Patent Publication Number: US-2023135955-A1

Title: Information processing apparatus, information processing method, and program

Description:
TECHNICAL FIELD 
     The present disclosure relates to an information processing apparatus, an information processing method, and a program. 
     BACKGROUND ART 
     Recently, it has been attempted to provide a service such as information provision or guidance using an autonomously movable robot in a store, an entertainment facility, a public facility, or the like (for example, PTL 1). 
     CITATION LIST 
     Patent Literature 
     PTL 1: Japanese Unexamined Patent Application Publication No. 2008-142876 
     SUMMARY OF THE INVENTION 
     In such a venue where a number of unspecified users are present, it is desired that a robot provide a service such as information provision or guidance to the users without any confusion even in a case where many users have come to the venue. 
     Therefore, it is desirable to provide an information processing apparatus, an information processing method, and a program that make it possible to create an action plan that causes a movable body to operate more efficiently even in an environment with presence of many users. 
     An information processing apparatus according to one embodiment of the present disclosure includes a crowd characteristic estimator and a plan controller. The crowd characteristic estimator estimates information regarding a characteristic of a crowd on the basis of a sensing result of an external environment. The crowd is a collection of individuals present in the external environment. The plan controller 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. 
     An information processing method according to one embodiment of the present disclosure includes: 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; and controlling 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. 
     A program according to one embodiment of the present disclosure causing a computer to function as a crowd characteristic estimator and a plan controller. The crowd characteristic estimator estimates information regarding a characteristic of a crowd on the basis of a sensing result of an external environment. The crowd is a collection of individuals present in the external environment. The plan controller 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. 
     According to the information processing apparatus, the information processing method, and the program according to one embodiment of the present disclosure, it is possible to estimate information regarding a characteristic of a crowd, which is a collection of individuals present in an external environment, on the basis of a sensing result of the external environment, and to control a creation mode of an action plan for the movable body in the external environment on the basis of the information regarding the estimated characteristic. This allows the information processing apparatus to take into consideration, in the action plan for the movable body, for example, not the move of each of the individuals but a move or a characteristic of the crowd which is a collection of the individuals. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a schematic explanatory diagram describing about a movable body to be a target of a technology according to the present disclosure. 
         FIG.  2    is a block diagram illustrating a functional configuration of an information processing apparatus according to one embodiment of the present disclosure. 
         FIG.  3 A  is a schematic explanatory diagram describing about a characteristic of a crowd. 
         FIG.  3 B  is a schematic explanatory diagram describing about the characteristic of the crowd. 
         FIG.  4    is a schematic explanatory diagram describing about the characteristic of the crowd. 
         FIG.  5    is a schematic explanatory diagram describing about a configuration example in a case where the information processing apparatus according to the embodiment, the movable body, and an external sensor section are provided individually. 
         FIG.  6    is a flowchart illustrating a flow of a first operation example of the information processing apparatus according to the embodiment. 
         FIG.  7 A  is an explanatory diagram describing about estimation of a moving route of the crowd in the first operation example. 
         FIG.  7 B  is an explanatory diagram describing about creation of an action plan for the movable body in the first operation example. 
         FIG.  8    is a flowchart illustrating a flow of a second operation example of the information processing apparatus according to the embodiment. 
         FIG.  9    is an explanatory diagram describing about creation of the action plan for the movable body in the second operation example. 
         FIG.  10    is a block diagram illustrating a hardware configuration example of the information processing apparatus according to the embodiment. 
     
    
    
     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. Outline   2. Configuration Examples   3. Relationship between Information Processing Apparatus and Movable Body   4. Operation Examples 
   4.1. First Operation Example   4.2. Second Operation Example   
   5. Hardware Configuration Examples   

     1. Outline 
     First, an outline of the technology according to the present disclosure is to be described with reference to  FIG.  1   .  FIG.  1    is a schematic explanatory diagram describing about a movable body  10  to be a target of the technology according to the present disclosure. 
     As illustrated in  FIG.  1   , the movable body  10  to 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 space  50  where many individuals  20  are present. 
     Specifically, the space  50  is 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 individual  20  is, for example, a customer who has come to the space  50 . The movable body  10  is, for example, a robot apparatus that autonomously moves and performs guidance or the like for the individual  20  who has come to the space  50 . 
     In such a use case, it is desired that the movable body  10  move avoiding movement lines of the individuals  20  each moving individually, and thereby avoid blocking the movements of the individuals  20 . 
     In order to achieve the above, for example, it is conceivable to cause the movable body  10  to operate in accordance with a policy that “in a case of becoming close to the individual  20 , perform an action of avoiding the individual  20 ”. However, in such a case, as the number of the individuals  20  increases, the movable body  10  may have an ad hoc or staggering move because of performing the avoiding action for each individual  20 . Such an unstable move may make the individual  20  feel uneasy, and this is not desirable. 
     Further, in order to achieve the above, for example, it is conceivable not to cause the movable body  10  to move in a normal situation where the respective moves of the individuals  20  are non-uniform, and to cause the movable body  10  to move for guidance or the like of the individuals  20  only in an evacuation situation such as a hazard situation where the respective movements of the individuals  20  are made uniform easily. According to this, it seems that to cause the movable body  10  to move only in a situation where the movement line of the individual  20  is simple makes it possible to reduce the possibility that the movable body  10  blocks the movement line of the individual  20 . However, in such a case, the movable body  10  has 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 body  10 . 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 body  10  more 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 body  10  by recognizing a collection of the individuals  20  as a crowd  30  and estimating a characteristic of the recognized crowd  30 . According to the technology according to the present disclosure, even in the space  50  where many individuals  20  are present, it is possible to cause the movable body  10  to move smoothly so as not to adversely affect the individuals  20 . 
