Information processing device and mobile robot

Abstract

An information processing device (100) comprises: an inference unit (140) that infers the state of a traffic light; a calculation unit (150) that calculates a standby time on the basis of the inferred state of the traffic light; and a control unit (130) that, when the mobile robot (10) arrives at the traffic light, causes a presentation device (15) provided to the mobile robot (10) to present a message only after the standby time has elapsed from the arrival of the mobile robot (10) at the traffic light.

Description

Information processing device and mobile robot

[0001] The present disclosure relates to an information processing device and a mobile robot.

[0002] Mobile robots are effective for delivering messages such as advertisements or public service announcements to a large number of people. Patent Document 1 discloses a system that, when a change in the vehicle's motion is detected, stops displaying an advertisement on a display attached to the vehicle and displays an image showing the change in the vehicle's motion on the display. Patent Document 2 discloses a mobile advertising system that can be mounted on a vehicle and displays multiple advertising messages. Patent Document 3 discloses a system for distributing advertisements to multiple vehicles.

[0003] US Patent Application Publication No. 2019 / 0266930 US Patent No. 7,154,383 Patent Publication No. 2021-515319

[0004] For example, pedestrians waiting at traffic lights are likely to pay attention to messages delivered by vehicles or other mobile robots, making them important targets for message delivery. Therefore, it is desirable to be able to efficiently present messages to pedestrians and other target audiences.

[0005] The present disclosure provides an information processing device and the like that can efficiently present messages.

[0006] An information processing device according to one aspect of the present disclosure includes an estimation unit that estimates the state of a traffic light, a calculation unit that calculates a waiting time based on the estimated state of the traffic light, and a control unit that causes a presentation device provided in the mobile robot to present a message after the waiting time has elapsed since the mobile robot arrived at the traffic light.

[0007] A mobile robot according to one aspect of the present disclosure includes the information processing device described above and the presentation device.

[0008] According to an information processing device according to an aspect of the present disclosure, messages can be presented efficiently.

[0009] FIG. 1 is a block diagram showing the configuration of a mobile robot according to an embodiment. FIG. 2A is a diagram for explaining a distribution strategy of a mobile robot according to a comparative example. FIG. 2B is a diagram for explaining a distribution strategy of a mobile robot according to an embodiment. FIG. 3 is a diagram showing a specific example of n(t). FIG. 4 is a diagram showing a specific example of m(t, b). FIG. 5 is a diagram showing u ｓ FIG. 6 is a diagram showing a specific example of p(a, μ, σ). FIG. 7A is a diagram showing a specific example of u ｗ FIG. 7B is a diagram showing a specific example of (μ, σ, w, b). ｗ FIG. 7C shows a specific example of (μ, σ, w, b). ｗ FIG. 8A is a diagram showing a specific example of (μ, σ, w, b). ＊ FIG. 8B is a diagram showing a specific example of (μ, σ, b). ＊ FIG. 8C shows a specific example of (μ, σ, b). ＊ 9 shows a specific example of (μ, σ, b). ｉ u ＊ (μ, σ, b ｉ 10A shows a specific example of the difference between two messages. ＊ FIG. 10B shows a specific example of w when presenting one of two messages. ＊ FIG. 10C shows a specific example of the w ＊ FIG. 10D shows a specific example of w when presenting one of two messages. ＊ 11 is a diagram showing a specific example of multiple routes. FIG. 12 is a diagram showing the u when the waiting time is 0. ｗ FIG. 13 is a diagram showing a specific example of μ ＊ 14A is a diagram illustrating a specific example of a waiting time calculation method. FIG. 14B is a diagram illustrating a waiting time calculation method. FIG. 14C is a diagram illustrating a waiting time calculation method. FIG. 15 is a flowchart illustrating an information processing method according to an embodiment.

[0010] (Findings underlying this disclosure) As noted above, mobile robots are effective in delivering messages, such as advertisements or public service announcements, to large numbers of people.

[0011] Conventionally, there are mobile advertising systems that depend on context. For example, Patent Documents 1 and 2 above describe mobile advertising systems in which the information displayed depends on the vehicle's speed, traffic conditions, location obtained via a GPS (Global Positioning System) sensor, and other location-based information such as local demographics. Patent Document 3 above deals with the synchronization of multiple advertising vehicles.

[0012] Here, pedestrians waiting at traffic lights are important targets for delivering messages because they are likely to pay attention to the message.

[0013] One way to convey a message is to repeat the same message continuously, but while this method can reach many people, it can be annoying for those who keep hearing the same message.

[0014] Therefore, the inventors of the present application have discovered a method for efficiently presenting a message so that the message can be conveyed to as many people as possible while suppressing the occurrence of such inconveniences.

[0015] The present disclosure proposes a mobile advertising system in which the time for delivering a message depends on, for example, the cycle time of a traffic light (traffic signal). For example, in an information processing method according to one aspect of the present disclosure, a message is presented at an optimal timing, for example, only once. In this way, the present disclosure maximizes the number of people who receive the message without repeating the message.

[0016] Hereinafter, the embodiments will be specifically described with reference to the drawings.

[0017] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection configurations, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not recited in the independent claims of the present disclosure are described as optional components. Furthermore, the drawings are not necessarily strict illustrations. In the drawings, substantially identical components are denoted by the same reference numerals, and redundant descriptions may be omitted or simplified.

[0018] (Embodiment) [Outline] FIG. 1 is a block diagram showing the configuration of a mobile robot 10 according to an embodiment.

[0019] The mobile robot 10 is an autonomous moving body. The mobile robot 10 is, for example, a self-driving vehicle. The mobile robot 10 is a moving body that performs tasks such as cleaning, minesweeping, or data collection while moving along a travel path calculated using, for example, Simultaneous Localization and Mapping (SLAM) technology. The mobile robot 10 also displays messages such as advertisements or public service announcements using a display device 15.

[0020] The mobile robot 10 detects information indicating the positions of walls, objects, and the like located around the mobile robot 10 using, for example, sensors such as a camera and LIDAR, each of which is an example of the sensor 11, and estimates its own position using the detected information and a map of the area in which the mobile robot 10 is traveling. The mobile robot 10 also estimates its own position using, for example, odometry information obtained by an acceleration sensor and an angular velocity sensor, each of which is an example of the sensor 11. The mobile robot 10 moves (travels) from its own position to a predetermined destination, for example, along the calculated travel route.

[0021] The mobile robot 10 includes a plurality of sensors 11 , a communication module 12 , a database 13 , an actuator 14 , a presentation device 15 , and an information processing device 100 .

[0022] Each of the multiple sensors 11 is a sensor unit that detects the environment around the mobile robot 10 so that the mobile robot 10 can move autonomously. The multiple sensors 11 are realized by, for example, a camera that detects the surroundings of the mobile robot 10 and the road surface, a 3D camera, a LiDAR (Light Detection and Ranging), an angular velocity sensor that detects the orientation of the mobile robot 10 (e.g., the direction of travel of the mobile robot 10), and an odometry sensor that measures the number of rotations (odometry information) of wheels provided on the mobile robot 10.

[0023] The communication module 12 is a communication module that communicates with a computer, such as an external server device, that is not mounted on the mobile robot 10. The communication module 12 is realized by, for example, an antenna and a wireless communication circuit.

[0024] The database 13 is a storage device that stores various types of information. The database 13 stores, for example, information indicating a plurality of messages to be displayed by the display device 15, information indicating a map on which the mobile robot 10 travels, including information indicating the locations of traffic lights, and information indicating the cycle length of the traffic lights included in the information (specifically, information indicating the timing at which the traffic light changes from red to green). The information indicating the messages may be audio information for displaying the messages by sound, text information for displaying the messages by text, or video information for displaying the messages by video.

