Mobile body control device, mobile body control method, and program

Abstract

This mobile body control device comprises: a recognition unit that recognizes, on the basis of an image in which the surrounding situation of a mobile body is captured, one or more objects present around the mobile body and a user of the mobile body; a generation unit that generates, on the basis of an index value related to the positions of the recognized one or more objects, a target trajectory for following the user; and a control unit that controls a movement mechanism of the mobile body so that the mobile body moves along the target trajectory. The generation unit narrows down an object for which the index value is to be considered, according to whether each of the recognized one or more objects is present in a first region extending between the mobile body and the user and a second region positioned outside the first region, and generates the target trajectory.

Description

MOBILE BODY CONTROL DEVICE, MOBILE BODY CONTROL METHOD, AND PROGRAM

[0001] The present invention relates to a control device for a mobile object, a control method for a mobile object, and a program.

[0002] Conventionally, robots that guide users to desired locations or transport luggage are known (see, for example, Patent Document 1). The robots refer to a movement speed database that associates maximum movement speeds with each area in an environment, and move at a speed that is up to the set maximum movement speed.

[0003] JP 2012-111011 A

[0004] However, with conventional technology, there are many parameters that need to be taken into consideration when autonomously driving (for example, pedestrians that need to be avoided), and it is sometimes not possible to efficiently generate a target trajectory for the robot.

[0005] The present invention has been made in consideration of the above circumstances, and one of its objects is to provide a control device for a moving body, a control method for a moving body, and a program that can efficiently generate a target trajectory for a robot.

[0006] The present invention provides a control device for a moving body, a control method for a moving body, and a program, each having the following configuration: (1): A control device for a moving body according to one aspect of the present invention includes a recognition unit that recognizes one or more objects present around the moving body and a user of the moving body based on an image capturing the situation around the moving body, a generation unit that generates a target trajectory for following the user based on index values ​​related to the positions of the recognized one or more objects, and a control unit that controls a movement mechanism of the moving body so that the moving body moves along the target trajectory, and the generation unit narrows down the objects for which the index values ​​are taken into consideration depending on whether each of the recognized one or more objects is present in a first region extending between the moving body and the user or a second region located outside the first region, and generates the target trajectory.

[0007] (2): In the aspect (1) above, when an object among the one or more recognized objects is present in the first area, the generation unit generates the target trajectory taking into account an index value related to the position of the object, regardless of whether the object is stationary or moving.

[0008] (3): In the aspect (1) above, when an object among the one or more recognized objects is present in the second area, the generation unit generates the target trajectory taking into account an index value related to the position of the object only when the object is moving.

[0009] (4): Another aspect of the present invention relates to a method for controlling a moving body, in which a computer recognizes one or more objects present around the moving body and the user of the moving body based on an image capturing the situation around the moving body, generates a target trajectory for following the user based on index values ​​relating to the positions of the recognized one or more objects, controls a movement mechanism of the moving body so that the moving body moves along the target trajectory, and generates the target trajectory depending on whether each of the recognized one or more objects is present in a first region extending between the moving body and the user or a second region located outside the first region.

[0010] (5): A program storing a program relating to another aspect of the present invention causes a computer to recognize one or more objects present around a moving body and the user of the moving body based on an image capturing the situation around the moving body, generate a target trajectory for following the user based on index values ​​relating to the positions of the recognized one or more objects, control the movement mechanism of the moving body so that the moving body moves along the target trajectory, and generate the target trajectory depending on whether each of the recognized one or more objects is present in a first area extending between the moving body and the user or in a second area located outside the first area.

[0011] According to the above aspects (1) to (5), it is possible to efficiently generate a target trajectory for the robot.

[0012] 1 is a diagram showing an example of the configuration of a mobile body system 1 including a mobile body 100. FIG. 2 is a diagram for explaining an example of a usage mode of the mobile body 100. FIG. 3 is a diagram for explaining a guidance mode. FIG. 4 is a perspective view showing the mobile body 100. FIG. 5 is a diagram showing an example of the functional configuration of the mobile body 100. FIG. 6 is a diagram showing an overview of processing executed by a risk distribution prediction unit 204 and a trajectory generation unit 206. FIG. 7 is a diagram showing an example of a first area AR1 and a second area AR2 set when the trajectory generation unit 206 generates a trajectory. FIG. 8 is a flowchart showing an example of the flow of processing executed by a control device 200.