     Specifically, in the technology of the present disclosure, first, the characteristic of the crowd  30  is estimated with use of a sensing result regarding the crowd  30  which is a collection of the individuals  20 . The characteristic of the crowd  30  is, for example, a moving characteristic such as a moving direction or a moving speed of the crowd  30 , a density of the individuals  20  in the crowd  30 , a mutual social connection degree of the individuals  20 , or randomness of the moves of the individuals  20 , or the like. Further, in the technology of the present disclosure, the creation mode of the action plan for the movable body  10  is controlled on the basis of the estimated characteristic of the crowd  30 . This makes it possible to cause the movable body  10  to so move as to avoid blocking the movement lines of the individuals  20 . 
     According to this, for example, in a case where the density of the individuals  20  in the crowd  30  is high, the movable body  10  is able to create an action plan including a route R2 or a route R3 that does not pass through the inside of the crowd  30  and avoids the movement line of the crowd  30 . Further, in a case where the density of the individuals  20  in the crowd  30  is low, the movable body  10  is able to create an action plan including a route R1 that passes between the individuals  20  inside the crowd  30 . 
     Note that it is described above that the individual  20  is a user who has come to the space  50  such 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 individual  20  is 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 space  50  such as a farm. 
     2. Configuration Examples 
     In the following, referring to  FIGS.  2  to  4   , a description is given of a configuration of an information processing apparatus  100  of one embodiment of the technology according to the present disclosure the outline of which has been described above.  FIG.  2    is a block diagram illustrating a functional configuration of the information processing apparatus  100  according to the present embodiment.  FIG.  3 A ,  FIG.  3 B , and  FIG.  4    are each a schematic explanatory diagram describing about the characteristic of the crowd  30 . 
     As illustrated in  FIG.  2   , the information processing apparatus  100  according to the present embodiment includes, for example, an external-environment recognizer  101 , a crowd recognizer  102 , a crowd characteristic estimator  103 , a plan controller  104 , a plan creator  105 , a state recognizer  106 , a task manger  107 , and a driving controller  108 . 
     The external-environment recognizer  101  recognizes an external environment around the movable body  10  on the basis of a sensing result of the external environment acquired from an external sensor section  110 . Specifically, the external-environment recognizer  101  recognizes a position, a move, and the like of the individual  20  present around the movable body  10  on the basis of the sensing result obtained by the external sensor section  110 . Further, the external-environment recognizer  101  may recognize positions, kinds, moves, and the like of various objects other than individual  20  that are present around the movable body  10 , on the basis of the sensing result obtained by the external sensor section  110 . Thus, the external-environment recognizer  101  is able to recognize the external environment in which the movable body  10  is present. Note that the external-environment recognizer  101  may 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 recognizer  101  may create an environment map representing an environment around the movable body  10  on the basis of a result of the recognition of the external environment. Specifically, the external-environment recognizer  101  may create an environment map representing the positions and the moves of the individual  20  and various objects other than the individual  20  that are present around the movable body  10 . For example, the external-environment recognizer  101  may create an environment map of an environment around the movable body  10  on the basis of a result of image recognition performed on an image capturing the environment around the movable body  10 . The environment map created by the external-environment recognizer  101  may 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 section  110  may include, for example, an imaging device that detects information regarding the environment around the movable body  10 , such as a stereo camera, a monocular camera, a color camera, an infrared-ray camera, or a polarization camera. Further, the external sensor section  110  may 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 section  110  may be provided, for example, in the movable body  10 , or may be provided on a wall, a ceiling, or the like in the space  50  separately from the movable body  10 . 
     The crowd recognizer  102  recognizes the crowd  30 , which is a collection of the individuals  20 , on the basis of the sensing result of the external environment acquired from the external sensor section  110 . Specifically, the crowd recognizer  102  may recognize, as the crowd  30 , each cluster obtained by clustering the individuals  20  with use of an overlapping degree of respective bodies of the individuals  20  recognized by the external-environment recognizer  101  and a direction of the overlapping degree as parameters. Thus, the crowd recognizer  102  is able to recognize the plurality of individuals  20  present in the external environment as a crowd  30  performing similar moves. 
     Note that the crowd recognizer  102  may recognize the crowd  30 , which is a collection of the individuals  20 , by using a method other than the above-described clustering. For example, in a case where distances between the two or more individuals  20  are within a threshold, the crowd recognizer  102  may recognize the two or more individuals  20  as one crowd  30 . 
     The crowd characteristic estimator  103  estimates information regarding a characteristic of each crowd  30  recognized by the crowd recognizer  102 . 
     For example, the crowd characteristic estimator  103  may estimate information regarding the number of the individuals  20  in the crowd  30 , the occupied area of the crowd  30 , or the density of the individuals  20  in the crowd  30  from the sensing result of the external sensor section  110 . Specifically, the crowd characteristic estimator  103  may estimate the information regarding the number of the individuals  20  in the crowd  30 , the occupied area of the crowd  30 , or the density of the individuals  20  in the crowd  30  from an image captured by the imaging device included in the external sensor section  110 . 
     Note that the occupied area of the crowd  30  may be estimated as, for example, the area of a region within a closed curve connecting the individuals  20  present on the outermost periphery of the crowd  30  with 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 crowd  30  may be estimated as the area of a region within a closed curve including a polygonal line having the respective vertices corresponding to the individuals  20 , 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 estimator  103  may further estimate other information regarding the characteristic of the crowd  30  (i.e., corresponding to secondary information) from the density of the individuals  20  in the crowd  30  (i.e., corresponding to primary information). Specifically, the crowd characteristic estimator  103  may estimate information regarding the mutual social connection degree of the individuals  20  included in the crowd  30  or the randomness of the moves of the individuals  20  included in the crowd  30  from the density of the individuals  20  included in the crowd  30 . 
