Patent Publication Number: US-11380103-B2

Title: Coverage device, moving body, control device, and moving body distributed control program

Description:
TECHNICAL FIELD 
     The present disclosure relates to a coverage device that uses a sensor to provide coverage of a target region, a moving body, a control device configured to control movement to bring a designated monitoring target into a monitoring range, and a moving body distributed control program. 
     BACKGROUND ART 
     Japanese Patent Application Laid-Open (JP-A) No. 2016-118996 discloses a monitoring system in which a flying device is controlled so as to move to an appropriate position to monitor a monitoring target. 
     To explain this in more detail, the monitoring system includes at least a flying device that monitors the ground from the air, and a sensor device. The sensor device includes a storage unit that stores an elevation angle for a monitoring target for each control type, a target calculating unit that, with reference to the storage unit, calculates a target position equivalent to an elevation angle corresponding to the control type when a control signal including the control type is input; and a flying device controlling unit for moving the flying device to the target position. Note that JP-A No. 2016-118996 does not employ logic that envisages plural moving bodies (flying devices). Moreover, it is assumed that the monitoring target has been identified in advance. 
     By way of reference, distributed management technology in which Voronoi regions are defined exists as technology for controlling plural moving bodies without resorting to centralized management. 
     For example, plural camera-equipped moving bodies are moved to potential risk areas (monitoring targets) set within a predetermined region. To monitor the potential risk areas, the predetermined region is partitioned into Voronoi regions, and each of the partitioned regions is set so as to be under the responsibility of a moving body. Collisions between the moving bodies can be avoided in this manner. 
     Technology for defining Voronoi regions is capable of providing logic ideally suited to cases in which plural moving bodies are envisaged. Moreover, there is no need to implement detailed action plans for each of the moving bodies, enabling the moving bodies to perform autonomous distributed decision-making while communicating with their surroundings. 
     Moreover, employing distributed processing instead of centralized processing reduces the calculation load, enabling solutions to be found in a realistic timeframe that is not dependent on scale. 
     Each moving body repeatedly moves toward the median point of the potential risk area within a Voronoi region surrounded by perpendicular bisectors between respective moving bodies. The definitions of the Voronoi regions are capable of changing from moment to moment. 
     SUMMARY 
     Technical Problem 
     However, in monitoring systems of related technology, including that of JP-A No. 2016-118996, in monitoring control involving plural moving bodies it is difficult to optimize selection of monitoring targets that require monitoring in cases in which the monitoring targets include unknown monitoring targets corresponding to regions in which a monitoring target has not hitherto been set, as well as known monitoring targets corresponding to regions that require continued monitoring. 
     An object of the present disclosure is to obtain a coverage device, a moving body, a control device, and a moving body distributed control program that are capable of performing monitoring control with mutual optimization even when monitoring targets include both unknown monitoring targets and known monitoring targets. 
     Solution to Problem 
     A coverage device of the disclosure includes: a sensor section that senses external environmental information; a defining section that defines an external environmental region, for which sensing information has not been obtained, as a target region; a determination section that determines the target region to be covered, based on a prediction result of prediction processing to predict a state quantity including uncertainty and an importance level of each target region, such that a surveillance performance according to the uncertainty of each target region and a tracking performance according to the importance level of each target region respectively become a predetermined level; and an instruction section that instructs allotting of actuators so as to provide coverage of the target region determined to be covered by the determination section. 
     According to the coverage device of the present disclosure, an external environmental region for which sensing information has not been obtained from the sensor section is defined as a target region. The target region is determined based on the prediction result of the prediction processing so as to make the surveillance performance and the tracking performance reach their predetermined levels. 
     For example, in cases in which target regions determined to require coverage include both an unknown target region that has hitherto not been a coverage target, and a known target region that has a coverage history and that requires continued coverage, an instruction is given to allot the actuators so as to provide coverage of the target regions such that the surveillance performance for the unknown target region and the tracking performance for the known target region reach the respective predetermined levels. This enables mutual optimization when performing monitoring control. 
     In the present disclosure, the surveillance performance of each target region is an Lx-norm of the uncertainty of the target region; and the tracking performance is a product of the importance level of the target region and a coverage ratio of the target region when covered. 
     Expressing the surveillance performance and the tracking performance as numerical values makes allotting of the actuators capable of simultaneous optimization more obvious. 
     In the present disclosure, the determination section determines that the target region is a region to be covered in a case in which a determination has been made that the target region is not covered globally. 
     This enables the target regions to receive global coverage. 
     In the present disclosure, the prediction processing includes: an acquisition section that acquires a transition of the state quantity of the target region as prior knowledge information; and a prediction section that predicts the state quantity of each of the target regions from a present point to a predetermined waypoint using a model to predict a change amount in the state quantity of each of the target regions from the state quantity of a corresponding target region that is defined based on the prior knowledge information acquired by the acquisition section and observation history for an actuator from the present point to a predetermined past point. 
     As the prediction processing to determine whether or not a target region requires coverage, the transition of the state quantity of the target region is acquired as the prior knowledge information, and the state quantity of each of the target regions from the present to a predetermined waypoint is predicted using the model to predict a change amount in the state quantity of each of the target regions. The model performs determination based on the acquired prior knowledge information and the observation history for the actuator from the present to a predetermined point in the past. 
     Predicting future transition from past transition enables prediction to be performed with a high level of precision. 
     The present disclosure further includes a gathering section that gathers observation history of a plurality of actuators. The gathering section gathers observation history of an actuator other than a given actuator, the actuator being capable of sharing coverage with the given actuator and the observation history that is used for determining the model is the observation history gathered by the gathering section. 
     Gathering observation history obtained by peripheral actuators to be employed in the prediction enables the prediction to be performed with a higher level of precision than when observation history acquired by a single actuator is employed to determine the model. 
     In the present disclosure, respective dedicated actuators address coverage of an unknown target region, which is a target region to be covered in order for the surveillance performance to satisfy the predetermined level, and coverage of a known target region, which is a target region to be covered in order for the tracking performance to satisfy the predetermined level. 
     Splitting responsibility in advance between a team responsible for coverage of unknown target regions and a team responsible for coverage of known target regions enables management control during allotting to be simplified. Note that the movement spaces (for example flight altitudes) may also be separated. 
     In the present disclosure, the target region is a blind spot region other than a region that the sensor section is capable of sensing, and the position and size of the blind spot region change over time. 
     If, for example, the sensor section used in the target region defined as an external environmental region for which sensing information has not been obtained is an imaging device, blind spot regions arise that are hidden by obstacles and cannot be captured. For example, if the obstacle is a vehicle, the blind spot region would change were the vehicle to move. Note that the type of obstacle depends on the type of sensor; for example if the sensor section is an infrared sensor, heat generating bodies become obstacles. 
     A moving body of the disclosure, by being disposed in a region of responsibility where the moving body does not interfere with other moving bodies, by including a sensor with a variable monitoring range, and by exchanging position information with other moving bodies, moves so as to change the region of responsibility while avoiding a collision. The moving body includes: a movement control section that, according to a predetermined prediction processing, moves the moving body based on prediction results of the prediction processing predicting a state quantity including an uncertainty and an importance level of respective monitoring targets within the region of responsibility of the moving body, such that a surveillance performance relating to the uncertainty of each of the monitoring targets within the region of responsibility of the moving body and a tracking performance relating to the importance level of each of the monitoring targets within the region of responsibility of the moving body satisfy respective predetermined levels. 
     The moving bodies of the present disclosure are moved based on the prediction results of predictions of a state quantity including uncertainty and an importance level of each monitoring target within the region of responsibility of the moving body itself, such that the surveillance performance relating to the uncertainty of the respective monitoring targets within the region of responsibility of the moving body itself and the tracking performance relating to the importance level of the respective monitoring targets within the region of responsibility of the moving body itself each reach the predetermined levels. 
     For example, in cases in which monitoring targets determined to require monitoring include both an unknown monitoring target that has hitherto not been a monitoring target, and a known monitoring target that has a coverage history and that requires continued monitoring, the moving bodies are moved such that the surveillance performance for the unknown monitoring target and the tracking performance for the known monitoring target reach the respective predetermined levels. This enables mutual optimization in monitoring control of both unknown monitoring target regions and known monitoring target regions. 
     A control device of the disclosure in a moving body distributed control system moves a plurality of moving bodies, the plurality of moving bodies, by respectively being disposed in regions of responsibility not interfering with each other, by respectively including a sensor with a variable monitoring range, and by exchanging position information with each other, moving so as to change the region of responsibility while avoiding collisions. The control device includes: a designation section that, according to a prediction processing, designates a monitoring target of a movement destination for a first moving body based on prediction results of the prediction processing predicting a state quantity including an uncertainty and an importance level for respective monitoring targets within the region of responsibility of the first moving body, such that a surveillance performance relating to the uncertainty of each of the monitoring targets within the region of responsibility of the first moving body and a tracking performance relating to the importance level of each of the monitoring targets within the region of responsibility of the first moving body satisfy respective predetermined levels; and a movement control section that moves the first moving body so that a monitoring target designated by the designation section falls within a monitoring range. 
     In the control device of the present disclosure, the designation section designates a monitoring target of a movement destination for each of the moving bodies based on prediction results predicting a state quantity including uncertainty and an importance level for respective monitoring targets within the region of responsibility of the moving body itself such that the surveillance performance relating to the uncertainty of each of the monitoring targets within the region of responsibility of the moving body itself and the tracking performance relating to the importance level of each of the monitoring targets within the region of responsibility of the moving body itself reach the respective predetermined levels. 
     The movement control section moves the moving body to bring a monitoring target designated by the designation section into the monitoring range. 
     For example, in cases in which monitoring targets determined to require monitoring include both an unknown monitoring target that has hitherto not been a monitoring target, and a known monitoring target that has a observation history and that requires continued monitoring, the monitoring targets configuring the movement destinations of the respective moving bodies are designated such that the surveillance performance for the unknown monitoring target and the tracking performance for the known monitoring target reach the respective predetermined levels. This enables mutual optimization in monitoring control of both unknown monitoring target regions and known monitoring targets. 
