Patent ID: 12222719

MODES FOR CARRYING OUT THE INVENTION

In the following, embodiments of the present disclosure will be described in detail with reference to the drawings. The embodiments described below are specific examples of the present disclosure, and the technology according to the present disclosure is not limited to the following embodiments. In addition, arrangements, dimensions, dimensional ratios, and the like of the respective constituent elements of the present disclosure are not limited to the embodiments illustrated in each of the drawings.

Note that the description will be given in the following order.1. Outline2. Configuration Examples3. Relationship between Information Processing Apparatus and Movable Body4. Operation Examples4.1. First Operation Example4.2. Second Operation Example5. Hardware Configuration Examples

1. Outline

First, an outline of the technology according to the present disclosure is to be described with reference toFIG.1.FIG.1is a schematic explanatory diagram describing about a movable body10to be a target of the technology according to the present disclosure.

As illustrated inFIG.1, the movable body10to be the target of the technology according to the present disclosure is, for example, a movable body that is able to autonomously determine a route and move in a space50where many individuals20are present.

Specifically, the space50is a space in which a number of unspecified users are to come, such as a showroom, an exhibition, a bank, a store, an art museum, an entertainment facility, or a public facility, and the individual20is, for example, a customer who has come to the space50. The movable body10is, for example, a robot apparatus that autonomously moves and performs guidance or the like for the individual20who has come to the space50.

In such a use case, it is desired that the movable body10move avoiding movement lines of the individuals20each moving individually, and thereby avoid blocking the movements of the individuals20.

In order to achieve the above, for example, it is conceivable to cause the movable body10to operate in accordance with a policy that “in a case of becoming close to the individual20, perform an action of avoiding the individual20”. However, in such a case, as the number of the individuals20increases, the movable body10may have an ad hoc or staggering move because of performing the avoiding action for each individual20. Such an unstable move may make the individual20feel uneasy, and this is not desirable.

Further, in order to achieve the above, for example, it is conceivable not to cause the movable body10to move in a normal situation where the respective moves of the individuals20are non-uniform, and to cause the movable body10to move for guidance or the like of the individuals20only in an evacuation situation such as a hazard situation where the respective movements of the individuals20are made uniform easily. According to this, it seems that to cause the movable body10to move only in a situation where the movement line of the individual20is simple makes it possible to reduce the possibility that the movable body10blocks the movement line of the individual20. However, in such a case, the movable body10has to include various sensors that measure a surrounding environment and a processor circuit that determines the situation on the basis of the sensing results from the various sensors in order to determine whether it is the normal situation or the emergency situation. This results in an increase in operation cost of the movable body10. Further, in a case where the robustness (i.e., erroneous detection resistance) of the various sensors is low, erroneous detection by the various sensors may make the move of the movable body10more unstable.

The technology according to the present disclosure has been conceived in view of the above circumstances. The technology according to the present disclosure is able to optimize a creation mode of an action plan for the movable body10by recognizing a collection of the individuals20as a crowd30and estimating a characteristic of the recognized crowd30. According to the technology according to the present disclosure, even in the space50where many individuals20are present, it is possible to cause the movable body10to move smoothly so as not to adversely affect the individuals20.

Specifically, in the technology of the present disclosure, first, the characteristic of the crowd30is estimated with use of a sensing result regarding the crowd30which is a collection of the individuals20. The characteristic of the crowd30is, for example, a moving characteristic such as a moving direction or a moving speed of the crowd30, a density of the individuals20in the crowd30, a mutual social connection degree of the individuals20, or randomness of the moves of the individuals20, or the like. Further, in the technology of the present disclosure, the creation mode of the action plan for the movable body10is controlled on the basis of the estimated characteristic of the crowd30. This makes it possible to cause the movable body10to so move as to avoid blocking the movement lines of the individuals20.

According to this, for example, in a case where the density of the individuals20in the crowd30is high, the movable body10is able to create an action plan including a route R2or a route R3that does not pass through the inside of the crowd30and avoids the movement line of the crowd30. Further, in a case where the density of the individuals20in the crowd30is low, the movable body10is able to create an action plan including a route R1that passes between the individuals20inside the crowd30.

Note that it is described above that the individual20is a user who has come to the space50such as a showroom, an exhibition, a bank, a store, an art museum, an entertainment facility, or a public facility; however, the technology according to the present disclosure is not limited to the above example. The individual20is not limited to a human as long as it is a target that moves individually, and may be a domestic animal such as a cow, a horse, or sheep grazed in the space50such as a farm.

2. Configuration Examples

In the following, referring toFIGS.2,3A,3B, and4, a description is given of a configuration of an information processing apparatus100of one embodiment of the technology according to the present disclosure the outline of which has been described above.FIG.2is a block diagram illustrating a functional configuration of the information processing apparatus100according to the present embodiment.FIGS.3A,3B, and4are each a schematic explanatory diagram describing about the characteristic of the crowd30.

As illustrated inFIG.2, the information processing apparatus100according to the present embodiment includes, for example, an external-environment recognizer101, a crowd recognizer102, a crowd characteristic estimator103, a plan controller104, a plan creator105, a state recognizer106, a task manger107, and a driving controller108.

The external-environment recognizer101recognizes an external environment around the movable body10on the basis of a sensing result of the external environment acquired from an external sensor section110. Specifically, the external-environment recognizer101recognizes a position, a move, and the like of the individual20present around the movable body10on the basis of the sensing result obtained by the external sensor section110. Further, the external-environment recognizer101may recognize positions, kinds, moves, and the like of various objects other than individual20that are present around the movable body10, on the basis of the sensing result obtained by the external sensor section110. Thus, the external-environment recognizer101is able to recognize the external environment in which the movable body10is present. Note that the external-environment recognizer101may perform recognition of the external environment on the basis of a predetermined rule, or may perform the recognition of the external environment on the basis of a machine-learning algorithm.

