as a smartphone or tablet operated by the user U1. In this case, the user terminal device 300 may also have a touch panel, which can accept instructions from the user U1 inputted via the touch panel. In addition, the user terminal device 300 may also have a voice recognition function, which can also accept instructions based on the user U1's voice recognized by the voice recognition function.

[0051] [Moving entity]

[0052] Figure 2 This is a perspective view of the mobile body 100. The mobile body 100 includes, for example, a base 10, a door 60 provided on the base 10, and wheels (first wheel 20, second wheel 30, and third wheel 40) assembled on the base 10. For example, user U1 can open the door 60 to insert or remove luggage from the storage compartment provided on the base 10. The first wheel 20 and second wheel 30 are drive wheels, and the third wheel 40 is an auxiliary wheel (driven wheel).

[0053] A cylindrical support 50 extending upwards is provided on the upper surface of the base 10. A camera 80 for taking pictures around the moving body 100 is provided at the end of the support 50. The position of the camera 80 can also be any position different from the one described above.

[0054] Camera 80 is, for example, a camera capable of capturing the periphery of the moving object 100 with a wide angle (e.g., 360 degrees). Camera 80 may also include multiple cameras. For example, camera 80 may be implemented by combining multiple 120-degree cameras or multiple 60-degree cameras.

[0055] Figure 3 This diagram illustrates the structure of a mobile body 100 equipped with a control device 200. The mobile body 100 includes, for example: a base 160 on which an HMI 110, a detection device 120, a position determination device 130, a communication device 170, a working device 180, and a control device 200 are mounted; a moving mechanism 140 mounted on the base 160; and a sensor 150 mounted on the moving mechanism 140, etc. The base 160 can be connected to... Figure 2The matrix 10 is the same.

[0056] The HMI110 displays various information to the user U1 and accepts input from the user U1. The HMI110 includes various display devices, speakers, microphones, buzzers, touch panels, switches, buttons, etc.

[0057] The detection device 120 is a device that generates data for identifying objects present in the vicinity of the moving body 100. The detection device 120 may include, for example, an object recognition device that identifies objects based on the output of the camera 80. It should be noted that the detection device 120 may also identify objects by performing sensor fusion processing based on sensors other than the camera 80, such as radar devices, LIDAR (Light Detection and Range), ultrasonic sensors, and the outputs of these sensors.

[0058] The position determination device 130 is a device for determining the position of the moving body 100. The position determination device 130 may include, for example, a GNSS receiver that determines the position of the moving body 100 based on signals received from GNSS (Global Navigation Satellite System) satellites. The position determination device 130 may also determine or supplement the position of the moving body 100 using INS (Inertial Navigation System) outputs from the sensor 150 described later. Additionally, the position determination device 130 may also have an electromagnetic wave receiving function and determine or supplement the position of the moving body 100 based on the intensity of electromagnetic waves arriving from nearby electromagnetic wave transmitting sources (whose locations are known).

[0059] The movement mechanism 140 is a mechanism for moving the moving body 100 in any direction. The movement mechanism 140 may include, for example, multiple wheels (first wheel 20, second wheel 30, and third wheel 40), drive motors mounted on one or more wheels, and steering devices mounted on one or more wheels. There are no particular restrictions on the structure of the movement mechanism 140; it may also include structures other than wheels, such as feet for bipedal walking simulation, tracks, etc.

[0060] Sensor 150 is a sensor used to detect the behavior of the moving body 100. Sensor 150 includes, for example, a wheel speed sensor for detecting the speed of the wheels, an acceleration sensor for detecting the acceleration acting on the moving body 100, a yaw rate sensor mounted near the center of gravity in the horizontal direction of the base 160, a steering angle sensor for detecting the steering angle of the steering wheel (steering wheel), and an orientation sensor for detecting the orientation of the moving body 100 in the horizontal direction.

[0061] Communication device 170 is a wireless communication module that communicates wirelessly with a wireless base station connected to a network NW. Communication device 170 communicates with user terminal device 300 via network NW. Alternatively, communication device 170 can also communicate directly with user terminal device 300 via short-range wireless communication.

[0062] The work device 180 is a device that outputs work data. For example, in the case of using a mobile unit 100 in a hospital, the work device 180 can be a thermometer or a blood pressure monitor. In this case, the work device 180 can output the temperature and blood pressure data measured from the patient as work data. The work device 180 is not limited to this; it can be any device that outputs work data through human intervention.

[0063] Figure 4 This diagram illustrates an example of the structure of the control device 200. The control device 200 includes, for example, a first detection unit 210, a first decision unit 220, a second detection unit 230, a second decision unit 240, a calculation unit 250, a generation unit 260, a control unit 270, a judgment unit 280, a mode switching unit 282, a job data acquisition unit 284, a job data transmission unit 286, a data anomaly judgment unit 288, a call detection unit 290, and a response output unit 292. These components are implemented, for example, by executing a program (software) using a hardware processor such as a CPU (Central Processing Unit). Some or all of these components can be implemented using hardware (including the circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Processing Unit), or through a combination of software and hardware. The program can be pre-saved on storage devices such as HDDs (Hard Disk Drives) and flash memory (storage devices with non-transitory storage media), or it can be saved on removable storage media such as DVDs and CD-ROMs (non-transitory storage media), and installed on the storage device by mounting the storage media onto the drive device. It should be noted that the control device 200 can also store map information, including at least a local map of the location where the mobile body 100 is operating, on the storage device.

[0064] The first detection unit 210 detects the location of the destination in the delivery mode based on the destination information received by the communication device 170 from the user terminal device 300. Additionally, the first detection unit 210 detects the user U1's operation on the mobile body 100 based on the operation information received by the communication device 170 from the user terminal device 300.

[0065] The second detection unit 230 detects the positions of interference objects that the moving body 100 should avoid around the moving body 100 based on information input from the detection device 120. An interference object refers to an object that interferes with the movement of the moving body 100. For example, interference objects are objects that the moving body 100 should avoid, such as environmental objects, traffic participants, and obstacles. Environmental objects include, for example, walls or lawns, objects within the boundaries of the driving road that the moving body 100 cannot enter. Traffic participants include, for example, pedestrians and vehicles. Obstacles include, for example, stationary objects existing on the driving road that impede the movement of the moving body 100. The moving body 100 repeatedly detects the position of the interference object at predetermined time intervals to avoid contact with it during movement.

[0066] The first decision unit 220 determines a remote operation benefit function representing the recommended degree of travel of the mobile body 100 based on the user U1's operation on the mobile body 100 detected by the first detection unit 210. Furthermore, the first decision unit 220 determines an environmental object target-based benefit function based on the location of the destination in the delivery mode detected by the first detection unit 210 and the location of environmental object targets detected by the second detection unit 230. Details regarding the remote operation benefit function and the environmental object target-based benefit function will be described later.