     Other characteristics to be estimated from the density of the individuals  20  in the crowd  30  are to be described with reference to  FIGS.  3 A and  3 B . As illustrated in  FIG.  3 A , in a case where the density of the individuals  20  in the crowd  30  is low, the distances between the individuals  20  in the crowd  30  are greater. In contrast, as illustrated in  FIG.  3 B , in a case where the density of the individuals  20  in the crowd  30  is high, the distances between the individuals  20  in the crowd  30  are 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 estimator  103  is able to estimate that the higher the density of the individuals  20  in the crowd  30  is, the higher the mutual social connection degree of the individuals  20  included in the crowd  30  is (that is, the deeper the social relationship is). Note that the crowd characteristic estimator  103  is also able to estimate the mutual social connection degree of the individuals  20  in the crowd  30 , further taking into consideration the consistency of the moving direction of the individuals  20  in the time axis, a body language such as the facial expression of the individuals  20 , and the like. 
     In addition, it is conceivable that the closer the individuals  20  are to each other, the higher the possibility that individuals  20  act in a coordinated or synchronized way with each other. Accordingly, the crowd characteristic estimator  103  is able to estimate that the higher the density of the individuals  20  in the crowd  30  is, the lower the possibility that each of the individuals  20  makes a sudden move is. Accordingly, the crowd characteristic estimator  103  is able to estimate that the higher the density of the individuals  20  in the crowd  30  is, the lower the randomness of the respective moves of the individuals  20  included in the crowd  30  is. 
     Further, for example, the crowd characteristic estimator  103  may estimate information regarding a moving characteristic of the crowd  30  from the sensing result of the external sensor section  110 . Specifically, the crowd characteristic estimator  103  may estimate information regarding the moving direction and the moving speed of the crowd  30  on the basis of a captured image acquired by the external sensor section  110 . Note that the crowd characteristic estimator  103  may regard the average of the moving directions and the average of the moving speeds of the respective individuals  20  included in the crowd  30  as the moving direction and the moving speed of the crowd  30 , or may regard the moving direction and the moving speed of a representative individual  20  of the individuals  20  included in the crowd  30  as the moving direction and the moving speed of the crowd  30 . 
     In addition, the crowd characteristic estimator  103  may further estimate another information regarding the characteristic of the crowd  30  (i.e., corresponding to secondary information) from the moving direction and the moving speed of the crowd  30  (i.e., corresponding to primary information). For example, the crowd characteristic estimator  103  may estimate information regarding a moving route of the crowd  30  or a region which the crowd  30  passes through upon moving, from the moving direction and the moving speed of the crowd  30 . 
     Other characteristics to be estimated from the moving direction and the moving speed of the crowd  30  is to be described with reference to  FIG.  4   . As illustrated in  FIG.  4   , the crowd characteristic estimator  103  is able to estimate the moving routes of crowds  30 A and  30 B by referring to the positions of the crowds  30 A and  30 B and the position of an object to be a destination such as a doorway  51  in addition to the moving directions and the moving speeds of the crowds  30 A and  30 B. Note that the crowd characteristic estimator  103  may estimate the moving routes of the crowds  30 A and  30 B only from the moving directions and the moving speeds of the crowds  30 A and  30 B, or may estimate the moving routes of the crowds  30 A and  30 B from the positions of the crowds  30 A and  30 B and the position of the object such as the doorway  51 . 
     Here, the crowd characteristic estimator  103  is able to estimate the regions which the crowds  30 A and  30 B pass through upon moving, from the estimated moving routes of the crowds  30 A and  30 B. For example, the crowd characteristic estimator  103  may estimate a region  35 A obtained by projecting the outline of the occupied area of the crowd  30 A to the object such as the doorway  51  to be the destination, as the region which the crowd  30 A passes through upon moving. 
     Alternatively, the crowd characteristic estimator  103  may regard, as the region which the crowd  30 A passes through upon moving, a region  33 A having a predetermined width from a straight line connecting the center of gravity  31 A of the occupied area of the crowd  30 A and the center of gravity  52  of the object such as the doorway  51  to be the destination. Similarly, the crowd characteristic estimator  103  may regard, as the region which the crowd  30 B passes through upon moving, a region  33 B having a predetermined width from a straight line connecting the center of gravity  31 B of the occupied area of the crowd  30 B and the center of gravity  52  of the object such as the doorway  51  to be the destination. In such cases, the crowd characteristic estimator  103  may cause the widths of the regions which the crowds  30 A and  30 B pass through upon moving to be greater with the increasing numbers of the individuals  20  included in the crowds  30 A and  30 B. Alternatively, the crowd characteristic estimator  103  may cause the widths of the regions which the crowds  30 A and  30 B pass through upon moving to be greater with the increasing occupied areas of the crowds  30 A and  30 B. 
     The plan controller  104  controls the creation mode of the action plan for the movable body  10  on the basis of the information regarding the characteristic of the crowd  30  estimated by the crowd characteristic estimator  103 . Specifically, the plan controller  104  may control at least one or more of an algorithm or a parameter to be used in creating the action plan for the movable body  10  on the basis of the information regarding the estimated characteristic of the crowd  30 . 
     For example, in a case where the movable body  10  is included in the moving route of the crowd  30  or the region which the crowd  30  passes through upon moving, the plan creator  105 , which will be described later, creates an action plan that causes the movable body  10  to so move as to be out of the moving route of the crowd  30  or the region which the crowd  30  passes through upon moving. 