[0025] The database 13 may also store information indicating the travel route and the destination of the mobile robot 10. This information may be acquired from an external server device or the like via the communication module 12.

[0026] The database 13 is realized by, for example, a hard disk drive (HDD) and / or a semiconductor memory such as a flash memory.

[0027] The actuator 14 is a drive device for driving (e.g., moving) the mobile robot 10. For example, the actuator 14 is two motors of a differential drive robot.

[0028] The presentation device 15 is a device that presents various types of information. In this embodiment, the presentation device 15 presents a message. The presentation device 15 is, for example, a media player that presents the message. The media player may be a speaker, a multimedia device that can play audio and / or video, or the like.

[0029] The presentation mode in which the presentation device 15 presents a message is not particularly limited. The presentation device 15 may present the message by outputting a sound indicating the message, or may present the message by displaying a video (specifically, a still image or a moving image) indicating the message. In other words, for example, the presentation device 15 presents the message using at least one of sound and video. The presentation device 15 is realized, for example, by an audio device including an amplifier and a speaker for outputting sound, and / or a display for displaying video.

[0030] The information processing device 100 is a computer that controls the mobile robot 10. The information processing device 100 controls, for example, a plurality of sensors 11, a communication module 12, a database 13, an actuator 14, and a presentation device 15. The information processing device 100 is a computer realized by, for example, a non-volatile memory that stores a program, a volatile memory that is a temporary storage area for executing the program, an input / output port for sending and receiving signals, and a processor that executes the program.

[0031] The information processing device 100 may be mounted on the mobile robot 10, or may be arranged outside the mobile robot 10 as a server device or the like so as to be able to communicate with the mobile robot 10. The information processing device 100 may be provided with a communication interface for communicating with the mobile robot 10. The information processing device 100 may also be provided with a database 13.

[0032] The information processing device 100 includes an acquisition unit 110 , a planning unit 120 , a control unit 130 , an estimation unit 140 , and a calculation unit 150 .

[0033] The acquisition unit 110 is a processing unit that acquires various types of information. For example, the acquisition unit 110 acquires various types of information from a plurality of sensors 11, a communication module 12, and a database 13. For example, the acquisition unit 110 may acquire information from an external server device or the like via the communication module 12.

[0034] The planner 120 is a processing unit that calculates (plans) the travel path and travel speed of the mobile robot 10. For example, the planner 120 calculates the self-position of the mobile robot 10, the travel path that the mobile robot 10 will travel, and the travel speed of the mobile robot 10 using the SLAM technology described above, based on the detection results of the multiple sensors 11 and information indicating a map contained in a database.

[0035] The control unit 130 is a processing unit that controls the mobile robot 10. The control unit 130 controls the actuator 14, for example, to make the mobile robot 10 run (move) at the running speed calculated by the planning unit 120 along the running path calculated by the planning unit 120.

[0036] In this way, the mobile robot 10 travels along a predetermined travel route by the acquisition unit 110, the planning unit 120, and the control unit 130.

[0037] Furthermore, for example, the control unit 130 controls the presentation device 15 to cause the presentation device 15 to present a predetermined message.

[0038] The estimation unit 140 is a processing unit that estimates the state of a traffic light. Specifically, the estimation unit 140 estimates the state of a traffic light at the time when the mobile robot 10 arrives. More specifically, the estimation unit 140 estimates, as the state of the traffic light, whether the color indication of the traffic light is green or red at the time when the mobile robot 10 arrives (i.e., whether the traffic light is lit green or lit red). Of course, the estimation unit 140 may also estimate that the traffic light is flashing green or lit yellow.

[0039] The calculation unit 150 is a processing unit that calculates the waiting time. Specifically, the calculation unit 150 calculates the waiting time, which is the time from when the mobile robot 10 arrives at a traffic light until the presentation device 15 presents a message. For example, the calculation unit 150 calculates the waiting time based on the time when the mobile robot 10 arrives at the traffic light, the color displayed on the traffic light at that time, and the cycle length of the traffic light.

[0040] The information processing device 100 may include a clock unit such as a real time clock (RTC).

[0041] Here, for example, when the mobile robot 10 arrives at a traffic light, the control unit 130 causes the presentation device 15 provided in the mobile robot 10 to present a message only after a waiting time has elapsed since the mobile robot 10 arrived at the traffic light. For example, the estimation unit 140 estimates the color of the traffic light at the time the mobile robot 10 arrives at the traffic light as the state of the traffic light. In this case, for example, when the mobile robot 10 arrives at a traffic light and the estimated color of the traffic light is red, the control unit 130 causes the presentation device 15 provided in the mobile robot 10 to present a message only after a waiting time has elapsed since the mobile robot 10 arrived at the traffic light.

[0042] When the mobile robot 10 arrives at a traffic light, if the estimated color indication of the traffic light is green, the control unit 130 may not cause the presentation device 15 to present a message.

[0043] Each processing unit of the acquisition unit 110, the planning unit 120, the control unit 130, the estimation unit 140, and the calculation unit 150 is realized by a control program for executing the above-mentioned processing, a memory for storing the control program, and a processor such as a CPU (Central Processing Unit) that executes the control program.

[0044] [Specific Example] Next, a specific example of the process executed by the information processing device 100 will be described.

[0045] Fig. 2A is a diagram for explaining a distribution strategy of a mobile robot according to a comparative example, and Fig. 2B is a diagram for explaining a distribution strategy of the mobile robot 10 according to an embodiment.

[0046] In each of Figures 2A and 2B, the horizontal axis indicates time, (a) indicates whether the traffic light at which the mobile robot 10 arrives is red or green, (b) indicates the number of people waiting at the location where the traffic light is located, and (c) indicates the timing at which the mobile robot displays a message.

[0047] While the traffic light is red, people who arrive at the traffic light stop to wait for the light to change. Therefore, the number of people stopping in the area near the traffic light (i.e., the number of people waiting for the light to change) is expected to increase over time until the light changes to green. For example, the traffic light is red from time t1 to time t2 and from time t3 to time t5. Furthermore, as schematically shown in (b) of each of Figures 2A and 2B, the number of people waiting for the light to change is expected to increase from time t1 to time t2. Similarly, the number of people waiting for the light to change is expected to increase from time t3 to time t5.

[0048] After that, when the traffic light turns green, the people waiting to cross the road, and the number of people waiting to cross the road decreases rapidly. As shown in (b) of Figures 2A and 2B, the number of people waiting to cross the road decreases from time t2 and from time t5.

[0049] Therefore, when presenting a message to a person waiting at a traffic light, it is thought that the most effective time to present the message is just before the traffic light changes from red to green.

[0050] Here, as shown in (c) of Fig. 2A, if the mobile robot 10 continues to deliver (present) a message repeatedly, a person who has been waiting at a traffic light for a long time will see and hear the message many times. For example, in (c) of Fig. 2A, the message is presented three times between time t1 and time t2. For example, a person who has been waiting at a traffic light since time t1 will see and hear the message three times. In this case, if the same message is presented three times, for example, it may be annoying for the person who keeps seeing and hearing the same message.

[0051] Furthermore, the third message may not be fully conveyed to people waiting at the traffic light because the traffic light changes from red to green while the message is being displayed.

[0052] 2B(c), when the mobile robot 10 arrives at a traffic light at time t4, the mobile robot 10 presents a message only once before the traffic light changes from red to green. For example, the mobile robot 10 waits a predetermined time (waiting time) after arriving at the traffic light, and then presents the message so that it ends just as the traffic light changes to green.

[0053] The time for which the message is presented may be determined arbitrarily and is not particularly limited.