     The present disclosure further includes a determination section that determines, in a case in which the surveillance performance relating to the uncertainty of each of the monitoring targets within the region of responsibility of the first moving body and the tracking performance relating to the importance level of each of the monitoring targets within the region of responsibility of the first moving body satisfy the respective predetermined levels, to move the first moving body to a region of responsibility of a second moving body for which the surveillance performance relating to the uncertainty of each of the monitoring targets within the region of responsibility and the tracking performance relating to the importance level of each of the monitoring targets within the region of responsibility do not satisfy the respective predetermined levels. The movement control section moves the first moving body to the region of responsibility of the second moving body where the determination section has determined to move the first moving body. 
     Providing the determination section configured to determine, in cases in which the surveillance performance relating to the uncertainty of each of the monitoring targets within the region of responsibility of moving body itself and the tracking performance relating to the importance level of each of the monitoring targets within the region of responsibility of moving body itself have reached the respective predetermined levels, that the moving body itself should move to a region of responsibility of another moving body for which the surveillance performance relating to the uncertainty of each of the monitoring targets within the region of responsibility and the tracking performance relating to the importance level of each of the monitoring targets within the region of responsibility have not reached the respective predetermined levels enables the movement control section to move the moving body itself to the region of responsibility of the other moving body to which determination to be moved has been made. 
     The present disclosure further includes an information intercommunication section that, after the first moving body has been moved by being controlled by the movement control section, transmits information indicating a monitoring level of a monitoring target within the region of responsibility of the first moving body, and receives information indicating the monitoring level of the monitoring target within the region of responsibility of the second moving body; a judgement section that judges, based on the information received by the information intercommunication section, whether or not a condition for moving toward a monitoring target in the region of responsibility of the second moving body, which is outside of the current region of responsibility of the first moving body, has been met; and a mediation section that mediates, in order to finalize a moving body to move among a plurality of moving bodies including the first moving body, for which the judgement section has judged that the condition to move has been met. 
     The plural moving bodies are provided so as to be disposed in regions of responsibility not interfering with each other, and the each of the moving bodies is installed with a sensor capable of varying a monitoring range by moving within the regions of responsibility. The plural moving bodies exchange position information with each other to move to change the regions of responsibility while avoiding collisions. For example, Voronoi partition control may be applied as control to move the moving bodies while avoiding collisions. 
     The information intercommunication section exchanges information indicating the monitoring level of a monitoring target after being moved by the moving control means. 
     The judgement section make a judgement based on the received information as to whether or not the condition for moving to the region of responsibility of another moving body has been met. The mediation section executes mediation in order to finalize which moving body out of the plural moving bodies will move, including the moving body itself for which the judgement section has judged the condition to move to have been met. 
     This enables movement to a monitoring target region outside of the region of responsibility while avoiding collisions between the plural moving bodies, enabling a reduction in the coverage ratio of a monitoring target region. 
     In the present disclosure, the judgement section judges based on whether or not a collision with another moving body would occur on a movement path to an objective monitoring target. 
     The absence of any other moving bodies on the movement path to the objective monitoring target region is set as a condition. In cases in which another moving body is present on the movement path to the objective monitoring target region, collision avoidance is prioritized, and the movement is abandoned. 
     In the present disclosure, the mediation section mediates based on at least one of a time until the condition is met, a time required for movement, or demanded sensor functionality. 
     For example, in mediation by the mediation section based on the time until the condition is met, out of the plural moving bodies, the moving body for which the condition was met in the shortest time transmits information to limit the movement of the other moving bodies, thereby selecting this moving body. In mediation based on the time required for movement, the moving body capable of moving to the objective monitoring target soonest is selected. In mediation based on the demanded sensor functionality, for example, the moving body is selected based on the demanded information in cases in which respective moving bodies include a moving body that obtains captured visual information as information, and a moving body that obtains specific signals using an ultrasound sensor or an infrared sensor. 
     In the present disclosure, the prediction processing includes: an acquisition section that acquires a transition of the state quantity of the monitoring target as prior knowledge information; and a prediction section that predicts the state quantity of each of the monitoring targets from a present point to a predetermined waypoint using a model to predict a change amount in the state quantity of each of the monitoring targets from the state quantity of each of the monitoring targets, the model being determined based on the prior knowledge information acquired by the acquisition section and observation history information for the moving body from the present point to a predetermined point in the past. 
     Predicting future transition from past transition enables prediction to be performed with a high level of precision. 
     The present disclosure further includes a gathering section that gathers observation history information of the plurality of moving bodies. The gathering section gathers observation history information of a moving body other than a given moving body, the other moving body being capable of sharing monitoring with the given moving body and the observation history information that is used for determining the model is the observation history information gathered by the gathering section. 
     Gathering observation history information obtained by peripheral moving bodies to be employed in prediction enables the prediction to be performed with a higher level of precision than when observation history information obtained by a single moving body is employed in the prediction. 
     In the present disclosure, respective dedicated moving bodies independently address monitoring of an unknown monitoring target that is a monitoring target requiring monitoring in order for the surveillance performance to satisfy the predetermined level, and monitoring of a known monitoring target that is a monitoring target requiring monitoring in order for the tracking performance to satisfy the predetermined level. 
     Splitting responsibility in advance between a team responsible for monitoring of unknown monitoring targets and a team responsible for monitoring of known monitoring targets enables management control during allotting to be simplified. Note that the movement spaces (for example flight altitudes) may also be separated. 
     In the present disclosure, each surveillance performance is an Lx-norm of the uncertainty of a monitoring target and each tracking performance is the product of the importance level of the monitoring target and a coverage ratio when monitoring a monitoring target. 
     Expressing the surveillance performance and the tracking performance as numerical values makes allotting of the actuators capable of simultaneous optimization more obvious. 
     The present disclosure further includes an acquisition section that acquires movement speed information and movement plan information relating to the monitoring target; a determination section that determines a speed difference between the movement speed of the monitoring target and a movement speed of the moving body based on the movement speed information acquired by the acquisition section; an identification section that, based on the movement plan information acquired by the acquisition section, identifies an estimated position of the monitoring target after a predetermined time that is estimated from a current position of the monitoring target, and an estimated movement route along which movement of the monitoring target is estimated to the estimated position after the predetermined time in a case in which the movement speed of the monitoring target is determined to be faster than the movement speed of the moving body according to a determination result by the determination section; and a shortcut control section that controls such that the moving body moves toward the estimated position after the predetermined time identified by the identification section and the moving body also moves along the estimated movement route. 
     A monitoring target tracking control device that controls the moving bodies performs basic control to move plural moving bodies. Each moving body is disposed in a region of responsibility where the moving body will not interfere with other moving bodies, is installed with a sensor section with a variable monitoring range, and is configured to exchange position information with the other plural moving bodies in order to move to change the region of responsibility while avoiding collisions. 
     Under this basic control, the acquisition section acquires the movement speed information and movement plan information relating to a monitoring target. The speed difference between the movement speed of the monitoring target and the movement speed of the moving body is determined based on the acquired movement speed information. 
     In cases in which the movement speed of the monitoring target has been determined to be faster than the movement speed of the moving body from a determination result of the determination section, based on the movement plan information acquired by the acquisition section the identification section identifies the estimated position of the monitoring target estimated for the predetermined timing ahead of the current position of the monitoring target, and the estimated movement route estimated for movement of the monitoring target to the estimated position at the predetermined timing ahead. The shortcut control section performs control such that the moving body moves to the estimated position at the predetermined timing ahead as identified by the identification section, and also such that the moving body moves in a manner reflecting the estimated movement route. 
     This enables monitoring of the monitoring target by the moving bodies to be continued by monitoring target tracking control even in cases in which the movement speed of the monitoring target is faster than the movement speed of the moving body. 
     In the present disclosure, the identification section identifies an estimated position selected from a plurality of important waypoints that are present on a predetermined movement route of the monitoring target and that are prioritized for monitoring. 
     Identifying the estimated position selected from the important waypoints present on the movement route of the monitoring target that are prioritized for monitoring enables lag-free monitoring of important monitoring regions. 
     In the present disclosure, the shortcut control section calculates a remaining time for the moving body to arrive at the estimated position earlier than the monitoring target in a case in which the moving body is moved to the estimated position along a shortest route and continues to track movement of the monitoring target so as to minimize both the calculated remaining time and a distance to the monitoring target. 
     In the present disclosure, the shortcut control section controls such that the moving body moves along the movement route, the movement route being the estimated movement route and being a circular arc shape connecting a current position of the moving body to the estimated position after the predetermined time identified by the identification section. 
     When the estimated position has been identified, although movement in a straight line would enable the estimated position to be reached in the shortest amount of time, the remaining time to arrive at the estimated position earlier than the monitoring target is calculated, and movement to track the monitoring target is continued so as to minimize the calculated remaining time and the distance to the tracking target. For example, in contrast to movement in a straight line, movement along a circular arc shaped path keeping as close as possible to the monitoring target is performed so as to arrive ahead. Although the level of monitoring (coverage ratio) of the monitoring target is not ideal, it is possible to maintain the minimum required monitoring of the monitoring target along the movement path of the monitoring target. 
     Advantageous Effects 
     The present disclosure enables monitoring control to be performed with mutual optimization even when monitoring targets include both unknown monitoring targets and known monitoring targets. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic view of a moving body distributed control system according to an exemplary embodiment. 
         FIG. 2  is a block diagram illustrating a control system for operating moving bodies in a moving body distributed control system according to an exemplary embodiment. 
         FIG. 3  is a block diagram illustrating execution of a distributed control program executed by a moving body CPU, classified by function. 
         FIG. 4A  is a schematic diagram illustrating an observational history for a moving body according to an exemplary embodiment, the observational history illustrating transition of potential risk areas within a region running back in time from the current time. 
         FIG. 4B  is a schematic diagram illustrating a gathered state of observational histories from plural moving bodies. 
         FIG. 5  is a schematic diagram illustrating observational prediction of transition of potential risk areas within a region going forward from the current time for a moving body according to an exemplary embodiment. 
         FIG. 6A  is a coordinate plot illustrating sampled points in a region within which moving bodies according to an exemplary embodiment move. 
         FIG. 6B  illustrates graphs illustrating characteristics of norm values representing importance levels and uncertainty dependent on state quantities of sampled points at respective coordinates. 
         FIG. 7  is a graph illustrating characteristics in a case in which state quantities and uncertainty of sampled points at respective coordinates are expressed using positive and negative numerical values. 
