Patent Publication Number: US-9885580-B2

Title: Navigation apparatus, simulation apparatus, moving device and navigation method

Description:
BACKGROUND OF THE INVENTION 
     Technical Field 
     The present invention relates to a navigation apparatus, a simulation apparatus, a moving device and a navigation method. 
     Background Art 
     In farms, disaster sites or the like, autonomous moving devices that do not require driving or operating by drivers are sometimes used. Even in a situation where there is no prior information of a path, such a moving device is required to be capable of autonomously moving from an arbitrary point to a destination. In Teppei Saitoh and Yoji Kuroda, “FastSLAM based Global Path Planning Strategy in Unknown Environment”, research report by department of science and technology of Meiji University, department of science and technology of Meiji University, No. 40, pp. 1-8 (31 Mar. 2009; hereinafter “Saitoh”), an autonomous moving robot capable of autonomously moving from a predetermined start point to a predetermined finish point is described. 
     SUMMARY OF THE INVENTION 
     Technical Problem 
     Even when no prior information such as that of obstacles is supplied, the autonomous moving robot described in Saitoh utilizes self-position estimation and map construction to perform a path planning. However, when there is no prior information of a path, the autonomous moving robot described in Saitoh is likely to encounter a dead end on the path. In this case, the autonomous moving robot must turn back and cannot efficiently move to the finish point. 
     An object of the present invention is to provide a navigation apparatus capable of efficiently moving a moving device to a destination even when there is no prior information of a path, and a moving device including the navigation apparatus, and a navigation method. 
     Another object of the present invention is to provide a simulation apparatus capable of generating weight information used to estimate worth (value) of a path to a destination in a navigation apparatus capable of efficiently moving a moving device to the destination even when there is no prior information of the path. 
     Solution to Problem 
     (1) According to one aspect of the present invention, a navigation apparatus carried in a moving device includes a path detector that detects one or a plurality of paths through which the moving device is capable of moving, a destination positional information acquirer that acquires information regarding a position of a destination with respect to the moving device as destination positional information, a topographical characteristics information acquirer that acquires information regarding characteristics of topography in surroundings of the moving device as topographical characteristics information, a storage portion that stores weight information including a plurality of weight coefficients respectively corresponding to the destination positional information and the topographical characteristic information and for calculating information that correlates with worth of the path to the destination from the destination positional information and the topographical characteristics information as worth correlation information, and a worth estimator that calculates the worth correlation information regarding the one or plurality of paths detected by the path detector to the destination from the destination positional information acquired by the destination positional information acquirer and the topographical characteristics information acquired by the topographical characteristics acquirer based on the weight information stored in the storage portion, and estimates worth of the one or plurality of paths to the destination based on the calculated worth correlation information. 
     In this navigation apparatus, the one or plurality of paths through which the moving device are capable of moving are detected. Further, the destination positional information regarding the position of the destination with respect to the moving device is acquired. The topographical characteristics information regarding the characteristics of the topography in the surroundings of the moving device is acquired. 
     The weight information for calculating the worth correlation information that correlates with the worth of the path to the destination from the destination positional information and the topographical characteristics information is stored in advance. Here, the weight information includes the plurality of weight coefficients respectively corresponding to the destination positional information and the topographical characteristics information. 
     The worth correlation information regarding the one or plurality of detected paths to the destination is calculated from the acquired destination positional information and topographical characteristics information based on the stored weight information. The worth of the one or plurality of paths to the destination is estimated based on the calculated worth correlation information. 
     This configuration enables estimation of the worth of the one or plurality of paths to the destination even when there is no prior information of the path. The moving device can reach the destination at a high probability by moving through the path estimated to have high worth. Thus, even when there is no prior information of the path, the navigation apparatus can efficiently move the moving device to the destination. 
     (2) The plurality of weight coefficients in the weight information may be calculated by a canonical correlation analysis using a plurality of destination positional information regarding a position of an arbitrary destination with respect to an arbitrary point, a plurality of topographical characteristics information regarding characteristics of topography in surroundings of the arbitrary point and worth of each of the plurality of paths that is acquired in advance in virtual topography including the plurality of paths. 
     In this case, the plurality of destination positional information, the plurality of topographical characteristics information and the worth of each of the plurality of paths to the destination are acquired using the virtual topography including the plurality of paths. Thus, the plurality of destination positional information, the plurality of topographical characteristics information and the worth of each of the plurality of paths to the destination of arbitrary topography can be easily acquired, and the weight coefficient of the arbitrary topography can be easily calculated. 
     (3) A first function that includes the plurality of destination positional information regarding the position of the arbitrary destination with respect to the arbitrary point and the plurality of topographical characteristics information regarding the characteristics of the topography in the surroundings of the arbitrary point in the virtual topography including the plurality of paths as a plurality of first variables and includes a plurality of first coefficients respectively corresponding to the plurality of first variables may be set, a second function that includes worth of each of the plurality of paths to the destination in the virtual topography as one or a plurality of second variables and includes one or a plurality of second coefficients respectively corresponding to the one or plurality of second variables may be set, and numerical values of the plurality of first coefficients and numerical values of the one or plurality of second coefficients may be determined such that a correlation between a numerical value of the first function and a numerical value of the second function is maximum, and the weight information may include the determined numerical values of the plurality of first coefficients as the plurality of weight coefficients. 
     In this case, the determined first coefficient is used, so that reliability of the worth correlation information regarding the one or plurality of paths to the destination calculated from the destination positional information and the topographical characteristics information in an actual path is improved. Thus, the reliability of the estimated worth of the one or plurality of paths to the destination can be improved. 
     (4) The navigation apparatus may further include a path selector that selects a path having highest worth of the worth estimated by the worth estimator. 
     In this case, the moving device moves through the path estimated to have the highest worth. Thus, the moving device can reach the destination through the best path. As a result, even when there is no prior information of the path, the navigation apparatus can more efficiently move the moving device to the destination. 
     (5) The navigation apparatus may further include an operation portion operated by a user in order to designate the position of the destination, and a position orientation sensor that receives positioning information of the moving device from a global positioning system, wherein the destination positional information acquirer may acquire the destination positional information based on the position of the destination designated by the operation portion and the positioning information of the moving device received from the position orientation sensor. In this case, the destination positional information can be easily and accurately acquired. 
     (6) The navigation apparatus may further include an environment recognition sensor that measures a first number of points in surroundings of the moving device, wherein the path detector may detect the one or plurality of paths through which the moving device is capable of moving based on the first number of information of measurement respectively corresponding to the first number of points by the environment recognition sensor, and the topographical characteristics information acquirer may extract a second number, smaller than the first number, of information of measurement of the one or plurality of paths from the first number of the information of measurement as the topographical characteristics information. 
     In this case, the number of dimensions of the plurality of topographical characteristics information is decreased from the first number to the second number. Thus, it is possible to quickly estimate the worth of the one or plurality of paths to the destination without largely reducing reliability. 
     (7) The environment recognition sensor may include a laser range finder. In this case, the first number of points can be measured with a simple configuration. 