[0067] The second decision unit 240 determines a risk function representing the degree of risk of interference between the moving body 100 and the interfering object based on the position of the interfering object detected by the second detection unit 230. Specifically, the second decision unit 240 determines an environmental object target risk function based on the position of an environmental object target detected by the second detection unit 230. Furthermore, the second decision unit 240 determines a traffic participant / obstacle risk function based on the positions of traffic participants and obstacles detected by the second detection unit 230. It should be noted that details regarding the environmental object target risk function and the traffic participant / obstacle risk function will be described later.

[0068] The calculation unit 250 calculates an evaluation function for evaluating the path traveled by the mobile body 100 based on the remote operation benefit function determined by the first decision unit 220 and the benefit function based on environmental object targets, and the environmental object target risk function and traffic participant / obstacle risk function determined by the second decision unit 240. Details regarding the evaluation function will be described later.

[0069] The generation unit 260 generates the target trajectory of the moving body 100 based on the evaluation function calculated by the calculation unit 250. For example, the generation unit 260 uses a model representing a portion of the outer periphery of a geometric shape to generate the target trajectory of the moving body 100. The model can be an arc model representing a circular arc. The generation unit 260 generates the target trajectory of the moving body 100 by modeling the trajectory of the moving body 100 using the arc model. The generation process of generating the target trajectory by the generation unit 260 will be described below.

[0070] [Target orbit generation and processing]

[0071] Figure 5 This is a diagram showing an example of a target trajectory generated by the generation unit 260. (See diagram for example.) Figure 5 As shown, the generation unit 260 generates the target trajectory of the moving body 100 by combining the first to third predicted trajectories in an arc shape.

[0072] First, let's explain the first predicted trajectory. In Figure 5 In the definition, an XY coordinate system is defined with the center of the current position of the moving body 100 as the origin, the front of the moving body 100 as the X-axis, and the left side of the moving body 100 as the Y-axis.

[0073] Furthermore, the position Z, starting from the origin of the XY coordinate system after the first prediction time, is set as the first prediction position. m1 It will be from the origin to the first predicted position Z. m1 The orbit is defined as the first predicted orbit in the shape of a circular arc.

[0074] Additionally, the radius of curvature of the first predicted trajectory is set to R. m1 And set the rotation angle of the first predicted trajectory to θ. m1 Rotation angle θ m1 This is equivalent to the angle between the Y-axis and the Y'-axis, which will be discussed later.

[0075] Additionally, regarding the distance from the origin to the first predicted position Z... m1 The predicted position when the orbit is divided into three equal parts, from the origin toward the first predicted position Z. m1 The three prediction positions Z are set sequentially. m11 Z m12 Z m13 (=Z) m1 ).

[0076] Next, the second predicted orbit will be explained. Figure 5 The first predicted position Z is defined in the middle. m1 (=Predicted position Z) m13 Set the first predicted position Z of the first predicted trajectory as the origin. m1The direction of the tangent at the point is set as the X' axis, and the direction orthogonal to the tangent is set as the Y' axis in the X'-Y' coordinate system.

[0077] Furthermore, starting from the origin of the X'-Y' coordinate system (i.e., the first predicted position Z), m1 The position after the second prediction time is set as the second prediction position Z. m2 It will start from the first predicted position Z m1 To the second predicted position Z m2 The orbit is defined as the second predicted orbit in the shape of a circular arc.

[0078] Additionally, the radius of curvature of the second predicted trajectory is set to R. m2 The rotation angle of the second predicted orbit is set to θ. m2 Rotation angle θ m2 This is equivalent to the angle between the Y' axis and the Y” axis, which will be discussed later.

[0079] Next, we will explain the third predicted orbit. Figure 5 In the middle, the second predicted position Z is defined. m2 Let Z be the origin, and set the second predicted position of the second predicted trajectory. m2 The X-Y coordinate system is formed by setting the direction of the tangent at a given point as the X-axis and setting the direction orthogonal to the tangent as the Y-axis.

[0080] Furthermore, the origin of the X”-Y” coordinate system (i.e., the second predicted position Z) will be used... m2 The position after the third prediction time is set as the third prediction position Z. m3 It will start from the second predicted position Z m2 To the third predicted position Z m3 The orbit is defined as a third predicted orbit in the shape of a circular arc.

[0081] As described above, the first predicted trajectory is determined by combining three predicted trajectories that are shorter than the second and third predicted trajectories. Specifically, this is the trajectory obtained by combining the three predicted trajectories from the current position to the predicted position Z. m11 The orbit from the predicted position Z m11 To the predicted position Z m12 The orbit, and from the predicted position Z m12 To the predicted position Z m13 The orbit obtained from the orbit determines the first predicted orbit.

[0082] This is because the first predicted trajectory is closer to the moving body 100 than the second and third predicted trajectories, thus requiring the generation of a predicted trajectory in a way that can more reliably avoid interference with the object being interfered with. It should be noted that the generation unit 260 may also determine the first predicted trajectory as a combination of two or more predicted trajectories to obtain a trajectory.

[0083] The generation unit 260 determines the rotation angle θ m1 ~θ m3 and radius of curvature R m1 ~R m3 Multiple tracks are generated by combining multiple patterns. Then, the generation unit 260 evaluates each of the generated tracks using an evaluation function calculated by the calculation unit 250. Afterward, based on the evaluation result using the evaluation function, the generation unit 260 determines one of the generated tracks as the target track for the moving body 100. By evaluating the generated tracks based on the evaluation function, the generation unit 260 can generate a target track for the moving body 100 that has a low risk of interference with the interference object and is suitable for follow-up or lead-ahead modes. It should be noted that details of the evaluation process using the evaluation function will be described later.

[0084] return Figure 4 As explained, the control unit 270 controls the moving mechanism 140 to move the moving body 100 along the target track generated by the generating unit 260. The control unit 270 controls the drive motor and the steering device to bring the position and behavior of the moving body 100, obtained from the output of the sensor 150, closer to the target track.

[0085] The determination unit 280 determines whether the mobile body 100 has arrived at its destination. For example, the determination unit 280 may determine that the mobile body 100 has arrived at its destination if the current location of the mobile body 100, as determined by the location determination device 130, is within a predetermined distance from the location of the destination detected by the first detection unit 210.

[0086] The mode switching unit 282 switches the operating mode of the mobile body 100 (e.g., delivery mode and remote operation mode). For example, when the mobile body 100 is operating in delivery mode, the mode switching unit 282 switches the operating mode of the mobile body 100 from delivery mode to remote operation mode when a condition including the mobile body 100 arriving at its destination is met. Details of this mode switching will be described later.

[0087] The work data acquisition unit 284 acquires work data from the work equipment 180 mounted on the mobile body 100. For example, if the work equipment 180 is a thermometer or a blood pressure monitor, the work data acquisition unit 284 acquires body temperature data and blood pressure data from the work equipment 180 as work data.

[0088] The work data transmission unit 286 uses the communication device 170 mounted on the mobile body 100 to transmit work data to an external information processing device. For example, the external information processing device could be a server that manages data of hospitalized patients. In this case, the work data transmission unit 286 could also transmit body temperature data and blood pressure data as work data to the external information processing device.