         FIG. 8  is a flowchart illustrating a main routine of moving body distributed control executed by a distributed control section according to an exemplary embodiment. 
         FIG. 9A  is a flowchart of control illustrating a prior knowledge information subroutine to elaborate on the processing of step  300  in  FIG. 8 . 
         FIG. 9B  is a flowchart of control illustrating an observational history analysis subroutine to elaborate on the processing of step  302  in  FIG. 8 . 
         FIG. 9C  is a flowchart of control illustrating an observational history gathering subroutine to elaborate on the processing of step  304  in  FIG. 8 . 
         FIG. 10A  is a flowchart of control illustrating a prediction control subroutine to elaborate on the processing of step  306  in  FIG. 8 . 
         FIG. 10B  is a flowchart of control illustrating a moving body allotting subroutine to elaborate on the processing of step  308  in  FIG. 8 . 
         FIG. 10C  is a flowchart of control illustrating an information provision subroutine to elaborate on the processing of step  310  in  FIG. 8 . 
         FIG. 11  is a plan view illustrating a situation in an exemplary embodiment in which respective moving bodies are moving around, each of the moving bodies being allotted as either a known potential risk area or a known potential risk area in a specific region. 
         FIG. 12A  is a schematic view illustrating a moving body distributed control system according to a modified example of an exemplary embodiment, illustrating a state in which a fixed camera is incorporated into an autonomous distributed network. 
         FIG. 12B  illustrates a state in which a fixed camera is not incorporated into an autonomous distributed network. 
         FIG. 13  is a plan view illustrating a moving body distributed control system according to an Example 1 of an exemplary embodiment, illustrating a region in which moving bodies move. 
         FIG. 14A  is a plan view illustrating Voronoi partitioned regions during potential risk area designation according to Example 1 of the exemplary embodiment. 
         FIG. 14B  illustrates moving bodies after moving based on control rule 1. 
         FIG. 15A  is a plan view illustrating a correlative relationship between a moving body and a potential risk area according to Example 1 of the exemplary embodiment in a case in which a coverage ratio=1. 
         FIG. 15B  illustrates a case in which there is no coverage. 
         FIG. 15C  illustrates a case in which the coverage ratio is less than 1. 
         FIG. 15D  illustrates a case in which the coverage ratio is greater than 0 but less than 1. 
         FIG. 15E  illustrates moving bodies capable of moving beyond a Voronoi region within a region. 
         FIG. 16A  is a coverage transition diagram illustrating change in coverage ratio according to a priority level when potential risk areas are set with an importance level. 
         FIG. 16B  is a coverage transition diagram illustrating change in coverage ratio according to a priority level when potential risk area are set with an importance level. 
         FIG. 17  is a plan view of Voronoi partitioned regions according to Example 1 of the exemplary embodiment, illustrating moving bodies after moving from the state in  FIG. 14B  based on control rule 2. 
         FIG. 18  is a flowchart illustrating a potential risk area monitoring control routine according to Example 1 of the exemplary embodiment. 
         FIG. 19A  is a flowchart illustrating a potential risk area monitoring control routine according to Example 2 of the exemplary embodiment, in which the moving body to move to an objective potential risk area is decided on a first-come-first-served basis. 
         FIG. 19B  is a flowchart illustrating a potential risk area monitoring control routine according to Example 2 of the exemplary embodiment, in which the moving body to move to an objective potential risk area is decided on a first-come-first-served basis. 
         FIG. 20  illustrates an example according to Example 3 of the exemplary embodiment, in which a moving body distributed control system is applied to monitoring by a home security system. 
         FIG. 21A  illustrates an example according to Example 4 of the exemplary embodiment, in which a moving body distributed control system is applied to monitoring of a crossroad for accident prevention. 
         FIG. 21B  is illustrates an example according to Example 4 of the exemplary embodiment, in which a moving body distributed control system is applied to monitoring of a crossroad for accident prevention. 
         FIG. 22  is a flowchart illustrating a tracking mode selection control routine corresponding to control in an input calculation subroutine under control rule 1 at step  104  in  FIG. 18 , and an input calculation subroutine under control rule 1 at step  108  in  FIG. 18 . 
         FIG. 23A  is a diagram illustrating transition of moving bodies and potential risk areas over a time series based on control rule 3 according to Example 5 of the exemplary embodiment. 
         FIG. 23B  is a diagram illustrating transition of moving bodies  10  and potential risk areas  28  over a time series based on control rule 4. 
         FIG. 24A  is a plan view demonstrating the advantageous effects of tracking control based on control rule 4 according to Example 5 by drawing comparison to a comparative example. 
         FIG. 24B  illustrates a comparative example corresponding to  FIG. 24A . 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
       FIG. 1  is a schematic view of a moving body distributed control system  50  according to the present exemplary embodiment. 
     The moving body distributed control system  50  of the present exemplary embodiment is illustrated by plural moving bodies  10 , and plural regions  12  within which the moving bodies  10  move in order to perform monitoring. 
     In  FIG. 1 , the plural moving bodies  10  are categorized into three groups  52 . The groups  52  are responsible for monitoring three respective regions  12 . 
     The group configurations of the moving bodies  10 , the number of monitoring regions, and the like are not limited to being in threes as in  FIG. 1 . Configuration may be made in which a single group  52  monitors a single region  12 , or configuration may be made in which two, or four or more groups  52  each monitor two, or four or more regions  12 . 
       FIG. 2  is a block diagram illustrating a control system for operating the moving bodies  10  (see  FIG. 1 ) applied with the present exemplary embodiment. 
     Each of the moving bodies  10  is capable of unmanned movement within the range of the corresponding region  12 , and is installed with a control device  14  provided with a microcomputer to execute control, including of this movement, as illustrated in  FIG. 2 . 
     The microcomputer of the control device  14  includes a CPU  16 A, RAM  16 B, ROM  16 C, an input/output port (I/O)  16 D, and a bus  16 E such as a data bus or control bus connecting these components together. A monitoring module  18 , a movement module  20 , a position recognition module  22 , and a communication module  24  are connected to the I/O  16 D. 
     The control device  14  loads and executes a moving body distributed control program pre-stored in the ROM  16 C in the CPU  16 A, for example, in order to control operation of the monitoring module  18 , the movement module  20 , the position recognition module  22 , and the communication module  24 . Note that the distributed control program may be recorded on a non-transitory recording medium such as a CD-ROM or a DVD and loaded into the ROM  16 C from the non-transitory recording medium. 
     Monitoring Module  18   
     A camera configured to image a specific monitoring range (field of view) from the position of the moving body  10  is a typical example of a device applied as the monitoring module  18 . 
     The monitoring module  18  is not limited to a camera that performs image capture, and may, for example, be configured to emit electromagnetic waves (radar, laser, ultrasound, or the like) to detect geographical features (landmarks) or the like. 
     Movement Module  20   
     Each of the moving bodies  10  of the present exemplary embodiment is a flying object (for example a drone), and includes plural propellers driven by independent drive sources (motors) as devices applied as the movement module  20 . The moving body  10  is capable of flying in a direction toward a destination and stopping (hovering) at a spatial destination position by controlling drive of the motors. 
     Note that the moving bodies  10  are not limited to flying objects, and configuration may be made with movement modules  20  for ground-based or water-based movement. Moreover, plural devices may be employed in combination. As an even broader concept, fixed-installation monitoring cameras may also be included under the definition of the movement module  20  if incorporated into an autonomous distributed network. 
     Note that such monitoring cameras may have a fixed imaging range, or may be provided with a swinging mechanism to enable the monitoring range of the monitoring module  18  to be changed. 
     Position Recognition Module  22   
     The position recognition module  22  has a function of recognizing the position of the moving body  10  itself. The position recognition module  22  includes a sensor for at least one out of GPS, laser, radar, ultrasound, motion capture, a camera, wireless communication, or wireless signal strength (distance information) as a device to obtain position information. 
     The position recognition module  22  recognizes the position of the moving body  10  itself using three-dimensional spatial coordinates or the like based on detection results (detection signals) of the sensor. 
     As well as recognizing the position of the moving body  10  itself, the position recognition module  22  also acquires position information relating to other moving bodies  10  through the communication module  24 , described below, and calculates distances therebetween in order to recognize the relative positional relationships of the plural moving bodies  10 . 
     Communication Module  24   
     A wireless communication-equipped device is provided as a device of the communication module  24 . Functionality of the wireless communication of the communication module  24 , which is used to communicate between a base station (server)  54  (see  FIG. 1 ) performing overall monitoring management and/or other moving bodies  10 , includes a position information exchange section that exchanges position information, a coverage ratio exchange section that exchanges information (coverage ratio information) relating to the monitoring level (referred to as “coverage ratio”) of a designated monitoring target (also referred to as a “potential risk area” or “target”), and a mediation information exchange section that exchanges mediation information relating to the allotting of potential risk areas. Note that such communication may also be performed exclusively between the moving bodies  10 , without involving the base station (server)  54 . 
     Note that the base station  54  is not a necessity if the moving body distributed control system  50  can be achieved by mutual information exchange functionality of the respective moving bodies  10 . The installation or otherwise of the base station  54  may be decided in consideration of the amount of past information (big data) to be managed. 
     The mediation information is information used to determine whether or not a moving body  10  will move toward a potential risk area, and distinguishes potential risk areas using labels (positive or negative). For example, a potential risk area defined as “positive” requires monitoring, while a potential risk area defined as “negative” does not require monitoring. The potential risk areas are regions that can be designated as either surveillance regions or tracking regions based on determination through predictive control executed in the present exemplary embodiment. 
     The wireless communication of the communication module  24  further includes a monitoring information transmission section that transmits monitoring results of the monitoring module  18  (for example captured information in the case of a camera) to the base station  54 . 
     As illustrated in  FIG. 1 , one example of the regions  12  monitored by the moving bodies  10  is a road (parking lot or the like) where vehicles  56  are present. The moving bodies  10  monitor the road on which the vehicles  56  travel in order to provide the vehicles  56  (drivers) with spatial data in which no blind spots are present. 
     Potential risk areas  28  present in the regions  12  illustrated in  FIG. 1  include known potential risk areas  28  and unknown potential risk areas  28 . 
     The known potential risk areas  28  (shaded within solid rectangular lines in  FIG. 1 ) are regions (tracking regions) toward which the moving bodies  10  are guided based on an importance level, described later, since it is necessary to track the potential risk area. 
     The unknown potential risk areas  28  (illustrated by question marks inside dotted rectangular lines in  FIG. 1 ) are regions (surveillance regions) toward which the moving bodies  10  need to be guided based on a surveillance performance, described later. 
     The unknown potential risk areas  28  include regions from which a known potential risk area  28  has been expunged from a history with the passage of time so as to become an unknown potential risk area  28 . 
     Information Processing Control by Distributed Control Program 
       FIG. 3  is a block diagram illustrating functionality of a distributed control program (referred to hereafter as a distributed control section  58 ) executed by the CPU  16 A (see  FIG. 2 ) of each moving body  10 . Although the distributed control section  58  illustrated in  FIG. 3  is not limited to a hardware configuration for distributed control, the distributed control section  58  be realized by a dedicated device such as an Application Specific Integrated Circuit (ASIC), or a hardware configuration such as a programmable logic device instead of by a microcomputer, or may be realized by a combination of plural different types of hardware configuration. 
     As illustrated in  FIG. 3 , the distributed control section  58  includes a prior knowledge acquisition section  60 . The prior knowledge acquisition section  60  accesses a big data management server (this may be either offline or online) in the base station  54  (see  FIG. 1 ) or the like, and acquires prior knowledge information relating to the regions  12  in advance. 
     The prior knowledge information relating to each of the regions  12  includes at least parameters used when determining dynamics of the potential risk areas  28  in the region  12 . The prior knowledge information relating to the regions  12  further includes information relating, for example, to the frequency with which potential risk areas  28  appear in the region  12 , how such potential risk areas  28  behave, how movement of the potential risk areas  28  is distributed, or where importance levels are highest. 
     The following formula (Equation 1) is established when a state quantity of a potential risk area  28  is denoted x.
 