[0054] As described above, when the mobile robot 10 arrives at a traffic light at a certain time (time t4 in the above example), in many cases, waiting for a while before starting to present a message is effective for presenting the message efficiently (i.e., presenting the message less frequently while allowing more people to recognize the message).

[0055] FIG. 3 is a diagram showing n(t). Specifically, FIG. 3 is a graph that schematically shows the change over time in the number of people waiting at a traffic light. The horizontal axis of the graph shown in FIG. 3 represents time, and the vertical axis represents the relative number of people. Note that n(t) represents the number of people waiting at a traffic light at time t, and is normalized so that the maximum value is 1.

[0056] To simplify the calculation, we define a time unit that corresponds to the total time it takes for a traffic light to change from red to green in one cycle.

[0057] In the example shown in FIG. 3 , the time when the traffic light turns red is set to t=0.0. The timing when the traffic light changes from red to green is set to t=1.0. In this example, people are expected to arrive at the traffic light and stop (i.e., wait) between t=0.0 and t=1.0. It is also assumed that people arrive while the traffic light is red and do not leave until the traffic light turns green. It is also assumed that the number of people returns to zero when the traffic light changes from red to green. It is also assumed that the arrival times of people at the traffic light are uniformly distributed. The expected number of people at time t relative to the maximum number of people at the end of one traffic light cycle is calculated using the following equation (1):

[0058]

[0059] Now, let us consider the message to be presented. The duration of time that one message is presented is defined as b. We also use the period of the traffic light color display (particularly the time when the color display is red) shown in Figure 3. We also define a weighting function m(t, b) to define the importance of a person seeing or hearing a particular part of the message.

[0060] Fig. 4 is a diagram showing m(t, b). Specifically, Fig. 4 is a graph showing a schematic diagram of the change in the importance of a presented message over time. The horizontal axis of the graph shown in Fig. 4 represents time, and the vertical axis represents importance.

[0061] m(t, b) is used to consider, for example, that the product name or manufacturer name is the most important part of the message being presented. For example, when the message is presented by voice, m(t, b) has a large value at the point (time) when a part (e.g., a word) determined to be important is pronounced.

[0062] If t=0.0 denotes the start of the presentation of a message and t=b denotes the end of the presentation of a message, then the message has no significance before the start of the presentation (t<0) or after the end of the presentation (t>0). Therefore, the following equation (2) is defined:

[0063]

[0064] For ease of calculation, assume that the importance of a message remains the same throughout its presentation. In this case, the importance function m(t, b) is defined as follows:

[0065]

[0066] In the example shown in FIG. 4, the case where b=0.2 is indicated by a solid line, the case where b=0.4 is indicated by a dashed line, and the case where b=0.6 is indicated by a dotted line.

[0067] Now, consider the effect of a message on a person when the message begins to be presented at time s.

[0068] FIG. 5 shows the ｓ 5 shows a specific example of (s, b). Specifically, FIG. 5 shows the effect of a presented message on a person. ｓ 1 is a graph showing the time change of u ｓ The higher the value of u, the more effectively the message is delivered to a larger number of people. ｓ The more people waiting at the traffic light when the message is displayed, the higher the value of u. ｓ The value of is higher when more important parts are presented to the person compared to less important parts.

[0069] u ｓ is calculated as in the following equations (4) and (5).

[0070]

[0071] In the example shown in FIG. 5, the case where b=0.2 is indicated by a solid line, the case where b=0.4 is indicated by a dashed line, and the case where b=0.6 is indicated by a dotted line.

[0072] As shown in FIG. ｓThe maximum value of is when s=1−b.

[0073] The arrival time of the mobile robot 10 relative to the period of change in the traffic light indication may not be known accurately. For example, the traffic light cycle length may be unknown. In such cases, the traffic light cycle length may be estimated based on statistics of traffic lights on roads of similar size. Even if the cycle length is known, the timing at which the traffic light color indication changes to red (start time of red) may not be known. In such cases, the traffic light color indication may be estimated by observing the traffic light using a camera, which is an example of the sensor 11 mounted on the mobile robot 10. Furthermore, if this information is known, it may be stored in the database 13.

[0074] Now, let us consider the time when the mobile robot 10 arrives at the traffic light. The time when the mobile robot 10 arrives at the traffic light (also simply referred to as the arrival time) is a. Furthermore, a = 0.0 indicates the timing when the traffic light changes from green to red, which is the timing when the mobile robot 10 arrives at the traffic light, and a = 1.0 indicates the timing when the traffic light changes from red to green.

[0075] Here, consider the probability function of the arrival time of the mobile robot 10 at a traffic light. A truncated normal distribution can be used as the probability function. Here, when the probability function is considered as a truncated normal distribution, it is expressed by the following equation (6). Note that Φ in the following equation (6) is the cumulative distribution function of the standard normal component.

[0076]

[0077] FIG. 6 is a diagram showing a specific example of p(a, μ, σ). Specifically, FIG. 6 is a graph showing a truncated normal distribution. The horizontal axis of the graph shown in FIG. 6 indicates the time when the mobile robot 10 arrives at the traffic light, and the vertical axis indicates the probability that the mobile robot 10 arrives at the traffic light. Note that μ indicates the mode of the arrival time, and σ indicates a scale parameter.

[0078] In the example shown in FIG. 6, the case where μ=0.2 and σ=0.03 is indicated by a solid line, the case where μ=0.6 and σ=0.1 is indicated by a dashed line, and the case where μ=0.4 and σ=0.5 is indicated by a dotted line.

[0079] As shown in Figure 6, it can be seen that the most probable arrival time is when a = μ. Also, as shown in Figure 6, it can be seen that the higher the value of σ, in other words, the lower the overall probability.

[0080] Next, let us consider the waiting time from when the mobile robot 10 arrives at the traffic light until it starts displaying a message. Here, the waiting time is denoted as w. The period of the color display of the traffic light shown in Figure 3 (particularly, the time when the color display is red) is used.

[0081] If the time when the message presentation starts is s, the time s is expressed by the following equation (7).

[0082] s = a + w (7) where u indicates the effect when a waiting time w is provided. ｗ is calculated by the following formula (8).

[0083]

[0084] 7A to 7C are all u ｗ 7A to 7C are diagrams showing specific examples of (μ, σ, w, b). ｗ 7A is a graph showing the effect of each parameter on (μ, σ, w, b). More specifically, FIG. 7A shows the effect of u on w when the values ​​of μ and σ are fixed and the value of b is varied. ｗ 7B shows the relationship between u and w when the values ​​of σ and b are fixed and the value of μ is changed. ｗ 7C shows the relationship between w and u when the values ​​of b and μ are fixed and the value of σ is changed. ｗ FIG.

[0085] In the graphs shown in FIGS. 7A to 7C, the horizontal axis is w and the vertical axis is u. ｗ7A, the case where μ=0.2, σ=0.1, and b=0.2 is shown by a solid line, the case where μ=0.2, σ=0.1, and b=0.4 is shown by a dashed line, and the case where μ=0.2, σ=0.1, and b=0.6 is shown by a dotted line. 7B, the case where μ=0.2, σ=0.1, and b=0.2 is shown by a solid line, the case where μ=0.4, σ=0.1, and b=0.2 is shown by a dashed line, and the case where μ=0.6, σ=0.1, and b=0.2 is shown by a dotted line. Also, in Figure 7C, the case where μ = 0.2, σ = 0.03 and b = 0.2 is shown by a solid line, the case where μ = 0.2, σ = 0.1 and b = 0.2 is shown by a dashed line, and the case where μ = 0.2, σ = 0.5 and b = 0.2 is shown by a dotted line.