     On this occasion, the plan controller  104  may control a distance from the movable body  10  to the moving route of the crowd  30  or the region which the crowd  30  passes through upon moving, on the basis of the information regarding the characteristic of the crowd  30 . For example, in a case where the moving speed of the crowd  30  is high, the plan controller  104  may so control various parameters as to allow for creation of an action plan that causes the movable body  10  to move with a greater distance from the moving route of the crowd  30  or the region which the crowd  30  passes through upon moving. Further, in a case where the region which the crowd  30  passes through upon moving is large, or in a case where the possibility that the movement of the crowd  30  and 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 individuals  20  included in the crowd  30  is high, or the like, the plan controller  104  may so control the various parameters as to allow for creation of the action plan that causes the movable body  10  to move with a greater distance from the moving route of the crowd  30  or the region which the crowd  30  passes through upon moving in a similar manner. Thus, the plan controller  104  is able to so control the plan creator  105 , which will be described later, as to allow for creation of an action plan that causes the movable body  10  to move along a route with a greater distance from the crowd  30 . 
     Further, the plan controller  104  may control a destination of the movable body  10  or a route to the destination on the basis of the position or the moving route of the crowd  30 . For example, in a case where a plurality of destinations of the mobile body  10  or a plurality of routes to the destination are conceivable by the plan creator  105 , the plan controller  104  may 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 crowd  30 . 
     Further, the plan controller  104  may control whether or not to regard a region between the individuals  20  in the crowd  30  as a passable region of the movable body  10 , depending on the mutual social connection degree of the individuals  20  in the crowd  30 . For example, in a case where the mutual social connection degree of the individuals  20  in the crowd  30  is low, the plan controller  104  may regard the region between the individuals  20  in the crowd  30  as the passable region of the movable body  10 . A reason for this is that, in the case where the mutual social connection degree between the individuals  20  is low, a psychological effect on the individuals  20  caused by the movable body  10  passing through or across the region between the individuals  20  seems to be small. In contrast, in a case where the mutual social connection degree of the individuals  20  in the crowd  30  is high, the plan controller  104  may regard the region between the individuals  20  in the crowd  30  as the non-passable region of the movable body  10 . A reason for this is that, in the case where the mutual social connection degree between the individuals  20  is high, the psychological effect on the individuals  20  caused by the movable body  10  passing through or across the region between the individuals  20  seems to be great. 
     Further, the plan controller  104  may control the algorithm to be used by the plan creator  105  in creating the action plan, on the basis of the randomness of the moves of the individuals  20  in the crowd  30 . 
     For example, in a case where the randomness of the moves of the individuals  20  in the crowd  30  is high, the plan controller  104  may 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 individuals  20  in the crowd  30  is low, the plan controller  104  may 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 controller  104  is 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 controller  104  may control the creation mode of the action plan for the movable body  10  on the basis of information regarding a characteristic of the movable body  10  and a task to be executed by the movable body  10 . 
     Specifically, the plan controller  104  may control the creation mode of the action plan for the movable body  10  on the basis of a moving characteristic of the movable body  10 . For example, the plan controller  104  may control the plan creator  105  to use a moving speed, a moving method, or a control method in moving of the movable body  10  as a parameter used in planning a route which the movable body  10  moves along. Thus, the plan controller  104  is 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 body  10 . For example, in a case where the moving speed of the movable body  10  is high, the plan controller  104  may control the creation mode of the plan creator  105  as to allow for creation of an action plan causing the movable body  10  to move in a direction away from the crowd  30  or the moving route of the crowd  30 . 
     Further, the plan controller  104  may control the creation mode of the action plan for the movable body  10  on the basis of a content of the task to be executed by the movable body  10 . For example, in a case where the task to be executed by the movable body  10  is a task configured to give higher priority to the movement of the movable body  10 , the plan controller  104  may so control the creation mode of the plan creator  105  as to allow for creation of an action plan that causes the moving distance of the movable body  10  to be shorter. Note that as the task configured to give higher priority to the movement of the movable body  10 , 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 creator  105  creates the action plan for the movable body  10  on the basis of the creation mode controlled by the plan controller  104 . Specifically, the plan creator  105  may create an action plan that causes the movable body  10  to move on the environment map created by the external-environment recognizer  101 , on the basis of the algorithm and the parameter controlled by the plan controller  104 . For example, the plan creator  105  may create the action plan that causes the movable body  10  to move, with use of the algorithm selected by the plan controller  104  from among a plurality of algorithms, including a machine-learning algorithm a rule-based algorithm, and the like, prepared in advance. Further, the plan creator  105  may set a condition for the route along which the movable body  10  is caused to move, or may change priorities in creating the action plan, on the basis of the various parameters controlled by the plan controller  104 . 
     The state recognizer  106  recognizes a state of the movable body  10  on the basis of the sensing result acquired from an internal sensor section  120 . Specifically, the state recognizer  106  may recognize a position, an attitude, and the like of the movable body  10  on the basis of the sensing result obtained by the internal sensor section  120  provided in the movable body  10 . 
     For example, in a case where the internal sensor section  120  includes an encoder provided at each joint of a leg or an arm, the state recognizer  106  may recognize the attitude of the movable body  10  by 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 section  120  includes an encoder provided on each wheel, the state recognizer  106  may recognize the position of the movable body  10  by calculating the moving direction and the moving distance of the movable body  10  from the sensing result obtained by the encoder. Further, in a case where the internal sensor section  120  includes an IMU (Inertial Measurement Unit) having a three-axis gyroscope and a three-way accelerometer, the state recognizer  106  may recognize the attitude or the position of the movable body  10  from the three-dimensional angular velocity and the acceleration of the movable body  10  measured by the IMU. In addition, in a case where the internal sensor section  120  includes a GNSS (Global Navigation Satellite System) sensor, the state recognizer  106  may recognize the position of the movable body  10  on the basis of position information from the GNSS sensor. 