 {dot over (x)}=f ( q,x )  Equation 1
 
     wherein 
     {dot over (x)}: predicted target dynamics (including deterministic elements and probabilistic elements) 
     x: target state quantity 
     q: parameter for determining target dynamics (for example in a linear discrete-time system, the variable of matrix F, G in x[k+1]=F(q)×[k]+G(q) would be denoted q) 
     The state quantity x of the potential risk area  28  includes the uncertainty and importance level of the potential risk area  28 . 
     The prior knowledge information acquisition section  60  is connected to a history analysis section  62 . As illustrated in  FIG. 4A , the history analysis section  62  acquires an observational history of each of the moving bodies  10  for a predetermined timeframe (in the present exemplary embodiment, a timeframe corresponding to a number of instances Tp at unit time intervals running back in time from the current time t (namely, t−Tp)) for each of the moving bodies  10 . For example, if the number of instances Tp is 100, an observational history corresponding to the past 100 frames would be acquired. 
     Precision increases the finer the time resolution (unit time) of acquisition, whereas the processing speed increases the lower the time resolution. The time resolution may be decided according to the processing power of the CPU  16 A (see  FIG. 2 ), the precision with which the regions  12  to be monitored are monitored, and so on. The number of instances Tp may be decided in consideration of the rate of change of the potential risk areas within the region  12  and the like. 
     For example, the observational history includes both known potential risk areas  28  and unknown potential risk areas  28  as described above. It is expected that the state quantities of these potential risk areas  28  will change over time. 
     The history analysis section  62  is used to ascertain, for each of the moving bodies  10 , the state quantities of the known potential risk areas  28  and the unknown potential risk areas  28  in the timeframe spanning back in time by the last number of instances Tp at unit time intervals from the current time t. 
     The observational history of potential risk areas  28  by an i th  moving body  10  (i being a number (positive integer) allotted to each of the plural moving bodies  10  present in a region  12  under their responsibility) can be expressed as in the following formula (Equation 2).
 
 X   i : =( x   i ( t ), x   i ( t− 1), . . . , X   i ( t−T   p ))  Equation 2
 
     wherein 
     Xi: observational history of i th  moving body 
     t: current time 
     t−1: timing at one unit time before current time 
     t−Tp: timing at Tp unit times before current time 
     The history analysis section  62  is connected to a history gathering section  64 . Information (Xi) corresponding to each of the moving bodies  10  analyzed by the history analysis section  62  is gathered in the history gathering section  64 . For example, as illustrated in  FIG. 4B , a sum of sets of including information relating to the observational histories of the moving body  10  itself as well as information relating from other moving bodies  10  in the vicinity thereof is calculated (see Equation 3). 
     
       
         
           
             
               
                 
                   
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     wherein 
     j: total number of moving bodies 
     The history gathering section  64  is connected to a prediction control section  66 . The gathered information is sent to the prediction control section  66 . 
     As illustrated in  FIG. 5 , the prediction control section  66  predicts future dynamics q′ of each moving body  10  for a predetermined timeframe (in the present exemplary embodiment, a timeframe going forward from the current time t by a number of instances Tf at unit time intervals (namely, t+Tf)) for each of the moving bodies  10 . For example, if the number of instances Tf is 100, future observational predictions corresponding to 100 frames would be acquired. 
     Precision increases the finer the time resolution (unit time) of acquisition, whereas the processing speed increases the lower the time resolution. The time resolution may be decided according to the processing power of the CPU  16 A (see  FIG. 2 ), the monitoring precision with which the regions  12  to be monitored are monitored, and so on. The number of instances Tf may be decided in consideration of the rate of change of the potential risk areas within the region  12  and the like. Moreover, the number of instances Tf does not necessarily have to be correlated to the number of instances Tp applied for the history analysis section  62 . 
     For example, the future prediction includes both known potential risk areas  28  and unknown potential risk areas  28  as described above. It is expected that the positions of these potential risk areas  28  will change over time. 
     The prediction control section  66  is used to ascertain (predict), for each of the moving bodies  10 , the state quantities of the known potential risk areas  28  and the unknown potential risk areas  28  in the timeframe spanning the next number of instances Tf at each unit time from the current time t. 
     Prediction of the state quantities of potential risk areas  28  by an i th  moving body  10  (i being a number (positive integer) allocated to each of the plural moving bodies  10  present in a region  12  under their responsibility) can be expressed as in the following formula (Equation 4).
 
( {circumflex over (x)}   i ( t+ 1), . . . ,  {circumflex over (x)}   i ( t+T   f ))  (4)
 
     Next, change amounts in the state quantities of these potential risk areas are predicted as expressed in the following formula (Equation 5), using the parameter qi used in determining dynamics of the potential risk areas for each moving body  10 . 
     
       
         
           
             
               
                 
                   
                     
                       
                         