Further, the external-environment recognizer101may create an environment map representing an environment around the movable body10on the basis of a result of the recognition of the external environment. Specifically, the external-environment recognizer101may create an environment map representing the positions and the moves of the individual20and various objects other than the individual20that are present around the movable body10. For example, the external-environment recognizer101may create an environment map of an environment around the movable body10on the basis of a result of image recognition performed on an image capturing the environment around the movable body10. The environment map created by the external-environment recognizer101may be, for example, an occupancy grid map (Occupancy Grid Map), a lane map (Lane Map), a point cloud map (Point Cloud Map), or the like.

The external sensor section110may include, for example, an imaging device that detects information regarding the environment around the movable body10, such as a stereo camera, a monocular camera, a color camera, an infrared-ray camera, or a polarization camera. Further, the external sensor section110may include an environment sensor that detects weather, a weather condition, or the like, a microphone that detects a sound, or a ranging sensor that measures a distance to a nearby object such as an ultrasonic sensor (Sound Navigation And Ranging: SONAR), a ToF (Time of Flight) sensor, or a LiDAR (Light Detection And Ranging) sensor. The various sensors included in the external sensor section110may be provided, for example, in the movable body10, or may be provided on a wall, a ceiling, or the like in the space50separately from the movable body10.

The crowd recognizer102recognizes the crowd30, which is a collection of the individuals20, on the basis of the sensing result of the external environment acquired from the external sensor section110. Specifically, the crowd recognizer102may recognize, as the crowd30, each cluster obtained by clustering the individuals20with use of an overlapping degree of respective bodies of the individuals20recognized by the external-environment recognizer101and a direction of the overlapping degree as parameters. Thus, the crowd recognizer102is able to recognize the plurality of individuals20present in the external environment as a crowd30performing similar moves.

Note that the crowd recognizer102may recognize the crowd30, which is a collection of the individuals20, by using a method other than the above-described clustering. For example, in a case where distances between the two or more individuals20are within a threshold, the crowd recognizer102may recognize the two or more individuals20as one crowd30.

The crowd characteristic estimator103estimates information regarding a characteristic of each crowd30recognized by the crowd recognizer102.

For example, the crowd characteristic estimator103may estimate information regarding the number of the individuals20in the crowd30, the occupied area of the crowd30, or the density of the individuals20in the crowd30from the sensing result of the external sensor section110. Specifically, the crowd characteristic estimator103may estimate the information regarding the number of the individuals20in the crowd30, the occupied area of the crowd30, or the density of the individuals20in the crowd30from an image captured by the imaging device included in the external sensor section110.

Note that the occupied area of the crowd30may be estimated as, for example, the area of a region within a closed curve connecting the individuals20present on the outermost periphery of the crowd30with one another, or may be estimated as the area in a region in which a region having a predetermined width is further added to the outside of the above-described closed curve. Note that the occupied area of the crowd30may be estimated as the area of a region within a closed curve including a polygonal line having the respective vertices corresponding to the individuals20, or may be estimated as the area of a region within a closed curve including a curve obtained by smoothing such vertices.

In addition, the crowd characteristic estimator103may further estimate other information regarding the characteristic of the crowd30(i.e., corresponding to secondary information) from the density of the individuals20in the crowd30(i.e., corresponding to primary information). Specifically, the crowd characteristic estimator103may estimate information regarding the mutual social connection degree of the individuals20included in the crowd30or the randomness of the moves of the individuals20included in the crowd30from the density of the individuals20included in the crowd30.

Other characteristics to be estimated from the density of the individuals20in the crowd30are to be described with reference toFIGS.3A and3B. As illustrated inFIG.3A, in a case where the density of the individuals20in the crowd30is low, the distances between the individuals20in the crowd30are greater. In contrast, as illustrated inFIG.3B, in a case where the density of the individuals20in the crowd30is high, the distances between the individuals20in the crowd30are shorter.

Here, it is conceivable that people who have a deeper social relationship and are more intimate have a higher degree of personal space sharing, which results in a shorter distance therebetween. Accordingly, the crowd characteristic estimator103is able to estimate that the higher the density of the individuals20in the crowd30is, the higher the mutual social connection degree of the individuals20included in the crowd30is (that is, the deeper the social relationship is). Note that the crowd characteristic estimator103is also able to estimate the mutual social connection degree of the individuals20in the crowd30, further taking into consideration the consistency of the moving direction of the individuals20in the time axis, a body language such as the facial expression of the individuals20, and the like.

In addition, it is conceivable that the closer the individuals20are to each other, the higher the possibility that individuals20act in a coordinated or synchronized way with each other. Accordingly, the crowd characteristic estimator103is able to estimate that the higher the density of the individuals20in the crowd30is, the lower the possibility that each of the individuals20makes a sudden move is. Accordingly, the crowd characteristic estimator103is able to estimate that the higher the density of the individuals20in the crowd30is, the lower the randomness of the respective moves of the individuals20included in the crowd30is.

Further, for example, the crowd characteristic estimator103may estimate information regarding a moving characteristic of the crowd30from the sensing result of the external sensor section110. Specifically, the crowd characteristic estimator103may estimate information regarding the moving direction and the moving speed of the crowd30on the basis of a captured image acquired by the external sensor section110. Note that the crowd characteristic estimator103may regard the average of the moving directions and the average of the moving speeds of the respective individuals20included in the crowd30as the moving direction and the moving speed of the crowd30, or may regard the moving direction and the moving speed of a representative individual20of the individuals20included in the crowd30as the moving direction and the moving speed of the crowd30.

In addition, the crowd characteristic estimator103may further estimate another information regarding the characteristic of the crowd30(i.e., corresponding to secondary information) from the moving direction and the moving speed of the crowd30(i.e., corresponding to primary information). For example, the crowd characteristic estimator103may estimate information regarding a moving route of the crowd30or a region which the crowd30passes through upon moving, from the moving direction and the moving speed of the crowd30.