[0086] As can be seen from Figure 7B, the higher μ is, that is, the later the arrival time of the mobile robot 10 at the traffic light, the more effective the shorter the waiting time. Also, as can be seen from Figure 7C, the right panel shows the effect of σ. As σ increases, that is, as uncertainty increases, it becomes less likely that a certain value of w will be effective. Also, the higher σ is, the more effective the shorter the waiting time.

[0087] Next, consider the optimal waiting time. ＊ If (μ, σ, b), then w ＊ (μ, σ, b) are expressed by the following equation (9).

[0088]

[0089] Also, let uw be the maximum. ＊ (μ, σ, b), then u ＊ (μ, σ, b) are expressed by the following equation (10).

[0090]

[0091] 8A to 8C are all w ＊ 8A to 8C are diagrams showing specific examples of (μ, σ, b). ＊ 10 is a graph showing the optimum waiting time obtained by changing each parameter w in (μ, σ, b).

[0092] In the graph shown in FIG. 8A, the horizontal axis is b and the vertical axis is w＊ In addition, the graph shown in FIG. 8B has a horizontal axis of μ and a vertical axis of w ＊ In addition, the graph shown in FIG. 8C has a horizontal axis of σ and a vertical axis of w ＊ is.

[0093] 8A, the case where μ=0.2 and σ=0.1 is shown by a solid line, the case where μ=0.2 and σ=0.5 is shown by a dashed line, the case where μ=0.4 and σ=0.1 is shown by a dotted line, and the case where μ=0.4 and σ=0.5 is shown by a dashed line. Also, in FIG. 8B, the case where σ=0.1 and b=0.2 is shown by a solid line, the case where σ=0.1 and b=0.4 is shown by a dashed line, the case where σ=0.5 and b=0.2 is shown by a dotted line, and the case where σ=0.5 and b=0.4 is shown by a dashed line. Also, in Figure 8C, the case where μ = 0.2 and b = 0.2 is shown by a solid line, the case where μ = 0.2 and b = 0.4 is shown by a dashed line, the case where μ = 0.4 and b = 0.2 is shown by a dotted line, and the case where μ = 0.4 and b = 0.4 is shown by a dotted line.

[0094] As shown in FIG. 8A, the optimal waiting time decreases monotonically as b increases, and continues to decrease until it reaches zero.

[0095] Also, as shown in FIG. 8B, the optimal waiting time monotonically decreases as μ increases, and continues to decrease until it reaches zero.

[0096] Also, as shown in FIG. 8C, the optimal waiting time monotonically decreases as σ increases, and continues to decrease until it reaches zero.

[0097] As described above, the planner 120 calculates a route along which the mobile robot 10 will travel (also simply referred to as a travel route). Information indicating the travel route is output to the control unit 130. The control unit 130 drives the actuator 14. The planner 120 may obtain information indicating the travel route from the database 13. The planner 120 may also receive commands from a remote control station such as an external server device via the communication module 12. The planner 120 may modify the route, for example, in accordance with sensor information indicating the detection results of the sensor 11.

[0098] As described above, the estimation unit 140 estimates, for example, whether the traffic light is green or red when the mobile robot 10 arrives. The estimation unit 140 also calculates μ, σ, and b. The traffic light state is estimated using sensor information from the sensor 11, information from the database 13, and / or information received via the communication module 12. The information in the database 13 may include information indicating the traffic light cycle length. The information indicating the cycle length may be acquired in real time via the communication module 12. The traffic light cycle length may also be estimated based on the results of photography by a camera that captures the traffic light from a distance. The camera may be the sensor 11 mounted on the mobile robot 10, or a camera located outside the mobile robot 10.

[0099] Furthermore, when the cycle length is estimated, the information processing device 100 may store information indicating the estimated cycle length in the database 13 for future use when the mobile robot 10 arrives at the same traffic light. Furthermore, when the information processing device 100 estimates the traffic light cycle length multiple times, for example, when the mobile robot 10 passes the same traffic light multiple times, the information processing device 100 may calculate an average of the estimation results and use the calculated result as the cycle length of the traffic light in order to improve the estimation result.

[0100] As described above, the database 13 stores information indicating one or more messages. For example, the information processing device 100 may calculate the relative message length as the above b from the actual length of the message (e.g., the message playback time, such as the number of seconds it takes for the message to be played) and the traffic light cycle length. The relative message length is calculated, for example, as the length of time the message is presented relative to the time from when the traffic light changes from green to red until it changes back to green.

[0101] The arrival time (estimated arrival time) of the mobile robot 10 at the traffic light, represented by μ and σ, is calculated using the travel route, cycle length, and the time when the traffic light changes from green to red, all of which are calculated by the planner 120. In this case, information about the transition time may be stored in the database 13, may be acquired via the communication module 12, or may be estimated using information obtained from the sensor 11, such as a camera.

[0102] The calculation unit 150 calculates the optimal waiting time w given by the above equation (9) based on the values ​​of μ, σ, and b. ＊ The information indicating the calculated waiting time is output to the control unit 130. The control unit 130 obtains information indicating a message from the database 13 in accordance with the obtained waiting time, and plays the message using the presentation device 15 such as a media player.

[0103] If the mobile robot 10 arrives at a traffic light while the traffic light is green, it may simply pass through the traffic light and cross the road without displaying a message. For example, if the mobile robot 10 arrives at a traffic light while the traffic light is red, it waits for the waiting time calculated as above before displaying a message.

[0104] [Modification] The processing executed by the mobile robot 10 is not limited to the above example. For example, the estimation unit 140 estimates the color of the traffic light when the mobile robot 10 arrives at the traffic light as the state of the traffic light. If the estimated color of the traffic light is green, the control unit 130 may cause the mobile robot 10 to wait for a time corresponding to one or more cycle lengths of the traffic light.

[0105] Specifically, when the traffic light is green, the control unit 130 may cause the mobile robot 10 to wait (stop) in a location that does not interfere with the flow of pedestrian traffic, for example. Furthermore, the control unit 130 may cause the mobile robot 10 to travel so that it arrives at the traffic light when the traffic light turns red. Furthermore, the control unit 130 may further wait for people to gather while the traffic light is red, then display a message, and cause the mobile robot 10 to pass through the traffic light when the traffic light turns green.

[0106] The control unit 130 may, based on a predetermined condition, cause the mobile robot 10 to wait in a location that does not interfere with pedestrian traffic, as described above. The predetermined condition may be set arbitrarily and is not particularly limited. Examples of the predetermined condition include when the number of people around the mobile robot 10 is less than a threshold value or when the volume of sound around the mobile robot 10 is equal to or greater than a threshold value. Each threshold may be set arbitrarily and is not particularly limited. Furthermore, the surroundings of the mobile robot 10 refer to, for example, the vicinity of the mobile robot 10, and the range is not particularly limited. The surroundings of the mobile robot 10 may be defined as a distance, such as within 10 meters of the mobile robot 10. Alternatively, the surroundings of the mobile robot 10 may be defined as an area captured by a camera, which is an example of a sensor 11, provided on the mobile robot 10, or an area in which sound is detected by a microphone, which is an example of a sensor 11, provided on the mobile robot 10. For example, when the predetermined condition is met, the control unit 130 may cause the mobile robot 10 to wait for a time equal to or greater than two traffic light cycles. Furthermore, an upper limit may be set on the time the mobile robot 10 is made to wait. The upper limit is set to, for example, 270 seconds, but any time may be set as the upper limit.

[0107] Furthermore, for example, the control unit 130 may determine the message to be presented by the presentation device 15 from among multiple messages of different lengths based on the cycle length of the traffic light and the arrival time of the mobile robot 10 at the traffic light.