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     The prediction control section  66  is connected to a moving body allotting section  68 . Prediction results of the prediction control section  66  (estimated dynamics q′) are sent to the moving body allotting section  68 . 
     The moving body allotting section  68  determines potential risk areas that require monitoring so as to satisfy predetermined levels for both a surveillance performance and a tracking performance. The plural moving bodies  10  are allocated the potential risk areas determined to be potential risk areas that require monitoring (simultaneous optimization). 
     The respective moving bodies  10  subjected to simultaneous optimization start moving toward their allocated potential risk areas to monitor the potential risk areas. 
     Surveillance Performance 
     The surveillance performance is a value determined according to the uncertainty of the information relating to each potential risk area  28  that requires monitoring in the region  12 . The surveillance performance is, for example, an Lx-norm of a vector expressing the uncertainty of the information relating to the respective potential risk area  28 . 
     Tracking Performance 
     The tracking performance represents the product of the importance level of a potential risk area and the coverage ratio (an overlap rate of potential risk areas and moving bodies  10 ). The tracking performance enables, for example, a judgement to be made to continue tracking a known potential risk area with a high importance level, or whether to continue or stop tracking of a known potential risk area with a low importance level. 
     In the present exemplary embodiment, the surveillance performance and the tracking performance are mutually analyzed to determine potential risk areas that require monitoring, and to allot the plural moving bodies  10  to potential risk areas determined to be potential risk areas that require monitoring. 
     For example, as illustrated in  FIG. 6A , each region  12  is classified into rectangular potential risk areas  28 , and labels are used to distinguish respective points P. A moving body  10  moves as illustrated by the intermittent line A. 
     Each point P is set with a state quantity x (distinguished by the label of the point P). The potential risk areas  28  include unsampled points (white sectors) and sampled points (shaded sectors). 
     From the illustration of  FIG. 6A , uncertainty can be expressed in terms of uncertainty of information, as illustrated in  FIG. 6B . 
     In the upper graph in  FIG. 6B , a characteristics curve B illustrated by a solid line represents the importance levels of the potential risk areas, as estimated based on the sampled points in  FIG. 6A  (for which uncertainty is at a minimum value). When the characteristics curve B expresses a high importance level, the importance level of the corresponding potential risk area is estimated to be high. 
     However, the larger the number of unsampled points present, the greater the uncertainty, resulting in greater divergence from the characteristics curve B illustrated by the solid line. This causes a large discrepancy in importance levels, as between the characteristics curve C (illustrated by a dashed line) and the characteristics curve D (illustrated by a single-dotted dashed line). This large discrepancy corresponds to the magnitude of uncertainty. 
     Greater emphasis is placed on surveillance of points with a large degree of uncertainty, and points with a high importance level. A permissible level is set in advance for the uncertainty (norm value), and the moving bodies are guided toward potential risk areas where the uncertainty is greater than the permissible level, in order to lower the surveillance performance (uncertainty). 
     Even if the uncertainty is lower than the level, points having a high importance level are subjected to further tracking. 
     The moving bodies  10  are allotted in this manner in order to perform simultaneous optimization for both the surveillance performances and tracking performance. 
     Note that as illustrated in  FIG. 6B , as time elapses after sampling a sampled point (for example point P 1 ), the uncertainty increases with the passage of time. Namely, the point P 1  is an example where a rate of forgetting is set so as to gradually deviate from the characteristics curve B illustrated by the solid line, from the perspective that old data is less reliable. 
     As illustrated in  FIG. 7 , the uncertainty of unknown potential risk areas may be simply expressed using negative numbers (for example less than 0 down to −1), and the importance levels of known potential risk areas may be simply expressed using positive numbers (for example from 0 to +1). A distinction may be drawn such that a known potential risk area in which a target object (for example a vehicle) is present may be expressed by “+1”, and a known potential risk area in which a target object is not present may be expressed by “0”. 
     As illustrated in  FIG. 3 , the moving body allotting section  68  is connected to an information processing section  70 . The information processing section  70  acquires information from the moving bodies  10  allotted by the moving body allotting section  68 , and provides reproduced spatial data in which no blind spots are present to a driver of a vehicle  56  (see  FIG. 1 ) present in the region  12  or the like, and warns of unexpected situations. 
     Explanation follows regarding operation of the present exemplary embodiment, with reference to the flowcharts illustrated in  FIG. 8  to  FIG. 10C . 
       FIG. 8  is a flowchart illustrating a main routine of moving body distributed control executed by the distributed control section  58 . 
     At step  300 , the prior knowledge information is acquired (see  FIG. 9A , described later). 
     At the next step  302 , the observational histories are analyzed (see  FIG. 9B , described later). 
     At the next step  304 , the observational histories of plural of the moving bodies  10  are gathered (see  FIG. 9C , described later). 
     At the next step  306 , the optimal value of the parameter q for determining dynamics of the potential risk areas  28  is obtained, and the state quantities of the potential risk area  28  are predicted (see  FIG. 10A , described later). 
     At the next step  308 , potential risk areas that require monitoring are determined based on the prediction results for the state quantity of the potential risk area  28 , and the moving bodies  10  are allocated (see  FIG. 10B , described later). 
     At the next step  310 , the acquired information is applied. Namely, the information is provided to users that require it. 
       FIG. 9A  is a flowchart of control illustrating a prior knowledge information subroutine to illustrate the processing of step  300  in  FIG. 8  in more detail. 
     At step  312 , the big data management server is accessed, and processing transitions to step  314 , where a region  12  to be monitored is designated. Processing then transitions to step  316 . 
     At step  316 , the parameter q used for determining the dynamics of the potential risk areas  28  is acquired from the big data management server as prior knowledge information relating to the region  12  designated at step  314 . Moreover, for example, information relating to the frequency with which potential risk areas  28  appear in the region  12 , how such potential risk areas  28  behave, how movement of the potential risk areas  28  is distributed, or where the importance level is highest is also acquired, and then the routine is ended. 
       FIG. 9B  is a flowchart of control illustrating an observational history analysis subroutine to illustrate the processing of step  302  in  FIG. 8  in more detail. 
     At step  320 , the number of instances Tp running back in time from the current time t at unit time intervals is set, and processing transitions to step  322 . 
     At step  322 , the observational history for each unit time interval is acquired, and processing transitions to step  324 . 
     At step  324 , a state quantity x representing the transition of the potential risk areas  28  since Tp instances of the unit time previously is identified based on the formula of Equation 2, and the routine is ended. 
       FIG. 9C  is a flowchart of control illustrating an observational history gathering subroutine to illustrate the processing of step  304  in  FIG. 8  in more detail. 
     At step  326 , first, the moving bodies  10  subject to the gathering are identified. Although gathering may be performed for all of the moving bodies  10  in a single group  52 , limiting the gathering to other moving bodies  10  positioned in the vicinity of the moving body  10  itself prevents a drop in precision, thereby enabling the precision of information to be efficiently raised. 
     At the next step  328 , the state quantities of the potential risk areas as obtained by the plural moving bodies  10  are gathered based on the formula of Equation 3, and the routine is ended. This enables the precision of the observational history ascertained by the moving body  10  itself to be raised. 
       FIG. 10A  is a flowchart of control illustrating a prediction control subroutine to illustrate the processing of step  306  in  FIG. 8  in more detail. 
     At step  330 , the optimal value of the parameter q used for determining the dynamics of the potential risk areas  28  is obtained, based on the parameter q used for determining the dynamics of the potential risk areas  28  that was acquired as prior knowledge information and the result of gathering the state quantities of the potential risk areas obtained from the plural moving bodies  10 . 
     At step  331 , the number of instances Tf advancing forward from the current time t at unit time intervals is set, and processing transitions to step  332 . 
     At step  332 , change in the state quantities of the potential risk areas  28  is predicted based on the formula of Equation 5, and prediction of the state quantities of the potential risk areas  28  is repeated to predict state quantities x′ of the potential risk areas  28  for the number of instances Tf, and the routine is ended. 
       FIG. 10B  is a flowchart of control illustrating a moving body allotting subroutine to illustrate the processing of step  308  in  FIG. 8  in more detail. 
     At step  336 , a numerical value for the uncertainty of each potential risk area  28  is taken from the prediction result for the state quantities of the potential risk areas  28  to calculate the surveillance performance. For example, if the surveillance performances are expressed by norm values of a vector expressing the uncertainty of each potential risk area, the surveillance performance becomes greater the greater the uncertainty. 
     At the next step  338 , potential risk areas  28  that require monitoring in order for the surveillance performance computed at step  336  to satisfy a predetermined level are determined. 
     At the next step  340 , an importance level for each potential risk area  28  is taken from the prediction results for the state quantities of the potential risk areas  28 , and processing transitions to step  342 . 
     At step  342 , the tracking performance are computed. Namely, the tracking performance is expressed by the importance level multiplied by the coverage ratio. At the next step  344 , the potential risk areas  28  that require monitoring in order for the tracking performance obtained at step  342  to satisfy a predetermined level are determined, and processing transitions to step  346 . 
     At step  346 , the moving bodies  10  are allotted to the potential risk areas  28  so as to perform simultaneous optimization of surveillance and tracking. Processing then transitions to step  348 , surveillance and tracking are instructed, and the routine is ended. 
       FIG. 10C  is a flowchart of control illustrating an information provision subroutine to illustrate the processing of step  310  in  FIG. 8  in more detail. 
     At step  350 , a judgement is made as to whether or not information has been acquired. In cases in which determination is negative, the routine is ended. 
     In cases in which determination is affirmative at step  350 , processing transitions to step  352 , and blind spot coverage information is generated. For example, as information for a vehicle, reproduced spatial data in which no blind spots are present is generated, processing transitions to step  354 , and this information is provided to a user. To explain in more detail, a driver who is looking for a space to park their car in a parking lot is provided with a map of the entire parking lot, enabling the driver to move directly to a parking space. Moreover, warning of the presence of pedestrians in the parking lot enables safer driving to be promoted. 
       FIG. 11  is a plan view illustrating a situation in which respective moving bodies  10  are moving around in a particular region  12 , each of the moving bodies  10  being allotted to either an unknown potential risk area  28  that requires monitoring in order for the surveillance performance to meet the predetermined level, or a known potential risk area  28  that requires monitoring in order for the tracking performance to meet the predetermined level. Note that in  FIG. 11 , the suffixes A, B, C are appended in order to distinguish between the individual moving bodies  10 , and the suffixes A, B, C, D, E are appended in order to distinguish between the individual potential risk areas  28 . 
     A moving body  10 A heads toward a known potential risk area  28 A, and is capable of collecting information while attaining a coverage ratio of 1 (100%). 
     A moving body  10 B heads toward a known potential risk area  28 B, and in order to raise the coverage ratio, the moving body  10 B moves within the range of the potential risk area  28 B, enabling the coverage ratio to be raised and information to be collected. 
     A moving body  10 C is initially allotted to an unknown potential risk area  28 C, and moves within the range thereof. As a result, the unknown potential risk area  28 C becomes a known potential risk area, and the moving body  10 C is then allotted to the next unknown potential risk area  28 D, and moves in the range thereof. As a result, the unknown potential risk area  28 D becomes a known potential risk area, and the moving body  10 C is then allotted to a known potential risk area  28 E, where it is capable of collecting information while attaining a coverage ratio of 1 (100%). 
     MODIFIED EXAMPLES OF PRESENT EXEMPLARY EMBODIMENT 
     In the present exemplary embodiment, out of the plural moving bodies  10  present in each group  52  that monitors a particular region  12 , the proportion of moving bodies  10  allotted to unknown potential risk areas  28  that require monitoring in order for the surveillance performance to satisfy the predetermined level, as opposed to moving bodies  10  allotted to known potential risk areas  28  that requires monitoring in order for the tracking performance to satisfy the predetermined level is 1:1 (there is a 50% probability of either). In other words, the moving bodies  10  are allotted to either surveillance or tracking dependent on the result of simultaneous optimization of surveillance and tracking. 