[0089] The data anomaly determination unit 288 determines whether the work data is abnormal. For example, the data anomaly determination unit 288 can determine that the work data is abnormal if the value shown by the work data does not fall within a preset range. For example, if the work data is a patient's body temperature data, the data anomaly determination unit 288 can determine that the work data is abnormal if the patient's body temperature shown by the body temperature data does not fall within a specified range (e.g., above 36.0°C and below 37.0°C). Similarly, if the work data is a patient's blood pressure data, the data anomaly determination unit 288 can determine that the work data is abnormal if the patient's blood pressure shown by the blood pressure data does not fall within a specified range (e.g., below 140 / 90 mmHg).

[0090] The call detection unit 290 detects calls to the mobile unit 100. For example, the call detection unit 290 can use a microphone provided in the HMI 110 to detect calls to the mobile unit 100. Specifically, the call detection unit 290 can use a voice recognition function to identify the sounds around the mobile unit 100 detected by the microphone, thereby detecting calls to the mobile unit 100.

[0091] The response output unit 292 generates and outputs response content to the call detected by the call detection unit 290. For example, the response output unit 292 may also automatically generate response content for small talk or simple chat and output it from the speaker installed in the HMI 110. The response output unit 292 may also use a learning model generated by pre-machine learning of conversational interactions to generate response content.

[0092] [Risk function for environmental objects and benefit function based on environmental object objectives]

[0093] Next, we will explain the environmental object target risk function and the benefit function based on the environmental object target. The environmental object target risk function represents the degree of risk of interference between the moving body 100 and the environmental object target (e.g., a wall, a lawn, etc.). Conversely, the environmental object target benefit function represents the degree of non-interference between the moving body 100 and the environmental object target.

[0094] Figure 6 This is a graph used to illustrate the risk function of environmental object targets and the benefit function based on environmental object targets. For example... Figure 6 As shown, the direction of travel of the moving body 100 is set as the Y-axis, and the direction orthogonal to the Y-axis is set as the X-axis. There are multiple environmental object targets R1 to R3 around the moving body 100, so the moving body 100 needs to travel in a way that does not interfere with these multiple environmental object targets R1 to R3.

[0095] exist Figure 6 In the example shown, the second detection unit 230 detects the position of the environmental object target R2 based on information input from the detection device 120. Here, the detection position of the environmental object target R2 is set as E_l. Figure 6 In the diagram, the detection location E_l only shows one location, but in reality, environmental object target R2 was detected at multiple locations.

[0096] It should be noted that in the XY coordinate system defined by the X and Y axes, the coordinates of the detection position E_l are defined as (xe_l, ye_l). The suffix l represents the number of the detected environmental object target, denoted as l = 1, 2, ..., n. e n e This represents the total number of detected locations of environmental objects.

[0097] Additionally, the direction from the center of the moving body 100 toward the detection position E_l is defined as X. e_l axis, and will be with X e_l Let the direction of the orthogonal axis be Y. e_l Axis. Additionally, when the X-axis is compared with the X... e_l Let the angle between the axes be ψ. e_l At that time, ψ e_l As shown in the following formula (1).

[0098] ψ e_l =tan -1 (ye_l / xe_l) …(1)

[0099] Furthermore, the coordinates of the predicted position on the trajectory generated by the generation unit 260 in the XY coordinate system are defined as Z. mj (X) mj Y mj The suffix j represents the number of the predicted position on the track generated by the generation unit 260, denoted as j=1, 2, ..., n. m n m This represents the total number of predicted positions on the orbit generated by the generation unit 260. Figure 5 In the example shown, five predicted locations (Z) are obtained. m11 Z m12 Z m13 (=Z) m1 Z m2 Z m3Therefore, it becomes n. m =5.

[0100] In addition, the coordinates Z of the predicted location are determined based on the following equations (2) and (3). mj (X) mj Y mj Transformed into X e_l axis and Y e_l The X-axis is defined by the axis. e_l -Y e_l Coordinate Z in the coordinate system mj_el (X) mj_el Y mj_el ).

[0101] X mj_el = (X mj -xe_l)cosψ e_l +(Y mj -ye_l)sinψ e_l

[0102] …(2)

[0103] Y mj_el =-(X mj -xe_l)sinψ e_l +(Y mj -ye_l)cosψ e_l

[0104] …(3)

[0105] Figure 7 It represents X e_l A diagram illustrating an example of the risk function for an environmental object target along the axis and the payoff function based on the environmental object target. (See diagram below.) Figure 7 As shown, the first decision unit 220 defines a reward function Pe_x_b based on the environmental object target R2, with the detection position E_l of the environmental object target R2 set as the origin. Additionally, the second decision unit 240 defines X... e_l The environmental object target risk function Pe_x_r along the axis.

[0106] It should be noted that the second decision unit 240 defines X in a manner that has a gradient in the first sign direction (positive direction). e_l The risk function for the environmental object target along the axis is Pe_x_r. Additionally, the first decision unit 220 defines a reward function Pe_x_b based on the environmental object target in a manner that has a gradient in a second sign direction (negative direction) opposite to the first sign direction (positive direction).

[0107] like Figure 7 As shown, X e_lThe environmental object target risk function Pe_x_r along the axis is X e_l A function whose risk function value increases with larger coordinates on the X-axis is only defined on the X-axis. e_l The region where the axis values ​​are positive. Additionally, X... e_l The reward function Pe_x_b based on the environment object target along the axis is X e_l A function whose payoff value is smaller for coordinates on the X-axis; this function is only defined on the X-axis. e_l The values ​​of the axes are in the negative region. It should be noted that this indicates the following: the larger the risk function value, the higher the risk of interference between the moving body 100 and the object being interfered with; the smaller the benefit function value, the lower the risk of interference between the moving body 100 and the object being interfered with.

[0108] Figure 8 It represents Y e_l A diagram of an example of the potential field function along the axial direction. (See diagram below.) Figure 8 As shown, the potential field function Pe_y is a function with the detection position E_l of the environmental object target R2 set as the origin. The second decision unit 240 defines the potential field function Pe_y as Y e_l Risk function of environmental object targets along the axis.

[0109] Y e_l The environmental object target risk function (potential field function Pe_y) along the axis is Y e_l A function whose risk function value increases the closer the coordinates of the axes are to 0. For example... Figure 7 and Figure 8 As shown, X e_l The environmental object target risk function Pe_x_r and Y along the axis e_l The environmental object target risk function Pe_y along the axis becomes a function of mutually different shapes. By X e_l The environmental object target risk function Pe_x_r along the axis is defined as X e_l A larger value for the axis coordinate results in a larger risk function value, thus preventing the generation of a target trajectory for the moving body 100 towards a position beyond the front of the target object in the environment. On the other hand, regarding Y... e_l The environmental object target risk function Pe_y along the axis is also defined as X. e_l When the environmental object target risk function Pe_x_r in the axial direction is the same, the computational load on the control device 200 increases. Therefore, regarding Y... e_l The environmental object target risk function Pe_y along the axis is defined such that the position of the origin (i.e. the position where the environmental object target R2 is detected) becomes the peak value.