Other characteristics to be estimated from the moving direction and the moving speed of the crowd30is to be described with reference toFIG.4. As illustrated inFIG.4, the crowd characteristic estimator103is able to estimate the moving routes of crowds30A and30B by referring to the positions of the crowds30A and30B and the position of an object to be a destination such as a doorway51in addition to the moving directions and the moving speeds of the crowds30A and30B. Note that the crowd characteristic estimator103may estimate the moving routes of the crowds30A and30B only from the moving directions and the moving speeds of the crowds30A and30B, or may estimate the moving routes of the crowds30A and30B from the positions of the crowds30A and30B and the position of the object such as the doorway51.

Here, the crowd characteristic estimator103is able to estimate the regions which the crowds30A and30B pass through upon moving, from the estimated moving routes of the crowds30A and30B. For example, the crowd characteristic estimator103may estimate a region35A obtained by projecting the outline of the occupied area of the crowd30A to the object such as the doorway51to be the destination, as the region which the crowd30A passes through upon moving.

Alternatively, the crowd characteristic estimator103may regard, as the region which the crowd30A passes through upon moving, a region33A having a predetermined width from a straight line connecting the center of gravity31A of the occupied area of the crowd30A and the center of gravity52of the object such as the doorway51to be the destination. Similarly, the crowd characteristic estimator103may regard, as the region which the crowd30B passes through upon moving, a region33B having a predetermined width from a straight line connecting the center of gravity31B of the occupied area of the crowd30B and the center of gravity52of the object such as the doorway51to be the destination. In such cases, the crowd characteristic estimator103may cause the widths of the regions which the crowds30A and30B pass through upon moving to be greater with the increasing numbers of the individuals20included in the crowds30A and30B. Alternatively, the crowd characteristic estimator103may cause the widths of the regions which the crowds30A and30B pass through upon moving to be greater with the increasing occupied areas of the crowds30A and30B.

The plan controller104controls the creation mode of the action plan for the movable body10on the basis of the information regarding the characteristic of the crowd30estimated by the crowd characteristic estimator103. Specifically, the plan controller104may control at least one or more of an algorithm or a parameter to be used in creating the action plan for the movable body10on the basis of the information regarding the estimated characteristic of the crowd30.

For example, in a case where the movable body10is included in the moving route of the crowd30or the region which the crowd30passes through upon moving, the plan creator105, which will be described later, creates an action plan that causes the movable body10to so move as to be out of the moving route of the crowd30or the region which the crowd30passes through upon moving.

On this occasion, the plan controller104may control a distance from the movable body10to the moving route of the crowd30or the region which the crowd30passes through upon moving, on the basis of the information regarding the characteristic of the crowd30. For example, in a case where the moving speed of the crowd30is high, the plan controller104may so control various parameters as to allow for creation of an action plan that causes the movable body10to move with a greater distance from the moving route of the crowd30or the region which the crowd30passes through upon moving. Further, in a case where the region which the crowd30passes through upon moving is large, or in a case where the possibility that the movement of the crowd30and the movement of the mobile body interfere with each other is determined to be high, for example, in a case where the randomness of the moves of the individuals20included in the crowd30is high, or the like, the plan controller104may so control the various parameters as to allow for creation of the action plan that causes the movable body10to move with a greater distance from the moving route of the crowd30or the region which the crowd30passes through upon moving in a similar manner. Thus, the plan controller104is able to so control the plan creator105, which will be described later, as to allow for creation of an action plan that causes the movable body10to move along a route with a greater distance from the crowd30.

Further, the plan controller104may control a destination of the movable body10or a route to the destination on the basis of the position or the moving route of the crowd30. For example, in a case where a plurality of destinations of the mobile body10or a plurality of routes to the destination are conceivable by the plan creator105, the plan controller104may so control the parameters for the creation of the action plan as to allow for selecting of a destination or a route to the destination having a greater distance from the position or the moving route of the crowd30.

Further, the plan controller104may control whether or not to regard a region between the individuals20in the crowd30as a passable region of the movable body10, depending on the mutual social connection degree of the individuals20in the crowd30. For example, in a case where the mutual social connection degree of the individuals20in the crowd30is low, the plan controller104may regard the region between the individuals20in the crowd30as the passable region of the movable body10. A reason for this is that, in the case where the mutual social connection degree between the individuals20is low, a psychological effect on the individuals20caused by the movable body10passing through or across the region between the individuals20seems to be small. In contrast, in a case where the mutual social connection degree of the individuals20in the crowd30is high, the plan controller104may regard the region between the individuals20in the crowd30as the non-passable region of the movable body10. A reason for this is that, in the case where the mutual social connection degree between the individuals20is high, the psychological effect on the individuals20caused by the movable body10passing through or across the region between the individuals20seems to be great.

Further, the plan controller104may control the algorithm to be used by the plan creator105in creating the action plan, on the basis of the randomness of the moves of the individuals20in the crowd30.

For example, in a case where the randomness of the moves of the individuals20in the crowd30is high, the plan controller104may control the algorithm to be used in the creation of the action plan to be a machine-learning algorithm. A reason for this is that the machine-learning algorithm is more compatible with an environment having higher randomness than other algorisms including a rule-based algorism, and therefore seems to allow for more efficient creation of the action plan. In contrast, in a case where the randomness of the moves of the individuals20in the crowd30is low, the plan controller104may control the algorithm to be used in the creation of the action plan to be a rule-based algorithm. A reason for this is that the rule-based algorism has a lower calculation load than the machine-learning algorism or the like, and seems to allow for faster answering for a simple problem. The environment having low randomness causes less difficulty in the creation of the action plan. Accordingly, the plan controller104is able to reduce the calculation load caused by the creation of the action plan by causing the action plan to be created by means of a simpler algorithm.

In addition, the plan controller104may control the creation mode of the action plan for the movable body10on the basis of information regarding a characteristic of the movable body10and a task to be executed by the movable body10.