[0013] Hereinafter, embodiments of a mobile object control device, a mobile object control method, and a program according to the present invention will be described with reference to the drawings.

[0014] 1 is a diagram showing an example of the configuration of a mobile body system 1 including a mobile body 100. The mobile body system 1 includes, for example, one or more terminal devices 2, a management device 10, an information providing device 20, and one or more mobile bodies 100. These communicate with each other, for example, via a network NW. The network NW is, for example, any network such as a LAN, a WAN, or an internet line.

[0015] The terminal device 2 is a computer device such as a smartphone, a tablet terminal, etc. The terminal device 2 requests, for example, from the management device 10, authorization to use the mobile object 100, and obtains information indicating that permission to use the mobile object 100 has been granted, based on a user operation.

[0016] [Management Device] In response to a request from the terminal device 2, the management device 10 grants the user of the terminal device 2 the authority to use the mobile object 100 and manages reservations for use of the mobile object 100. The management device 10 generates and manages, for example, schedule information that associates pre-registered user identification information with the date and time of reservations for use of the mobile object 100.

[0017] [Information Providing Device] The information providing device 20 provides the mobile body 100 with the location of the mobile body 100, the area in which the mobile body 100 is moving, and map information about the surrounding area. In response to a request from the mobile body 100, the information providing device 20 may generate a trajectory to the destination of the mobile body 100 and provide the generated trajectory to the mobile body 100.

[0018] [Mobile Object] The mobile object 100 is used by a user in the following manner. FIG. 2 is a diagram illustrating an example of a manner in which the mobile object 100 is used. The mobile object 100 is placed, for example, in a facility or a predetermined location in a city. When a user wants to use the mobile object 100, the user can start using the mobile object 100 by operating an operation unit (not shown) of the mobile object 100 or by operating the terminal device 2. For example, when a user goes shopping and has a lot of luggage, the user starts using the mobile object 100 and places the luggage in a storage unit of the mobile object 100. The mobile object 100 then moves together with the user, autonomously following the user. The user can continue shopping or head to their next destination with the luggage stored in the mobile object 100. For example, the mobile object 100 moves together with the user, traveling on sidewalks and crosswalks on roadways. The mobile object 100 can move in areas where pedestrians can pass, such as roadways and sidewalks. For example, the vehicle 100 may be used in indoor or outdoor facilities or private premises such as shopping centers, airports, parks, and theme parks, and can move in areas where pedestrians can pass through.

[0019] The moving body 100 may be capable of moving autonomously in modes such as a guidance mode or an emergency mode in addition to (or instead of) the following mode in which it follows the user as described above.

[0020] FIG. 3 is a diagram illustrating the guidance mode. The guidance mode is a mode in which the mobile object 100 guides the user to a destination specified by the user, autonomously moving in front of the user at a speed that matches the user's movement speed. As shown in FIG. 3 , when a user is searching for a specific product in a shopping center, the user requests the mobile object 100 to lead the user to the location of the specific product. The mobile object 100 then guides the user to the location of the product. This allows the user to easily find the specific product. When the mobile object 100 is used in a shopping center, the mobile object 100 or the information providing device 20 stores map information, including information on the locations of products, stores, and facilities within the shopping center, as well as map information for the shopping center. This map information includes detailed map information, such as the widths of roads and aisles.

[0021] The emergency mode is a mode in which the mobile object 100 moves autonomously to seek help from nearby people or facilities if something unusual happens to the user (for example, if the user falls) while moving with the user. In addition to (or instead of) following or guiding the user as described above, the mobile object 100 may move while maintaining a proper distance from the user.

[0022] 4 is a perspective view showing the moving body 100. In the following description, the forward direction of the moving body 100 is the positive x direction, the backward direction of the moving body 100 is the negative x direction, the width direction of the moving body 100, which is the left direction based on the positive x direction, is the positive y direction, the right direction based on the positive x direction, is the negative y direction, and the height direction of the moving body 100, which is a direction perpendicular to the x direction and y direction, is the positive z direction.