     (8) High worth of the path to the destination may include shortness of a moving time period during which the moving device moves through the path. In this case, the navigation apparatus can move the moving device to the destination in a short period of time. 
     (9) High worth of the path to the destination may include a small amount of energy consumption by which the moving device moves through the path. In this case, the navigation apparatus can move the moving device to the destination with a small amount of energy consumption. 
     (10) According to another aspect of the present invention, a simulation apparatus that generates weight information used to estimate worth of a path to a destination in the navigation apparatus according to the one aspect of the present invention includes a storage portion that stores topographical data indicating virtual topography including a plurality of paths, a virtual moving device capable of traveling on the plurality of paths in the virtual topography indicated by the topographical data stored in the storage portion, a worth calculator that acquires worth of each of the plurality of paths by moving the moving device from an arbitrary point to an arbitrary destination in the virtual topography and calculates worth of a most suitable path to the arbitrary destination based on the acquired worth of each of the plurality of paths, an information acquirer that acquires a plurality of destination positional information regarding a position of the arbitrary destination with respect to the arbitrary point and a plurality of topographical characteristics information regarding characteristics of topography in surroundings of the arbitrary point in the virtual topography, and a weight information calculator that calculates the weight information for calculating worth correlation information that correlates with worth of the path to the destination by a canonical correlation analysis based on worth of a most suitable path calculated by the worth calculator regarding each of the plurality of destinations and the plurality of destination positional information and the plurality of topographical characteristic information that are acquired by the information acquirer, wherein the weight information includes a plurality of weight coefficients respectively corresponding to the destination positional information and the topographical characteristics information. 
     In this simulation apparatus, the topographical data indicating the virtual topography including the plurality of paths is stored. The virtual moving device can travel on the plurality of paths in the virtual topography indicated by the stored topographical data. The moving device is moved from an arbitrary point to an arbitrary destination in the virtual topography, so that the worth of each of the plurality of paths is acquired, and the worth of the most suitable path to the arbitrary destination is calculated based on the acquired worth of each of the plurality of paths. The plurality of destination positional information regarding the position of the arbitrary destination with respect to the arbitrary point and the plurality of topographical characteristics information regarding the topographical characteristics in surroundings of the arbitrary point in the virtual topography are acquired. 
     The weight information for calculating the worth correlation information that correlates with the worth of the path to the destination is calculated by the canonical correlation analysis based on the worth of the most suitable path calculated regarding each of the plurality of destinations and the plurality of acquired destination positional information and the plurality of acquired topographical characteristics information. Here, the weight information includes the plurality of weight coefficients respectively corresponding to the destination positional information and the topographical characteristics information. 
     In the navigation apparatus, the weight information calculated by the simulation apparatus is stored. The worth correlation information regarding the one or plurality of detected paths to the destination is calculated from the acquired destination positional information and topographical characteristics information based on the stored weight information. The worth of the one or plurality of paths to the destination is estimated based on the calculated worth correlation information. 
     This configuration enables estimation of the worth of the one or plurality of paths to the destination even when there is no prior information of the path. The moving device can reach the destination at a high probability by moving through the path estimated to have high worth. Thus, even when there is no prior information of the path, the navigation apparatus can efficiently move the moving device to the destination. 
     Further, even when there is no prior information of the path, the simulation apparatus can generate the weight information used to estimate the worth of the path to the destination in the navigation apparatus capable of efficiently moving the moving device to the destination. 
     (11) According to yet another aspect of the present invention, an autonomous moving device includes a main body configured to be movable, and the navigation apparatus according the one aspect of the present invention carried in the main body, and a controller that controls movement of the main body based on worth of each path to a destination estimated by the navigation apparatus. 
     In this autonomous moving device, the above-mentioned navigation apparatus is carried in the main body. The main body moves based on the worth of each path to the destination estimated by the navigation apparatus. This configuration causes the autonomous moving device to include the above-mentioned navigation apparatus, so that the autonomous moving device can efficiently move to the destination even when there is no prior information of the path. 
     (12) According to yet another aspect of the present invention, a navigation method for navigating a moving device includes the steps of storing weight information that includes a plurality of weight coefficients respectively corresponding to destination positional information and topographical characteristics information and for calculating information that correlates with worth of a path to a destination from the destination positional information and the topographical characteristics information as worth correlation information, detecting one or a plurality of paths through which the moving device is capable of moving, acquiring information regarding a position of a destination with respect to the moving device as the destination positional information, acquiring information regarding characteristics of topography in surroundings of the moving device as the topographical characteristics information, and calculating the worth correlation information regarding the one or plurality of detected paths to the destination from the acquired destination positional information and the acquired topographical characteristics information based on the stored weight information, and estimating worth of the one or plurality of paths to the destination based on the calculated worth correlation information. 
     In this navigation method, the one or plurality of paths through which the moving device can move are detected. Further, the destination positional information regarding the position of the destination with respect to the moving device is acquired. The topographical characteristics information regarding the characteristics of the topography in the surroundings of the moving device is acquired. 
     The weight information for calculating the worth correlation information that correlates with the worth of the path to the destination from the destination positional information and the topographical characteristics information is stored in advance. Here, the weight information includes the plurality of weight coefficients respectively corresponding to the destination positional information and the topographical characteristics information. 
     The worth correlation information regarding the one or plurality of detected paths to the destination is calculated from the acquired destination positional information and topographical characteristics information based on the stored weight information. The worth of the one or plurality of paths to the destination is estimated based on the calculated worth correlation information. 
     This configuration enables estimation of the worth of the one or plurality of paths to the destination even when there is no prior information of the path. The moving device can reach the destination at a high probability by moving through the path estimated to have high worth. Thus, even when there is no prior information of the path, the navigation method can efficiently move the moving device to the destination. 
     Advantageous Effects of Invention 
     The present invention enables the moving device to efficiently move to the destination even when there is no prior information of the path. Further, even when there is no prior information, the weight information used to estimate the worth of the path to the destination in the navigation apparatus capable of efficiently moving the moving device to the destination can be generated. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing one example of configurations of a moving device according to one embodiment of the present invention. 
         FIG. 2  is a schematic diagram showing an appearance of the moving device. 
         FIG. 3  is a block diagram showing one example of configurations of a simulation apparatus according to one embodiment of the present invention. 
         FIG. 4  is a diagram showing virtual topography used in a path setting process. 
         FIG. 5  is a flow chart showing the path setting process. 
         FIG. 6  is a flow chart showing a worth calculation process by a worth calculator. 
         FIG. 7  is a flow chart showing a destination positional information acquisition process by an information acquirer. 
         FIG. 8  is a diagram for explaining an ‘r’ component, an ‘h’ component and a ‘0’ component of destination positional information. 
         FIG. 9  is a flow chart showing a topographical characteristics information acquisition process by the information acquirer. 
         FIG. 10  is a diagram for explaining f1 to f16 components of the topographical characteristics information. 
         FIG. 11  is a flow chart showing a weight information calculation process by a weight information calculator. 
         FIG. 12  is a flow chart showing a navigation process by a navigation apparatus. 