Specifically, the plan controller104may control the creation mode of the action plan for the movable body10on the basis of a moving characteristic of the movable body10. For example, the plan controller104may control the plan creator105to use a moving speed, a moving method, or a control method in moving of the movable body10as a parameter used in planning a route which the movable body10moves along. Thus, the plan controller104is able to so control the creation mode of the action plan as to allow for creation of an action plan taking into consideration a machine characteristic of the movable body10. For example, in a case where the moving speed of the movable body10is high, the plan controller104may control the creation mode of the plan creator105as to allow for creation of an action plan causing the movable body10to move in a direction away from the crowd30or the moving route of the crowd30.

Further, the plan controller104may control the creation mode of the action plan for the movable body10on the basis of a content of the task to be executed by the movable body10. For example, in a case where the task to be executed by the movable body10is a task configured to give higher priority to the movement of the movable body10, the plan controller104may so control the creation mode of the plan creator105as to allow for creation of an action plan that causes the moving distance of the movable body10to be shorter. Note that as the task configured to give higher priority to the movement of the movable body10, an urgent task that requires shorter-time execution can be assumed. Examples of such a task include a task of carrying an AED (Automated External Defibrillator) or the like.

The plan creator105creates the action plan for the movable body10on the basis of the creation mode controlled by the plan controller104. Specifically, the plan creator105may create an action plan that causes the movable body10to move on the environment map created by the external-environment recognizer101, on the basis of the algorithm and the parameter controlled by the plan controller104. For example, the plan creator105may create the action plan that causes the movable body10to move, with use of the algorithm selected by the plan controller104from among a plurality of algorithms, including a machine-learning algorithm a rule-based algorithm, and the like, prepared in advance. Further, the plan creator105may set a condition for the route along which the movable body10is caused to move, or may change priorities in creating the action plan, on the basis of the various parameters controlled by the plan controller104.

The state recognizer106recognizes a state of the movable body10on the basis of the sensing result acquired from an internal sensor section120. Specifically, the state recognizer106may recognize a position, an attitude, and the like of the movable body10on the basis of the sensing result obtained by the internal sensor section120provided in the movable body10.

For example, in a case where the internal sensor section120includes an encoder provided at each joint of a leg or an arm, the state recognizer106may recognize the attitude of the movable body10by calculating an attitude of the leg or an arm from the sensing result obtained by the encoder. Further, in a case where the internal sensor section120includes an encoder provided on each wheel, the state recognizer106may recognize the position of the movable body10by calculating the moving direction and the moving distance of the movable body10from the sensing result obtained by the encoder. Further, in a case where the internal sensor section120includes an IMU (Inertial Measurement Unit) having a three-axis gyroscope and a three-way accelerometer, the state recognizer106may recognize the attitude or the position of the movable body10from the three-dimensional angular velocity and the acceleration of the movable body10measured by the IMU. In addition, in a case where the internal sensor section120includes a GNSS (Global Navigation Satellite System) sensor, the state recognizer106may recognize the position of the movable body10on the basis of position information from the GNSS sensor.

The task manger107manages the task to be executed by the movable body10. For example, the task manger107may manage the timings or the priorities of execution of tasks inputted to the movable body10by the user by means of the input section130or of tasks autonomously set by the movable body10.

The input section130is, for example, an input device that accepts an input from a user, such as a mouse, a keyboard, a touch panel, a button, a switch, or a lever. Alternatively, the input section130may be a microphone that accepts an audio input from a user. The input section130may be provided on the movable body10, or may be provided outside the movable body10. In a case where the input section130is provided outside the movable body10, the input section130may be a remote control device that transmits the input content to the movable body10by means of wireless communication or the like.

The driving controller108controls a driver section140on the basis of the action plan. Specifically, the driving controller108controls the operation of the movable body10by controlling the driver section140on the basis of the action plan created by the plan creator105. For example, the driving controller108may so control the driver section140that the movable body10moves along a route included in the action plan.

The driver section140is, for example, a motor or an actuator that drives a moving mechanism included in the movable body10. Specifically, the driver section140may be a motor that drives a two-wheeled or four-wheeled movable device, or an actuator that drives a two-legged or four-legged movable device.

The information processing apparatus100including the above-described configuration is able to estimate the information regarding the characteristic of the crowd30, which is a collection of the individuals20, and to control the creation mode of the action plan for the movable body10on the basis of the estimated information regarding the characteristic of the crowd30. Accordingly, the information processing apparatus100is able to create, by referring to the moving characteristic or the density characteristic of the crowd30, an action plan for the movable body10that allows the movable body10to move efficiently without blocking the moving route of each of the individuals20included in the crowd30.

3. Relationship Between Information Processing Apparatus and Movable Body

Next, a relationship between the information processing apparatus100according to the present embodiment and the movable body10is to be described. The information processing apparatus100according to the present embodiment is able to configure various variations of systems together with the movable body10, as described below. That is, the information processing apparatus100is applicable to various control systems including the movable body10(e.g., a robot).

For example, the information processing apparatus100according to the present embodiment may be mounted on the movable body10. In such a case, the information processing apparatus100is able to perform the above-described control by performing transmission and reception of information with the external sensor section110, the internal sensor section120, the input section130, and the driver section140similarly mounted on the movable body10via an inner bus or the like.

In a case where the information processing apparatus100is mounted on the movable body10, it is possible to perform the above-described control by the movable body10alone even in a case where connection to a public communication network such as the Internet is lost in the event of a disaster. Accordingly, the movable body10is able to perform evacuation guidance or the like of the individuals20without blocking the movement lines of the individuals20who have come to the space50(i.e., customers) even in emergencies.

Further, the information processing apparatus100according to the present embodiment may be provided separately from the movable body10, for example. Such a case is to be described with reference toFIG.5.FIG.5is a schematic explanatory diagram describing about a configuration example in a case where the information processing apparatus100, the movable body10, and the external sensor section110are provided separately.