     However, in a modified example, the moving bodies may be classified in advance as either a surveillance-only moving body  10  (the shaded moving bodies  10 S in  FIG. 12 ), or a tracking-only moving body  10  (the unshaded moving bodies  10 F in  FIG. 12 ). Note that although three moving bodies  10 S are deployed as a surveillance team and two moving bodies  10 F are deployed as a tracking team in  FIG. 12 , there is no limitation to these numbers and ratio. In addition to the fixed-purpose moving bodies  10 S and  10 F, moving bodies  10  that are capable of being allotted to either surveillance or tracking, as in the present exemplary embodiment, may also be present. 
     Moreover, instead of being surveillance-only or tracking-only moving bodies  10 , some or all of the moving bodies  10  may be allotted in a ratio favoring one or the other out of surveillance or tracking (for example 8:2, 3:7, etc.). 
     Note that fixed cameras  72  installed in the regions  12  may configure a networked range in the distributed control system.  FIG. 12A  is a perspective view in which a fixed camera  72  configures a networked range in the distributed control system, and  FIG. 12A  is a perspective view in which a fixed camera  72  does not configure a networked range in the distributed control system. 
     EXAMPLES 
     Explanation follows regarding examples of application of the moving body distributed control system  50  of the present exemplary embodiment to control movement toward potential risk areas  28  using Voronoi regions. 
     Example 1 
       FIG. 13  illustrates moving bodies  10  applied in a moving body distributed control system according to Example 1, and a region  12  in which the moving bodies  10  move. 
     The region  12  illustrated in  FIG. 13  is Voronoi partitioned in the control device  14  of each moving body  10  based on position information from the position recognition module  22 . 
     Voronoi partitioning analyzes the domains of influence of each point (in this case, the positions of the moving bodies  10 ), and collections of points having the shortest distance to respective moving bodies  10  expressed as single polygons are referred to as Voronoi regions. For example, in  FIG. 13 , in Voronoi partitioning of a two-dimensional flat plane, boundary lines between the Voronoi partitions are perpendicular bisectors (dashed lines  26  in  FIG. 13 ) of line segments connecting between the moving bodies  10 . One of the moving bodies  10  is always present in each Voronoi region (1) to (n) demarcated by the dashed lines  26 . Note that the variable n is the number of Voronoi partitions, and n=17 in  FIG. 13 . 
     In Example 1, in the range of the region  12 , the moving bodies  10  move freely with respect to each other, and each time this occurs, the Voronoi regions change. The Voronoi regions in  FIG. 13  corresponding to when each of the moving bodies  10  has moved from the position indicated by dotted lines to the position indicated by solid lines. 
     Moreover, in Example 1, in the region  12  illustrated in  FIG. 13 , monitoring targets (potential risk areas)  28  corresponding to particular surveillance regions and tracking regions in the present exemplary embodiment are designated as illustrated in  FIG. 14A . 
     In Example 1, the area of a single unit of potential risk area  28  is equivalent to the area of a monitoring range which the monitoring module  18  of one moving body  10  is capable of monitoring. Namely, when the center of one moving body  10  overlaps with the center of a potential risk area  28  illustrated by a rectangular hatching pattern then the entire potential risk area  28  is within the monitoring range. 
     Note that the area of each of the potential risk areas  28  and the area of the monitoring range need not necessarily have a 1:1 relationship. 
     The positions of each moving body  10  in  FIG. 14A  are the same as the positions illustrated in  FIG. 13 . Each moving body  10  moves toward a potential risk area  28  in its own Voronoi region while the moving bodies  10  exchange position information with each other. 
       FIG. 14B  illustrates the result when the moving bodies  10  have moved from  FIG. 14A  under control of related technology that moves the moving bodies  10  toward the potential risk areas  28  while maintaining the Voronoi regions (control rule 1). 
     Here, under control rule 1, a situation arises in which it is not possible to make all of the potential risk areas  28  fall into the monitoring ranges of the moving bodies  10 . 
     Namely, the level of monitoring of the designated potential risk areas  28  can be expressed in terms of a coverage ratio. The coverage ratio is the area of monitoring regions of the moving bodies  10  divided by the area of the potential risk areas  28 . Note that this may be expressed as a percentage ((area of monitoring regions of the moving bodies  10 /area of the potential risk areas  28 )×100%). 
     Based on control rule 1, it is apparent that in  FIG. 14B  there are potential risk areas  28  present for which the coverage ratio is less than 1 (less than 100%). On the other hand, the moving bodies  10  fulfil no function whatsoever when in a Voronoi region in which there is no potential risk area  28  present. 
     Namely, even if the combined area of the monitoring regions of all of the moving bodies  10  is greater than the total area of the designated potential risk areas  28 , it is not possible to monitor all of the potential risk areas  28  when control is constrained by the control rule 1. 
     Accordingly, in the present exemplary embodiment, in addition to control rule 1, control (control rule 2) is also established to move a moving body  10  so as to leave its own Voronoi region and move toward a potential risk area  28  where the insufficient coverage ratio is insufficient (0&lt;coverage ratio&lt;1) based on positional relationships between (the monitoring ranges of) the moving bodies  10  and the potential risk areas  28 . 
       FIG. 15A  to  FIG. 15E  illustrate conceivable situations of positional relationships between (the monitoring ranges of) moving bodies  10  and potential risk areas  28 . 
       FIG. 15A  illustrates a situation in which within a single Voronoi region a single moving body  10  is addressing a single potential risk area  28  unit. The area of the monitoring range of the single moving body  10  matches the area of the potential risk area  28 , such that the coverage ratio is 1. This is an ideal relationship. 
       FIG. 15B  illustrates a case in which no potential risk area  28  is present in the Voronoi region under the responsibility of a moving body  10 . This is the most inefficient relationship (Situation 1). 
       FIG. 15C  illustrates a case in which a single designated potential risk area  28  unit straddling two Voronoi regions is being addressed by the two moving bodies  10  responsible for the respective Voronoi regions. The area of the monitoring ranges of the two moving bodies  10  is greater than the area of the potential risk area  28 , such that the coverage ratio is greater than 1 (i.e. 2 (=200%)), thus giving a relationship in which there is an excess in the monitoring ranges of the moving bodies  10  (Situation 2). 
     On the other hand,  FIG. 15D  illustrates a situation in which three potential risk area  28  units within a single Voronoi region are being addressed by a single moving body  10 . The coverage ratio is less than 1 (0.333 . . . ). This is a relationship in which the potential risk areas  28  are insufficiently monitored. 
     In the relationship in  FIG. 15A , the moving body  10  is preferably maintained in its current state. 
     The relationship in  FIG. 15B  (Situation 1) is a situation in which the monitoring range of one moving body  10  is going to waste. In other words, this is a situation in which the moving body  10  illustrated in  FIG. 15B  could be allocated to another potential risk area  28 . 
     The relationship in  FIG. 15C  (Situation 2) is a situation in which half of the monitoring range of each of the two moving bodies  10  is going to waste. In other words, this is a situation in which were it not for the constraint of the Voronoi regions, one of the moving bodies  10  out of the two moving bodies  10  in  FIG. 15C  could be allocated to another potential risk area  28 . 
     On the other hand, the relationship in  FIG. 15D  is a situation in which two of the potential risk area  28  units cannot be monitored. 
     The present exemplary embodiment acknowledges the situations in  FIG. 15B  and  FIG. 15C  (Situation 1 and Situation 2 in which there is excess of monitoring range of the moving bodies), and the situation in  FIG. 15D  (a situation in which the monitoring range of the moving bodies  10  is insufficient), and so deviates from Voronoi region maintenance control (control rule 1) to set the control rule 2 permitting the moving bodies  10  to be moved (see  FIG. 15E ). 
     Moreover, in the present exemplary embodiment, in cases in which the importance levels of the potential risk areas  28  differ, movement of the moving bodies  10  is controlled according to the importance level. 
       FIG. 16A  and  FIG. 16B  illustrate movement control of the moving bodies  10  based on the importance levels. 
     The importance levels are, for example, expressed by numerical values greater than 0 and up to and including 1, with 1 being the highest importance level.  FIG. 16A  and  FIG. 16B  envisage the presence of potential risk areas  28 A with an importance level of 1 and potential risk areas  28 B with an importance level of 0.5. 
     For example, as illustrated in  FIG. 16A , a potential risk area  28 B with an importance level of 0.5 is present in a first block. When this is covered (monitored) by a moving body  10  then, from the perspective of the importance level, since only 50% of the monitoring area capability of the moving body is being used, the coverage ratio is 1/0.5=2. Moreover, two potential risk areas  28 A with an importance level of 1 are present in a second block. When one of these potential risk areas  28 A is covered (monitored) by a moving body  10 , from the perspective of the importance level, 100% of the monitoring area capability of the moving body  10  is being used, such that the coverage ratio is 1/(1+1)=0.5. 
     Here, an overall evaluation of the coverage ratio taking into account the importance level is expressed as an evaluation index. The higher the numerical value of the evaluation index, the better the coverage ratio. 
     In  FIG. 16A , the evaluation index for the two blocks would be 0.5+1=1.5. 
     In contrast thereto, in a case in which a potential risk area  28 B with an importance level of 0.5 and a potential risk area  28 A with an importance level of 1 are present in the first block, as illustrated in  FIG. 16B , if the potential risk area  28 A is covered (monitored) by the moving body  10  then the coverage ratio would be 1/(0.5+1)=0.67. Moreover, if a potential risk area  28 A with an importance level of 1 were present in the second region and covered (monitored) by the moving body  10  then the coverage ratio would be 1/1=1. 
     In  FIG. 16B , the evaluation index for the two blocks would be 1+1=2. 
     Namely, the overall coverage ratio can be improved by prioritizing coverage of the potential risk areas  28  with a high importance level. 
     In the moving body  10  movement control performed under control rule 2 the following conditions are set, and the moving body  10  which is to be moved is selected by mediation between the moving bodies  10 . 
     Condition 1 
     There must be no other moving body  10  present on a movement path of a moving body  10  moving to a potential risk area  28  having an insufficient monitoring range (defined as “positive”). 
     Condition 2 
     A moving body  10  meeting Condition 1 must declare its movement to other moving bodies  10 . 
     Declaring movement is made by overwriting a potential risk area  28  that was defined as being “positive” to “negative”. Moreover, the movement path is also overwritten as “negative”. Accordingly, movement is only permitted for the moving body  10  that declared movement first, enabling a situation in which plural of the moving bodies  10  head toward a single potential risk area  28 , resulting in collisions or the like, to be avoided. 
       FIG. 17  illustrates the result after the moving bodies  10  have moved from the state in  FIG. 14B  under control of control rule 2. It is apparent that a 1:1 relationship is achieved between all of the potential risk areas  28  and the monitoring ranges of the moving bodies  10 . 
     Explanation follows regarding operation of Example 1, with reference to the flowchart of  FIG. 18 . Note that in this flowchart, it is assumed that the potential risk areas  28  have been set to surveillance regions and tracking regions identified by the prediction control of the present exemplary embodiment. 
       FIG. 18  is a flowchart illustrating a potential risk area monitoring control routine according to the present exemplary embodiment, and mainly illustrates a dedicated flow of movement control of the moving bodies  10 . 
     At step  100 , a moving body  10  collects information relating to itself. Namely, the moving body  10  recognizes its own position information in the region  12  (see  FIG. 13 ), and transmits this position information to the other moving bodies  10 . The information relating to the moving body  10  itself may also be transmitted to the base station. 
     At the next step  102 , the moving body  10  collects information relating to the other moving bodies  10  (moving bodies  10  present in the region  12  other than itself). Namely, the moving body  10  recognizes position information of the other devices, and processing transitions to step  104 . The information relating to the other moving bodies  10  may also be collected from the base station. 
     At step  104 , input calculation is executed under control rule 1. Namely, Voronoi regions are successively set for each of the moving bodies  10 , and in cases in which there is a potential risk area  28  present in a Voronoi region, movement control is executed so as to cover the potential risk area  28 . 
     At the next step  106 , the moving body  10  ascertains its own current state. Namely, the moving body  10  makes a judgement as to whether it is in a situation in which there is no potential risk area  28  present in its own moving body Voronoi region (Situation 1), or it is in a situation in which there is a potential risk area  28  in its own moving body Voronoi region that is smaller than the monitoring area of the moving body (the coverage ratio is greater than 1) (Situation 2), or it is in another situation. 
     In cases in which determination is affirmative at step  106 , namely in cases in which a judgement is made that either Situation 1 or Situation 2 exists, processing transitions to step  108 , and input calculation is executed following control rule 2. Namely, mediation is performed regarding which moving body  10  is to move outside its own Voronoi region and toward a potential risk area  28  having a coverage ratio of less than 1 (coverage ratio&lt;1), then control is executed to move the moving body  10  determined by the mediation so as to leave its own Voronoi region and move toward the potential risk area  28 , and processing transitions to step  110 . 