     The task manger  107  manages the task to be executed by the movable body  10 . For example, the task manger  107  may manage the timings or the priorities of execution of tasks inputted to the movable body  10  by the user by means of the input section  130  or of tasks autonomously set by the movable body  10 . 
     The input section  130  is, 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 section  130  may be a microphone that accepts an audio input from a user. The input section  130  may be provided on the movable body  10 , or may be provided outside the movable body  10 . In a case where the input section  130  is provided outside the movable body  10 , the input section  130  may be a remote control device that transmits the input content to the movable body  10  by means of wireless communication or the like. 
     The driving controller  108  controls a driver section  140  on the basis of the action plan. Specifically, the driving controller  108  controls the operation of the movable body  10  by controlling the driver section  140  on the basis of the action plan created by the plan creator  105 . For example, the driving controller  108  may so control the driver section  140  that the movable body  10  moves along a route included in the action plan. 
     The driver section  140  is, for example, a motor or an actuator that drives a moving mechanism included in the movable body  10 . Specifically, the driver section  140  may 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 apparatus  100  including the above-described configuration is able to estimate the information regarding the characteristic of the crowd  30 , which is a collection of the individuals  20 , and to control the creation mode of the action plan for the movable body  10  on the basis of the estimated information regarding the characteristic of the crowd  30 . Accordingly, the information processing apparatus  100  is able to create, by referring to the moving characteristic or the density characteristic of the crowd  30 , an action plan for the movable body  10  that allows the movable body  10  to move efficiently without blocking the moving route of each of the individuals  20  included in the crowd  30 . 
     3. Relationship Between Information Processing Apparatus and Movable Body 
     Next, a relationship between the information processing apparatus  100  according to the present embodiment and the movable body  10  is to be described. The information processing apparatus  100  according to the present embodiment is able to configure various variations of systems together with the movable body  10 , as described below. That is, the information processing apparatus  100  is applicable to various control systems including the movable body  10  (e.g., a robot). 
     For example, the information processing apparatus  100  according to the present embodiment may be mounted on the movable body  10 . In such a case, the information processing apparatus  100  is able to perform the above-described control by performing transmission and reception of information with the external sensor section  110 , the internal sensor section  120 , the input section  130 , and the driver section  140  similarly mounted on the movable body  10  via an inner bus or the like. 
     In a case where the information processing apparatus  100  is mounted on the movable body  10 , it is possible to perform the above-described control by the movable body  10  alone 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 body  10  is able to perform evacuation guidance or the like of the individuals  20  without blocking the movement lines of the individuals  20  who have come to the space  50  (i.e., customers) even in emergencies. 
     Further, the information processing apparatus  100  according to the present embodiment may be provided separately from the movable body  10 , for example. Such a case is to be described with reference to  FIG.  5   .  FIG.  5    is a schematic explanatory diagram describing about a configuration example in a case where the information processing apparatus  100 , the movable body  10 , and the external sensor section  110  are provided separately. 
     As illustrated in  FIG.  5   , the information processing apparatus  100  is provided separately from the movable body  10  and the external sensor section  110 . The information processing apparatus  100  is able to perform the above-described control by performing transmission and reception of information via a network  60 . Specifically, the information processing apparatus  100  is able to transmit the created action plan to the movable body  10  by creating the action plan for the movable body  10  on the basis of information acquired from the movable body  10  and the external sensor section  110  via the network  60 . For example, in order to perform sensing on the entire space  50 , the external sensor section  110  may be provided on the ceiling or the like of the space  50  in which the crowd  30 , which is a collection of the individuals  20 , and the movable body  10  are present. Further, the information processing apparatus  100  may be a server or the like provided outside the space  50 . 
     In a case where the information processing apparatus  100  is provided separately from the movable body  10 , even if the movable body  10  has a simpler configuration, the information processing apparatus  100  is able to perform the above-described control. 
     Further, the information processing apparatus  100  is able to acquire the information regarding the characteristic of the crowd  30  from the external sensor section  110  for the entire space  50 . The information processing apparatus  100  is therefore able to optimize the action plan for the movable body  10  for the entire space  50 . Accordingly, for example, the information processing apparatus  100  is able to create an action plan for guiding the crowd  30  equally to a plurality of doorways  51  present in the space  50  at the time of a disaster, and cause the movable body  10  to perform the created action plan. 
     4. Operation Examples 
     4.1. First Operation Example 
     Next, referring to  FIG.  6  to  7 B , a first operation example of the information processing apparatus  100  according to the present embodiment is to be described.  FIG.  6    is a flowchart illustrating a flow of the first operation example of the information processing apparatus  100  according to the present embodiment. 
     The first operation example is an operation example of the information processing apparatus  100  in a case of causing the movable body  10  to so move as to avoid the moving route of the crowd  30 . 
     As illustrated in  FIG.  6   , first, the external-environment recognizer  101  acquires information regarding the space  50  in which the movable body  10  and the individuals  20  are present from the external sensor section  110  (S 101 ). Thereafter, the crowd recognizer  102  recognizes the crowd  30  present in the space  50  (S 103 ). Thereafter, the crowd characteristic estimator  103  estimates the information regarding the characteristic of the crowd  30  including the moving route of the crowd  30  (S 105 ). 