         FIG. 13  is a flow chart showing the navigation process by the navigation apparatus. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     (1) Overall Configuration of the Navigation Apparatus and the Moving Device 
     The navigation apparatus and the moving device according to one embodiment of the present invention will be described below with reference to drawings. The moving device is an autonomous moving device that is used in a farm, a disaster site or the like and does not require driving and operating by a driver. 
       FIG. 1  is a block diagram showing one example of configurations of the moving device according to one embodiment of the present invention.  FIG. 2  is a schematic diagram showing an appearance of the moving device.  FIG. 2( a )  shows a plan view of the moving device, and  FIG. 2( b )  shows a side view of the moving device. As shown in  FIG. 1 , the moving device  200  includes the navigation apparatus  100 , a controller  210 , an actuator portion  220 , a moving mechanism portion  230 , a vehicle body  240  and wheels  250  ( FIG. 2 ). As shown in  FIG. 2( a ) , in the present embodiment, the moving device  200  is a UGV (Unmanned Ground Vehicle) including the four wheels  250 . 
     The navigation apparatus  100  includes a sensor portion  110 , an operation portion  120  and a storage portion  130 . Further, the navigation apparatus  100  includes a destination positional information acquirer  140 , a path detector  150 , a topographical characteristics information acquirer  160 , a worth (value) estimator  170  and a path selector  180 . The destination positional information acquirer  140 , the path detector  150 , the topographical characteristics information acquirer  160 , the worth estimator  170  and the path selector  180  are realized by a CPU (Central Processing Unit) and a computer program, for example. 
     The sensor portion  110  includes a position orientation sensor  111  and an environment recognition sensor  112 . The position orientation sensor  111  receives positioning information and orientation information of the moving device  200  from a GPS (Global Positioning System), an INS (Inertial Navigation System) and an IMU (Inertial Measurement Unit). The positioning information includes a position, a velocity and acceleration of the moving device  200 . Further, the orientation information includes an orientation of the vehicle body  240 . The position orientation sensor  111  supplies the received positioning information and orientation information of the moving device  200  to the destination positional information acquirer  140 . 
     The environment recognition sensor  112  is an LRF (Laser Range Finder), for example. As shown in  FIG. 2( b ) , the environment recognition sensor  112  sequentially emits a plurality ( 32  in the present example) of laser light beams in a top-and-bottom direction of the moving device  200  at predetermined angular intervals. In this state, as shown in  FIG. 2( a ) , the environment recognition sensor  112  rotates about an axis extending in the top-and-bottom direction of the vehicle body  240 . Thus, the environment recognition sensor  112  sequentially emits the plurality of laser light beams towards surroundings of the vehicle body  240  at the predetermined angular intervals. 
     The environment recognition sensor  112  measures a plurality of points in surroundings of the moving device  200  by detecting light beams reflected by objects in the surroundings. In the present example, the environment recognition sensor  112  emits about 64,000 laser light beams per second towards the surroundings of the vehicle body  240 . Therefore, the environment recognition sensor  112  measures points of 64,000 locations in the surroundings of the moving device  200 . The environment recognition sensor  112  supplies measurement information of the plurality of points to the path detector  150 . 
     The operation portion  120  of  FIG. 1  includes a user interface. A user can designate a position of a destination by operating the operation portion  120 . The operation portion  120  supplies the position of the destination designated by the user to the destination positional information acquirer  140 . 
     The destination positional information acquirer  140  estimates a position and an orientation of the moving device  200  based on the positioning information and the orientation information of the moving device  200 . Further, the destination positional information acquirer  140  acquires the position of the destination with respect to the moving device  200  as destination positional information based on the estimated position of the moving device  200  and the designated position of the destination. The destination positional information has 3 dimensions. The destination positional information acquirer  140  supplies the destination positional information to the worth estimator  170 . 
     The path detector  150  detects one or a plurality of paths through which the moving device  200  can move based on the measurement information of the plurality of points by the environment recognition sensor  112 . The path detector  150  supplies the detected paths to the topographical characteristics information acquirer  160 . 
     The topographical characteristics information acquirer  160  selects measurement information on the path from the measurement information of the plurality of points by the environment recognition sensor  112  and extracts the measurement information as topographical characteristics information. The topographical characteristics information indicates topographical characteristics in the surroundings of the moving device  200 . In the present example, topographical characteristics information of 16 locations on the path is extracted from the measurement information of the points of the 64,000 locations. That is, the topographical characteristics information has 16 dimensions. The topographical characteristics information acquirer  160  supplies the topographical characteristics information to the worth estimator  170 . 
     The storage portion  130  is a hard disc, for example. Weight information is stored in the storage portion  130  in advance. The weight information includes a plurality of weight coefficients respectively corresponding to the destination positional information and the topographical characteristics information. The weight information is the information that makes a link between the destination positional information and the topographical characteristics information, and weight correlation information correlating with worth of the path to the destination. The worth estimator  170  acquires the weight information stored in the storage portion  130 . 
     Here, the worth of the path to the destination may be a moving time period or energy consumption. When the worth of the path to the destination is the moving time period, the shorter the moving time period is when the moving device  200  moves through the path, the higher the worth of the path to the destination is. Therefore, the navigation apparatus  100  can move the moving device  200  to the destination in a short period of time by selecting a path having high worth. 
     When the worth of the path to the destination is the energy consumption, the smaller an amount of the energy consumption is when the moving device  200  moves through the path, the higher the worth of the path to the destination is. Therefore, the navigation apparatus  100  can move the moving device  200  to the destination with a small amount of energy consumption by selecting a path having high worth. 
     The worth estimator  170  calculates information that correlates with worth regarding one or a plurality of paths to the destination as worth correlation information from the destination positional information and the topographical characteristics information based on the weight information. Further, the worth estimator  170  estimates worth of the one or plurality of paths to the destination based on the calculated worth correlation information. The worth estimator  170  supplies the worth of the one or plurality of paths to the destination to the path selector  180 . 
     The path selector  180  selects a path having the highest worth of the worth estimated by the worth estimator  170 . The path selector  180  supplies information indicating the selected path to the controller  210 . 
     The controller  210  is an ECU (Electronic Control Unit), for example. The actuator portion  220  includes a driving system actuator  221  and a steering system actuator  222 . The moving mechanic portion  230  includes a driving mechanism  231  and a steering mechanism  232 . The driving system  231  includes a throttle valve and a brake, for example. Further, the steering mechanism  232  includes steering, for example. The driving system actuator  221  and the steering system actuator  222  are connected to the driving mechanism  231  and the steering mechanism  232 , respectively. 
     The controller  210  controls the driving system actuator  221  and the steering actuator  222  such that the moving device  200  moves through the path selected by the path selector  180 . The driving system actuator  221  adjusts an opening of the throttle valve and an amount of operation of the brake of the driving mechanism  231 , for example, based on the control by the controller  210 . Further, the steering system actuator  222  operates the steering of the steering mechanism  232 , for example. Thus, the moving device  200  can move through the path selected by the path selector  180  to the destination. 
     In this case, the moving device  200  moves through the path estimated to have the highest worth. Thus, the moving device  200  can reach the destination through the best path. As a result, the navigation apparatus  100  can more efficiently move the moving device  200  to the destination even when there is no prior information of the path. 
     (2) Basic Configuration of the Simulation Apparatus 
     The weight information stored in the storage portion  130  of  FIG. 1  is calculated by the simulation apparatus.  FIG. 3  is a block diagram showing one example of configurations of the simulation apparatus according to one embodiment of the present invention. As shown in  FIG. 3 , the simulation apparatus  300  includes an operation portion  310 , a display portion  320 , a storage portion  330 , a path setter  340 , a worth calculator  350 , an information acquirer  360  and a weight information calculator  370 . The path setter  340 , the worth calculator  350 , the information acquirer  360  and the weight information calculator  370  are realized by a CPU and a computer program, for example. 
