JP6357402B2 - Route optimization device, route optimization program, and route optimization method - Google Patents

Description

  The present invention relates to a route optimization device, a route optimization program, and a route optimization method that search for routes of a plurality of moving objects.

  Conventionally, it has been known that the problem of searching for an optimum route when a moving body moves to a plurality of points is applied to a traveling salesman problem (TSP). The traveling salesman problem is a problem in which “a route where the travel distance is the shortest when a certain salesman visits several cities once and returns to the starting point” is obtained.

  Specifically, conventionally, it is known that a traveling salesman problem solution is used for searching for a delivery route of an article or searching for a processing route by a processing robot used in the manufacturing industry.

JP 2006-53951 A Japanese Patent Laid-Open No. 10-39909

  The traveling salesman problem is a problem of searching for a traveling route of a moving body when there is a single moving body, and is not considered for a case where there are a plurality of moving bodies.

  For this reason, in the case where there are a plurality of moving bodies, there is a possibility that interference between the moving bodies may occur in the traveling path of each moving body searched by applying the solution of the traveling salesman problem. Therefore, when trying to find a cyclic route that does not cause interference between moving objects, the number of variables to be considered increases and a huge amount of calculation is required, making it difficult to obtain an optimum cyclic route.

  In one aspect, an object is to provide a route optimization device, a route optimization program, and a route optimization method that search for an optimum cyclic route of each mobile body in a plurality of mobile bodies.

  According to one embodiment, the closest distance between a plurality of moving bodies and the longest time required for the patrol when each of the plurality of moving bodies visits a plurality of points are output variables, and the movement of each moving body A process of obtaining the output variable that is an optimal solution by optimizing the input variable, using a starting point, a moving direction of each moving body, and region information for specifying a region in which each moving body moves, as input variables; And causing the computer to execute a process of outputting a circular route for each of the moving bodies calculated using the input variable when the output variable becomes an optimal solution.

  Each of the above processes may be a functional unit that realizes each of the above processes, a method of causing each computer to execute each of the processes as a procedure, and a computer-readable storage medium that stores the program.

  It is possible to search for an optimal patrol route of each moving body among a plurality of moving bodies.

It is a figure explaining a route optimization device. It is a figure explaining the outline | summary of the process by a route optimization apparatus. It is a figure which shows an example of the hardware constitutions of a route optimization apparatus. It is a figure which shows an example of a movement destination database. It is a figure which shows an example of an initial input variable database. It is a figure explaining the function of a route optimization device. It is a flowchart explaining operation | movement of a route optimization apparatus. It is a figure explaining the circulation route for every moving body. It is a figure which shows an example of output variable information. It is a figure which shows the relationship between the nearest distance calculated | required for every input variable set by the input variable reset part, and the longest traveling time. It is a figure which shows the example by which the patrol route of each moving body was displayed.

  Embodiments will be described below with reference to the drawings. FIG. 1 is a diagram illustrating a route optimization device.

  The route optimization device 100 according to the present embodiment provides route information 300 indicating a traveling route when each of the plurality of moving bodies moves to a plurality of points to the moving body control device 200 having a plurality of moving bodies. .

  The moving body control apparatus 200 according to the present embodiment is a scanning probe microscope that moves a plurality of probes so as to trace the surface of a substance and magnifies and observes the surface state. In this case, a plurality of probes become moving bodies. In addition, the moving body control device 200 of the present embodiment is a drilling device having a plurality of drills that drill holes in a circuit board or the like, for example. In this case, a plurality of drills become moving bodies.

  The route optimization apparatus 100 according to the present embodiment has a route optimization program 110 installed therein, and includes a movement destination database 120 and an initial input variable database 130. In the destination database 120 of the present embodiment, information indicating a point where the moving body circulates and a staying time at which the moving object stays are stored in association with each other. The initial input variable database 130 stores various variables given to the route optimization apparatus 100 as initial values.