As illustrated inFIG.5, the information processing apparatus100is provided separately from the movable body10and the external sensor section110. The information processing apparatus100is able to perform the above-described control by performing transmission and reception of information via a network60. Specifically, the information processing apparatus100is able to transmit the created action plan to the movable body10by creating the action plan for the movable body10on the basis of information acquired from the movable body10and the external sensor section110via the network60. For example, in order to perform sensing on the entire space50, the external sensor section110may be provided on the ceiling or the like of the space50in which the crowd30, which is a collection of the individuals20, and the movable body10are present. Further, the information processing apparatus100may be a server or the like provided outside the space50.

In a case where the information processing apparatus100is provided separately from the movable body10, even if the movable body10has a simpler configuration, the information processing apparatus100is able to perform the above-described control.

Further, the information processing apparatus100is able to acquire the information regarding the characteristic of the crowd30from the external sensor section110for the entire space50. The information processing apparatus100is therefore able to optimize the action plan for the movable body10for the entire space50. Accordingly, for example, the information processing apparatus100is able to create an action plan for guiding the crowd30equally to a plurality of doorways51present in the space50at the time of a disaster, and cause the movable body10to perform the created action plan.

4. Operation Examples

4.1. First Operation Example

Next, referring toFIGS.6,7A, and7B, a first operation example of the information processing apparatus100according to the present embodiment is to be described.FIG.6is a flowchart illustrating a flow of the first operation example of the information processing apparatus100according to the present embodiment.

The first operation example is an operation example of the information processing apparatus100in a case of causing the movable body10to so move as to avoid the moving route of the crowd30.

As illustrated inFIG.6, first, the external-environment recognizer101acquires information regarding the space50in which the movable body10and the individuals20are present from the external sensor section110(S101). Thereafter, the crowd recognizer102recognizes the crowd30present in the space50(S103). Thereafter, the crowd characteristic estimator103estimates the information regarding the characteristic of the crowd30including the moving route of the crowd30(S105).

Here, the plan controller104determines whether or not the moving route of the crowd30and the movable body10interfere with each other (S107). In a case where the moving route of the crowd30and the movable body10interfere with each other (S107/Yes), the plan controller104so controls the creation mode of the action plan for the movable body10as to allow for creation of an action plan in which the moving route of the crowd30and the movable body10do not interfere with each other (S109). In contrast, in a case where the moving route of the crowd30and the movable body10do not interfere with each other (S107/No), the information processing apparatus100skips the operation in step S109. Thereafter, the plan creator105creates the action plan for the movable body10on the basis of the creation mode (S111). In addition, the driving controller108controls the movement of the movable body10by controlling the driver section140in accordance with the created action plan (S113).

Referring toFIGS.7A and7B, the above-described first operation example is described more specifically.FIG.7Ais an explanatory diagram describing about estimation of the moving route of the crowd30in the first operation example.FIG.7Bis an explanatory diagram describing about creation of the action plan for the movable body10in the first operation example.

For example, as illustrated inFIGS.7A and7B, the external-environment recognizer101and the crowd recognizer102recognize the positions of crowds30A,30B, and30C and movable bodies10A and10B that are present in the space50.

Next, the crowd characteristic estimator103estimates moving-route candidates PAAand PABof the crowd30A as information regarding a characteristic of the crowd30A. Similarly, the crowd characteristic estimator103estimates moving-route candidates PBAand PBBof the crowd30B as information regarding a characteristic of the crowd30B, and estimates moving-route candidates PCAand PCBof the crowd30C as information regarding a characteristic of the crowd30C. The moving-route candidates of the crowds30A,30B, and30C can be estimated, for example, from the respective positions of the crowds30A,30B, and30C and the positions of the doorways51A and51B.

Thereafter, the crowd characteristic estimator103estimates a region WAA which the crowd30A passes through upon moving, as the information regarding the characteristic of the crowd30A. Similarly, the crowd characteristic estimator103estimates a region WBBwhich the crowd30B passes through upon moving, as the information regarding the characteristic of the crowd30B, and estimates a region WCAwhich the crowd30C passes through upon moving, as the information regarding the characteristic of the crowd30C. For example, it is possible to estimate the region which the crowd30A passes through upon moving by providing a width according to the number of people in the crowd30A to the moving-route candidate PAAhaving the shortest moving distance of the moving-route candidates PAAand PABof the crowd30A estimated above. This is similarly applicable to the crowds30B and30C.

On this occasion, a plurality of route candidates is conceivable as an action plan in which each of the movable body10A that interferes with the region WAA which the crowd30A passes through upon moving and the movable body10B that interferes with the region WBBwhich the crowd30B passes through upon moving is caused to move to any of escape areas53A,53B, and53C. For example, regarding the movable body10A, a route candidate RA1for moving to the escape area53A and a route candidate RA2for moving to the escape area53C are conceivable. Regarding the movable body10B, a route candidate RB1for moving to the escape area53A and a route candidate RB2for moving to the escape area53B are conceivable.

In the information processing apparatus100according to the present embodiment, the plan controller104controls the creation mode of the action plan of the plan creator105. Thus, the plan creator105is able to efficiently create an action plan that causes the movable bodies10A and10B to escape from the moving routes or the like of the crowds30A,30B, and30C and to move to any of the escape areas53A,53B, and53C.

Specifically, regarding the movable body10A, the plan controller104is able to so control the creation mode as to allow for creation of an action plan including the route candidate RA1that does not interfere with the moving route of the crowd30C although having a longer moving distance. Further, regarding the movable body10B, the plan controller104is able to so control the creation mode as to allow for creation of an action plan including the route candidate RB2that does not pass across the moving route of the crowd30B although having a longer moving distance.