     The operation portion  310  includes a key board and a pointing device. The user can supply various instructions to the path setter  340  by operating the operation portion  310 . The display portion  320  is a liquid crystal display panel or an organic EL (electroluminescence) display panel, for example. 
     The storage portion  330  is a hard disc, for example. Topographical data and moving device data are stored in the storage portion  330  in advance. Further, a path setting program, a worth calculation program, a destination positional information acquisition program, a topographical characteristics information acquisition program and a weight information calculation program are stored in the storage portion  330  in advance. 
     The topographical data is the data for displaying the plurality of paths and virtual topography including obstacles such as plants and rocks (hereinafter referred to as virtual topography) in the display portion  320 . As the topographical data, the topographical data having the virtual topography similar to topography through which the moving device  200  actually travels is preferably used. The moving device data is the data for displaying a virtual moving device corresponding to the moving device  200  of  FIG. 1  (hereinafter referred to as a virtual moving device) in the display portion  320 . 
     The path setter  340  displays the virtual moving device and the virtual topography in the display portion  320  based on an operation of the operation portion  310  by the user. The moving device data is made of the data for displaying virtual sensor portion, actuator portion, moving mechanism portion, vehicle body and wheels respectively corresponding to the sensor portion  110 , the actuator portion  220 , the moving mechanism portion  230  and the vehicle body  240  of  FIG. 1  and the wheels  250  of  FIG. 2  in the display unit  320 . The virtual moving device can travel on the plurality of paths in the virtual topography by driving the virtual actuator portion and moving mechanism portion. 
     Further, the path setter  340  sets combinations of the plurality of paths in the virtual topography through which the virtual moving device moves by performing a path setting process, described below. The path setter  340  stores the set combinations of the paths in the storage portion  330 . 
     The worth calculator  350  moves the virtual moving device from an arbitrary point to an arbitrary destination in the virtual topography by performing a worth calculation process, described below, based on the topographical data and the moving device data. Thus, the worth calculator  350  acquires worth of each of the plurality of paths. Further, the worth calculator  350  calculates worth of the most suitable path to the arbitrary destination based on the acquired worth of each of the plurality of paths. The worth calculator  350  stores the calculated worth of the most suitable path regarding each of the plurality of destinations in the storage portion  330 . 
     The information acquirer  360  acquires a plurality of destination positional information regarding a position of the arbitrary destination with respect to the arbitrary point in the virtual topography by performing a destination positional information acquisition process, described below, based on the topographical data and the moving device data. Further, the information acquirer  360  acquires a plurality of topographical characteristics information regarding characteristics of topography in surroundings of the arbitrary point in the virtual topography by performing a topographical characteristics information acquisition process, described below, based on the topographical data and the moving device data. The information acquirer  360  stores the plurality of acquired destination positional information and the plurality of acquired topographical characteristics information in the storage portion  330 . 
     The weight information calculator  370  calculates weight information based on the worth of each of the plurality of paths to the destination, the plurality of destination positional information and the plurality of topographical characteristics information by performing a weight information calculation process, described below. The weight information is the information for calculating the worth correlation information that correlates with the worth of the path to the destination, and calculation of the weight information is performed by a canonical correlation analysis. The weight information calculator  370  stores the calculated weight information in the storage portion  330 . The weight information stored in the storage portion  330  is stored in the storage portion  130  of the moving device  200 . 
     (3) Operation of the Simulation Apparatus 
     (a) Path Setting Process 
       FIG. 4  is a diagram showing virtual topography used in the path setting process. As shown in  FIG. 4 , the virtual topography  10  includes a plurality of virtual paths  11  through which the virtual moving device can move and virtual obstacles  12  such as plants and rocks through which the virtual moving device cannot move. Hereinafter, the virtual paths  11  and the virtual obstacles  12  are referred to as the virtual paths  11  and the virtual obstacles  12 , respectively. In  FIG. 4 , the virtual paths  11  are indicated by being filled in white, and portions in the diagram where the virtual obstacles  12  are located are indicated by a dotted pattern. In  FIG. 4 , differences in height in the virtual topography  10  are not shown. 
       FIG. 5  is a flow chart showing the path setting process. The path setting process is performed by execution of a path setting program by the CPU of the simulation apparatus  300 . In the path setting process, the user selects the topographical data stored in the storage portion  330  by operating the operation portion  310 . The path setter  340  displays the virtual topography  10  in the display portion  320  based on the selected topographical data (step S 1 ). Next, the path setter  340  sets a plurality of nodes  13  in a plurality of portions on the virtual path  11  (step S 2 ). Letting the number of the set nodes  13  be N. 
     Subsequently, the path setter  340  sets combinations of the virtual paths  11  through which the virtual moving device moves by linking each of the set arbitrary nodes  13  to another (step S 3 ). Thereafter, the path setter  340  stores the combinations of the plurality of set nodes and the virtual paths  11  in the storage portion  330  (step S 4 ). 
     In the present example, 83 nodes  13  are set in the step S 2 . Here, the number of combinations of the virtual paths  11  from one node  13  to another node  13 , which is a destination, is N×(N−1). Links among the nodes  13  are voluntarily set, so that combinations of an arbitrary number of virtual paths  11  can be set. 
     In the present example, 9378 types of combinations of the virtual paths  11  are set in the step S 3 . After the path setting process, each of the worth calculation process, the destination positional information acquisition process and the topographical characteristics information acquisition process is continued. 
     (b) Worth Calculation Process 
     The worth calculator  350  performs the worth calculation process after the path setting process is ended.  FIG. 6  is a flow chart showing the worth calculation process by the worth calculator  350 . The worth calculation process is performed by execution of a worth calculation program by the CPU of the simulation apparatus  300 . 
     The worth calculator  350  acquires the worth among all of the nodes  13  by allowing the virtual moving device to travel among all of the nodes  13  (step S 11 ). In the present example, worth between each two nodes  13  is a moving time period of the virtual moving device when the virtual moving device travels between the two nodes  13 . The worth between each two nodes  13  may be a ratio of fuel consumption of the virtual moving device when the virtual moving device moves among the two nodes  13 . 
     Next, the worth calculator  350  sets an arbitrary node  13  as a destination (step S 12 ). Subsequently, the worth calculator  350  calculates the worth of the most suitable virtual path  11  from a node  13  other than the destination to the destination (step S 13 ). The calculation of worth is performed by dynamic programming. Thereafter, the worth calculator  350  stores the acquired worth among all of the nodes  13  and the calculated worth in the storage portion  330  (step S 14 ). 
     Next, the worth calculator  350  determines whether all of the nodes  13  are set as destinations (step S 15 ). In the step S 15 , when all of the nodes  13  are not set as the destinations, the worth calculator  350  returns to the process of the step S 12 . Thus, the process from the steps S 12  to S 15  is repeated. On the one hand, in the step S 15 , when all of the nodes  13  are set as the destinations, the worth calculator  350  ends the worth calculation process. 
     In the step S 11 , the worth among the nodes  13  is acquired based on a moving velocity of the virtual moving device. For example, when unevenness between nodes  13  is large, the moving velocity of the virtual moving device decreases. Therefore, the worth between the nodes  13  is low. 
     Further, in the acquisition of the worth between the nodes  13 , an orientation of the virtual moving device is considered. In a case in which inclination from one node  13  to another node  13  is upward, the moving velocity of the virtual moving device decreases when the virtual moving device moves from the one node  13  to the other node  13 . In contrast, the moving velocity of the virtual moving device increases when the virtual moving device moves from the other node  13  to the one node  13 . Therefore, the worth between the nodes  13  decreases when the virtual moving device moves from the one node  13  to the other node  13 , and the worth between the nodes  13  increases when the virtual moving device moves from the other node  13  to the one node  13 . 
     (c) Destination Positional Information Acquisition Process 
     The information acquirer  360  performs a destination positional information acquisition process after the path setting process is ended.  FIG. 7  is a flow chart showing the destination positional information acquisition process by the information acquirer  360 . The destination positional information acquisition process is performed by execution of a destination positional information acquisition program by the CPU of the simulation apparatus  300 . 