     In cases in which determination is negative at step  106 , processing transitions to step  110 . 
     At step  110 , a judgement is made as to whether or not coverage of all of the potential risk areas  28  has been achieved. In cases in which determination is affirmative, the current routine is ended. In cases in which determination is negative at step  110 , processing returns to step  100 , and the above process is repeated. 
     Note that in cases in which the total area of the potential risk areas  28  is greater than the total area capable of being monitored by the plural moving bodies  10 , the moving bodies  10  may tasked to address plural potential risk areas  28  over a time series so as to obtain information about the potential risk areas  28 . 
     With the aim of avoiding collisions as the plural moving bodies  10  move freely in the region  12 , Example 1 enables the coverage ratio of the potential risk areas  28  to be improved by, as control rule 2, mediating movement to potential risk areas  28  with low coverage ratios (for example on a first-come-first-served basis) and moving the moving bodies  10  so as to leave their current Voronoi regions when there is a need to correct a reduction in coverage ratio of the potential risk areas  28  caused as a result of setting the Voronoi regions based on control rule 1. 
     Operation and Advantageous Effects of Example 1 
     Example 1 applies distributed management technology that defines Voronoi regions as technology for controlling plural moving bodies without resorting to centralized management. 
     For example, in cases in which plural moving bodies are moved to potential risk areas  28  set within a predetermined region so as to monitor the potential risk areas  28 , partitioning the predetermined region into Voronoi regions and setting each partitioned region as a region of responsibility for the respective moving body enables collisions between the moving bodies to be avoided. 
     Technology to define Voronoi regions is capable of providing logic ideally suited to cases in which plural moving bodies are envisaged. Moreover, there is no need to implement a detailed action plan for each of the moving bodies, and the respective moving bodies are capable of autonomous, distributed decision making while communicating with their surroundings. 
     However, conventional autonomous distributed control in which Voronoi regions are defined works well as long as the region of responsibility of each single moving body contains potential risk areas  28  capable of being addressed by that moving body. In reality, however, it may be envisaged that situations such as the following might occur on a constant basis. 
     Situation 1 
     No potential risk area  28  is present in a region of responsibility, with the result that a moving body does not fulfil a monitoring function (what is referred to as an idle moving body is present). An example thereof is a case in which the surveillance performance according to the uncertainty of each potential risk area  28  in the region of responsibility of a moving body and the tracking performance according to the importance level of each potential risk area  28  in the region of responsibility are each at a predetermined level. 
     Situation 2 
     In cases in which there is a potential risk area  28  present that could be monitored by a single moving body straddling two or more regions of responsibility, monitoring is performed by the moving bodies in their respective regions of responsibility, such that two or more moving bodies monitor the potential risk area with excess capability. 
     Situation 3 
     A potential risk area for which monitoring by a single moving body would be insufficient is present in a single region of responsibility. 
     Example 1 enables an improvement in the coverage ratio, this being a ratio of the monitoring regions capable of being monitored by the moving bodies to the potential risk areas  28 , under autonomous distributed control. 
     Example 2 
     In the present exemplary embodiment, when potential risk area monitoring control is executed based on the flowchart of  FIG. 18 , there is a need for mediation in order to avoid interference (collisions) between the moving bodies  10  moving toward potential risk areas  28 , in particular in the case of control rule 2.  FIG. 19A  and  FIG. 19B  are flowcharts illustrating a potential risk area monitoring control routine as Example 2 (Example 2 being embellished from Example 1) in which the moving body  10  to be moved toward an objective potential risk area  28  is decided based on the principle of first-come-first-served. 
     As illustrated in  FIG. 19A , at step  120 , initial Voronoi regions are set in the region  12  as illustrated in  FIG. 13 . Next, processing transitions to step  122  and the moving bodies  10  are moved randomly within their respective Voronoi regions. Processing then transitions to step  124 . 
     At step  124 , a judgement is made as to whether or not potential risk areas  28  have been designated. In cases in which determination is negative, processing transitions to step  126 , and each moving body  10  transmits its own position information and receives position information of other moving bodies  10 . Next, processing transitions to step  128  and Voronoi regions are set based on the relative positions of the moving bodies  10 . Processing then returns to step  120 . 
     Moreover, in cases in which determination is affirmative at step  124 , processing transitions to step  130 , and the presence or absence of a potential risk area  28  in the current Voronoi region is searched for. Next, processing transitions to step  132 , the coverage ratio is calculated, and processing transitions to step  134  of  FIG. 19B . 
     At step  134 , processing branches depending on the calculated coverage ratio. 
     Namely, in cases in which a judgement has been made at step  134  that the coverage ratio=1, there is a 1:1 relationship between the area of the potential risk area  28  and the area of the monitoring range of the moving body  10 . Determination is thus made that monitoring of the potential risk area  28  is possible under control by control rule 1, processing transitions to step  136 , and the moving body  10  is moved to the potential risk area  28  based on control rule 1. Processing then transitions to step  156 . In such cases, the moving body  10  is capable of covering all of the potential risk areas  28  inside its own Voronoi region (region of responsibility) (see the state in  FIG. 15A ). 
     Moreover, in cases in which determination has been made at step  134  that the coverage ratio&lt;1, processing transitions to step  138 , and the moving body  10  is moved to a potential risk area  28  inside its own Voronoi region based on control rule 1. Processing then transitions to step  140 , and mediation information (regarding a positive potential risk area indicating that the potential risk areas  28  cannot be fully covered) is transmitted, before processing transitions to step  156 . In such cases, the moving body  10  is capable of at least partially covering of the potential risk areas  28  in its own Voronoi region (region of responsibility) (see the state in  FIG. 15D ). 
     On the other hand, in cases in which determination is made at step  134  that the coverage ratio&gt;1 (including cases in which there is no potential risk area  28  present), a judgement is made that a potential risk area  28  present in a Voronoi region of another device could be covered (see the states in  FIG. 15B  and  FIG. 15C ). Processing transitions to step  142 , and a judgement is made as to whether or not mediation information (a positive potential risk area) has been received. 
     In cases in which determination is negative at step  142 , processing returns to step  120 . In cases in which determination is affirmative at step  142 , processing transitions to step  144 , and a judgement is made as to whether or not an obstacle would be present when moving toward the positive potential risk area. Processing transitions to the next step  146 , and a judgement is made as to whether or not an obstacle is present. In cases in which a judgement is made at step  146  that an “obstacle is present”, processing returns to step  120 . In cases in which a judgement is made at step  146  that “no obstacle is present”, processing transitions to step  148 , a judgement is made as to whether or not overwrite information (a negative potential risk area and a negative potential for a movement path, indicating that the potential risk area  28  is being covered) has been received. In cases in which determination is negative at step  148 , processing transitions to step  150 . At this point in time, the exclusive right to move to the potential risk area  28  is attained (on the principle of first-come-first-served). In cases in which determination is affirmative at step  148 , a judgement is made that the exclusive right has not been attained, and processing returns to step  120 . 
     At step  150 , the moving body  10  is moved toward the potential risk area based on control rule 2, and processing transitions to the next step  152  where the moving body  10  transmits its own position information and receives position information of the other devices. Then, processing transitions to step  154 , and, in order to declare that it has attained the exclusive right, the moving body  10  transmits overwrite information to overwrite the mediation information (changing the positive potential risk area to a negative potential risk area) and transmits negative potential information for the movement path, before processing transitions to step  156 . 
     At step  156 , Voronoi regions are set based on the relative positions of the moving bodies  10 . Processing then transitions to step  158 , and potential risk area  28  monitoring information (for example imaging information) is transmitted to the base station. Processing then transitions to step  160 . At step  160 , a judgement is made as to whether or not the potential risk area  28  has been resolved. In cases in which determination is negative, processing returns to step  158 . In cases in which determination is affirmative at step  160 , processing returns to step  120 . 
     Example 3 
       FIG. 20  illustrates an Example 3, this being an example in which the moving body  10  distributed control system is employed in the monitoring of a home security system. 
     As illustrated in  FIG. 20 , a wall  32  is provided around the periphery of a house  30 , and a site enclosed by the wall  32  is monitored by fixed monitoring cameras  34 . Three of the moving bodies  10  are deployed within the site, the site is Voronoi partitioned, and the moving bodies  10  move while avoiding collisions with each other. 
     For example, in cases in which the presence of a suspicious person  36  has been detected by the fixed monitoring cameras  34 , potential risk areas  28  are set in order to focus monitoring on the region where the suspicious person  36  was detected. When this is performed, although the potential risk areas  28  are not able to be sufficiently covered by a single moving body  10  in cases in which there are Voronoi regions present, the three moving bodies  10  can be moved to the potential risk areas  28  based on control rule 2. 
     Note that in cases in which there is a blind spot present on the site, monitoring may be focused on the blind spot. Moreover, although in the monitoring example of Example 3 it is a site around the house  30  that is monitored, a site employed as a parking lot may be monitored. In the case of a parking lot, blind spots for fixed monitoring cameras change depending on the number and positions of parked vehicles, and therefore monitoring using the moving bodies  10  is effective. 
     Example 4 
       FIG. 21A  and  FIG. 21B  illustrate an Example 4, this being an example in which the moving body  10  distributed control system is employed for monitoring at a crossroad in order to prevent accidents. 
     As illustrated in  FIG. 21A , at a crossroad  44  where a side-road  42  intersects a main road  40  on which vehicles  38  can travel, monitoring cameras  46  are installed on the main road  40  to monitor the approach of vehicles  38 . Three moving bodies  10  are deployed in a preset region  12  at the crossroad  44 . The site is Voronoi partitioned and the moving bodies  10  move while avoiding collisions with each other. 
     For example, as illustrated in  FIG. 21B , in cases in which the approach of a vehicle  38  has been detected by a monitoring camera  46 , a prediction is made that a bicycle  48  or the like might suddenly emerge from the side-road  42  onto the main road  40  at the side the vehicle  56  is travelling on (from the side-road  42  on the upper side in  FIG. 21B ), and potential risk areas  28  are set in order to focus monitoring on a region including the side-road  42 . When this is performed, although the potential risk areas  28  are not able to be sufficiently covered by a single moving body  10  in cases in which there are Voronoi regions present, the three moving bodies  10  can be moved to the potential risk areas  28  based on control rule 2. 
     Image information imaged by the moving bodies  10  is transmitted to the vehicle  38  to enable the driver to recognize the danger in advance. 
     Although examples have been given of monitoring of the property of the house  30  as illustrated in  FIG. 20 , and of monitoring to recognize danger at the crossroad  44  in advance as illustrated in  FIG. 21A  and  FIG. 21B , other examples include applications executed by plural moving bodies  10  that normally monitor a wide monitoring range, such as a mountain or an office building, to identify people in need of rescue in disaster situations by designating potential risk areas in regions smaller than the usual range in order to carry out focused search and rescue operations. 
     Example 5 
     In  FIG. 17 , it is assumed that the positions of the potential risk areas  28  do not change (do not move) prior to the moving bodies  10  moving (see  FIG. 14B ). Hereafter, potential risk areas  28  that do not move are referred to as static potential risk areas  28 . 
     However, the potential risk areas  28  may move. For example, in cases in which a potential risk area  28  corresponds to a person, motorcycle, vehicle, or the like, the position of the potential risk area  28  will change from moment to moment at its own movement speed. Such potential risk areas  28  that are capable of movement are defined as “dynamic potential risk areas  28 ”, in contrast to the static potential risk areas  28 . Note that in the present exemplary embodiment, the static potential risk areas  28  and the dynamic potential risk areas  28  are allocated the same reference numerals from the perspective that they are both potential risk areas  28 , and are distinguished using the words “static” and “dynamic” where necessary. 