[0108] The length of the message may be determined, for example, by the time the message is presented, or by the number of characters in the message or the amount of data in the information indicating the message.

[0109] Here, the ratio of the length (time) of message i to the time the traffic light is red is defined as b ｉ In addition, each message has a weight β ｉ are given (or linked). For example, the longer a message is, the more information it contains. Therefore, for example, a longer message is given a higher weight.

[0110] When a plurality of pieces of information indicating messages are stored in the database 13, for example, the message to be presented may be the message with the highest value of β ｉ u ＊ (μ, σ, b ｉ ) is selected.

[0111] Next, a specific example of which message is selected and presented when there are two messages will be described.

[0112] FIG. 9 shows the β ｉ u ＊ (μ, σ, b ｉ 9 shows an example of the difference between the values ​​of μ and σ. Specifically, FIG. 9 shows how the value of μ affects the message selected as the value of σ changes. In the example shown in FIG. 9, message 1 (i.e., the message with i=1) has a length of 0.4, and message 2 (i.e., the message with i=2) has a length of 0.2. The weight assigned to message 1 is β １ = 1.5, and the weight given to message 2 is β ２ = 1. The horizontal axis of the graph shown in FIG. 9 is μ, and the vertical axis is β ２ u ＊ (μ, σ, b ２ ) -β １ u ＊ (μ, σ, b １) In other words, when the value on the vertical axis is positive, it indicates that it is more effective to present message 2 first, and when the value on the vertical axis is negative, it indicates that it is more effective to present message 1 first.

[0113] In FIG. 9, the case where σ=0.03 is indicated by a solid line, the case where σ=0.1 is indicated by a dashed line, the case where σ=0.2 is indicated by a dotted line, and the case where σ=0.5 is indicated by a dashed line.

[0114] Intuitively, it would seem that the earlier the mobile robot 10 arrives at the traffic light (i.e., the lower the value of μ), the more effective it would be to present a longer message (Message 1 in this example). Conversely, the later the mobile robot 10 arrives at the traffic light (i.e., the higher the value of μ), the more effective it would be to present a shorter message (Message 2 in this example). However, the example shown in FIG. 9 contradicts this intuition, showing that when the value of σ is high, the wider the range of μ, the more effective a longer message is than a short message. This is thought to be due to the fact that if the time is not known accurately and the weight of a longer message is sufficiently high, presenting a longer message has a higher expected value. In fact, in this example, when σ = 0.03, Message 1 is selected if μ ≦ 0.74, and Message 2 is selected if μ > 0.74. Furthermore, when σ = 0.1, Message 1 is selected if μ ≦ 0.78, and Message 2 is selected if μ > 0.78. Also, when σ=0.2, if μ≦0.93, message 1 is selected, and if μ>0.93, message 2 is selected. Also, when σ=0.5, message 1 is selected regardless of the value of μ.

[0115] 10A to 10D show the results of presenting either of the two messages. ＊ 10A to 10D are graphs showing a specific example of the optimum waiting time. Specifically, FIGS. 10A to 10D are graphs showing whether message 1 or message 2 is selected for each of the four values ​​of σ described with reference to FIG. 9. The horizontal axis of the graphs shown in FIGS. 10A to 10D is μ, and the vertical axis is w. ＊ is.

[0116] Fig. 10A shows the case where σ = 0.03. Note that in Fig. 10A, the case where message 1 (M1) is selected is shown by a thick solid line, and the case where message 2 (M2) is selected is shown by a thin solid line.

[0117] In the example shown in FIG. 10A, the higher the value of μ, the shorter the optimal waiting time. When μ=0.6, the optimal waiting time is 0. When μ exceeds 0.7, message 2, which is shorter than message 1, is selected instead of message 1. The optimal waiting time at the timing when the selected message switches is 0.037. If μ is further increased, ＊ decreases again, and at μ = 0.77, w ＊ becomes 0 again.

[0118] Fig. 10B shows the case where σ = 0.1. In Fig. 10B, the case where message 1 (M1) is selected is shown by a thick dashed line, and the case where message 2 (M2) is selected is shown by a thin dashed line.

[0119] In the example shown in Figure 10B, the higher the value of μ, the shorter the optimal waiting time. Also, when μ is about 0.6, the optimal waiting time is 0. Also, when μ exceeds about 0.7, message 2, which is shorter than message 1, is selected instead of message 1. Also, when message 2 is selected, the optimal waiting time is always 0.

[0120] 10C shows the case where σ=0.2. In FIG. 10C, the case where message 1 (M1) is selected is shown by a thick dotted line, and the case where message 2 (M2) is selected is shown by a thin dotted line.

[0121] In the example shown in Figure 10C, the higher the value of μ, the shorter the optimal waiting time. Also, when μ is around 0.5, the optimal waiting time is 0. Also, when μ exceeds 0.9, message 2, which is shorter than message 1, is selected instead of message 1. Also, when message 2 is selected, the optimal waiting time is always 0.

[0122] Fig. 10D shows the case where σ = 0.5. In Fig. 10D, the case where message 1 (M1) is selected is shown by the dashed dotted line.

[0123] In the example shown in Figure 10D, message 2 is never selected, and message 1 is always selected. From this, it can be assumed that if the value of σ becomes higher than a predetermined value, the longer message will be selected. Also, as the value of μ increases, the optimal waiting time becomes shorter. Furthermore, when μ = 0.4 or so, the optimal waiting time becomes 0, and even if the value of μ is further increased, the optimal waiting time remains 0.

[0124] Furthermore, as described above, when one message is selected from a plurality of messages and presented, for example, the estimation unit 140 outputs multiple values ​​of b (specifically, the number of messages) instead of one. Furthermore, for example, the calculation unit 150 may output information indicating the selected message (for example, an ID unique to each message, i ＊ The control unit 130 outputs, for example, i ＊ One message is determined from a plurality of messages based on the above, and the determined message is displayed by a display device 15 only after the mobile robot 10 has waited for an optimum waiting time since it arrived at the traffic light.

[0125] Furthermore, for example, if there are multiple routes that the mobile robot 10 can take to reach the traffic light, the planning unit 120 may determine the travel route of the mobile robot 10 to the traffic light based on the arrival time of the mobile robot 10 at the traffic light when the mobile robot 10 travels along each of the multiple routes.

[0126] For example, when the mobile robot 10 can select from multiple routes to travel until it arrives at a traffic light, the planner 120 assigns a weight to each route. The weight assigned to each route may be determined arbitrarily and is not particularly limited. For example, the planner 120 calculates the time it would take for the mobile robot 10 to arrive at the traffic light from its current location if it traveled each route, and determines the weight according to the calculated time. For example, the planner 120 determines the weight so that the longer the time, the smaller the weight value. Information indicating the relationship between the time and the weight may be stored in the database 13.

[0127] In the following explanation, the weight is α ｊLet's say.

[0128] Fig. 11 is a diagram showing specific examples of multiple routes. In the example shown in Fig. 11, the mobile robot 10 located at point A (current point) passes through an intersection where four traffic lights 200 are located and heads toward point D. In the example shown in Fig. 11, there are two routes from point A to point D. These two routes are referred to as a first route and a second route.

[0129] The first route is indicated by a solid line in Fig. 11 and is a route in the order of point A → point B → point C → point D. The second route is indicated by a dashed line in Fig. 11 and is a route in the order of point A → point E → point F → point D.