     The information acquirer  360  sets an arbitrary node  13  as a destination (step S 21 ). Next, the information acquirer  360  calculates the destination positional information at a node  13  other than the destination (step S 22 ). The destination positional information is calculated based on the positioning information by the position orientation sensor of the sensor portion of the virtual moving device. The destination positional information is made of 3 components (an ‘r’ component, an ‘h’ component and a ‘θ’ component). That is, the destination positional information has 3 dimensions. 
     Subsequently, the information acquirer  360  stores the calculated destination positional information in the storage portion  330  (step S 23 ). Thereafter, the information acquirer  360  determines whether the destination positional information at all of the nodes  13  other than the destination are stored in the storage portion  330  (step S 24 ). In the step S 24 , when the destination positional information at all of the nodes  13  other than the destination are not stored in the storage portion  330 , the information acquirer  360  returns to the process of the step S 22 . Thus, the process from the steps S 22  to S 24  is repeated. 
     On the one hand, in the step S 24 , when the destination positional information at all of the nodes  13  other than the destination is stored in the storage portion  330 , the information acquirer  360  determines whether all of the nodes  13  are set as the destinations (step S 25 ). In the step S 25 , when all of the nodes  13  are not set as the destinations, the information acquirer  360  returns to the process of the step S 21 . Thus, the process from the step S 21  to S 25  is repeated. On the one hand, in the step S 25 , when all of the nodes  13  are set as the destinations, the information acquirer  360  ends the destination positional information acquisition process. 
       FIG. 8  is a diagram for explaining the r component, the h component and the 0 component of the destination positional information.  FIG. 8( a )  shows a side view of part of the virtual topography  10 , and  FIG. 8( b )  shows a plan view of part of the virtual topography  10 . In the  FIGS. 8( a ), 8( b ) , a node  13 G is set as a destination. Attention is paid to a node  13 A as a node  13  other than the node  13 G. 
     In this case, as shown in  FIG. 8( a ) , a distance in a horizontal direction to the node  13 G with respect to the node  13 A is the r component of the destination positional information at the node  13 A. Further, a distance in a vertical direction to the node  13 G with respect to the node  13 A is the h component of the destination positional information at the node  13 A. 
     Further, an angle formed by a line extending from the node  13 A towards another node  13  other than the destination and a line extending from the node  13 A towards the node  13 G is the θ component of the destination positional information at the node  13 A. In the example of  FIG. 8( b ) , nodes  13 B,  13 C are set as nodes  13  adjacent to the node  13 A. An angle formed by a line extending from the node  13 A towards the node  13 B and a line extending from the node  13 A towards the node  13 G is θ 1 , and an angle formed by a line extending from the node  13 A towards the node  13 C and the line extending from the node  13 A toward the node  13 G is θ 2 . 
     Therefore, when the virtual moving device moves from the node  13 A to the node  13 B, the angle θ 1  is the θ component of the destination positional information at the node  13 A. On the one hand, when the virtual moving device moves from the node  13 A to the node  13 C, the angle θ 2  is the θ component of the destination positional information at the node  13 A. In this manner, the θ components of the destination positional information of each node  13  are different from one another depending on the path of the virtual moving device. 
     (d) Topographical Characteristics Information Acquisition Process 
     The information acquirer  360  performs the topographical characteristics information acquisition process after the path setting process is ended.  FIG. 9  is a flow chart showing the topographical characteristics information acquisition process by the information acquirer  360 . The topographical characteristics information acquisition process is performed by execution of a topographical characteristics information acquisition program by the CPU of the simulation apparatus  300 . 
     The information acquirer  360  calculates the topographical characteristics information at an arbitrary node  13  (step S 31 ). The topographical characteristics information is made of 16 components (f1 to f16 components, described below). That is, the destination positional information has 16 dimensions. 
     Next, the information acquirer  360  stores the calculated topographical information in the storage portion  330  (step S 32 ). Subsequently, the information acquirer  360  determines whether the topographical characteristics information at all of the nodes  13  is stored in the storage portion  330  (step S 33 ). In the step S 33 , when the topographical information at all of the nodes  13  is not stored in the storage portion  330 , the information acquirer  360  returns to the process of the step S 31 . Thus, the process from the steps S 31  to S 33  is repeated. On the one hand, in the step S 33 , when the topographical characteristics information at all of the nodes  13  is stored in the storage portion  330 , the information acquirer  360  ends the topographical characteristics information acquisition process. 
       FIG. 10  is a diagram for explaining the f1 to f16 components of the topographical characteristics information.  FIG. 10( a )  shows a plan view of part of the virtual topography  10 , and  FIG. 10( b )  shows a side view of part of the virtual topography  10 . In  FIGS. 10( a ), 10( b ) , attention is paid to the node  13 A as an arbitrary node  13 . The virtual moving device  1  is arranged at the node  13 A while having an orientation of facing in an advancing direction. 
     As shown in  FIGS. 10( a ), 10( b ) , laser light beams are emitted from the environment recognition sensor of the sensor portion of the virtual moving device  1  in a range of 180° of a front surface of the virtual moving device  1 . Further, laser light beams reflected by objects in surroundings of the virtual moving device  1  are detected by the environment recognition sensor. Here, the objects include obstacles such as ground surfaces, plants and rocks. 
     Out of the detected positions of the plurality of objects that have reflected the laser light beams, a position closest to the virtual moving device  1  is P 1 , and a position farthest from the virtual moving device  1  is P 2 . Laser light beams L 1  to L 16  respectively reflected by objects at 16 positions that equally divide a distance between the positions P 1 , P 2  into 15 are extracted. Distances to the objects measured by the laser light beams L 1  to L 16  are d 1  to d 16 , respectively. 
     Next, a plurality of laser light beams respectively reflected by a plurality of objects near the distance d 1  are detected. An average worth of distances to the plurality of objects measured by these plurality of laser light beams is the f1 component of the topographical characteristics information. A plurality of laser light beams respectively reflected by a plurality of objects near the distance d 2  are detected. An average worth of distances to the plurality of objects measured by these plurality of laser light beams is the f2 component of the topographical characteristics information. 
     A plurality of laser light beams respectively reflected by a plurality of objects near the distance d 3  are detected. An average worth of distances to the plurality of objects measured by these plurality of laser light beams is the f3 component of the topographical characteristics information. Similarly, a plurality of laser light beams respectively reflected by a plurality of objects near the distances d 4  to d 16  are detected. Average numerical values of distances to the plurality of objects measured by the plurality of laser light beams corresponding to the distances d 4  to d 16  are the f4 to f16 components of the topographical characteristics information, respectively. 
     From the above process, in the step S 31 , the topographical characteristics information having 16 components at the node  13 A of when the virtual moving device  1  has one orientation is calculated. That is, the number of dimensions of components of the topographical characteristics information is decreased from the large number of 64,000 (in the present example) to 16. 
     In the present example, the number of orientations of the virtual moving device  1  that moves through the virtual path  11  is limited to 2, so that 2 topographical characteristics information are calculated according to orientations of the virtual moving device  1  at each node  13 . Therefore, when the virtual moving device  1  has another orientation (an orientation facing in an opposite direction to the advancing direction of  FIGS. 10( a ), 10( b ) ), the above-mentioned process is repeated. Thus, in the step S 31 , the topographical characteristics information at the node  13 A of when the virtual moving device  1  has another orientation is calculated. 
     (e) Weight Information Calculation Process 
     The weight information calculator  370  performs the weight information calculation process after the worth calculation process, the destination positional information acquisition process and the topographical characteristics information acquisition process are ended.  FIG. 11  is a flow chart showing the weight information calculation process by the weight information calculator  370 . The weight information calculation process is performed by execution of a weight information calculation program by the CPU of the simulation apparatus  300 . 
     The weight information calculator  370  sets a first function in the plurality of virtual paths  11  set by the path setter  340  (step S 41 ). The first function includes a plurality of first variables and a plurality of first coefficients. The plurality of first variables include a plurality of destination positional information and a plurality of topographical characteristics information. The plurality of first coefficients respectively correspond to the plurality of first variables. 
     Here, the first function x is given by a following formula (1). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Formula 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   x 
                   = 
                   