[0110] Next, we will explain how to calculate the risk function value of the environmental object target. As mentioned above, the coordinates Z of the predicted position on the predicted trajectory in the XY coordinate system are... mj (X) mj Y mj Transform into X e_l -Y e_l The coordinates Z of the predicted position in the coordinate system mj_el (X) mj_el Y mj_el At the current time k, the X coordinates X of the predicted position will be used. mj_el The obtained risk function value is set as Pe_x_r_j_l(k), and the Y coordinate Y based on the predicted location is used. mj_el The risk function value is obtained and set as Pe_y_j_l(k). Pe_x_r_j_l(k) can be determined based on... Figure 7 Pe_x_r is given. Pe_y_j_l(k) can be obtained from Pe_x_r. Figure 8 Pe_y is obtained from the given information.

[0111] In this case, the risk function value Pe_r_j_l(k) for each detection location on the environmental object target can be expressed as shown in equation (4). That is, Pe_r_j_l(k) can be obtained by multiplying Pe_x_r_j_l(k) by Pe_y_j_l(k).

[0112] Pe_r_j_l(k)=Pe_x_r_j_l(k)×Pe_y_j_l(k)

[0113] …(4)

[0114] Furthermore, the total risk function value Pe_r_j(k) for the coordinates of the predicted positions on the predicted trajectory can be expressed as shown in equation (5). That is, by taking the risk function values ​​Pe_r_j_l(k) (l=1, 2, ..., n) for all detection positions on the environmental object target. e In total, Pe_r_j(k) can be calculated.

[0115] Pe_r_j(k)=Σ l=1 ne (Pe_r_j_l(k))…(5)

[0116] Furthermore, the environmental object target risk function value Pe_r(k) can be expressed as shown in equation (6). That is, by using the total risk function value Pe_r_j(k) (j=1, 2, ..., n) for the coordinates of all predicted positions on the predicted trajectory. m In total, the environmental object target risk function value Pe_r(k) can be obtained.

[0117] Pe_r(k) = Σj=1 nm (Pe_r_j(k))…(6)

[0118] Next, we will explain how to calculate the benefit function value based on the environmental object target. For example... Figure 7 As explained, the first decision unit 220 defines a reward function Pe_x_b based on the environmental object target R2, with the detection position E_l of the environmental object target R2 set as the origin. Furthermore, the first decision unit 220... Figure 8 The potential field function Pe_y shown is defined as Y e_l A axial benefit function based on environmental object targets.

[0119] As mentioned above, the Z coordinates of the predicted position on the predicted trajectory in the XY coordinate system are... mj (X) mj Y mj Transform into X e_l -Y e_l The coordinates Z of the predicted position in the coordinate system mj_el (X) mj_el Y mj_el At the current time k, the X coordinates X of the predicted position will be used. mj_el The obtained profit function value is set as Pe_x_b_j_l(k), and the Y coordinate Y based on the predicted location is used. mj_el The resulting profit function value is denoted as Pe_y_j_l(k). Pe_x_b_j_l(k) can be determined based on... Figure 7 Pe_x_b is shown. Pe_y_j_l(k) can be obtained from Pe_x_b. Figure 8 Pe_y is obtained from the given information.

[0120] In this case, the profit function value Pe_b_j_l(k) for each detection position on the environmental object target can be expressed as shown in equation (7). That is, Pe_b_j_l(k) can be obtained by multiplying Pe_x_b_j_l(k) by Pe_y_j_l(k).

[0121] Pe_b_j_l(k)=Pe_x_b_j_l(k)×Pe_y_j_l(k)

[0122] …(7)

[0123] Furthermore, the total reward function value Pe_b_j(k) for the coordinates of the predicted positions on the predicted trajectory can be expressed as shown in equation (8). That is, the reward function value Pe_b_j_l(k) (l=1, 2, ..., n) for all detection positions on the environmental object target can be obtained. e The maximum value of ) is Pe_b_j(k).

[0124] Pe_b_j(k)=max Pe_b_j_l(k)

[0125] =max Pe_x_b_j_l(k)×Pe_y_j_l(k)

[0126] …(8)

[0127] Figure 9 This is a schematic diagram illustrating the reward function based on environmental objects when environmental objects are detected at multiple detection locations. For example... Figure 9 As shown, for example, assuming there are walls on both the left and right sides, environmental object targets are detected on the right and left sides relative to the travel direction of the moving body 100. Furthermore, the reward function based on the detection position on the left side relative to the travel direction of the moving body 100 is set as Pe_x_b_1, and the reward function based on the detection position on the right side relative to the travel direction of the moving body 100 is set as Pe_x_b_2.

[0128] In this case, the first decision unit 220 sets the maximum value among the multiple benefit function values ​​(Pe_x_b_1 and Pe_x_b_2) as X. e_l The benefit function value based on the environmental object target along the axis. The benefit function should be set considering the balance of all environmental object targets, so the maximum value is selected. Therefore, in the aforementioned equation (8), Pe_b_j(k) is obtained by considering the maximum value.

[0129] Furthermore, the reward function value Pe_b(k) based on the environmental object target can be expressed as shown in equation (9). That is, it is expressed as the total reward function value Pe_b_j(k) (j=1, 2, ..., n) for the coordinates of all predicted positions on the predicted trajectory. m In total, the benefit function value Pe_b(k) based on the environmental object target can be obtained.

[0130] Pe_b(k) = Σ j=1 nm (Pe_b_j(k))…(9)

[0131] [Traffic Participant / Obstacle Risk Function]

[0132] Next, we will explain the traffic participant / obstacle risk function. The traffic participant / obstacle risk function is a function that represents the degree of risk of interference between the moving body 100 and traffic participants and obstacles.

[0133] Figure 10 This is a graph used to illustrate the risk function of traffic participants and obstacles. For example... Figure 10As shown, the direction of travel of the moving body 100 is set as the Y-axis, and the direction orthogonal to the Y-axis is set as the X-axis. There are multiple traffic participants U2 and U3 around the moving body 100, so the moving body 100 needs to travel in a way that does not interfere with these multiple traffic participants U2 and U3.

[0134] exist Figure 10 In the example shown, the second detection unit 230 detects the positions of traffic participants U2 and U3 based on information input from the detection device 120. Furthermore, the second decision unit 240 sets a traffic participant / obstacle risk function based on the positions of traffic participants U2 and U3 detected by the second detection unit 230.

[0135] Figure 11 This is a diagram used to illustrate the traffic participant / obstacle risk function obtained based on traffic participant U2. The following describes the decision process by which the second decision unit 240 determines the traffic participant / obstacle risk function obtained based on traffic participant U2. Figure 11 In this context, the detection location for traffic participant U2 is set to TP_l. Figure 11 The diagram only shows the detection location TP_l for traffic participant U2, but traffic participant U3 is also detected. It should be noted that if there are obstacles on the road besides traffic participants, the second detection unit 230 also detects the obstacles.