  The route optimization apparatus 100 according to the present embodiment refers to the destination database 120 and the initial input variable database 130 by executing the route optimization program 110, and interferes with each other when a plurality of moving objects move. As a result, the traveling route of each mobile unit is obtained.

  Note that the route optimization device 100 of the present embodiment may provide the route information 300 to the mobile control device 200 via a network or the like. In addition, the route optimization apparatus 100 according to the present embodiment may store the route information 300 in, for example, a portable storage medium. In this case, the route information 300 stored in the portable storage medium is read by the mobile control device 200.

  In the example of FIG. 1, the route optimization device 100 and the mobile control device 200 are separate devices, but the present invention is not limited to this. In the present embodiment, the route optimization device 100 may be included in the mobile control device 200.

  Hereinafter, with reference to FIG. 2, an outline of processing by the route optimization device 100 of the present exemplary embodiment will be described. FIG. 2 is a diagram for explaining an outline of processing by the route optimization apparatus. In FIG. 2, a description will be given of processing in a case where 120 points are set as moving destinations, four moving bodies are provided, and all 120 places are visited by four moving bodies.

  FIG. 2A shows a state in which 120 points that are moving destinations of the moving object are randomly plotted in the region S. FIG. In this embodiment, as shown in FIG. 2B, the region S is divided by the line T1 in the x-axis direction, and the region S is divided by the line T2 in the y-axis direction, so that the region S is the number of moving bodies. Is divided into four regions S1 to S4, which are the same number as. In FIG. 2B, the intersection of the line T1 and the line T2 is P.

  And in this embodiment, a mobile body is allocated to each area | region S1-S4, and circulation route R1-R4 of each mobile body in area | region S1-S4 is calculated | required. At this time, in the present embodiment, cyclic routes R1 to R4 in which the moving bodies do not interfere with each other are obtained.

  Here, when obtaining the circulation routes R1 to R4, the position of the boundary between the regions S1 to S4, the position at which each mobile object starts to move, the direction of movement, and the point at which each mobile object travels. Stay time is an item to consider.

  Further, it is preferable that the traveling routes R1 to R4 do not interfere with each other and the time required for the traveling is shortened.

  Therefore, the route optimization device 100 according to the present embodiment includes information indicating an area in which each mobile object circulates (information indicating a boundary position between the areas S1 to S4), information indicating a position at which each mobile object starts moving, Information indicating the moving direction of each moving body and information indicating the moving speed of each moving body are used as input variables. Further, the route optimization apparatus 100 according to the present embodiment uses the closest distance between moving bodies and the longest longest traveling time among traveling times of a plurality of moving bodies as output variables.

  Then, the route optimization apparatus 100 according to the present embodiment acquires an output variable that is an optimal solution by optimizing the input variables so that the closest distance is maximized and the longest tour time is minimized. That is, in the present embodiment, it is possible to calculate a patrol route in which a plurality of moving bodies do not interfere with each other and travel around all points with the shortest route.

  Also, in this embodiment, by optimizing the input variables, compared to the case where the traveling salesman problem is applied to each moving body under all conceivable conditions and a traveling route of a plurality of moving bodies is calculated. Unnecessary calculations can be omitted and processing time can be shortened.

  Below, the route optimization apparatus 100 of this embodiment is demonstrated. FIG. 3 is a diagram illustrating an example of a hardware configuration of the route optimization device.

  The route optimization apparatus 100 of this embodiment includes an input device 11, an output device 12, a drive device 13, an auxiliary storage device 14, a memory device 15, an arithmetic processing device 16, and an interface device 17 that are connected to each other via a bus B. including.

  The input device 11 includes a keyboard and a mouse, and is used for inputting various signals. The output device 12 includes a display device and the like, and is used to display various windows and data. The interface device 17 includes a modem, a LAN card, and the like, and is used for connecting to a network.

  The route optimization program 110 is at least a part of various programs that control the route optimization device 100. The route optimization program 110 is provided, for example, by distributing the recording medium 18 or downloading from the network. The recording medium 18 on which the route optimization program 110 is recorded is a recording medium such as a CD-ROM, a flexible disk, a magneto-optical disk, etc. for recording information optically, electrically or magnetically, a ROM, a flash memory, etc. Various types of recording media such as a semiconductor memory that electrically records information can be used.