                     aX 
                     = 
                     
                       
                         [ 
                         
                           a 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           1 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           a 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           a 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           3 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           … 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           a 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           19 
                         
                         ] 
                       
                       ⁡ 
                       
                         [ 
                         
                           
                             
                               
                                 r 
                                 
                                   ( 
                                   1 
                                   ) 
                                 
                               
                             
                             
                               
                                 r 
                                 
                                   ( 
                                   2 
                                   ) 
                                 
                               
                             
                             
                               … 
                             
                             
                               
                                 r 
                                 
                                   ( 
                                   n 
                                   ) 
                                 
                               
                             
                           
                           
                             
                               
                                 h 
                                 
                                   ( 
                                   1 
                                   ) 
                                 
                               
                             
                             
                               
                                 h 
                                 
                                   ( 
                                   2 
                                   ) 
                                 
                               
                             
                             
                               … 
                             
                             
                               
                                 h 
                                 
                                   ( 
                                   n 
                                   ) 
                                 
                               
                             
                           
                           
                             
                               
                                 θ 
                                 
                                   ( 
                                   1 
                                   ) 
                                 
                               
                             
                             
                               
                                 θ 
                                 
                                   ( 
                                   2 
                                   ) 
                                 
                               
                             
                             
                               … 
                             
                             
                               
                                 θ 
                                 
                                   ( 
                                   n 
                                   ) 
                                 
                               
                             
                           
                           
                             
                               
                                 f 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   1 
                                   
                                     ( 
                                     1 
                                     ) 
                                   
                                 
                               
                             
                             
                               
                                 f 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   1 
                                   
                                     ( 
                                     2 
                                     ) 
                                   
                                 
                               
                             
                             
                               … 
                             
                             
                               
                                 f 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   1 
                                   
                                     ( 
                                     n 
                                     ) 
                                   
                                 
                               
                             
                           
                           
                             
                               ⋮ 
                             
                             
                               ⋮ 
                             
                             
                               … 
                             
                             
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                               … 
                             
                             
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                               … 
                             
                             
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                                 f 
                                 ⁢ 
                                 
                                     
                                 
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                                   16 
                                   
                                     ( 
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                                     ) 
                                   
                                 
                               
                             
                             
                               
                                 f 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   16 
                                   
                                     ( 
                                     2 
                                     ) 
                                   
                                 
                               
                             
                             
                               … 
                             
                             
                               
                                 f 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   16 
                                   
                                     ( 
                                     n 
                                     ) 
                                   
                                 
                               
                             
                           
                         
                         ] 
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     As shown in the formula (1), the first function x is multiplication of a first coefficient matrix ‘a’ and a first variable matrix X. The first coefficient matrix ‘a’ is a data matrix of 1 row×p column. The first coefficient matrix ‘a’ includes p first coefficients. p is an integer of not less than 2. In the present embodiment, the first coefficient matrix ‘a’ includes 19 first coefficients a1, a2, a3, . . . , a19. The first variable matrix X is a data matrix of p row×n column. p is an integer indicating the number of the first coefficient, and n is an integer indicating the number of combinations of the virtual paths  11 . In the present example, p is 19, and n is 9378. 
     Each column of the first variable matrix X includes p first variables. In the present example, each column includes p first variables r (i) , h (i) , θ (i) , f1 (i)  to f16 (i) . Here, i is an integer from 1 to n. In the present embodiment, the r component, the h component and the θ component of the destination positional information corresponding to an ‘i’th virtual path  11 , and the f1 to f16 components of the topographical characteristics information are the equivalent of p first variables r (i) , h (i) , θ (i) , f1 (i)  to f16 (i) . 
     In this manner, the plurality of first coefficients a1, a2, a3, a19 of the first coefficient matrix ‘a’ correspond to the plurality of first variables r (i) , h (i) , θ (i) , f1 (i)  to f16 (i)  of each column of the first variable matrix X, respectively. 
     Further, the weight information calculator  370  sets a second function in the plurality of virtual paths  11  set by the path setter  340  (step S 42 ). The second function includes one or a plurality of second variables and one or a plurality of second coefficients. The one or plurality of second variables include worth of the most suitable virtual path  11  to the destination. The one or plurality of second coefficients correspond to the one or plurality of second variables, respectively. 
     Here, the second function y is given by a following formula (2).
 
[Formula 2]
 
 y=bY=b [ υ     (   1) υ     (   2) . . .  υ     (n)   ]  (2)
 
     As shown in the formula (2), the second function y is multiplication of a second coefficient matrix b and a second variable matrix Y. The second coefficient matrix b is a data matrix of 1 row×q column. In the present example, q is 1. Therefore, the second coefficient matrix b only includes 1 second coefficient, and the second coefficient matrix b is equal to the second coefficient b. The second variable matrix Y is a data matrix of q row×n column. As described above, n is an integer indicating the number of combinations of the virtual paths  11 . In the present example, q is 1, and n is 9378. 
     Each row (1 row in the present example) of the second variable matrix Y includes n second variables. In the present example, each row includes n second variables v (1) , v (2) , v (3) , . . . , v (n) . In the present embodiment, worth v corresponding to an ‘i’th virtual path  11  is the equivalent of the second variable v (i) . Here, i is an integer from 1 to n. 
     Next, the weight information calculator  370  determines numerical values of the plurality of first coefficients and numerical values of the one or plurality of second coefficients such that a correlation between the first function x and the second function y is maximum (step S 43 ). 
     In the step S 43 , numerical values of the first coefficients a1, a2, a3, a19 of the plurality of first coefficient matrices ‘a’ and numerical values of the second coefficient matrix b (a numerical value of the second coefficient b) are determined by a following step. First, covariance matrices S XX , S YY , S YX  given by following formulas (3), (4) and (5) are calculated. A superscript “T” indicates a transposed matrix. 
     