[0023] The mobile body 100 includes, for example, a base body 110, a door unit 112 provided on the base body 110, and wheels (first wheel 120, second wheel 130, and third wheel 140) attached to the base body 110. For example, a user can open the door unit 112 to put luggage into a storage compartment provided on the base body 110 or take luggage out of the storage compartment. The first wheel 120 and the second wheel 130 are driving wheels, and the third wheel 140 is an auxiliary wheel (driven wheel). The mobile body 100 may be movable using a configuration other than wheels, such as caterpillars.

[0024] A cylindrical support body 150 extending in the positive z direction is provided on the positive z direction surface of the base body 110. A camera 180 that captures images of the surroundings of the moving body 100 is provided on the end of the support body 150 in the positive z direction. The position at which the camera 180 is provided may be any position different from the above.

[0025] Camera 180 is, for example, a camera that can capture images of the periphery of moving body 100 at a wide angle (e.g., 360 degrees). Camera 180 may include multiple cameras. Camera 180 may be realized by combining, for example, multiple 120-degree cameras or multiple 60-degree cameras.

[0026] 5 is a diagram showing an example of the functional configuration of the mobile body 100. In addition to the functional configuration shown in FIG. 4, the mobile body 100 further includes a first motor 122, a second motor 132, a battery 134, a brake device 136, a steering device 138, a communication unit 190, and a control device 200. The first motor 122 and the second motor 132 are operated by power supplied to the battery 134. The first motor 122 drives the first wheel 120, and the second motor 132 drives the second wheel 130. The first motor 122 may be an in-wheel motor provided in the wheel of the first wheel 120, and the second motor 132 may be an in-wheel motor provided in the wheel of the second wheel 130.

[0027] The brake device 136 outputs a brake torque to each wheel based on an instruction from the control device 200. The steering device 138 includes an electric motor. The electric motor applies a force to a rack and pinion mechanism based on an instruction from the control device 200, for example, to change the direction of the first wheel 120 or the second wheel 130, thereby changing the course of the mobile object 100.

[0028] The communication unit 190 is a communication interface for communicating with the terminal device 2 , the management device 10 , or the information providing device 20 .

[0029] [Control Device] The control device 200 includes, for example, a recognition unit 202, a risk distribution prediction unit 204, a trajectory generation unit 206, a drive control unit 208, and a storage unit 220. The recognition unit 202, the risk distribution prediction unit 204, the trajectory generation unit 206, and the drive control unit 208 are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware. The program may be stored in advance in a storage device (a storage device with a non-transitory storage medium) such as a hard disk drive (HDD) or flash memory, or may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM, and installed by inserting the storage medium into a drive device. The storage unit 220 is realized by a storage device such as a HDD, flash memory, or RAM (Random Access Memory). The storage unit 220 stores map information 222 referenced by the mobile object 100. The map information 222 is, for example, map information provided by the information providing device 20, such as the location of the mobile object 100, the area in which the mobile object 100 moves, and the surrounding area. Some or all of the functional components included in the control device 20 may be included in another device. For example, the mobile object 100 and another device may communicate with each other and cooperate to control the mobile object 100.

[0030] The recognition unit 202 recognizes the positions (distance from the mobile body 100 and direction relative to the mobile body 100) and speed, acceleration, and other statuses of objects around the mobile body 100 based on, for example, images captured by the camera 180. Objects include traffic participants (pedestrians) and obstacles present in facilities or on roads. The recognition unit 202 recognizes and tracks the user of the mobile body 100. For example, the recognition unit 202 tracks the user based on an image (e.g., a user's facial image) captured by a user registered when the user uses the mobile body 100, or a user's facial image (or feature values ​​obtained from the user's facial image) provided by the terminal device 2 or the management device 10. The recognition unit 202 recognizes gestures made by the user. The mobile body 100 may be provided with a detection unit other than a camera, such as a radar device or LIDAR. In this case, the recognition unit 202 recognizes the situation around the mobile body 100 using detection results from the radar device or LIDAR instead of (or in addition to) images.

[0031] The risk distribution prediction unit 204 sets a risk, which is an index value indicating the degree to which the mobile body 100 should not enter or approach, for the object (and its surrounding area) recognized by the recognition unit 202. A higher risk value indicates that the mobile body 100 should not enter or approach, and a value closer to zero indicates that it is preferable for the mobile body 100 to pass through. Therefore, generally, the closer to the position of the recognized object, the higher the risk value, while the farther away from the position of the recognized object, the lower the risk value. However, this relationship may be reversed. The risk is an example of a "position-related index value" in the claims.