  The route optimization program 110 is installed from the recording medium 18 to the auxiliary storage device 14 via the drive device 13 when the recording medium 18 on which the route optimization program 110 is recorded is set in the drive device 13. The route optimization program 110 downloaded from the network is installed in the auxiliary storage device 14 via the interface device 17.

  The auxiliary storage device 14 stores the installed route optimization program 110 and also stores necessary files, data, and the like. The memory device 15 reads and stores the route optimization program 110 from the auxiliary storage device 14 when the computer is activated. The arithmetic processing unit 16 implements various processes as described later in accordance with the route optimization program 110 stored in the memory device 15.

  Next, with reference to FIGS. 4 and 5, each database included in the route optimization apparatus 100 will be described. FIG. 4 is a diagram illustrating an example of the movement destination database. The destination database 120 of this embodiment is given in advance to the route optimization device 100 in correspondence with the mobile control device 200.

  The destination database 120 of this embodiment has coordinates and stay time as information items.

  The value of the item “coordinate” is a coordinate indicating a point where the mobile body circulates and stays. Note that the value of the item “coordinate” in the present embodiment is a two-dimensional coordinate. More specifically, the value of the item “coordinate” is information indicating a point in the region S.

  The value of the item “stay time” indicates the time during which the moving object stays at a point specified by the coordinate value.

  In the following description, information in which the value of the item “coordinate” is associated with the value of the item “stay time” is referred to as destination information.

  FIG. 5 is a diagram illustrating an example of the initial input variable database. The initial input variable database 130 of the present embodiment may be given, for example, when the route optimization program 110 is executed. Further, the initial input variable database 130 may be given in advance corresponding to the mobile control device 200.

  In the example of FIG. 5, four moving bodies included in the moving body control device 200 are illustrated as moving bodies 1 to 4.

  The initial input variable database 130 of the present embodiment includes boundary coordinates, a moving direction, a moving start point, and a moving speed as information items. In the present embodiment, these items are input variables given during processing by the route optimization program 110. In the present embodiment, the value of the item “boundary coordinate”, the value of another item associated with the value of the item “boundary coordinate”, and input variable information are referred to. The initial input variable database 130 of this embodiment stores a plurality of pieces of input variable information in advance, and values included in each input variable information are referred to as input variable initial values.

  The value of the item “boundary coordinates” is the coordinates of the boundary of the area allocated for each moving object. In other words, the item “boundary coordinates” in the present embodiment is region information that identifies the region in which each mobile object circulates. In the following description, the areas S1 to S4 allocated for each moving object in the area S where the destination point exists are referred to as divided areas. The value of the item “boundary coordinates” in this embodiment is the coordinate of the intersection point P between the line T1 dividing the region S in the x-axis direction and the line T2 dividing the region S in the y-axis direction (see FIG. 2). .

  The value of the item “movement direction” indicates the movement direction of the moving bodies 2 to 4. The moving direction of the present embodiment indicates whether to move clockwise or counterclockwise within the area allocated to the moving object. In the present embodiment, when the moving body moves in the clockwise direction in the divided area, the value of the item “movement direction” is set to “1”. In the present embodiment, when the moving body moves in the counterclockwise direction in the divided area, the value of the item “movement direction” is set to “−1”. For example, in FIG. 2C, the moving direction of the moving body moving in the area S1 is given by “1” indicating clockwise.

  The value of the item “movement start point” indicates a start point when the moving bodies 2 to 4 start moving. In the present embodiment, for example, a number is given to each point where the mobile body circulates, and the value of the item “movement start point” may be indicated by a number assigned to each point.

  In the present embodiment, the reference moving body, the moving direction of the reference moving body, and the movement start point are set in advance in the plurality of moving bodies. Accordingly, the item “movement direction” and the item “movement start point” of the initial input variable database 130 of the present embodiment do not store values corresponding to the reference moving body. In the example of FIG. 5, since the moving body 1 is a reference moving body, the value corresponding to the moving body 1 is not stored in each of the item “movement direction” and the item “movement start point”.