[0136] It should be noted that in the XY coordinate system defined by the X and Y axes, the coordinates of the detection position TP_l are defined as (xtp_l, ytp_l). The suffix l represents the number of the detected traffic participant / obstacle location, denoted as l = 1, 2, ..., n. t n t This represents the total number of detected locations of traffic participants and obstacles. Additionally, the direction of travel of traffic participant U2 is defined as the Ytp_l axis, and the direction orthogonal to the Ytp_l axis is defined as the Xtp_l axis.

[0137] Furthermore, the coordinates of the predicted position on the trajectory generated by the generation unit 260 in the XY coordinate system are defined as Z. mj (X) mj Y mj Using the same method as in equations (2) and (3) above, the coordinates Z of the predicted position are... mj (X) mj Y mj Transformed into X tp_l axis and Y tp_l The X-axis is defined by the axis. tp_l -Y tp_l Coordinate Z in the coordinate system mj_tpl (X) mj_tpl Ymj_tpl ).

[0138] Figure 12 It represents X tp_l A graph illustrating an example of the traffic participant / obstacle risk function along the axial direction. (See figure.) Figure 12 As shown, the second decision unit 240 defines X by setting the detection position TP_l of traffic participant U2 as the origin. tp_l The traffic participant / obstacle risk function Ptp_x along the axial direction. It should be noted that the second decision unit 240 defines X in a manner that has a gradient in the first sign direction (positive direction). tp_l The traffic participant / obstacle risk function Ptp_x along the axis.

[0139] like Figure 12 As shown, X tp_l The traffic participant-obstacle risk function Ptp_x along the axis is X tp_l A function whose risk function value increases the closer the coordinates of the axis are to 0.

[0140] Figure 13 It represents Y tp_l A graph illustrating an example of the traffic participant / obstacle risk function along the axial direction. (See figure.) Figure 13 As shown, the second decision unit 240 defines Y by setting the detection position TP_l of traffic participant U2 as the origin. tp_l The traffic participant / obstacle risk function Ptp_y along the axial direction. It should be noted that the second decision unit 240 defines Y in a manner that has a gradient in the first sign direction (positive direction). tp_l The traffic participant / obstacle risk function Ptp_y along the axis.

[0141] like Figure 13 As shown, Y tp_l The traffic participant-obstacle risk function Ptp_y along the axis is Y tp_l A function whose risk function value increases the closer the coordinates of the axis are to 0.

[0142] Next, we will explain how to calculate the traffic participant / obstacle risk function value. As mentioned earlier, the coordinates Z of the predicted position on the predicted track in the XY coordinate system are... mj (X) mj Y mj Transform into X tp_l -Y tp_l The coordinates Z of the predicted position in the coordinate system mj_tpl (X) mj_tpl Y mj_tpl At the current time k, the X coordinates X of the predicted position will be used. mj_tplThe obtained risk function value is set as Ptp_x_j_l(k), and the Y-coordinate based on the predicted location is used. mj_tpl The obtained risk function value is set as Ptp_y_j_l(k). Ptp_x_j_l(k) can be determined based on... Figure 12 Ptp_x is obtained from the given Ptp_x. Ptp_y_j_l(k) can be obtained from... Figure 13 The value of Ptp_y is obtained from the given information.

[0143] In this case, the risk function value Ptp_r_j_l(k) for each detection location of traffic participants and obstacles can be expressed as shown in equation (10). That is, Ptp_r_j_l(k) can be obtained by multiplying Ptp_x_j_l(k) by Ptp_y_j_l(k).

[0144] Ptp_r_j_l(k)=Ptp_x_j_l(k)×Ptp_y_j_l(k)

[0145] …(10)

[0146] Furthermore, the total risk function value Ptp_r_j(k) for the coordinates of the predicted position on the predicted track can be expressed as shown in Equation (11). That is, by using the risk function values ​​Ptp_r_j_l(k) (l=1, 2, ..., n) for all detected positions of traffic participants and obstacles. t In total, Ptp_r_j(k) can be calculated.

[0147] Ptp_r_j(k)=Σ l=1 nt (Ptp_r_j_l(k))…(11)

[0148] Furthermore, the traffic participant-obstacle risk function value Ptp_r(k) can be expressed as shown in equation (12). That is, it is expressed as the total risk function value Ptp_r_j(k) (j=1, 2, ..., n) for the coordinates of all predicted positions on the predicted track. m In total, the traffic participant-obstacle risk function value Ptp_r(k) can be obtained.

[0149] Ptp_r(k) = Σ j=1 nm (Ptp_r_j(k))…(12)

[0150] [Remote Operation Revenue Function]

[0151] Next, the remote operation benefit function will be explained. The remote operation benefit function is a function that represents the recommended degree of movement of the mobile body 100, determined based on the user U1's operation on the mobile body 100. Furthermore, the remote operation benefit function is set in both remote operation mode and delivery mode.

[0152] Figure 14 This diagram illustrates the automatic movement of the mobile unit 100 in delivery mode. (Example) Figure 14 As shown, the direction of travel of the moving body 100 is set as the Y-axis, and the direction orthogonal to the Y-axis is set as the X-axis. There are multiple environmental objects R4~R6 and traffic participants U4 (hereinafter referred to as pedestrians U4) around the moving body 100, so the moving body 100 needs to travel in a way that does not interfere with these multiple environmental objects R4~R6 and pedestrians U4.

[0153] When the mode switching unit 282 switches the operation mode of the mobile body 100 to the delivery mode, the first decision unit 220 determines the approximate path to the destination based on the destination and map information, and sets a reward function based on environmental object targets along the path. On the other hand, the first decision unit 220 does not set a reward function based on environmental object targets for areas outside the path.

[0154] Therefore, in Figure 14 In the example shown, the first decision unit 220 does not set a benefit function based on the environmental object targets in the space S between the environmental object target R4 and the environmental object target R5. If a benefit function based on the environmental object targets is set in the space S, the mobile body 100 is recommended to travel at the location where the benefit function is set, and therefore may not go to the destination but travel straight. In this way, the first decision unit 220 determines the approximate path to the destination based on the destination information and map information, and sets a benefit function based on the environmental object targets along the path, thereby guiding the mobile body 100 to the destination more reliably.

[0155] It should be noted that no destination is set in the remote operation mode, therefore the first decision unit 220 also sets a benefit function based on the environmental object target in the space S between the environmental object target R4 and the environmental object target R5. In the remote operation mode, the user U1 can use the user terminal device 300 to control the direction of travel of the mobile body 100, and control the start and stop of the mobile body 100.

[0156] exist Figure 14 In the example shown, the mobile body 100 avoids interfering objects (such as pedestrians U4, environmental objects R4~R6) without user U1 operation, while traveling on path A1 corresponding to the position where the environmental object benefit function is set.