[0130] For each route, the time when the mobile robot 10 arrives at the traffic light 200 is μ ｊ and σ ｊ If the cycle length of the traffic light 200 at which the mobile robot 10 arrives (specifically, the red cycle in which the color display turns red) is different, the relative message length b ｊ If multiple paths exist, α ｊ u ＊ (μ ｊ , σ ｊ , b ｊ ) is selected as the travel route for the mobile robot 10.

[0131] When a travel route along which the mobile robot 10 travels is selected from a plurality of routes, the estimation unit 140 does not output one piece of information (one tuple) for each of μ, σ, and b as described above, but rather outputs information for each of a plurality of routes (for example, μ ｊ , σ ｊ and b ｊ In this case, the calculation unit 150 may output a plurality of tuples, such as j ＊ For example, the planner 120 may output j ＊The control unit 130 selects one of the multiple routes as a travel route based on the selected route and outputs information indicating the selected travel route to the control unit 130. The control unit 130 causes the mobile robot 10 to travel along the selected travel route.

[0132] Furthermore, for example, the planner 120 determines the traveling speed of the mobile robot 10 based on the length of the traveling route until the mobile robot 10 arrives at the traffic light and the cycle length of the traffic light.

[0133] For example, the mobile robot 10 can change its running speed. In this case, the running speed of the mobile robot 10 can be changed so that the arrival time of the mobile robot 10 at the traffic light (the expected arrival time μ) corresponds to the optimal timing for presenting the message. Therefore, the optimal expected arrival time of the mobile robot 10 at the traffic light μ is defined as ＊ Then, μ ＊ is expressed as the following equation (11).

[0134]

[0135] FIG. 12 shows the u when the waiting time is 0. ｗ Specifically, FIG. 12 shows the change in u when the value of b and the value of σ are changed. ｗ The values ​​of (μ, σ, 0, b) are shown.

[0136] The horizontal axis of the graph shown in FIG. 12 is μ, and the vertical axis is u ｗ 12, the case where σ=0.03 and b=0.3 is indicated by a solid line, the case where σ=0.03 and b=0.6 is indicated by a dashed line, the case where σ=0.5 and b=0.3 is indicated by a dotted line, and the case where σ=0.5 and b=0.6 is indicated by a dashed line.

[0137] As shown in FIG. 12, when σ=0.03, u is larger than when σ=0.5. ｗ The maximum value of (μ, σ, 0, b) is higher. Also, when σ=0.03, the maximum value of u when b=0.6 is higher than when b=0.3. ｗ The value of μ at which (μ, σ, 0, b) is maximum becomes small. Also, when σ=0.03, the value of μ becomes approximately 1-b.ｗ This is the maximum value of (μ, σ, 0, b).

[0138] Therefore, the lower the value of σ, the ｗ The higher the maximum value of (μ, σ, 0, b), and the higher the value of b, the ｗ The value of μ at which (μ, σ, 0, b) is maximum becomes lower.

[0139] Also, when σ=0.5 and b=0.3, u ｗ (μ, σ, 0, b) is at its maximum when μ is higher than μ=1−b. Also, when σ=0.5 and b=0.6, u ｗ (μ,σ,0,b) has a maximum value when the value of μ is very low.

[0140] Therefore, for high values ​​of σ, u ｗ (μ, σ, 0, b) has a maximum value above 1-b if b is low, and a maximum value at very low μ if b is high.

[0141] FIG. 13 shows the μ ＊ Specifically, FIG. 13 shows the change in μ when the value of b and the value of σ are changed. ＊ 13 is a graph showing the relationship between the horizontal axis and the vertical axis of the graph shown in FIG. ＊ In the example shown in Fig. 13, the case where b = 0.2 is indicated by a solid line, the case where b = 0.3 is indicated by a dashed line, the case where b = 0.5 is indicated by a dotted line, and the case where b = 0.6 is indicated by a dashed line.

[0142] As can be seen from FIG. ＊ The value of μ is non-monotonic with respect to the change in the value of σ, regardless of the value of b. In addition, when b = 0.2 or 0.3, and when b = 0.5 or 0.6, ＊ Therefore, as σ increases, the best arrival time (i.e., the arrival time at which the message has the greatest effect on people) varies significantly depending on the length of the message presented.

[0143] When the traveling speed of the mobile robot 10 is selected to satisfy μ, which indicates the desired expected arrival time, various methods can be used for the selection. An example of such a method is described below. ｉ Note that one cycle includes a red light portion (t=0 to 1) and a green light portion (t>1). The running speed of the mobile robot 10 is set to the default speed v ｄ and the maximum speed is v ｍ It is assumed that the following is predetermined: ｄ Using the above, the period c of the mobile robot 10 ｉ The expected arrival time at μ ｄ Assume that μ ｄ ≦μ ＊ In this case, the mobile robot 10 moves in a period c ｉ μ in ＊ On the other hand, μ ｄ >μ ＊ Then, the mobile robot 10 ｉ μ in ＊ The speed required to reach ｍ Period c if and only if: ｉ μ in ＊ Increase the driving speed so that the vehicle arrives at μ ｄ >μ ＊ If so, the mobile robot 10 determines that the required velocity is v ｍ If it is greater than 1, the period c ｉ The next period is period c ｉ＋１ μ in ＊ Reduce your driving speed to arrive at

[0144] For example, the calculation unit 150 may calculate the waiting time based on the estimated traffic light status and the volume of sounds around the mobile robot 10. For example, when presenting a message by sound, if the noise around the mobile robot 10 is greater than a predetermined volume, the mobile robot 10 may not present the message for a predetermined time even after the waiting time has elapsed.

[0145] External noises can interfere with the clear transmission of a message. For example, an ambulance, a police car, or a billboard with a loudspeaker can be sources of noise that interfere with the transmission of a message.

[0146] The sensor 11 includes, for example, a microphone that detects sounds around the mobile robot 10. The microphone detects the above-mentioned noise. It is also assumed that the microphone can identify the intensity (volume) of a sound and the type of the sound. The volume and type of sound may be identified by the calculation unit 150. For example, the calculation unit 150 identifies the type of sound (e.g., a sound emitted from an ambulance siren, a sound emitted from a police car siren, or an advertising sound emitted from a signboard, etc.) based on, for example, a change in the frequency of the sound over time. Information for identifying the type of sound (e.g., information correlating a change in the frequency of the sound over time with the type of sound) may be stored in the database 13.

[0147] The time when the noise disappears is μ ｅ has a maximum value at μ ｅ > μ and uncertainty σ ｅ The model is a truncated Gaussian probability distribution where γ is a weighting factor representing the degradation of the message due to noise. γ may be set to any value as long as γ<1. γ may depend on the volume of the noise. For example, the louder the noise, the smaller γ may be. Using γ, the following equations (12) and (13) are calculated.

[0148]

[0149] u ＊ ａ denotes u when the message is presented even in the presence of noise. ＊ ｅ denotes u when the message is presented after waiting for the noise to die away.

[0150] Furthermore, the optimum waiting time taking into consideration the influence of noise is calculated by the following equations (14) and (15).

[0151]

[0152] w ＊ ａ indicates the waiting time when the message is presented without waiting for the noise to disappear. ＊ ｅ indicates the waiting time when a message is presented after waiting for the noise to disappear.

[0153] u ＊ ａ >u ＊ ｅ In this case, the waiting time is ＊ ａ is selected, and u ＊ ａ ≦u ＊ ｅ In this case, the waiting time is ＊ ｅ is selected.

[0154] 14A to 14C are diagrams for explaining how to calculate the waiting time. Specifically, FIGS. 14A to 14C are diagrams for explaining how to calculate the waiting time. ｅ , γ and b, the values ​​of σ and σ ｅ Depending on the value of , there are cases where the message is more effective when it is presented after waiting until the noise has disappeared, and cases where the message is more effective when it is presented without taking the noise into account.