  The value of the item “movement speed” indicates the movement speed of each moving object.

  Next, the function of the route optimization apparatus 100 of this embodiment will be described with reference to FIG. FIG. 6 is a diagram illustrating the function of the route optimization device.

  The route optimization apparatus 100 according to the present embodiment includes a route optimization processing unit 110A. The route optimization processing unit 110 </ b> A of the present embodiment is realized by the arithmetic processing device 16 executing the route optimization program 110.

  The route optimization apparatus 100 of this embodiment has a storage area 300. The storage area 300 may be provided in a predetermined area such as the auxiliary storage device 14 or the memory device 15. The storage area 300 of this embodiment stores output variable information 310, Pareto solution information 320, and the like output as processing results of the route optimization processing unit 110A.

  The route optimization processing unit 110A of the present embodiment includes a destination information acquisition unit 111, an initial input variable setting unit 112, an output variable acquisition unit 113, an input variable resetting unit 114, a Pareto solution extraction unit 115, and an optimal solution selection unit 116. And a route presentation unit 117.

  The destination information acquisition unit 111 according to the present embodiment acquires destination information from the destination database 120. The initial input variable setting unit 112 acquires input variable information from the initial input variable database 130 and sets each input variable.

  The output variable acquisition unit 113 obtains an optimal traveling route for each moving object by using the traveling salesman problem solution for one input variable information. The input variable information used for the calculation by the output variable acquisition unit 113 includes the input variable information stored in the initial input variable database 130 and the input variable information generated by the input variable resetting unit 114.

  Further, the output variable acquisition unit 113, when each mobile object circulates in the divided area with an optimal circulation route, the distance when the mobile objects are closest to each other and the traveling time in which the mobile object circulates in the divided area Among them, the longest traveling time is acquired as an output variable and output. Note that the traveling time of the moving object in the divided region includes the staying time for the moving object to stay at the destination point. That is, the traveling time of the present embodiment is a time including a travel time for moving from one point to the next point and a stay time at each point.

  In the following explanation, when each mobile object travels in the divided area with the optimum circulation route, the distance when the moving objects come closest to each other is called the closest distance, and the traveling time in the divided area for each moving object. Of these, the longest traveling time is called the longest traveling time.

  The output variable acquired by the output variable acquisition unit 113 of this embodiment is stored in the storage area 300 as the output variable information 310 together with the input variable information corresponding to the output variable.

  The input variable resetting unit 114 generates and sets new input variable information from the input variable information stored in the initial input variable database 130 by an optimization algorithm. The optimization algorithm of the present embodiment is, for example, a genetic algorithm or an annealing method.

  The Pareto solution extraction unit 115 extracts a Pareto solution from the output variables stored in the output variable information 310. Further, the Pareto solution extraction unit 115 stores the extracted Pareto solution and input variable information corresponding to the Pareto solution in the storage area 300 as Pareto solution information 320.

  The Pareto solution is also called a non-inferior solution, and indicates a solution that is excellent in one or more characteristic values as a plurality of objective functions and that is not inferior in other characteristics (not controlled). In other words, the Pareto solution is a solution that is highly evaluated compared to other solutions under certain conditions. The Pareto solution extraction unit 115 of the present embodiment extracts a Pareto solution when the closest distance and the longest traveling time are respectively set as objective functions.

  The optimum solution selection unit 116 selects, from the extracted Pareto solutions, a solution (output variable) that satisfies the constraint condition and has the shortest cyclic time as the optimum solution. The constraint condition of the present embodiment is that the closest distance is equal to or greater than a predetermined distance at which a plurality of moving bodies can move without colliding. The constraint condition is given in advance by, for example, a user of the mobile body control device 200 or the like.

  The route presenting unit 117 presents the traveling route of each mobile unit calculated using the input variable information corresponding to the optimum solution selected by the optimum solution selecting unit 116 as the optimum traveling route.