[0157] In both remote operation and delivery modes, the control device 200 controls the direction of travel of the mobile body 100 based on the operation instructions from the user U1 using the joystick or similar control from the user terminal device 300. However, when the mobile body 100 is operated remotely by the user U1 in a completely manual manner, the user U1 experiences stress due to the communication delay between the mobile body 100 and the user terminal device 300. Therefore, in this embodiment, the mobile body 100 essentially travels automatically, changing its path to the left or right based on the operation instructions received from the user U1. This reduces the stress experienced by the user U1 in operating the mobile body 100.

[0158] Figure 15 This diagram illustrates a scenario where user U1 instructs the moving body 100 to move to the left. Figure 15 In the example shown, when the mobile body 100 is traveling along path A1, the distance between the mobile body 100 and the pedestrian U4 is short. Therefore, moving the mobile body 100 to the left allows for more reliable avoidance of the pedestrian U4. Thus, when the user U1 uses the user terminal device 300 to instruct the mobile body 100 to move to the left, the first decision unit 220 sets a remote operation benefit function in the region to the left (the negative direction on the X-axis) relative to the direction of travel of the mobile body 100. The mobile body 100 is recommended to travel near the location where the benefit function is set, thus changing the path from path A1 to path A2. As a result, the mobile body 100 can travel towards its destination while more reliably avoiding the pedestrian U4.

[0159] Next, we will explain how to calculate the remote operation benefit function value. As mentioned earlier, the coordinates of the predicted position on the predicted trajectory in the XY coordinate system are represented as Z. mj (X) mj Y mj Additionally, the parameters of the operation information obtained based on the operation of user U1 on user terminal device 300 are set as xop_j (j=1, 2, ..., n). m For example, the parameter xop_j can be calculated based on the amount of operation (travel) of the user U1 on the joystick, the number of operations in the operation direction, and the operation time in the operation direction. It should be noted that the right direction relative to the travel direction of the moving body 100 is defined as the positive direction of the parameter xop_j, and the left direction relative to the travel direction of the moving body 100 is defined as the negative direction of the parameter xop_j.

[0160] The first detection unit 210 calculates the parameter xop_j based on operation information obtained from the user U1's operation of the joystick, etc., and outputs the parameter xop_j to the first decision unit 220. Thus, the first detection unit 210 can detect the user U1's operation on the moving body 100. Furthermore, the first decision unit 220 determines the position based on the parameter xop_j and the predicted X-coordinate X. mj To calculate the return function value. This point will be explained below.

[0161] Figure 16 This is a graph representing an example of a remote operation payoff function. For example... Figure 16 As shown, the first decision unit 220 defines the remote operation benefit function Pe_b_op_j (j=1, 2, ..., n) in such a way that it has a gradient in the second sign direction (negative direction). m The remote operation benefit function Pe_b_op_j is a function whose value decreases as Δxop_j approaches 0.

[0162] Here, Δxop_j is the parameter xop_j and the X-coordinate of the predicted position on the trajectory generated by the generator 260. mj The difference between them. Therefore, the difference Δxop_j(k) at the current time k is expressed as in equation (13).

[0163] Δxop_j(k) = X mj (k)-xop_j(k)…(13)

[0164] The first decision unit 220 calculates Δxop_j(k) based on equation (13). Furthermore, the first decision unit 220... Figure 16 The remote operation benefit function shown is used to calculate the benefit function value Pe_b_op_j(k) corresponding to the calculated Δxop_j(k).

[0165] Furthermore, the remote operation benefit function value Pe_b_op(k) can be expressed as in equation (14). That is, by using the benefit function value Pe_b_op_j(k) (j=1, 2, ..., n) for the coordinates of all predicted positions on the predicted orbit. m In total, the remote operation benefit function value Pe_b_op(k) can be obtained.

[0166] Pe_b_op(k) = Σ j=1 nm (Pe_b_op_j(k))…(14)

[0167] Figure 17 This diagram illustrates the change in the direction of travel of the moving body 100 when a remote operation benefit function is set. (Example) Figure 17As shown, for example, when user U1 uses user terminal device 300 to give an instruction to move the mobile body 100 to the right, a remote operation benefit function is set on the right side (positive direction of the X-axis) relative to the direction of travel of the mobile body 100.

[0168] Furthermore, the mobile body 100 is recommended to travel at the position where the remote operation benefit function value is minimized (becoming the position where Δxop_j=0), and therefore the direction of travel is changed to the right. In this way, the first decision unit 220 determines the remote operation benefit function, thereby enabling the mobile body 100 to change its direction of travel according to the operation of the user U1.

[0169] [Calculation and processing of the evaluation function, etc.]

[0170] Next, the calculation process of the evaluation function will be explained. The calculation unit 250 calculates the evaluation function J(k) based on the profit function determined by the first decision unit 220 and the risk function determined by the second decision unit 240. Specifically, the calculation unit 250 calculates the evaluation function J(k) according to the following equation (15). That is, the calculation unit 250 calculates the evaluation function J(k) by adding Pe_b_op(k), Pe_b(k), Ptp_r(k), and Pe_r(k) together.

[0171] J(k)=Pe_b_op(k)+Pe_b(k)+Ptp_r(k)+Pe_r(k)…(15)

[0172] The benefit functions Pe_b_op(k) and Pe_b(k) are negative values; the smaller the benefit function value, the higher the recommended travel degree of the moving body 100. The risk functions Ptp_r(k) and Pe_r(k) are positive values; the larger the risk function value, the higher the risk of interference between the moving body 100 and the object being interfered with. Therefore, the generation unit 260 needs to generate the target trajectory of the moving body 100 in a way that reduces the evaluation value calculated based on the evaluation function J(k).

[0173] As described above, the generation unit 260 Figure 5 As shown, the trajectory of the moving body 100 is modeled using a circular arc model, and the curvature or radius of curvature of the arc is calculated by varying the evaluation function J(k) in the second sign direction (negative direction), thereby generating the target trajectory. As previously described, the generation unit 260 determines... Figure 5 rotation angle θ m1 ~θ m3 and radius of curvature R m1 ~R m3Multiple tracks are generated by combining multiple patterns. Furthermore, the generation unit 260 uses the evaluation function J(k) calculated by the calculation unit 250 to calculate the evaluation value for each of the multiple generated tracks, and generates the track with the smallest evaluation value as the target track.

[0174] The control unit 270 controls the mobile body 100 based on the target track generated by the generation unit 260. Specifically, the control unit 270 controls the movement mechanism 140 (drive motor, steering device, etc.) in such a way that the mobile body 100 travels on the target track generated by the generation unit 260. As a result, the control device 200 can control the mobile body 100 to travel on the target track where the risk of interference between the mobile body 100 and the object of interference (pedestrian, obstacle, etc.) is low and suitable for the set mode (remote operation mode or delivery mode).