[0155] The horizontal axis of the graphs shown in FIGS. 14A to 14C is σ, and the vertical axis is σ ｅ σ ｅ is σ when noise is present. When there is no noise, it is simply written as σ. In the example shown in FIG. 14A, μ = 0.2, μ ｅ = 0.9, γ = 0.8 and b = 0.3. In the example shown in FIG. 14B, μ = 0.1, μ ｅ = 0.6, γ = 0.9 and b = 0.5. In the example shown in FIG. 14C, μ = 0.1, μ ｅ = 0.9, γ = 0.8 and b = 0.8.

[0156] In the graphs shown in FIGS. 14A to 14C, the values ​​of σ and σ ｅWhen the value of is located in area (a) (the white part of the graph), the waiting time is ＊ ａ This indicates that the message is more effective when σ is selected. ｅ If the value of is located in the area (a), the message will be more effective if you do not wait for the noise to disappear.

[0157] On the other hand, the values ​​of σ and σ ｅ When the value of is located in the area (b) (the hatched area of ​​the graph), ＊ ｅ This indicates that the message is more effective when σ is selected. ｅ If the value of is located in area (b), the message will be more effective if you wait until the noise disappears.

[0158] As described above, depending on the conditions, there are cases where the message is more effective when it is presented before the noise has disappeared, and cases where the message is more effective when it is presented after the noise has disappeared. For example, the calculation unit 150 calculates (determines) the waiting time in consideration of the above conditions.

[0159] As described above, for example, when the traffic light is green, the control unit 130 causes the mobile robot 10 to wait (stop) in a location that does not interfere with the flow of pedestrian traffic. In such a case, for example, when the noise is greater than a predetermined volume, the control unit 130 may cause the mobile robot 10 to wait for a period of time equal to or longer than multiple traffic light cycles.

[0160] The predetermined volume may be arbitrarily determined in advance and is not particularly limited. Furthermore, although the above microphone has been described as an example of the sensor 11, it may be a microphone that is not mounted on the mobile robot 10 and is attached to a traffic light or the like.

[0161] [Processing Procedure] Fig. 15 is a flowchart showing an information processing method according to an embodiment. The information processing device 100 includes, for example, a memory and a processor, and the processor performs the following processing using the memory. Before the processing shown in Fig. 15, for example, the planner 120 calculates a travel path for the mobile robot 10, and the controller 130 controls the actuator 14 to cause the mobile robot 10 to travel along the calculated travel path.

[0162] First, the estimation unit 140 estimates the state of a traffic light (S10). For example, the estimation unit 140 identifies a traffic light that the mobile robot 10 will arrive at if it travels along the travel route calculated by the planning unit 120, and estimates the state of the traffic light (e.g., the color of the traffic light) when the mobile robot 10 arrives at the identified traffic light. For example, the acquisition unit 110 acquires information indicating the travel speed of the mobile robot 10, the current time, the traffic light cycle length, etc. from the sensor 11, an external server device communicating with the mobile robot 10 via the communication module 12, and / or the database 13. For example, the estimation unit 140 estimates, based on the acquired information and the travel route, whether the color of the traffic light will be red or green when the mobile robot 10 arrives at the traffic light, as the state of the traffic light.

[0163] Next, the calculation unit 150 calculates the waiting time (S20). For example, the calculation unit 150 calculates the waiting time, which is the time from when the mobile robot 10 arrives at the traffic light until when the presentation device 15 presents a message, based on the state of the traffic light estimated by the estimation unit 140 and the cycle length of the traffic light.

[0164] Next, the control unit 130 determines whether the mobile robot 10 has arrived at a traffic light (S30). For example, the control unit 130 determines whether the mobile robot 10 has arrived at a traffic light based on information obtained from the sensor 11.

[0165] If the control unit 130 determines that the mobile robot 10 has not arrived at the traffic light (No in S30), it repeats the process of step S30.

[0166] On the other hand, if the control unit 130 determines that the mobile robot 10 has arrived at the traffic light (Yes in S30), it determines whether the calculated waiting time has elapsed since the mobile robot 10 arrived at the traffic light (S40).

[0167] If the control unit 130 determines that the calculated waiting time has not elapsed (No in S40), it repeats the process of step S40.

[0168] On the other hand, when the control unit 130 determines that the calculated waiting time has elapsed (Yes in S40), the control unit 130 controls the presentation device 15 to present a message (S50).

[0169] Steps S20 to S50 are performed, for example, when the mobile robot 10 arrives at a traffic light and the traffic light is red. For example, when the mobile robot 10 arrives at a traffic light and the traffic light is green, the control unit 130 causes the mobile robot 10 to pass through the traffic light. In this case, steps S20 to S50 do not need to be performed.

[0170] Also, in step S20, if it is estimated that the traffic light will be green when the mobile robot 10 arrives at the traffic light, the control unit 130 may control the traveling speed of the mobile robot 10 so that the mobile robot 10 arrives at the traffic light when the traffic light is red, based on information such as the distance from the current position of the mobile robot 10 to the traffic light, the current time, and the traffic light cycle length.

[0171] (Effects, etc.) Hereinafter, examples of techniques that can be obtained from the disclosure of this specification will be given, and effects, etc. that can be obtained from the exemplified techniques will be described.

[0172] Technique 1 is an information processing device 100 including an estimation unit 140 that estimates the state of a traffic light, a calculation unit 150 that calculates a waiting time based on the estimated state of the traffic light, and a control unit 130 that, when the mobile robot 10 arrives at the traffic light, causes a presentation device 15 provided in the mobile robot 10 to present a message only after the waiting time has elapsed since the mobile robot 10 arrived at the traffic light.

[0173] By repeatedly presenting a message, the message can be delivered to many people. On the other hand, for people who repeatedly see or hear the same message, the repeated presentation of the message may be annoying. Therefore, the information processing device 100 causes the presentation device 15 to present a message only after a waiting time has elapsed since the mobile robot 10 arrived at the traffic light. For example, it is expected that the number of targets, such as pedestrians waiting at the traffic light, will increase over time. Therefore, by having the presentation device 15 present a message after a waiting time corresponding to the traffic light status has elapsed, the possibility of presenting the message to many targets increases. Therefore, the information processing device 100 can efficiently present a message to many targets while minimizing the annoyance felt by the targets.

[0174] Technology 2 is the information processing device 100 described in Technology 1, in which the estimation unit 140 estimates the color display of the traffic light at the time when the mobile robot 10 arrives at the traffic light as the state of the traffic light, and the control unit 130, when the mobile robot 10 arrives at the traffic light and the estimated color display of the traffic light is red, causes the presentation device 15 provided in the mobile robot 10 to present a message only after a waiting time has elapsed since the mobile robot 10 arrived at the traffic light.

[0175] According to this, by displaying the message at a timing when the number of target people waiting at the traffic light is large, the possibility of displaying the message to many target people increases. Therefore, the information processing device 100 can display the message efficiently.

[0176] Technique 3 is the information processing device 100 according to Technique 1 or 2, in which the estimation unit 140 estimates the color indication of the traffic light at the time when the mobile robot 10 arrives at the traffic light as the state of the traffic light, and the control unit 130 makes the mobile robot 10 wait for a time corresponding to one or more cycle lengths of the traffic light if the estimated color indication of the traffic light is green.

[0177] When the estimated traffic light is green, the mobile robot 10 waits, for example, by stopping or slowing down in a position that does not obstruct traffic, and moves so as to arrive at the traffic light when the traffic light turns red. This increases the possibility of presenting the message to many people by presenting the message when there are many people waiting at the traffic light. Therefore, the information processing device 100 can present the message efficiently.