     Here, the plan controller  104  determines whether or not the moving route of the crowd  30  and the movable body  10  interfere with each other (S 107 ). In a case where the moving route of the crowd  30  and the movable body  10  interfere with each other (S 107 /Yes), the plan controller  104  so controls the creation mode of the action plan for the movable body  10  as to allow for creation of an action plan in which the moving route of the crowd  30  and the movable body  10  do not interfere with each other (S 109 ). In contrast, in a case where the moving route of the crowd  30  and the movable body  10  do not interfere with each other (S 107 /No), the information processing apparatus  100  skips the operation in step S 109 . Thereafter, the plan creator  105  creates the action plan for the movable body  10  on the basis of the creation mode (S 111 ). In addition, the driving controller  108  controls the movement of the movable body  10  by controlling the driver section  140  in accordance with the created action plan (S 113 ). 
     Referring to  FIGS.  7 A and  7 B , the above-described first operation example is described more specifically.  FIG.  7 A  is an explanatory diagram describing about estimation of the moving route of the crowd  30  in the first operation example.  FIG.  7 B  is an explanatory diagram describing about creation of the action plan for the movable body  10  in the first operation example. 
     For example, as illustrated in  FIG.  7 A  and  FIG.  7 B , the external-environment recognizer  101  and the crowd recognizer  102  recognize the positions of crowds  30 A,  30 B, and  30 C and movable bodies  10 A and  10 B that are present in the space  50 . 
     Next, the crowd characteristic estimator  103  estimates moving-route candidates P AA  and P AB  of the crowd  30 A as information regarding a characteristic of the crowd  30 A. Similarly, the crowd characteristic estimator  103  estimates moving-route candidates P BA  and P BB  of the crowd  30 B as information regarding a characteristic of the crowd  30 B, and estimates moving-route candidates P CA  and P CB  of the crowd  30 C as information regarding a characteristic of the crowd  30 C. The moving-route candidates of the crowds  30 A,  30 B, and  30 C can be estimated, for example, from the respective positions of the crowds  30 A,  30 B, and  30 C and the positions of the doorways  51 A and  51 B. 
     Thereafter, the crowd characteristic estimator  103  estimates a region W AA  which the crowd  30 A passes through upon moving, as the information regarding the characteristic of the crowd  30 A. Similarly, the crowd characteristic estimator  103  estimates a region W BB  which the crowd  30 B passes through upon moving, as the information regarding the characteristic of the crowd  30 B, and estimates a region W CA  which the crowd  30 C passes through upon moving, as the information regarding the characteristic of the crowd  30 C. For example, it is possible to estimate the region which the crowd  30 A passes through upon moving by providing a width according to the number of people in the crowd  30 A to the moving-route candidate P AA  having the shortest moving distance of the moving-route candidates P AA  and P AB  of the crowd  30 A estimated above. This is similarly applicable to the crowds  30 B and  30 C. 
     On this occasion, a plurality of route candidates is conceivable as an action plan in which each of the movable body  10 A that interferes with the region W AA  which the crowd  30 A passes through upon moving and the movable body  10 B that interferes with the region W BB  which the crowd  30 B passes through upon moving is caused to move to any of escape areas  53 A,  53 B, and  53 C. For example, regarding the movable body  10 A, a route candidate R A1  for moving to the escape area  53 A and a route candidate R A2  for moving to the escape area  53 C are conceivable. Regarding the movable body  10 B, a route candidate R B1  for moving to the escape area  53 A and a route candidate R B2  for moving to the escape area  53 B are conceivable. 
     In the information processing apparatus  100  according to the present embodiment, the plan controller  104  controls the creation mode of the action plan of the plan creator  105 . Thus, the plan creator  105  is able to efficiently create an action plan that causes the movable bodies  10 A and  10 B to escape from the moving routes or the like of the crowds  30 A,  30 B, and  30 C and to move to any of the escape areas  53 A,  53 B, and  53 C. 
     Specifically, regarding the movable body  10 A, the plan controller  104  is able to so control the creation mode as to allow for creation of an action plan including the route candidate R A1  that does not interfere with the moving route of the crowd  30 C although having a longer moving distance. Further, regarding the movable body  10 B, the plan controller  104  is able to so control the creation mode as to allow for creation of an action plan including the route candidate R B2  that does not pass across the moving route of the crowd  30 B although having a longer moving distance. 
     Thus, the information processing apparatus  100  is able to prevent the action plans for the movable bodies  10 A and  10 B and the moving routes of the crowds  30 A,  30 B, and  30 C from crossing, by estimating the moving routes of the crowds  30 A,  30 B, and  30 C and the regions which the crowds  30 A,  30 B, and  30 C pass through upon moving. Further, the information processing apparatus  100  is able to prevent the movable bodies  10 A and  10 B from becoming close to each of the individuals  20  included in the crowds  30 A,  30 B, and  30 C. The information processing apparatus  100  is therefore able to prevent ad hoc or staggering behavior of the movable bodies  10 A and  10 B. 
     4.2. Second Operation Example 
     Next, referring to  FIG.  8    and  FIG.  9   , a second operation example of the information processing apparatus  100  according to the present embodiment is to be described.  FIG.  8    is a flowchart illustrating a flow of the second operation example of the information processing apparatus  100  according to the present embodiment. 
     The second operation example is an operation example of the information processing apparatus  100  in a case where an action plan is created giving higher priority to moving the movable body  10  than preventing the crossing of the movable body  10  and the movement line of the crowd  30 . Note that in the second operation example, it is assumed that the operation corresponding to steps S 101  to S 103  of the first operation example has already been executed. 
     As illustrated in  FIG.  8   , first, the task manger  107  acquires a task to be executed by the movable body  10 , on the basis of an input received from the input section  130  (S201). Thereafter, the plan controller  104  determines whether or not the task to be executed by the movable body  10  is a highly urgent task (S 203 ). 