[0032] The risk distribution prediction unit 204 may set risks not only for the present time, but also for future time points specified at regular time intervals, such as the current time t, after Δt (time t + Δt), after 2Δt (time t + 2Δt), etc. The risk distribution prediction unit 204 predicts the risk at each future time point based on changes in the position of the moving target that is continuously recognized by the recognition unit 202.

[0033] The trajectory generation unit 206 generates a trajectory to the destination based on the risk predicted by the risk distribution prediction unit 204. Here, the destination refers to the user to be followed or a predetermined location near the user when the mobile object 100 is in the following mode. On the other hand, when the mobile object 100 is in the guidance mode, the destination refers to, for example, the location of a product or facility set by the user. In this case, the user specifies the location of the product or facility, and the mobile object 100 compares the location of the specified product or facility with the map information 222 and sets the location of the identified product or facility as the destination. Furthermore, when the mobile object 100 is in the guidance mode, if the location set by the user is far from the current location of the mobile object 100, the location set by the user may be set as the final destination, and a location within a predetermined range from the current location may be set as a temporary destination. Furthermore, in the guidance mode, the user does not necessarily have to set the destination; the mobile object 100 may predict the user's moving direction and autonomously move in front of the user at the user's moving speed. In this case, the destination of the mobile object 100 may be a location within a predetermined range ahead of the user.

[0034] The trajectory generation unit 206 generates multiple trajectories for reaching the destination, calculates the risk of each trajectory point constituting each trajectory, and calculates the total value of the calculated risks as the trajectory risk. If the risk of each trajectory point satisfies a predetermined criterion (e.g., if the risk of each trajectory point is equal to or less than a threshold value Th1) or if the risk of the trajectory satisfies a predetermined criterion (e.g., if the total value of the risk is equal to or less than a threshold value Th2), the trajectory generation unit 206 adopts the trajectory that satisfies the criterion as the target trajectory for the moving body 100 to move.

[0035] The drive control unit 208 controls the movement mechanisms such as the motors (first motor 122, second motor 132), braking device 136, steering device 138, etc. so that the moving body 100 travels along the trajectory generated by the trajectory generation unit 206.

[0036] FIG. 6 is a diagram illustrating an overview of the processing executed by the risk distribution prediction unit 204 and the trajectory generation unit 206. FIG. 6 illustrates, as an example, a case in which the mobile object 100 is traveling in follow-up mode, i.e., the user U itself is set as the destination. As shown in FIG. 6 , the risk distribution prediction unit 204, as an example, sets risks for objects other than the user U and the mobile object 100 on a bird's-eye view map, with contour lines of ellipses or circles based on the direction of travel and speed, and sets a fixed value of risk for an unmovable area BD such as a wall. R(OB1) is the risk for a stationary object (a person standing in place) OB1, R(OB2) is the risk for a moving object (pedestrian) OB2, R(OB3) is the risk for a moving object (pedestrian) OB3, and R(OB4) is the risk for a moving object (pedestrian) OB4. Because the pedestrian continues to move, risks are set at positions different from the current time for each future point in time. R(OB2)_t is the risk of object OB2 in a certain control cycle, R(OB2)_t+Δt is the risk of object OB2 in the next control cycle, and R(OB2)_t+Δ2t is the risk of object OB2 in the control cycle after that. R(BD) is the risk of the immovable area BD. In the figure, the density of the hatching indicates the risk value, with the darker the hatching, the greater the risk.