     Dynamic potential risk areas  28  can be classified into the following two types. 
     Classification 1 
     Dynamic potential risk areas  28  that have a slower maximum speed than the movement speed of the moving bodies  10 . 
     Classification 2 
     Dynamic potential risk areas  28  that have a faster maximum speed than the movement speed of the moving bodies  10 . 
     In the case of classification 1, a moving body  10  is capable of maintaining constant tracking of the dynamic potential risk area  28  when the dynamic potential risk area  28  is moving in a straight line. 
     However, in the case of classification 2, a situation in which the moving body  10  is unable to track the dynamic potential risk area  28  could arise when the dynamic potential risk area  28  is moving in a straight line. 
     In Example 5, in addition to the control rule 1 and the control rule 2 described above, a classification 1 situation and a classification 2 situation are distinguished. Movement control of the moving bodies  10  is configured by executing a control rule 3 for tracking control in the case of the classification 1, and executing a control rule 4 for tracking control in the case of the classification 2. 
     The control rule 3 or the control rule 4 needs to be selected during the processing of either the control rule 1 or the control rule 2 described above. 
     Accordingly, in the present exemplary embodiment, during potential risk area monitoring control (explained in detail in  FIG. 18 ), processing of the input calculation for movement control under control rule 1 or the input calculation for movement control under control rule 2 is assumed to have taken place when selection control (explained in detail in  FIG. 22 ) is executed in order to perform tracking. 
     Execution of the control rule 3 or the control rule 4 is selected based on the result of comparing a movement speed v 1  (maximum speed) of the moving body  10  itself against a movement speed v 2  (maximum speed) of a dynamic potential risk area  28  configuring a tracking target (v 1 :v 2 ). 
     In cases in which the movement speed of the moving body  10  is determined to be the same as the movement speed of the dynamic potential risk area  28  or faster than the movement speed of the dynamic potential risk area  28  (v 1 &gt;v 2 ), tracking control is executed (control rule 3) in which a position of the dynamic potential risk area  28  calculated using collected estimation information regarding the potential risk area  28  is set as a goal. 
     Namely, reliable tracking by the moving body  10  is possible when the dynamic potential risk area  28  is moving in a straight line. 
     However, in cases in which the movement speed of the moving body  10  is determined to be equivalent to the movement speed of the dynamic potential risk area  28  or slower than the movement speed of the dynamic potential risk area  28  (v 1 ≤v 2 ), tracking control (control rule 4) is performed in which an estimated position to which the potential risk area  28  is likely to move (that it has been estimated the potential risk area  28  will move to) several steps ahead of the position of the dynamic potential risk area  28  as calculated using collected estimation information regarding the potential risk area  28  is set as a goal. 
     Namely, in cases in which the dynamic potential risk area  28  moves in a straight line, the moving body  10  will gradually be left behind by the dynamic potential risk area  28 . 
     If there were no time lag in tracking movement control, the movement of the moving body  10  would theoretically be able to keep up when tracking if v 1 =v 2 . However, in reality, control rule 4 is also applied in the case of v 1 =v 2 . 
     In this manner, in tracking control under control rule 4, a movement path of the moving body  10  deviates from the movement path of the dynamic potential risk area  28 . However, in cases in which the estimated position corresponds to an important waypoint, for example, the important role of monitoring can be better secured by prioritizing monitoring at the estimated position even if this involves missing out part of the movement path. 
     In the potential risk area monitoring control routine illustrated in  FIG. 18 , the correlative relationship between the movement speeds of the moving body  10  and the potential risk area  28  (classification 1 or classification 2) affects both the input calculation under the control rule 1 executed at step  104  and the input calculation under the control rule 2 executed at step  108 . 
     Accordingly, when executing step  104  or step  108  in  FIG. 18 , processing (selection control for tracking) is executed to decide a tracking control rule mode (control rule 3 or control rule 4). 
       FIG. 22  is a flowchart illustrating a tracking mode selection control routine, this being control in a subroutine for the input calculation under control rule 1 at step  104  or a subroutine for the input calculation under control rule 1 at step  108  in  FIG. 18 . 
     At step  200  in  FIG. 22 , information relating to the moving body  10  itself is collected, and then at step  202 , information relating to the other moving bodies  10  is collected, and processing transitions to step  204 . 
     At step  204 , estimation information relating to the potential risk area  28  is collected. For example, information from a navigation system installed in a vehicle may be acquired in order to ascertain a future travel situation. 
     At the next step  206 , a judgement is made as to whether or not the movement control state in  FIG. 18  is within the Voronoi region or outside the Voronoi region. 
     In cases in which the movement control state is determined to be within the Voronoi region at step  206 , a judgement is made in the subroutine illustrated in  FIG. 22  that control is being executed under the control rule 1 at step  104  in  FIG. 18 , and processing transitions to step  208  to perform the input calculation according to the control rule 1. Processing then transitions to step  212 . 
     In cases in which the movement control state is determined to be outside the Voronoi region at step  206 , a judgement is made in the subroutine illustrated in  FIG. 22  that control is being executed under the control rule 2 at step  108  in  FIG. 18 , and processing transitions to step  210  to perform the input calculation according to the control rule 2. Processing then transitions to step  212 . 
     At the next step  212 , the movement speed v 1  (maximum speed) of the moving body  10  itself is compared against the movement speed v 2  (maximum speed) of the potential risk area  28  configuring the tracking target to determine which is faster (v 1 : v 2 ). 
     In cases in which the movement speed of the moving body  10  is determined to be the same as the movement speed of the potential risk area  28  or faster than the movement speed of the potential risk area  28  at step  212  (v 1 &gt;v 2 ), processing transitions to step  214 , and tracking control (control rule 3) is set such that a position of the potential risk area  28  calculated using the estimation information regarding the potential risk area  28  collected at step  204  is set as a goal. Processing then transitions to step  218 . 
     In cases in which the movement speed of the moving body  10  is determined to be equivalent to the movement speed of the potential risk area  28  or slower than the movement speed of the potential risk area  28  at step  212  (v 1 ≤v 2 ), processing transitions to step  216 , and tracking control (control rule 4) is set such that an estimated position to which the potential risk area  28  is likely to move several steps ahead of the position of the dynamic potential risk area  28 , as calculated using the estimation information regarding the potential risk area  28  collected at step  204 , is set as a goal. Processing then transitions to step  218 . 
     When this is performed, sometimes the moving body  10  will not keep up if the moving body  10  moves in a direction diverging from a line connecting the moving body  10  and the potential risk area  28 . However, in cases in which the potential risk area  28  is a vehicle, the moving body  10  can take a shortcut to the estimated position (for example so as to arrive ahead) if for example the vehicle meanders, turns left or right, performs a U-turn, or stops as it moves. 
     At step  218 , the set control inputs are executed, and the subroutine of  FIG. 22  is ended (processing returns to step  106  or step  110 ). 
     Note that at step  212 , the control rule 3 is adopted in cases in which the movement speed v 1  (maximum speed) of the moving body  10  is even slightly faster than the movement speed v 2  (maximum speed) of the potential risk area  28 . Although there is theoretically no problem with this, in practice, from the perspective of “the moving body  10  performing tracking”, preferably v 1 &gt;v 2  is satisfied by a given speed difference Δv (=v 1 −v 2 ) or greater. 
     Detailed explanation follows regarding Example 5 with reference to  FIG. 23A  and  FIG. 23B . 
       FIG. 23A  illustrates movement of moving bodies  10  and potential risk areas  28  over a time series under the control rule 3 explained in Example 5. 
     At time step  1 , the moving bodies  10  are covering the respective potential risk areas  28  in regions A 1 , A 2  demarcated by the dashed line  26  configuring the Voronoi region boundary. 
     At time step  2  to time step  4 , even though the potential risk areas  28  are moving, since the movement speed of the moving bodies  10  is faster than the movement speed of the potential risk areas  28 , the moving bodies  10  are capable of maintaining constant tracking and coverage of the potential risk areas  28 . 
       FIG. 23B  illustrates movement of moving bodies  10  and potential risk areas  28  over a time series under the control rule 4 explained in Example 5. 
     At time step  1 , the moving bodies  10  are covering the respective potential risk areas  28  in regions A 1 , A 2  demarcated by the dashed line  26  configuring the Voronoi region boundary. 
     At time step  2 , when the potential risk areas  28  move faster than the moving bodies  10 , the moving bodies  10  start moving in an attempt to track the potential risk areas  28 . When this is performed, the moving bodies  10  estimate for the time step  3  and the time step  4 , and the moving bodies  10  take shortcuts to the positions corresponding to the time step  4  while taking into account the positions of the potential risk areas  28  at time step  3 . In this manner, the moving bodies  10  do not lose sight of the positions of the potential risk areas  28 . 
       FIG. 24A  and  FIG. 24B  are diagrams to demonstrate the advantageous effects of the tracking control under the control rule 4 of Example 5 by drawing comparison to a comparative example. In  FIG. 24A  and  FIG. 24B , in regions A 1 , A 2  demarcated by the dashed line  26  configuring the Voronoi region boundary, each potential risk area  28  moves in a straight line from a first position A to a second position B and then turns by 90° to move to a third position C. 
     In a comparative example illustrated in  FIG. 24B , since the movement speed of the moving bodies  10  is slower than the movement speed of the potential risk areas  28 , when the potential risk areas  28  have started moving from the first positions A, at the same timing as the potential risk areas  28  arrive at the second positions B, the moving bodies  10  are in the process of moving in a straight line between the first position A and the second position B of the corresponding potential risk area  28 . 
     Then, since the potential risk areas  28  move to the third positions C, the moving bodies  10  move toward the potential risk areas  28  from their respective positions between the first position A and the second position B of the corresponding potential risk area  28 . However, the potential risk areas  28  arrive at the third positions C while the moving bodies  10  are still on their way, and the moving bodies  10  cannot keep up. In a worst-case scenario, the moving bodies  10  might lose sight of the potential risk areas. 
     However, in Example 2 illustrated in  FIG. 24A , the third position C has been estimated at the point in time when the potential risk areas  28  are moving from the first position A to the second position B. The moving bodies  10  accordingly move in a circular arc toward the third position C, while acknowledging the second position B. 
     By taking this shortcut, the moving bodies  10  are capable of arriving at the third position C at the point in time when the potential risk areas  28  arrive at the third position. Configuration may also be made such that the moving bodies  10  arrive earlier in time. 
     Note that the circular arc shaped paths of the moving bodies  10  illustrated in FIG.  24 A may be set such that the radii of curvature of the circular arcs are based on a level of importance attached to the second position B. The more important the second position B, the closer the moving bodies  10  will pass to the second position B. In such cases, the arrival at the third position C may be slightly delayed. 
     As described above, in Example 5 the tracking control mode (control rule 3 or control rule 4) is selected based on the relationship between the movement speed of the moving body  10  and the movement speed of the potential risk area  28  (classification 1 or classification 2). When the moving body  10  would be unable to keep up by moving along the same movement path as the potential risk area  28 , the movement of the potential risk area  28  several steps ahead is estimated, and the moving body  10  takes what is referred to as a shortcut (including arriving ahead). As a result, the moving body  10  does not lose sight of the potential risk area  28 . The path taken to arrive ahead enables the state of the potential risk area  28  to be reliably monitored en route in consideration of the importance level of the potential risk area  28  when on its movement path. 
     Note that in each of the exemplary embodiments described above the regions  12  (external environmental regions, regions of responsibility) may equally be set on land, sea, or air. 
     Applications of this sensing include monitoring, surveying, observation, rescue, and forecasting. More specifically, examples of applications over comparatively small regions include monitoring of vehicles and pedestrians in a parking lot, monitoring of vehicle movements at a crossroad, and monitoring for suspicious activity at a house or the like. Examples of applications over comparatively large regions include excavation surveys, searching for victims at the scene of a disaster (rescue operations), mountain and forest management, and weather surveillance for forecasting. 
     Although explanation has been given regarding an example in which drones capable of aerial photography are employed as the moving bodies  10  (actuators), the autonomous distributed control may be applied to other moving bodies such as vehicles or boats. 
     The disclosure of Japanese Patent Application No. 2017-135071, filed on Jul. 10, 2017, is incorporated in its entirety by reference herein.