Thus, the information processing apparatus100is able to prevent the action plans for the movable bodies10A and10B and the moving routes of the crowds30A,30B, and30C from crossing, by estimating the moving routes of the crowds30A,30B, and30C and the regions which the crowds30A,30B, and30C pass through upon moving. Further, the information processing apparatus100is able to prevent the movable bodies10A and10B from becoming close to each of the individuals20included in the crowds30A,30B, and30C. The information processing apparatus100is therefore able to prevent ad hoc or staggering behavior of the movable bodies10A and10B.

4.2. Second Operation Example

Next, referring toFIG.8andFIG.9, a second operation example of the information processing apparatus100according to the present embodiment is to be described.FIG.8is a flowchart illustrating a flow of the second operation example of the information processing apparatus100according to the present embodiment.

The second operation example is an operation example of the information processing apparatus100in a case where an action plan is created giving higher priority to moving the movable body10than preventing the crossing of the movable body10and the movement line of the crowd30. Note that in the second operation example, it is assumed that the operation corresponding to steps S101to S103of the first operation example has already been executed.

As illustrated inFIG.8, first, the task manger107acquires a task to be executed by the movable body10, on the basis of an input received from the input section130(S201). Thereafter, the plan controller104determines whether or not the task to be executed by the movable body10is a highly urgent task (S203).

In a case where the task to be executed by the movable body10is the highly urgent task (S203/Yes), the plan controller104so controls the creation mode as to allow for creation of an action plan giving higher priority to the movement of the movable body10. Specifically, the plan controller104determines whether or not the moving route of the crowd30included in the information regarding the characteristic of the crowd30and the movable body10interfere with each other (S211), and optimizes the creation mode of the action plan for the movable body10on the basis of the characteristic of the movable body and the characteristic of the task (S213). For example, the plan controller104may so optimize the algorithm and the parameter to be used in creation of the action plan as to allow the movable body10to execute the task with a shorter moving distance. Note that in a case where the moving route of the crowd30and the movable body10do not interfere with each other (S211/No), the plan controller104omits execution of step S213described above.

In a case where the task to be executed by the movable body10is not the highly urgent task (S203/No), the plan controller104so controls the creation mode as to allow for creation of an action plan in which the moving route of the crowd30and the movable body10do not interfere with each other, as in S107and S109of the first operation example. Specifically, the plan controller104determines whether or not the moving route of the crowd30included in the information regarding the characteristic of the crowd30and the movable body10interfere with each other (S221), and controls the creation mode of the action plan for the movable body10(S223). Note that in a case where the moving route of the crowd30and the movable body10do not interfere with each other (S221/No), the plan controller104skips the operation in step S223described above.

Thereafter, the plan creator105creates the action plan for the movable body10on the basis of the creation mode controlled by the plan controller104(S205). Further, the driving controller108controls the movement of the movable body10by controlling the driver section140in accordance with the created action plan (S207).

The above-described operation example is to be described more specifically with reference toFIG.9.FIG.9is an explanatory diagram describing about creation of the action plan for the movable body10in the second operation example.

For example, as illustrated inFIG.9, assume that the task is a highly urgent task that gives higher priority to moving the movable body10to a destination70, and that the crowds30A and30B are present between the movable body10and the destination70. Note that it is assumed that a moving route PA of the crowd30A and a region wAwhich the crowd30A passes through upon moving have already been estimated by operation corresponding to steps S101to S103of the first operation example, and a moving route PBof the crowd30B and a region wBwhich the crowd30B passes through upon moving have already been estimated in a similar manner.

In such a case, a plurality of route candidates r1, r2, and r3is conceivable as routes for the movable body10to reach the destination70. The plan controller104is able to control which of the route candidates r1, r2, and r3is adopted as the action plan by controlling the creation mode on the basis of the characteristic of the movable body10and the characteristic of the task. Further, the plan controller104is able to set an optimum algorism and an optimum parameter for each of the route candidates r1, r2, and r3on the basis of the characteristic of the movable body10and the characteristic of the task.

For example, the route candidate r1that bypasses the outside of the crowd30A is a route that allows for avoidance of the crowds30A and30B and has a relatively short distance to the destination70. However, it is estimated that the crowd30A is a group in which the density of the individuals20is low and the randomness of the respective moves of the individuals20is high. Therefore, it is estimated that, regarding the route candidate r1, the possibility of an increase in moving time caused by a random factor is high.

In such a case, the plan controller104may so control the creation mode that a more optimal action plan is created, by changing the algorithm to be used by the plan creator105to an algorithm suitable for an environment with high randomness, such as a machine-learning algorithm. Further, in a case where the randomness with respect to the movable body10is low or in a case where the task involves a strict time limitation, the plan controller104may so control the creation mode that the route candidate r1is not adopted as the action plan, by lowering the priority of the route candidate r1.

For example, the route candidate r2passing between the individuals20inside the crowd30A is a route having the shortest distance to the destination70. However, because the route candidate r2passes through the inside of the crowd30A, it is estimated that it is difficult to predict a specific route and specific moving time. Further, there is a possibility that the route candidate r2causes an unpleasant feeling in the individuals20by the movement of the movable body10depending on the density and the social connection degree of the individuals20in the crowd30A.

In such a case, the plan controller104may so control the creation mode that a more optimal action plan is created, by changing the algorithm to be used in the plan creator105to an algorithm suitable for an environment with high randomness, such as a machine-learning algorithm. Further, the plan controller104may control whether or not to regard the inside of the crowd30A as a passable region on the basis of the density and the social connection degree of the individuals20in the crowd30A. In addition, the plan controller104may so control the creation mode of the action plan that the movable body10does not approach the crowd30B in which the social connection degree of the individuals20is high.

The route candidate r3bypassing the outside of the crowd30B is a route that allows for avoidance of the crowds30A and30B but has the longest distance to the destination70. However, it is estimated that the crowd30B is a group in which the density of the individuals20is high and the randomness of the respective moves of the individuals20is low. Therefore, it is estimated that the route candidate r3is a route having the lowest randomness.

Accordingly, the plan controller104may so control the creation mode that the route candidate r3is easily adopted as the action plan by increasing the priority of the route candidate r3in a case where the randomness with respect to the movable body10is low.