[0037] The trajectory generation unit 206 generates multiple trajectories from the moving body 100 to the destination (i.e., user U), and in Fig. 6, trajectories TP1 and TP2 are generated as an example. The trajectory generation unit 206 obtains the risk for each trajectory point constituting each of the trajectories TP1 and TP2, and calculates the sum of the obtained risks as the risk of the trajectory. 6 , the trajectory generation unit 206 calculates the risk value for trajectory TP1 as the sum of the risk value r(TP1)_t+Δt at time t+Δt and the risk value r(TP1)_t+2Δt at time t+2Δt, r(TP1)_t+Δt+r(TP1)_t+2Δt, and calculates the risk value for trajectory TP2 as the sum of the risk value r(TP2)_t+Δt at time t+Δt and the risk value r(TP2)_t+2Δt at time t+2Δt, r(TP2)_t+Δt+r(TP2)_t+2Δt. The trajectory generation unit 206 compares the risk value for trajectory TP1 with the risk value for trajectory TP2. Since the risk value for trajectory TP1 is positive while the risk value for trajectory TP2 is zero, the trajectory generation unit 206 adopts trajectory TP2 as the target trajectory.

[0038] In this way, the risk distribution prediction unit 204 sets risks for objects recognized by the recognition unit 202 and their surrounding areas, and the trajectory generation unit 206 selects the trajectory with the smallest risk value from among multiple candidate trajectories. In this case, if the trajectory generation unit 206 performs route calculations taking into account the risks of all objects recognized by the recognition unit 202, it will also perform calculations for objects with an extremely low risk of contact, such as objects that are relatively far from the mobile body 100 or stationary objects, which is inefficient.

[0039] [Setting of first and second areas] Taking the above circumstances into consideration, the trajectory generation unit 206 sets a first area AR1 extending between the moving body 100 and the destination and a second area AR2 located outside the first area AR1, narrows down the objects for which risk is to be considered depending on whether each recognized object is located in the first area AR1 or the second area AR2, and generates a target trajectory TP based on the risk of the narrowed down objects.

[0040] 7 is a diagram showing an example of a first area AR1 and a second area AR2 set by the trajectory generation unit 206 when generating a trajectory. Fig. 7 shows, as an example, a case in which the recognition unit 202 recognizes objects OB1 to OB4. As shown in Fig. 7 , the trajectory generation unit 206 sets, for example, a first area AR1 as a region starting from the moving object 100 and ending at a destination (user U in Fig. 7 ), and sets, as a second area AR2, a region obtained by expanding the width of the set first area AR1 by a predetermined distance d.

[0041] For recognized objects present in the first area AR1, the trajectory generation unit 206 generates a target trajectory by taking into account the risk value of the object, regardless of whether the object is stationary or moving. For objects present in the second area AR2, the trajectory generation unit 206 generates a target trajectory by taking into account the risk value of the object only if the object is moving. This is because, as described above, the first area AR1 is set as an area starting from the mobile object 100 and ending at the destination. Therefore, if an object present outside the first area AR1 is stationary, it is assumed that there is little need to consider the risk value of the object. Therefore, in the case of FIG. 7 , the trajectory generation unit 206 calculates risk values ​​for multiple trajectories by taking into account the risk values ​​of the moving object OB1 and the stationary object OB2 within the first area AR1 and the risk value of the moving object OB3 within the second area AR2 (these objects are indicated by diagonal lines in FIG. 7 ), but does not take into account the risk value of the stationary object OB4 within the second area AR2. The trajectory with the smallest calculated risk value is adopted as the target trajectory TP.

[0042] [Processing Flow] Next, a processing flow executed by the control device 200 will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the processing flow executed by the control device 200. The processing shown in Fig. 8 is executed when the mobile object 100 is traveling in a following mode, that is, when the user U itself is set as the destination. However, even when the mobile object 100 is traveling in a guidance mode, the processing of this flowchart is executed in the same way as long as the destination is recognized by the recognition unit 202.

[0043] First, the recognition unit 202 recognizes one or more objects around the mobile object 100 and the user of the mobile object 100, for example, based on an image captured by the camera 180 (step S100). Next, the risk distribution prediction unit 204 sets a risk for the one or more objects recognized by the recognition unit 202 (step S102). Next, the trajectory generation unit 206 sets a first area AR1 and a second area AR2 based on the positions of the recognized user and the mobile object 100 (step S104).

[0044] Next, the trajectory generation unit 206 classifies the recognized object(s) as belonging to either the first area AR1 or the second area AR2 (step S106). Next, the trajectory generation unit 206 generates a target trajectory based on the risk of all objects included in the first area AR1 and the risk of moving objects included in the second area (step S108). Next, the drive control unit 208 controls the movement mechanism so that the moving body 100 moves along the generated target trajectory (step S110). This completes the processing of this flowchart.