  Specifically, the route presentation unit 117 of the present embodiment may display, for example, the traveling route of each mobile body obtained based on the input variable information corresponding to the selected Pareto solution. In addition, the route presentation unit 117 according to the present embodiment may provide the input variable information corresponding to the selected Pareto solution to the mobile control device 200 as the route information 300 indicating the optimum patrol route.

  Next, the operation of the route optimization apparatus 100 of this embodiment will be described with reference to FIG. FIG. 7 is a flowchart for explaining the operation of the route optimization apparatus.

  When the route optimization program 110 is activated, the route optimization apparatus 100 according to the present embodiment refers to the destination database 120 by the destination information acquisition unit 111 and acquires destination information (step S701). Subsequently, the route optimization apparatus 100 refers to the initial input variable database 130 by the initial input variable setting unit 112 and acquires the input variable information stored in the initial input variable database 130 (step S702). Variable information is set (step S703).

  Next, the route optimization apparatus 100 uses the output variable acquisition unit 113 to obtain an optimum cyclic route in the divided area of each mobile object for each set input variable information (step S704). The output variable acquisition unit 113 according to the present embodiment obtains the optimum traveling route of the moving object in the divided area by using, for example, a solution to the traveling sales man problem.

  Next, the output variable acquisition unit 113 calculates the traveling time related to the traveling of the optimum traveling route obtained in step S704 for each moving body. Specifically, the output variable acquisition unit 113 uses the patrol route obtained in step S704 and the moving speed of the moving body included in the input variable information to calculate the stay time at each point where the moving body circulates. Calculate the round trip time. Further, the output variable acquisition unit 113 calculates the distance (closest distance) when the moving body is closest to another moving body during the movement of each moving body.

  And the output variable acquisition part 113 of this embodiment acquires the longest traveling time which is the longest traveling time among the traveling time for every moving body, and the closest distance as an output variable (step S705). The output variable acquisition unit 113 of this embodiment stores the acquired output variable in the storage area 300 as output variable information 310 in association with the input variable information.

  Subsequently, the output variable acquisition unit 113 determines whether or not the input variable optimization processing has ended (step S706). More specifically, the output variable acquisition unit 113 performs, for example, a predetermined number of input variable information set by the input variable resetting unit 114 in addition to the input variable information acquired from the initial input variable database 130. Thus, it is determined whether or not the processing from step S703 to step S705 has been completed.

  If the optimization process has not been completed in step S706, the input variable resetting unit 114 generates input variable information by the optimization algorithm, sets it again (step S707), and returns to step S703.

  In the present embodiment, when the input variable resetting unit 114 generates the input variable information by, for example, a genetic algorithm, the first generation is based on the output variable obtained from the input variable information stored in the initial input variable database 130. To input variable information from a preset generation to a preset generation.

  In step S706, when the optimization process is completed, the route optimization apparatus 100 causes the Pareto solution extraction unit 115 to extract an output variable that can be regarded as optimal from the output variable information 310 as a Pareto solution (step S708).

  Subsequently, the route optimization apparatus 100 sets a constraint condition for obtaining a unique optimum solution from the Pareto solution by the optimum solution selection unit 116 (step S709). Details of the constraint conditions will be described later.

  Subsequently, in the route optimization apparatus 100, the optimum solution selection unit 116 selects an optimum solution that satisfies the constraint condition and has the shortest longest tour time from the Pareto solution extracted in step S708 (step S710).

  Subsequently, the route optimization device 100 moves the route information 300 indicating the optimum cyclic route in the divided area of each moving body based on the input variable information corresponding to the optimum solution selected in step S710 by the route presenting unit 117. This is presented to the body control device 200 (step S711). The method of presenting the route information 300 may be displayed on the display of the route optimization device 100 so that the user of the mobile control device 200 can visually recognize the traveling route of each mobile, for example. Further, input variable information corresponding to the optimal solution may be passed to the mobile control device 200.