Thus, the information processing apparatus100is able to more efficiently plan the route in a case where the movable body10passes a region close to the crowds30A and30B by understanding the characteristics of the crowds30A and30B.

5. Hardware Configuration Examples

In addition, referring toFIG.10, a hardware configuration of the information processing apparatus100according to the present embodiment is to be described.FIG.10is a block diagram illustrating a hardware configuration example of the information processing apparatus100according to the present embodiment.

The functions of the information processing apparatus100according to the present embodiment are implemented by cooperation between software and hardware described below. For example, the functions of the external-environment recognizer101, the crowd recognizer102, the crowd characteristic estimator103, the plan controller104, the plan creator105, the state recognizer106, the task manger107, and the driving controller108described above may be executed by a CPU901.

As illustrated inFIG.10, the information processing apparatus100includes a CPU (Central Processing Unit)901, a ROM (Read Only Memory)903, and a RAM (Random Access Memory)905.

In addition, the information processing apparatus100may further include a host bus907, a bridge909, an external bus911, an interface913, an input unit915, an output unit917, a storage unit919, a drive921, a coupling port923, and a communication unit925. In addition, the information processing apparatus100may include other processing circuits, for example, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), or the like, in place of the CPU901or together with the CPU901.

The CPU901functions as a calculation unit or a control unit, and controls the overall operation of the information processing apparatus100in accordance with various programs recorded in the ROM903, the RAM905, the storage unit919, or a removable recording medium927. The ROM903stores a program and arithmetic parameters to be used by the CPU901. The RAM905temporarily stores a program to be used in execution by the CPU901and parameters to be used in the execution thereof.

The CPU901, the ROM903, and the RAM905are mutually coupled by the host bus907including an internal bus such as a CPU bus. In addition, the host bus907is coupled to the external bus911such as a PCI (Peripheral Component Interconnect/Interface) bus via the bridge909.

The input unit915is a unit that accepts an input from a user, such as a mouse, a keyboard, a touch panel, a button, a switch, or a lever. Note that the input unit915may be a microphone or the like that detects a voice of a user. The input unit915may be, for example, a remote control unit utilizing infrared rays or other radio waves, and may be an external coupling apparatus929compatible with the operation of the information processing apparatus100.

The input unit915further includes an input control circuit that supplies, to the CPU901, an input signal generated on the basis of information inputted by the user. The user is able to input various data or give a process operation instruction to the information processing apparatus100by operating the input unit915.

The output unit917is a unit that is able to visually or audibly present information acquired or generated by the information processing apparatus100to a user. The output unit917may be, for example, a display unit such as an LCD (Liquid Crystal Display), a PDP (Plasma Display Panel), an OLED (Organic Light Emitting Diode) display, a hologram, or a projector. Further, the output unit917may be a sound output device such as a speaker or a headphone, or may be a printing device such as a printer. The output unit917may output information obtained by the process of the information processing apparatus100as an image such as a text or a picture, or a sound such as a voice or an acoustic sound.

The storage unit919is a data storage device configured as an example of a storage section of the information processing apparatus100. The storage unit919may include, for example, a magnetic storage device such as an HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like. The storage unit919is able to store a program to be executed by the CPU901, various data, various data acquired from outside, and the like.

The drive921is a reading or writing unit for the removable recording medium927such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory. The drive921is built in or externally attached to the information processing apparatus100. For example, the drive921is able to read information recorded in the removable recording medium927mounted thereon and supply the information to the RAM905. Further, the drive921is able to write a record in the mounted removable recording medium927.

The coupling port923is a port for directly coupling the external coupling apparatus929to the information processing apparatus100. The coupling port923may be, for example, a USB (Universal Serial Bus) port, an IEEE1394 port, an SCSI (Small Computer System Interface) port, or the like. Further, the coupling port923may be an RS-232C port, an optical audio terminal, an HDMI (registered trademark) (High-Definition Multimedia Interface) port, or the like. The coupling port923is able to perform transmission and reception of various data between the information processing apparatus100and the external coupling apparatus929by being coupled to the external coupling apparatus929.

The communication unit925is, for example, a communication interface including a communication device for coupling to the communication network931. The communication unit925may be, for example, a wired or wireless LAN (Local Area Network), Bluetooth (registered trademark), a communication card for WUSB (Wireless USB), or the like. Further, the communication unit925may be a router for optical communication, a router for ADSL (Asymmetric Digital Subscriber Line), a modem for various types of communication, or the like.

For example, the communication unit925is able to perform transmission and reception of a signal or the like with the Internet or another communication apparatus with use of a predetermined protocol such as TCP/IP. The communication network931coupled to the communication unit925may be a network coupled with or without a wire. The communication network931may be, for example, an Internet communication network, a domestic LAN, an infrared-ray communication network, a radio wave communication network, a satellite communication network, or the like.

Note that it is also possible to create a program for causing the hardware built in the computer such as the CPU901, the ROM903, and the RAM95to exhibit functions equivalent to those of the information processing apparatus100described above. Further, it is also possible to provide a recording medium in which such a program is recorded and that is readable by the computer.

The technology according to the present disclosure has been described above with reference to the embodiment. According to the information processing apparatus100of the present embodiment, a collection of the plurality of individuals20is recognized as the crowd30, and the characteristic of the recognized crowd30is taken into consideration in the action plan for the movable body10. This makes it possible to take into consideration a movement tendency of each of the individuals20in the action plan for the movable body10. Therefore, the information processing apparatus100is able to create an action plan for the movable body10that does not adversely affect the individuals20even in an environment in which many individuals20are present in a mixed manner.