[0178] Technique 4 is the information processing device 100 according to any one of techniques 1 to 3, in which the control unit 130 determines a message to be presented by the presentation device 15 from among a plurality of messages of different lengths, based on the cycle length of the traffic light and the arrival time of the mobile robot 10 at the traffic light.

[0179] The longer the message, the more information can be presented to the target person. On the other hand, if a message is presented after the mobile robot 10 arrives at a traffic light when the traffic light is red, if the time it takes for the traffic light to change from red to green is short compared to the length of the message, the target person may not be able to understand all of the content of the message. Therefore, by determining the message to be presented by the presentation device 15 from among multiple messages of different lengths based on the traffic light cycle length and the arrival time of the mobile robot 10 at the traffic light, a message of an appropriate length can be presented to the target person.

[0180] Technology 5 is an information processing device 100 according to any one of Technology 1 to Technology 4, which includes a planning unit 120 that determines a travel route of the mobile robot 10 to the traffic light based on the arrival time of the mobile robot 10 at the traffic light when the mobile robot 10 travels along each of the multiple routes, when there are multiple routes for the mobile robot 10 to arrive at the traffic light.

[0181] This allows the mobile robot 10 to move so as to arrive at the traffic light when the traffic light turns red without making unnecessary stops. This increases the likelihood of presenting a message to many people by presenting a message when there are many people waiting at the traffic light. Therefore, the information processing device 100 can present messages efficiently.

[0182] Technique 6 is the information processing device 100 according to any one of techniques 1 to 5, which includes a planning unit 120 that determines the traveling speed of the mobile robot 10 based on the length of the traveling path until the mobile robot 10 arrives at the traffic light and the cycle length of the traffic light.

[0183] This allows the mobile robot 10 to move so as to arrive at the traffic light when the traffic light turns red without making unnecessary stops. This increases the likelihood of presenting a message to many people by presenting a message when there are many people waiting at the traffic light. Therefore, the information processing device 100 can present messages efficiently.

[0184] Technique 7 is the information processing device 100 according to any one of techniques 1 to 6, wherein the calculation unit 150 calculates the waiting time based on the estimated state of the traffic light and the volume of sound around the mobile robot 10.

[0185] When the presentation device 15 presents a message by sound, if the surroundings of the mobile robot 10 are noisy, the message may not be conveyed to the target person. On the other hand, if the volume of the presentation device 15 is increased to convey the message, the volume of the message may be too loud and the target person may feel uncomfortable. Therefore, for example, by calculating the waiting time so that the waiting time is extended without presenting a message when the surroundings of the mobile robot 10 are noisy, the message can be presented at an appropriate time without being presented unnecessarily.

[0186] Technology 8 is a mobile robot 10 including the information processing device 100 according to any one of Technology 1 to Technology 7 and a presentation device 15.

[0187] This allows the mobile robot 10 to efficiently present messages to many people while minimizing the inconvenience felt by the people to whom the messages are presented.

[0188] Technology 9 is the mobile robot 10 according to technology 8, wherein the presentation device 15 presents the message using at least one of sound and video.

[0189] This allows the mobile robot 10 to efficiently present a message to a target person, for example, who is waiting at a traffic light.

[0190] The present disclosure may also be realized as an information processing method that is a process executed by an information processing device, as a program executed by a computer, or as a computer-readable non-transitory recording medium that stores the program.

[0191] Other Embodiments Although the information processing device and the like according to the present disclosure have been described above based on the above-described embodiments, the present disclosure is not limited to the above-described embodiments.

[0192] For example, in the above embodiment, each processing unit is described as being realized by a CPU and a control program. For example, each component of the processing unit may be composed of one or more electronic circuits. Each of the one or more electronic circuits may be a general-purpose circuit or a dedicated circuit. The one or more electronic circuits may include, for example, a semiconductor device, an integrated circuit (IC), or a large-scale integration (LSI). The IC or LSI may be integrated on a single chip or on multiple chips. Although the IC or LSI is referred to here as an IC or LSI, the name may vary depending on the degree of integration, and may be called a system LSI, a very large-scale integration (VLSI), or an ultra-large-scale integration (ULSI). Also, a field programmable gate array (FPGA), which is programmed after the LSI is manufactured, can be used for the same purpose.

[0193] In the above-described embodiment, the processing performed by a specific processing unit may be performed by another processing unit. The order of multiple processing operations may be changed, or multiple processing operations may be performed in parallel.

[0194] Furthermore, some or all of the components of the information processing device 100 do not need to be mounted on the mobile robot 10. For example, the present disclosure may be realized as an information processing system including a mobile body and a server device that has some or all of the functions of the information processing device 100 and communicates with the mobile robot 10.

[0195] Furthermore, the mobile robot is, for example, an autonomous vehicle, but may also be a remotely controlled vehicle or a vehicle directly driven by a driver.

[0196] Furthermore, the general or specific aspects of the present disclosure may be realized as a system, an apparatus, a method, an integrated circuit, or a computer program. Alternatively, they may be realized as a computer-readable non-transitory recording medium such as an optical disk, a HDD, or a semiconductor memory on which the computer program is stored. Alternatively, they may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

[0197] In addition, this disclosure also includes forms obtained by applying various modifications to each embodiment that a person skilled in the art would think of, and forms realized by arbitrarily combining the components and functions of each embodiment within the scope of this disclosure.

[0198] The present disclosure can be used for autonomously moving mobile robots.

[0199] REFERENCE SIGNS LIST 10 Mobile robot 11 Sensor 12 Communication module 13 Database 14 Actuator 15 Presentation device 100 Information processing device 110 Acquisition unit 120 Planning unit 130 Control unit 140 Estimation unit 150 Calculation unit 200 Traffic light

Claims

1. An information processing device comprising: an estimation unit that estimates a traffic light state; a calculation unit that calculates a waiting time based on the estimated traffic light state; and a control unit that causes a presentation device provided in the mobile robot to present a message after the waiting time has elapsed since the mobile robot arrived at the traffic light.

2. The information processing device of claim 1, wherein the estimation unit estimates the color indication of the traffic light at the time the mobile robot arrives at the traffic light as the state of the traffic light, and the control unit, when the estimated color indication of the traffic light is red, causes the presentation device equipped on the mobile robot to present a message after the waiting time has elapsed since the mobile robot arrived at the traffic light.

3. The information processing device of claim 1, wherein the estimation unit estimates the color indication of the traffic light at the time the mobile robot arrives at the traffic light as the state of the traffic light, and the control unit, when the estimated color indication of the traffic light is green, makes the mobile robot wait for a time corresponding to one or more cycle lengths of the traffic light.

4. The information processing device according to claim 1, wherein the control unit determines a message to be presented by the presentation device from among a number of messages of different lengths based on the cycle length of the traffic light and the arrival time of the mobile robot at the traffic light.

5. An information processing device as described in claim 1, further comprising a planning unit that, when there are multiple routes for the mobile robot to arrive at the traffic light, determines a travel route for the mobile robot to the traffic light based on the arrival time of the mobile robot at the traffic light when the mobile robot travels each of the multiple routes.

6. The information processing device according to claim 1, further comprising a planning unit that determines the traveling speed of the mobile robot based on the length of the travel path until the mobile robot arrives at the traffic light and the cycle length of the traffic light.

7. The information processing device according to claim 1, wherein the calculation unit calculates the waiting time based on the estimated state of the traffic light and a sound volume around the mobile robot.

8. A mobile robot comprising: an information processing device according to any one of claims 1 to 7; and the presentation device.

9. The mobile robot according to claim 8, wherein the presentation device presents a message using at least one of sound and video.

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