     In a case where the task to be executed by the movable body  10  is the highly urgent task (S 203 /Yes), the plan controller  104  so controls the creation mode as to allow for creation of an action plan giving higher priority to the movement of the movable body  10 . Specifically, the plan controller  104  determines whether or not the moving route of the crowd  30  included in the information regarding the characteristic of the crowd  30  and the movable body  10  interfere with each other (S 211 ), and optimizes the creation mode of the action plan for the movable body  10  on the basis of the characteristic of the movable body and the characteristic of the task (S 213 ). For example, the plan controller  104  may so optimize the algorithm and the parameter to be used in creation of the action plan as to allow the movable body  10  to execute the task with a shorter moving distance. Note that in a case where the moving route of the crowd  30  and the movable body  10  do not interfere with each other (S 211 /No), the plan controller  104  omits execution of step S 213  described above. 
     In a case where the task to be executed by the movable body  10  is not the highly urgent task (S 203 /No), the plan controller  104  so controls the creation mode as to allow for creation of an action plan in which the moving route of the crowd  30  and the movable body  10  do not interfere with each other, as in S 107  and S 109  of the first operation example. Specifically, the plan controller  104  determines whether or not the moving route of the crowd  30  included in the information regarding the characteristic of the crowd  30  and the movable body  10  interfere with each other (S 221 ), and controls the creation mode of the action plan for the movable body  10  (S 223 ). Note that in a case where the moving route of the crowd  30  and the movable body  10  do not interfere with each other (S 221 /No), the plan controller  104  skips the operation in step S 223  described above. 
     Thereafter, the plan creator  105  creates the action plan for the movable body  10  on the basis of the creation mode controlled by the plan controller  104  (S 205 ). Further, the driving controller  108  controls the movement of the movable body  10  by controlling the driver section  140  in accordance with the created action plan (S 207 ). 
     The above-described operation example is to be described more specifically with reference to  FIG.  9   .  FIG.  9    is an explanatory diagram describing about creation of the action plan for the movable body  10  in the second operation example. 
     For example, as illustrated in  FIG.  9   , assume that the task is a highly urgent task that gives higher priority to moving the movable body  10  to a destination  70 , and that the crowds  30 A and  30 B are present between the movable body  10  and the destination  70 . Note that it is assumed that a moving route p A  of the crowd  30 A and a region w A  which the crowd  30 A passes through upon moving have already been estimated by operation corresponding to steps S 101  to S 103  of the first operation example, and a moving route p B  of the crowd  30 B and a region w B  which the crowd  30 B passes through upon moving have already been estimated in a similar manner. 
     In such a case, a plurality of route candidates r 1 , r 2 , and r 3  is conceivable as routes for the movable body  10  to reach the destination  70 . The plan controller  104  is able to control which of the route candidates r 1 , r 2 , and r 3  is adopted as the action plan by controlling the creation mode on the basis of the characteristic of the movable body  10  and the characteristic of the task. Further, the plan controller  104  is able to set an optimum algorism and an optimum parameter for each of the route candidates r 1 , r 2 , and r 3  on the basis of the characteristic of the movable body  10  and the characteristic of the task. 
     For example, the route candidate r 1  that bypasses the outside of the crowd  30 A is a route that allows for avoidance of the crowds  30 A and  30 B and has a relatively short distance to the destination  70 . However, it is estimated that the crowd  30 A is a group in which the density of the individuals  20  is low and the randomness of the respective moves of the individuals  20  is high. Therefore, it is estimated that, regarding the route candidate r 1 , the possibility of an increase in moving time caused by a random factor is high. 
     In such a case, the plan controller  104  may so control the creation mode that a more optimal action plan is created, by changing the algorithm to be used by the plan creator  105  to 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 body  10  is low or in a case where the task involves a strict time limitation, the plan controller  104  may so control the creation mode that the route candidate r 1  is not adopted as the action plan, by lowering the priority of the route candidate r 1 . 
     For example, the route candidate r 2  passing between the individuals  20  inside the crowd  30 A is a route having the shortest distance to the destination  70 . However, because the route candidate r 2  passes through the inside of the crowd  30 A, 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 r 2  causes an unpleasant feeling in the individuals  20  by the movement of the movable body  10  depending on the density and the social connection degree of the individuals  20  in the crowd  30 A. 
     In such a case, the plan controller  104  may so control the creation mode that a more optimal action plan is created, by changing the algorithm to be used in the plan creator  105  to an algorithm suitable for an environment with high randomness, such as a machine-learning algorithm. Further, the plan controller  104  may control whether or not to regard the inside of the crowd  30 A as a passable region on the basis of the density and the social connection degree of the individuals  20  in the crowd  30 A. In addition, the plan controller  104  may so control the creation mode of the action plan that the movable body  10  does not approach the crowd  30 B in which the social connection degree of the individuals  20  is high. 
     The route candidate r 3  bypassing the outside of the crowd  30 B is a route that allows for avoidance of the crowds  30 A and  30 B but has the longest distance to the destination  70 . However, it is estimated that the crowd  30 B is a group in which the density of the individuals  20  is high and the randomness of the respective moves of the individuals  20  is low. Therefore, it is estimated that the route candidate r 3  is a route having the lowest randomness. 
     Accordingly, the plan controller  104  may so control the creation mode that the route candidate r 3  is easily adopted as the action plan by increasing the priority of the route candidate r 3  in a case where the randomness with respect to the movable body  10  is low. 
     Thus, the information processing apparatus  100  is able to more efficiently plan the route in a case where the movable body  10  passes a region close to the crowds  30 A and  30 B by understanding the characteristics of the crowds  30 A and  30 B. 