Specifically, the information processing apparatus100is able to create an action plan to take an avoidance action with respect to the crowd30which is a collection of the individuals20rather than each of the individuals20. This makes it possible to prevent the movable body10from making an unstable move to deal with each of the individuals20. Further, the information processing apparatus100is able to similarly create an action plan for the movable body10in either normal or urgent situation. This allows the configuration of the system including the movable body10to be simpler and more shared. Accordingly, the information processing apparatus100makes it possible to reduce the operation cost of the system including the movable body10. In addition, the information processing apparatus100is able to create the action plan for the movable body10without minutely dividing the surrounding environment of the movable body10. This makes it possible to improve robustness (erroneous detection resistance) at a time of detection of the surrounding environment of the movable body10.

Note that the technology according to the present disclosure is not limited to the above-described embodiments and the like, and is modifiable in a variety of ways.

Furthermore, not all of the configurations and the operations described in the respective embodiments are essential to the configurations and the operations of the present disclosure. For example, among the components in each embodiment, components not described in the independent claims describing the most superordinate concept of the present disclosure should be understood as optional components.

The terms used throughout the specification and the appended claims should be construed as “non-limiting” terms. For example, the terms “include” or “be included” should be construed as “not limited to the example described with the term included”. The term “have” should be construed as “not limited to the example described with the term have”.

The terms used herein include some terms that are used merely for convenience of description and are not used to limit the configuration and the operation. For example, the term such as “right,” “left,” “upper,” or “lower” merely indicates a direction in the referred drawing. Further, the terms “inner” and “outer” merely indicate a direction toward the center of the component of interest and a direction away from the center of the component of interest, respectively. This similarly applies to terms similar to the above-described terms and terms having similar meanings.

Note that the technology according to the present disclosure may have the following configurations. According to the technology according to the present disclosure having the following configurations, the information processing apparatus is able to take into consideration, in the action plan for the movable body, the move and the characteristic of the crowd which is a collection of individuals rather than each of the individuals. Accordingly, the information processing apparatus is able to create an action plan that efficiently causes the movable body to move without being disturbed by each of the individuals even in an environment where many individuals are present. Effects exerted by the technology according to the present disclosure are not necessarily limited to the effects described here, and may be any of the effects described in the present disclosure.

(1)

An information processing apparatus including:a crowd characteristic estimator that estimates information regarding a characteristic of a crowd on the basis of a sensing result of an external environment, the crowd being a collection of individuals present in the external environment; anda plan controller that controls a creation mode of an action plan for a movable body in the external environment on the basis of at least the information regarding the characteristic.
(2)

The information processing apparatus according to (1) described above, in which the information regarding the characteristic includes information regarding at least any one or more of a density of the individuals in the crowd, a mutual social connection degree of the individuals, or randomness of moves of the individuals.

(3)

The information processing apparatus according to (1) or (2) described above, in which the information regarding the characteristic includes information regarding a movement characteristic of the crowd.

(4)

The information processing apparatus according to (3) described above, in which the information regarding the movement characteristic of the crowd includes information regarding at least any one or more of a moving direction, a moving speed, a moving route, or a passing region upon moving of the crowd.

(5)

The information processing apparatus according to any one of (1) to (4) described above, further including a crowd recognizer that recognizes the crowd by performing clustering on the sensing result.

(6)

The information processing apparatus according to any one of (1) to (5) described above, further including a plan creator that creates the action plan for the movable body on the basis of the creation mode controlled by the plan controller.

(7)

The information processing apparatus according to (6) described above, in which the plan controller controls, as the creation mode, at least one or more of an algorithm or a parameter to be used when the plan creator creates the action plan.

(8)

The information processing apparatus according to (7) described above, in which the plan controller controls at least one or more of the algorithm or the parameter to be used by the plan creator, on the basis of information regarding a density of the individuals in the crowd.

(9)

The information processing apparatus according to (7) or (8) described above, in which the plan controller controls whether a machine-learning-based algorithm or a rule-based algorithm is to be used by the plan creator, on the basis of information regarding randomness of moves of the individuals in the crowd.

(10)

The information processing apparatus according to any one of (7) to (9) described above, in which the plan controller controls whether or not the plan creator determines that a space between the individuals in the crowd is passable, on the basis of information regarding a mutual social connection degree of the individuals in the crowd.

(11)

The information processing apparatus according to any one of (6) to (10) described above, in which the plan controller controls the creation mode to cause the plan creator to create the action plan in which a moving route of the crowd and the movable body do not interfere with each other.

(12)

The information processing apparatus according to (11) described above, in which the plan creator creates the action plan that causes the movable body to be out of the moving route of the crowd on the basis of the creation mode.

(13)

The information processing apparatus according to any one of (6) to (12) described above, in which the plan controller controls the creation mode of the action plan for the movable body further on the basis of information regarding a characteristic of the movable body or a task to be executed by the movable body.

(14)

The information processing apparatus according to (13) described above, in which the plan creator creates the action plan in which the moving route of the crowd and a moving route of the movable body interfere with each other, in a case where the task to be executed by the movable body is a predetermined content.

(15)

The information processing apparatus according to (14) described above, in which the plan controller optimizes the creation mode at a time when the plan creator creates the action plan in which the moving route of the crowd and the moving route of the movable body interfere with each other.

(16)

An information processing method including:by a calculation processor,estimating information regarding a characteristic of a crowd on the basis of a sensing result of an external environment, the crowd being a collection of individuals present in the external environment; andcontrolling a creation mode of an action plan for a movable body in the external environment on the basis of at least the information regarding the characteristic.
(17)

A program causing a computer to function asa crowd characteristic estimator that estimates information regarding a characteristic of a crowd on the basis of a sensing result of an external environment, the crowd being a collection of individuals present in the external environment, anda plan controller that controls a creation mode of an action plan for a movable body in the external environment on the basis of at least the information regarding the characteristic.

This application claims the priority on the basis of Japanese Patent Application No. 2020-039058 filed on Mar. 6, 2020 with Japan Patent Office, the entire contents of which are incorporated in this application by reference.

It should be understood by those skilled in the art that various modifications, combinations, sub-combinations, and alterations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims or the equivalents thereof.