[0045] In the processing of the above flowchart, after the risk distribution prediction unit 204 sets a risk for the recognized object, the trajectory generation unit 206 classifies the object for which a risk has been set into a first area AR1 and a second area AR2. However, the present invention is not limited to such a configuration, and the trajectory generation unit 206 may first classify the recognized object into a first area AR1 and a second area AR2, and then the risk distribution prediction unit 204 may set a risk for all objects included in the first area AR1 and moving objects included in the second area AR2.

[0046] Furthermore, in the above embodiment, as an example, the trajectory generation unit 206 generates a plurality of candidate trajectories and adopts the trajectory with the smallest risk value as the target trajectory, but the present invention is not limited to such a configuration, and the trajectory generation unit 206 may generate the target trajectory using any algorithm that generates a trajectory taking into account at least the risk value of the object.

[0047] Furthermore, in the above embodiment, as an example, the trajectory generation unit 206 sets the first area AR1 and the second area AR2 as rectangular areas, but the present invention is not limited to such a configuration, and the trajectory generation unit 206 may at least set the first area AR1 as a predetermined area from the moving body 100 to the destination, and set the second area AR2 as a different predetermined area located outside the first area AR1.

[0048] According to the embodiment described above, a target trajectory is generated depending on whether each of the recognized one or more objects is present in a first region extending between the moving body and the user, or in a second region located outside the first region, thereby enabling efficient generation of a target trajectory for the robot.

[0049] The above-described embodiment can be expressed as follows: A mobile body comprising: a storage medium storing computer-readable instructions; and a processor connected to the storage medium, wherein the processor executes the computer-readable instructions to: recognize one or more objects present around the mobile body and a user of the mobile body based on an image capturing an environment around the mobile body; generate a target trajectory for following the user based on index values ​​related to the positions of the recognized one or more objects; control a movement mechanism of the mobile body so that the mobile body moves along the target trajectory; and generate the target trajectory depending on whether each of the recognized one or more objects is present in a first region extending between the mobile body and the user, or in a second region located outside the first region.

[0050] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention.

[0051] 100 Mobile object 200 Control device 202 Recognition unit 204 Risk distribution prediction unit 206 Trajectory generation unit 208 Drive control unit 220 Storage unit

Claims

1. A control device for a moving body comprising: a recognition unit that recognizes one or more objects existing around the moving body and a user of the moving body based on an image capturing the situation around the moving body; a generation unit that generates a target trajectory to a destination based on an index value relating to the position of the recognized one or more objects; and a control unit that controls a movement mechanism of the moving body so that the moving body moves along the target trajectory, wherein the generation unit narrows down the objects for which the index value is taken into consideration depending on whether each of the recognized one or more objects exists in a first area extending between the moving body and the destination, or a second area located outside the first area, and generates the target trajectory.

2. The control device for a moving body as described in claim 1, wherein the generation unit generates the target trajectory taking into account an index value related to the position of an object when an object among the one or more recognized objects is present in the first area, regardless of whether the object is stationary or moving.

3. The control device for a moving body as described in claim 1, wherein the generation unit generates the target trajectory taking into account an index value related to the position of an object only when an object among the one or more recognized objects is present in the second area and the object is moving.

4. A method for controlling a moving body, in which a computer recognizes one or more objects existing around the moving body and a user of the moving body based on an image capturing the situation around the moving body, generates a target trajectory for following the user based on index values ​​related to the positions of the recognized one or more objects, controls a movement mechanism of the moving body so that the moving body moves along the target trajectory, and narrows down the objects for which the index values ​​are taken into consideration depending on whether each of the recognized one or more objects exists in a first area extending between the moving body and the user, or a second area located outside the first area, and generates the target trajectory.

5. A program which causes a computer to recognize one or more objects existing around a moving body and a user of the moving body based on an image capturing the situation around the moving body; generate a target trajectory for following the user based on an index value relating to the position of the recognized one or more objects; control the movement mechanism of the moving body so that the moving body moves along the target trajectory; and narrow down the objects for which the index value is taken into consideration depending on whether each of the recognized one or more objects exists in a first area extending between the moving body and the user, or in a second area located outside the first area, and generate the target trajectory.

Images