     5. Hardware Configuration Examples 
     In addition, referring to  FIG.  10   , a hardware configuration of the information processing apparatus  100  according to the present embodiment is to be described.  FIG.  10    is a block diagram illustrating a hardware configuration example of the information processing apparatus  100  according to the present embodiment. 
     The functions of the information processing apparatus  100  according to the present embodiment are implemented by cooperation between software and hardware described below. For example, the functions of the external-environment recognizer  101 , the crowd recognizer  102 , the crowd characteristic estimator  103 , the plan controller  104 , the plan creator  105 , the state recognizer  106 , the task manger  107 , and the driving controller  108  described above may be executed by a CPU  901 . 
     As illustrated in  FIG.  10   , the information processing apparatus  100  includes a CPU (Central Processing Unit)  901 , a ROM (Read Only Memory)  903 , and a RAM (Random Access Memory)  905 . 
     In addition, the information processing apparatus  100  may further include a host bus  907 , a bridge  909 , an external bus  911 , an interface  913 , an input unit  915 , an output unit  917 , a storage unit  919 , a drive  921 , a coupling port  923 , and a communication unit  925 . In addition, the information processing apparatus  100  may include other processing circuits, for example, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), or the like, in place of the CPU  901  or together with the CPU  901 . 
     The CPU  901  functions as a calculation unit or a control unit, and controls the overall operation of the information processing apparatus  100  in accordance with various programs recorded in the ROM  903 , the RAM  905 , the storage unit  919 , or a removable recording medium  927 . The ROM  903  stores a program and arithmetic parameters to be used by the CPU  901 . The RAM  905  temporarily stores a program to be used in execution by the CPU  901  and parameters to be used in the execution thereof. 
     The CPU  901 , the ROM  903 , and the RAM  905  are mutually coupled by the host bus  907  including an internal bus such as a CPU bus. In addition, the host bus  907  is coupled to the external bus  911  such as a PCI (Peripheral Component Interconnect/Interface) bus via the bridge  909 . 
     The input unit  915  is 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 unit  915  may be a microphone or the like that detects a voice of a user. The input unit  915  may be, for example, a remote control unit utilizing infrared rays or other radio waves, and may be an external coupling apparatus  929  compatible with the operation of the information processing apparatus  100 . 
     The input unit  915  further includes an input control circuit that supplies, to the CPU  901 , 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 apparatus  100  by operating the input unit  915 . 
     The output unit  917  is a unit that is able to visually or audibly present information acquired or generated by the information processing apparatus  100  to a user. The output unit  917  may 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 unit  917  may be a sound output device such as a speaker or a headphone, or may be a printing device such as a printer. The output unit  917  may output information obtained by the process of the information processing apparatus  100  as an image such as a text or a picture, or a sound such as a voice or an acoustic sound. 
     The storage unit  919  is a data storage device configured as an example of a storage section of the information processing apparatus  100 . The storage unit  919  may 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 unit  919  is able to store a program to be executed by the CPU  901 , various data, various data acquired from outside, and the like. 
     The drive  921  is a reading or writing unit for the removable recording medium  927  such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory. The drive  921  is built in or externally attached to the information processing apparatus  100 . For example, the drive  921  is able to read information recorded in the removable recording medium  927  mounted thereon and supply the information to the RAM  905 . Further, the drive  921  is able to write a record in the mounted removable recording medium  927 . 
     The coupling port  923  is a port for directly coupling the external coupling apparatus  929  to the information processing apparatus  100 . The coupling port  923  may 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 port  923  may be an RS-232C port, an optical audio terminal, an HDMI (registered trademark) (High-Definition Multimedia Interface) port, or the like. The coupling port  923  is able to perform transmission and reception of various data between the information processing apparatus  100  and the external coupling apparatus  929  by being coupled to the external coupling apparatus  929 . 
     The communication unit  925  is, for example, a communication interface including a communication device for coupling to the communication network  931 . The communication unit  925  may 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 unit  925  may 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 unit  925  is 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 network  931  coupled to the communication unit  925  may be a network coupled with or without a wire. The communication network  931  may 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 CPU  901 , the ROM  903 , and the RAM  95  to exhibit functions equivalent to those of the information processing apparatus  100  described 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 apparatus  100  of the present embodiment, a collection of the plurality of individuals  20  is recognized as the crowd  30 , and the characteristic of the recognized crowd  30  is taken into consideration in the action plan for the movable body  10 . This makes it possible to take into consideration a movement tendency of each of the individuals  20  in the action plan for the movable body  10 . Therefore, the information processing apparatus  100  is able to create an action plan for the movable body  10  that does not adversely affect the individuals  20  even in an environment in which many individuals  20  are present in a mixed manner. 
     Specifically, the information processing apparatus  100  is able to create an action plan to take an avoidance action with respect to the crowd  30  which is a collection of the individuals  20  rather than each of the individuals  20 . This makes it possible to prevent the movable body  10  from making an unstable move to deal with each of the individuals  20 . Further, the information processing apparatus  100  is able to similarly create an action plan for the movable body  10  in either normal or urgent situation. This allows the configuration of the system including the movable body  10  to be simpler and more shared. Accordingly, the information processing apparatus  100  makes it possible to reduce the operation cost of the system including the movable body  10 . In addition, the information processing apparatus  100  is able to create the action plan for the movable body  10  without minutely dividing the surrounding environment of the movable body  10 . This makes it possible to improve robustness (erroneous detection resistance) at a time of detection of the surrounding environment of the movable body  10 . 
     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. 
     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; and   a 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.   

     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. 
     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. 
     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. 
      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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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; and   controlling 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.   

     A program causing a computer to function as 
     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, and   a 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.