[0175] [Specific examples of model change]

[0176] Next, as a specific example of a mode change, we will describe an example of the mobile unit 100 being used as a ward round cart that automatically travels within a hospital. It should be noted that the mobile unit 100 is provided with a thermometer and a blood pressure monitor as a work device 180. Furthermore, the mobile unit 100 is arranged at a nurse station 400.

[0177] Figure 18 This diagram illustrates the scenario where a mobile device 100 travels to a patient's room. (Example) Figure 18 As shown, patient P1 is lying on bed B1, and patient P2 is lying on bed B2. User U1 is a nurse working at the hospital.

[0178] User U1 uses user terminal device 300 to set patient P1's ward as the destination and sets the motion mode of mobile body 100 to delivery mode. As a result, mobile body 100 automatically moves to patient P1's ward.

[0179] When the mobile device 100 arrives at patient P1's ward, it measures patient P1's temperature and blood pressure. It should be noted that the mobile device 100 is equipped with a thermometer and blood pressure monitor, allowing patient P1 to perform these measurements independently. The mobile device 100 can also provide audio instructions on temperature and blood pressure measurement via a speaker. This enables patient P1 to understand how to measure temperature and blood pressure. It should be noted that the nurse user U1 does not need to accompany the mobile device 100 to patient P1's ward; they can wait at the nurses' station 400.

[0180] Next, the work data acquisition unit 284 acquires work data from the work equipment 180 mounted on the mobile body 100. For example, the work data acquisition unit 284 can acquire the body temperature data of patient P1 from a thermometer and the blood pressure data of patient P1 from a blood pressure monitor. Afterwards, the work data transmission unit 286 uses the communication device 170 mounted on the mobile body 100 to transmit the work data to an external information processing device. For example, the work data transmission unit 286 can use the communication device 170 to transmit the body temperature data and blood pressure data to the external information processing device. For example, the external information processing device could be a server that manages data of hospitalized patients. Thus, work data (e.g., body temperature data, blood pressure data) can be collected automatically.

[0181] Next, the data anomaly determination unit 288 determines whether the work data is abnormal. For example, the data anomaly determination unit 288 can determine that the work data is abnormal if the body temperature of patient P1, as shown by the body temperature data, is not within the specified range (e.g., above 36.0°C but below 37.0°C). Additionally, the data anomaly determination unit 288 can determine that the work data is abnormal if the blood pressure of patient P1, as shown by the blood pressure data, is not within the specified range (e.g., below 140 / 90 mmHg).

[0182] Next, the mode switching unit 282 can switch from delivery mode to remote operation mode when the conditions are met, including the arrival of the mobile body 100 at its destination and the occurrence of abnormal operation data. At this time, the switch to remote operation mode can also be notified to the user U1, who is the nurse, by notifying the computer at the nurse station 400.

[0183] When the operation mode of the mobile device 100 is switched to remote operation mode, the user U1, acting as a nurse, can remotely operate the mobile device 100 using the user terminal device 300. This allows the user to communicate with the patient P1 using the display device, speaker, and microphone of the HMI 110 installed on the mobile device 100. Therefore, if there are abnormalities in the operational data (such as body temperature data and blood pressure data), the user U1 can operate the mobile device 100 to confirm the situation.

[0184] When user U1 finishes the status confirmation operation, mode switching unit 282 switches the operation mode of mobile body 100 from remote operation mode to delivery mode based on the instruction from user terminal device 300. At this time, if the ward for the next ward round is pre-set, mode switching unit 282 can set the location of the ward for the next ward round (e.g., patient P2's ward) as the destination for delivery mode. It should be noted that the method of setting the destination is not limited to this. For example, mode switching unit 282 can also set user U1's current location as the destination for delivery mode. Specifically, mode switching unit 282 can receive user U1's current location, determined using GPS, from user terminal device 300. Alternatively, mode switching unit 282 can set a pre-determined location, such as nurse station 400, as the destination for delivery mode. Thus, mobile body 100 can automatically move to the designated location.

[0185] It should be noted that, when there are no abnormalities in the work data, the mode switching unit 282 does not switch from delivery mode to remote operation mode, but instead sets the destination of the movement. Therefore, even when there are no abnormalities in the work data, the mobile unit 100 can automatically move to the designated location.

[0186] Furthermore, the mode switching unit 282 can determine whether to switch to remote operation mode based on the content of a call detected by the call detection unit 290 during the delivery mode operation of the mobile body 100. For example, if the content of the call detected by the call detection unit 290 is related to medical procedures (such as needing help from a nurse), the mode switching unit 282 can switch the operation mode of the mobile body 100 to remote operation mode. Thus, the user U1 can check the status of the person who spoke to the mobile body 100 by operating the mobile body 100.

[0187] On the other hand, if it is determined that the remote operation mode will not be switched, the response output unit 292 can generate and output a response to the call detected by the call detection unit 290. For example, the response output unit 292 can also automatically generate a response to small talk or simple chatter and output it from the speaker installed in the HMI 110. This saves the user U1 the effort of dealing with someone who has spoken to the mobile device 100.

[0188] Furthermore, when setting a delivery mode, the user terminal device 300 can select a mobile body 100 from a plurality of waiting mobile bodies 100 for use in the delivery mode. For example, the user terminal device 300 can select one of the plurality of mobile bodies 100 based on at least one of the types of operating devices 180 mounted on each of the plurality of mobile bodies 100, the location of each of the plurality of mobile bodies 100, and the charging state of each of the plurality of mobile bodies 100. Thus, the user terminal device 300 can select the optimal mobile body 100 for use in the delivery mode.

[0189] It should be noted that the computer at the nurse station 400 can also have the same structure and functions as the user terminal device 300, enabling remote operation of the mobile device 100 from the computer at the nurse station 400. Thus, the user U1 can remotely operate the mobile device 100 while remaining in a waiting state at the nurse station 400.

[0190] [flow chart]

[0191] Figure 19 This is a flowchart illustrating an example of a process performed by the control device 200. The process based on this flowchart is executed according to the start-up of the moving body 100.

[0192] First, the mode switching unit 282 sets the delivery mode based on the instruction from user U1 (step S101). At this time, the mode switching unit 282 also sets the destination based on the instruction from user U1. For example, the mode switching unit 282 can also accept the instruction from user U1 based on information input by user U1 into user terminal device 300. In addition, the mode switching unit 282 can accept the instruction from user U1 based on user U1's operation on HMI 110. In addition, the mode switching unit 282 can also accept the instruction from user U1 by detecting user U1's gesture using camera 80. Moreover, the mode switching unit 282 can also use voice recognition function to recognize the voice emitted by the user and accept the instruction from user U1 based on the recognized voice. By setting the delivery mode, the mobile body 100 moves towards the destination.

[0193] Next, the determination unit 280 determines whether the mobile body 100 has arrived at its destination (step S102). For example, the determination unit 280 may determine that the mobile body 100 has arrived at its destination if the current location of the mobile body 100, as determined by the location determining device 130, is within a predetermined distance from the destination. If the mobile body 100 has not arrived at its destination, the determination unit 280 waits until the mobile body 100 arrives at its destination.