       
         
           
             
               
                 
                   [ 
                   
                     Formula 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     3 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     S 
                     XX 
                   
                   = 
                   
                     
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                     ⁢ 
                     X 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     σ 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     X 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       σ 
                       T 
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
             
               
                 
                   [ 
                   
                     Formula 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     4 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     S 
                     YY 
                   
                   = 
                   
                     
                       1 
                       n 
                     
                     ⁢ 
                     Y 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     σ 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     Y 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       σ 
                       T 
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
             
               
                 
                   [ 
                   
                     Formula 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     5 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     S 
                     YX 
                   
                   = 
                   
                     
                       1 
                       n 
                     
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                     Y 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     σ 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     X 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       σ 
                       T 
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     Here, Xσ is an average deviation matrix acquired by subtraction of an average worth of a component in a row direction of the first variable matrix X from each component in a row direction of the first variable matrix X. Yσ is an average deviation matrix acquired by subtraction of an average worth of a component in a row direction of the second variable matrix Y from each component in a row direction of the second variable matrix Y. Next, a function r (a, b) given by a following formula (6) is calculated. 
     
       
         
           
             
               
                 
                   [ 
                   
                     Formula 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     6 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     r 
                     ⁡ 
                     
                       ( 
                       
                         a 
                         , 
                         b 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       
                         bS 
                         YX 
                       
                       ⁢ 
                       
                         a 
                         T 
                       
                     
                     
                       
                         
                           
                             aS 
                             XX 
                           
                           ⁢ 
                           
                             a 
                             T 
                           
                         
                       
                       ⁢ 
                       
                         
                           
                             bS 
                             YY 
                           
                           ⁢ 
                           
                             b 
                             T 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     Here, the function r (a, b) is a correlation function of a linear combination of the first coefficient matrix ‘a’, the second coefficient matrix b, the first variable matrix X and the second variable matrix Y. The first coefficient matrix ‘a’ and the second coefficient matrix b that maximizes the function r (a, b) are first canonical correlation vectors and respectively indicated by A and B. 
     The first and second functions x m , y m  having a maximum correlation is found by the above-mentioned method as shown in following formulas (7) and (8). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Formula 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     7 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     x 
                     m 
                   
                   = 
                   
                     
                       AX 
                       m 
                     
                     = 
                     
                       
                         [ 
                         
                           A 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           1 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           A 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           A 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           3 
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                           … 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           A 
                           ⁢ 
                           
                               
                           
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                           19 
                         
                         ] 
                       
                       ⁡ 
                       
                         [ 
                         
                           
                             
                               r 
                             
                           
                           
                             
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                                 f 
                                 ⁢ 
                                 
                                     
                                 
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                                 1 
                               
                             
                           
                           
                             
                               ⋮ 
                             
                           
                           
                             
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                               ⋮ 
                             
                           
                           
                             
                               
                                 f 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 16 
                               
                             
                           
                         
                         ] 
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
             
               
                 
                   [ 
                   
                     Formula 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     8 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     y 
                     m 
                   
                   = 
                   
                     
                       BY 
                       m 
                     
                     = 
                     Bv 
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
     The first canonical correlation vector A of the above formula (7) includes p first weight coefficients A1, A2, A3, . . . , A19. The first variable matrix X m  of the above formula includes p first variables r, h, θ, f1 to f16. The first variables r, h, θ are the equivalent of the r component, the h component and the θ component of the destination positional information, and the first variables f1, to f16 are equivalent of the f1 to f16 components of the topographical characteristics information. 
     In the above formula (8), a first canonical correlation vector B includes q (1 in the present example) second weight variable B. A first variable matrix Y m  of the above formula includes q (1 in the present example) second variable v. The second variable v is the equivalent of worth v. 
     A correlation between the first function x m  of the above formula (7) and the second function y m  of the above formula (8) is maximized. Therefore, a worth of the first function x m  of when numerical values are substituted into the first variables r, h, θ, f1, . . . , f16 of the formula (7) indicates how high the worth v is. In this case, a numerical value of the first function x m  is the equivalent of the worth correlation information to the destination. 
     The path selector  180  of  FIG. 1  can select a path having the highest worth by selecting a path at which a numerical value of the first function x is maximum based on the formula (7). 
     The weight information calculator  370  stores the determined numerical values of the plurality of first coefficients in the storage portion  330  as the plurality of above-mentioned weight coefficients A1 to A19 (step S 44 ). The determined numerical values of the plurality of first coefficients (the plurality of weight coefficients A1 to A19) are weight information. Thus, the topographical characteristics information acquisition process is ended. 
     In this manner, the plurality of destination positional information, the plurality of topographical characteristics information and the worth of each of the plurality of paths to the destination are acquired using the virtual topography  10  including the plurality of paths. Thus, a plurality of destination positional information, a plurality of topographical characteristics information and worth of each of a plurality of paths to the destination, of arbitrary topography can be easily acquired, and a weight coefficient of the arbitrary topography can be easily calculated. 
     (4) Operation of the Navigation Apparatus 
       FIGS. 12 and 13  are flow charts showing a navigation process by the navigation apparatus  100 . The navigation process is performed by execution of a navigation program by the CPU of the navigation apparatus  100 . The moving device  200  is arranged in a farm or a disaster site where the moving device  200  is actually used. The weight information calculated in the step S 44  of the weight information calculation process is stored in the storage portion  130 . 
     The user operates the operation portion  120  to designate a position of a destination in advance. The destination positional information acquirer  140  sets the position of the designated destination (step S 51 ). The controller  210  of the moving device  200  drives the moving mechanism portion  230  by controlling the actuator portion  220 . Thus, movement of the moving device  200  is started. 
     Next, the destination positional information acquirer  140  acquires positioning information and orientation information from the position orientation sensor  111  (step S 52 ). The destination positional information acquirer  140  estimates a position and an orientation of the moving device  200  based on the acquired positioning information and the orientation information (step S 53 ). Further, the destination positional information acquirer  140  acquires the destination positional information (the r component, the h component and the θ component) regarding the position of the destination with respect to the moving device  200  based on the position of the moving device  200  and the position of the destination (step S 54 ). 
     The path detector  150  acquires measurement information of a plurality of points from the environment recognition sensor  112  (step S 55 ). The path detector  150  detects the path through which the moving device  200  can move based on the acquired measurement information of the plurality of points (step S 56 ). 
     The topographical characteristics information acquirer  160  extracts the topographical characteristics information (the f1 to f16 components) from the measurement information of the plurality of points by the environment recognition sensor  112  (step S 57 ). The three components (the r component, the h component and the θ component) of the destination positional information acquired in the step S 54  and the θ components (the f1 to f16 components) of the topographical characteristics information extracted in the step S 57  are the first variable matrix X m  of the formula (7). 
     Steps of extraction of the topographical characteristics information are similar to steps of extraction of the f1 to f16 components of 16 topographical characteristics information from the topographical characteristics information having 64,000 components in  FIGS. 10( a ), 10( b ) . Thus, in the step S 58 , the worth estimator  170  can quickly estimate the worth of the one or plurality of paths to the destination without largely reducing reliability. 
     The worth estimator  170  estimates worth of the one or plurality of paths to the destination (step S 58 ). The estimation of the worth of the paths to the destination is performed by calculation of the worth correlation information that correlates with the worth regarding the one or plurality of paths to the destination from the destination positional information and the topographical characteristics information based on the weight information stored in the storage portion  130 . Here, the weight information stored in the storage portion  130  is the first canonical correlation vector A (the plurality of weight coefficients A1 to A19) of the formula (7), and the worth correlation information to the destination is the first function x m  of the formula (7). Therefore, a numerical value of the first function x m  is calculated from the first canonical correlation vector A and the first variable matrix X m  regarding the one or plurality of paths based on the formula (7), whereby worth v of the paths to the destination is estimated. 
     The path selector  180  selects the path having the highest worth of the worth estimated by the worth estimator  170  (step S 59 ). The controller  210  moves through the path designated by the path selector  180 . Here, the destination positional information acquirer  140  determines whether a position of the moving device  200  is a position of the destination (step S 60 ). 
     In the step S 60 , when the position of the moving device  200  is not the position of the destination, the CPU returns to the process of the step S 52 . Thus, the process from the step S 52  to S 60  is repeated. On the one hand, in the step S 60 , when the position of the moving device  200  is the position of the destination, the CPU ends the navigation process. 
     (5) Effects 
     In the navigation apparatus  100  according to the present embodiment, the one or plurality of paths are detected by the path detector  150 . The destination positional information (the r component, the h component and the θ component) is acquired by the destination positional information acquirer  140 . The topographical characteristics information (the f1 to f16 components) is acquired by the topographical characteristics information acquirer  160 . The weight information (the first canonical correlation vector A including the plurality of weight coefficients A1 to A19) is stored in the storage portion  130  in advance. 
     The worth correlation information (a numerical value of the first function x m ) regarding the one or plurality of detected paths to the destination is calculated by the worth estimator  170  from the acquired destination positional information (the r component, the h component and the θ component), the topographical characteristics information (the f1 to f16 components) based on the stored weight information and the formula (7). Further, the worth v of the one or plurality of paths to the destination is estimated by the worth estimator  170  based on the calculated worth correlation information. 
     Thus, even when there is no prior information of the path, the worth of the one or plurality of paths to the destination can be estimated. The moving device  200  can reach the destination at a high probability by moving through the path estimated to have high worth. As a result, even when there is no prior information of the path, the navigation apparatus  100  can efficiently move the moving device  200  to the destination. 
     Further, in the simulation apparatus  300  according to the present embodiment, the worth of the most suitable path regarding each of the plurality of destinations in the virtual topography  10  is calculated by the worth calculator  350 . The plurality of destination positional information and the plurality of topographical characteristics information in the virtual topography  10  are acquired by the information acquirer  360 . The weight information is calculated by the weight information calculator  370  based on the acquired worth of each of the plurality of the paths to the destination and the plurality of acquired destination positional information and the plurality of acquired topographical characteristics information. 
     Thus, even when there is no prior information of the path, the weight information used for estimation of worth of the path to the destination in the navigation apparatus  100  capable of efficiently moving the moving device  200  to the destination can be generated. 
     (6) Other Embodiments 
     (a) While the worth of the path to the destination is the moving time period or the energy consumption in the above-mentioned embodiment, the invention is not limited to this. The worth of the path to the destination may be a moving time period and energy consumption. In this case, the shorter the moving time period is and the smaller an amount of the energy consumption is when the moving device  200  moves through the path, the higher the worth of the path to the destination is. Therefore, the navigation apparatus  100  can move the moving device  200  to the destination in a short period time and with a small amount of energy consumption by selecting the path having high worth. 
     In this manner, when the worth of the path to the destination includes a plurality of elements, a second function y set by the weight information calculator  370  of the simulation apparatus  300  in the weight information calculation process is given by a following formula (9) instead of the formula (2). Further, a second function y m  having a maximum correlation with a first function x m  of the formula (7) is given by a following formula (10) instead of the formula (8). Here, v1, v2 are worth of the most suitable virtual paths to the destination respectively corresponding to the moving time period and the energy consumption. 
     
       
         
           
             
               
                 
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     (b) While the path selector  180  selects the path having the highest worth of the worth estimated by the worth estimator  170  in the above-mentioned embodiment, the invention is not limited to this. When the selected path is a dead end, the moving device  200  returns to the latest branch point of the path. Here, the path selector  180  may select a path having worth other than the highest worth (second highest worth, for example) of the worth estimated by the worth estimator  170 . 
     (c) In the above-mentioned embodiment, the number of dimensions of components of the topographical characteristics information is decreased from the large number to 16. However, the invention is not limited to this. The number of dimensions of components of the topographical characteristics information may be decreased from the large number to not more than 15. In this case, the worth estimator  170  can more quickly estimate the worth of the one or plurality of paths to the destination. Alternatively, the number of dimensions of the components of the topographical characteristics information may be decreased from the large number to not less than 17 components. In this case, the worth estimator  170  can more accurately estimate the worth of the one or plurality of paths to the destination. 
     (d) While the moving device  200  is a four-wheeled vehicle in the above-mentioned embodiment, the invention is not limited to this. The moving device  200  may be another moving device such as a two-wheeled vehicle, a three-wheeled vehicle, a vessel or a walking robot. 
     (7) Correspondences Between Constituent Elements in Claims and Parts in Preferred Embodiments 
     In the following paragraphs, non-limiting examples of correspondences between various elements recited in the claims below and those described above with respect to various preferred embodiments of the present invention are explained. 
     In the above-mentioned embodiment, the moving device  200  is an example of a moving device or an autonomous moving device, the navigation apparatus  100  is an example of a navigation apparatus, and the path detector  150  is an example of a path detector. The destination positional information acquirer  140  is an example of a destination positional information acquirer, the topographical characteristics information acquirer  160  is an example of a topographical characteristics information acquirer, the storage portion  130  is an example of a storage portion of the navigation apparatus, and the worth estimator  170  is an example of a worth estimator. 
     The component in the column direction of the first variable matrix X is an example of a first variable, the component in the row direction of the first coefficient matrix ‘a’ is an example of a first coefficient, and the first function x is an example of a first function. The component in the column direction of the second variable matrix Y is an example of a second variable, the component in the row direction of the second coefficient matrix b is an example of a second function, and the second function Y is an example of a second function. 
     The path selector  180  is an example of a path selector, the operation portion  120  is an example of an operation portion, the position orientation sensor  111  is an example of a position orientation sensor, the environment recognition sensor  112  is an example of an environment recognition sensor. The storage portion  330  is an example of a storage portion of a simulation apparatus, the virtual moving device  1  is an example of a virtual moving device, the worth calculator  350  is an example of a worth calculator, and the information acquirer  360  is an example of an information acquirer. The weight information calculator  370  is an example of a weight information calculator, the vehicle body  240  is an example of a main body, and the controller  210  is an example of a controller. 
     As each of constituent elements recited in the claims, various other elements having configurations or functions described in the claims can be also used. 
     INDUSTRIAL APPLICABILITY 
     The present invention can be effectively utilized for various autonomous mobile objects including navigation apparatuses.