[0194] On the other hand, when the mobile body 100 arrives at its destination, the work data acquisition unit 284 acquires work data from the work equipment 180 mounted on the mobile body 100 (step S103). For example, if the work equipment 180 is a thermometer or a blood pressure monitor, the work data acquisition unit 284 acquires temperature data and blood pressure data from the work equipment 180 as work data. At this time, the work data transmission unit 286 uses the communication device 170 to send the work data to an external information processing device (such as a server that manages patient data).

[0195] Next, the data anomaly determination unit 288 determines whether the work data is abnormal (step S104). For example, the data anomaly determination unit 288 may determine that the work data is abnormal if the value shown by the work data does not fall within a preset range.

[0196] If the operation data is determined to be abnormal, the mode switching unit 282 switches the operation mode of the mobile body 100 to the remote operation mode (step S105). As a result, the user U1 can remotely operate the mobile body 100 using the user terminal device 300.

[0197] On the other hand, if the operation data is determined to be normal, the mode switching unit 282 does not switch from delivery mode to remote operation mode. At this time, the mode switching unit 282 sets the destination in delivery mode as the next moving destination. Therefore, even if the operation data is normal, the mobile unit 100 can automatically move to the designated location.

[0198] It should be noted that, according to the control device 200 of this embodiment, the target trajectory of the moving body 100 is generated by modeling the trajectory of the moving body 100 using an arc model, thus enabling the movement of the moving body 100 to be smooth. Therefore, traffic participants (pedestrians, etc.) can easily predict the movement of the moving body 100, and contact between the moving body 100 and traffic participants can be effectively prevented.

[0199] As explained above, the control device 200 of this embodiment is a device that switches between a delivery mode for moving to a destination and a remote operation mode for remote operation of the mobile body 100 by the user U1, and includes a determination unit 280, a mode switching unit 282, a generation unit 260, and a control unit 270. The determination unit 280 determines whether the mobile body 100 has arrived at its destination. When the mobile body 100 is operating in delivery mode, the mode switching unit 282 switches the operation mode of the mobile body 100 from delivery mode to remote operation mode when a condition including the mobile body 100 having arrived at its destination is met. The generation unit 260 generates a target track for the mobile body 100 based on the switched operation mode. The control unit 270 controls the mobile body 100 based on the target track. Thus, the control device 200 of this embodiment enables the mobile body 100 to switch between delivery mode and remote operation mode for movement.

[0200] The implementation methods described above can be performed as follows.

[0201] A control device comprising:

[0202] Storage device, which stores a program; and

[0203] A hardware processor that switches between a delivery mode for moving to the destination and a remote operation mode for remotely operating the mobile body, thereby causing the mobile body to move.

[0204] The hardware processor performs the following processing by executing a program stored in the storage device:

[0205] Determine whether the moving body has arrived at the destination;

[0206] While the mobile body is operating in the delivery mode, when a condition including the mobile body arriving at the destination is met, the operation mode of the mobile body is switched from the delivery mode to the remote operation mode.

[0207] The target trajectory of the moving body is generated based on the switched action mode; and

[0208] The moving body is controlled based on the target trajectory.

[0209] The above description uses the embodiments described in relation to the specific implementation, but the present invention is not limited to such embodiments in any way, and various modifications and substitutions can be made without departing from the spirit of the present invention.

[0210] [Explanation of Labels in the Attached Image]

[0211] 100 moving bodies

[0212] 200 control device

[0213] 210 First Testing Department

[0214] 220 First Decision Department

[0215] 230 Second Inspection Department

[0216] 240 Second Decision Department

[0217] 250 Calculation Department

[0218] 260 Generation Department

[0219] 270 Control Department

[0220] 280 Judgment Department

[0221] 282 Mode Switching Unit

[0222] 284 Operational Data Acquisition Department

[0223] 286 Operation Data Transmission Department

[0224] 288 Data Anomaly Detection Department

[0225] 290 Call the Inspection Department

[0226] 292 Response Output Section

[0227] 300 User terminal devices.

Claims

1. A control device that switches between a delivery mode for moving to a destination and a remote operation mode for remotely operating the mobile body by a user, thereby causing the mobile body to move, wherein, The control device includes: The determination unit determines whether the moving body has reached the destination; The mode switching unit switches the operation mode of the mobile body from the delivery mode to the remote operation mode when a condition including the mobile body arriving at the destination is met during the operation of the mobile body in the delivery mode. The generation unit generates the target trajectory of the moving body based on the switched action mode; as well as The control unit controls the moving body based on the target trajectory.

2. The control device according to claim 1, wherein, The control device also includes: The work data acquisition unit acquires work data from the work equipment mounted on the mobile body; and The work data transmission unit uses a communication device mounted on the mobile body to transmit the work data to an external information processing device.

3. The control device according to claim 2, wherein, The control device also includes a data anomaly determination unit, which determines whether the operation data is abnormal. When the conditions of the mobile body arriving at the destination and the operation data being abnormal are met, the mode switching unit switches from the delivery mode to the remote operation mode.

4. The control device according to claim 1, wherein, The control device also includes a call detection unit that detects calls to the moving body. If a call to the mobile body is detected while the mobile body is operating in the delivery mode, the mode switching unit determines whether to switch to the remote operation mode based on the content of the call.

5. The control device according to claim 4, wherein, The control device also includes a response output unit, which generates and outputs a response to the call when it is determined that the remote operation mode will not be switched.

6. A mobile system comprising a plurality of mobile bodies and a terminal device capable of communicating with the plurality of mobile bodies, wherein, Each of the plurality of moving bodies has a control device as described in any one of claims 1 to 5. The terminal device selects one of the plurality of mobile bodies based on at least one of the following: the type of operating equipment mounted on each of the plurality of mobile bodies, the location of each of the plurality of mobile bodies, and the charging status of each of the plurality of mobile bodies.

7. A control method, wherein processing is performed by a control device, wherein, The control device switches between a delivery mode that moves to the destination and a remote operation mode that allows the user to remotely operate the mobile device, thereby causing the mobile device to move. The control method causes the control device to perform the following processing: Determine whether the moving body has arrived at the destination; While the mobile body is operating in the delivery mode, when a condition including the mobile body arriving at the destination is met, the operation mode of the mobile body is switched from the delivery mode to the remote operation mode. The target trajectory of the moving body is generated based on the switched action mode; as well as The moving body is controlled based on the target trajectory.

8. A program that performs processing via a processor of a control device, wherein, The control device switches between a delivery mode that moves to the destination and a remote operation mode that allows the user to remotely operate the mobile device, thereby causing the mobile device to move. The program is used to cause the processor of the control device to perform the following processing: Determine whether the moving body has arrived at the destination; While the mobile body is operating in the delivery mode, when a condition including the mobile body arriving at the destination is met, the operation mode of the mobile body is switched from the delivery mode to the remote operation mode. The target trajectory of the moving body is generated based on the switched action mode; as well as The moving body is controlled based on the target trajectory.

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