  As described above, in the route optimization device 100 according to the present embodiment, by optimizing the input variables using the optimization algorithm, the two output variables have the minimum value for the longest cycle time, and the closest distance is obtained. Finds the combination of input variables with the maximum value. That is, the route optimization apparatus 100 according to the present embodiment obtains a combination of input variables for a plurality of moving objects so that the moving objects do not approach each other and the traveling route is shortened.

  Therefore, according to the present embodiment, when a plurality of moving bodies circulate around a plurality of points, it is possible to search for an optimum traveling route that allows the moving bodies to move efficiently without interfering with each other.

  Hereinafter, the processing of the route optimization program 110 of this embodiment will be described in detail with reference to FIGS. 8 to 12.

  FIG. 8 is a diagram for explaining a patrol route for each moving object. In the example of FIG. 8, as input variable information, boundary coordinates (5, 5), the moving direction “1” of the moving body 2, the moving direction “−1” of the moving body 3, and the moving direction “−1” of the moving body 4 are used. Is given. Further, in the example of FIG. 8, as input variable information, the movement start point “1” of the moving body 2, the movement start point “1” of the moving body 3, the movement start point “1” of the moving body 4, Moving speed “0.07 [m / min]”, moving speed 2 of moving body 2 “0.78 [m / min]”, moving speed of moving body 3 “0.65 [m / min]”, moving body 4 The moving speed of “0.31 [m / min]” is given.

  In the present embodiment, the output variable acquisition unit 113 et al., Based on this input variable information, the optimal traveling route R1 of the moving body 1 in the divided region S1, the optimal traveling route R2 of the moving body 1 in the divided region S2, The optimum traveling route R3 of the moving body 3 in the divided region S3 and the optimum traveling route R4 of the moving body 4 in the divided region S4 are obtained.

  Then, the output variable acquisition unit 113 calculates the traveling time required for the moving bodies 1 to 4 to move on the traveling paths R1 to R4 from the moving speeds of the moving bodies 1 to 4 and the traveling paths R1 to R4. And among the mobile bodies 1-4, the traveling time of the mobile body with the longest traveling time is set as the longest traveling time, and is set as an output variable.

  Moreover, the output variable acquisition part 113 calculates | requires the distance when each of the mobile bodies 1-4 approaches the other mobile body most. And the output variable acquisition part 113 sets the shortest distance among the calculated | required distances as the closest distance, and makes it an output variable.

  FIG. 9 is a diagram illustrating an example of output variable information. The output variable acquisition unit 113 of the present embodiment stores the output variable 92 acquired for each input variable information in the storage area 300 as output variable information 310 associated with the input variable information 91.

  FIG. 10 is a diagram illustrating the relationship between the closest distance obtained for each input variable set by the input variable resetting unit and the longest traveling time.

  The optimum condition in the present embodiment is that the closest distance value is the maximum and the longest tour time value is the minimum. Therefore, as shown in FIG. 10, the solutions existing in the frame lines W1 to W4 shown in the lower right of the figure are Pareto solutions (groups of optimal solutions).

  In the present embodiment, as a constraint condition for obtaining a unique optimal solution from this Pareto solution, a distance that ensures that a plurality of moving bodies do not contact each other is set. In the present embodiment, in FIG. 10, an area in which the closest distance is equal to or greater than a distance that ensures that a plurality of moving objects do not contact each other is referred to as an operation guarantee area.

  The optimal solution selection unit 116 of the present embodiment selects a solution having the shortest longest cyclic time as an optimal solution from among the Pareto solutions in the operation guarantee area. Therefore, in the example of FIG. 10, the solution within the frame line W4 is selected as the optimal solution.

  When the optimal solution is selected, the route optimization device 100 according to the present embodiment uses the route presentation unit 117 to obtain and display the traveling routes of the mobile objects 1 to 4 using the input variable information corresponding to the optimal solution.

  In the present embodiment, as described above, when obtaining a single optimum solution, the constraint condition is applied to the Pareto solution obtained after extensively searching for the solution without setting the constraint condition in advance. Therefore, in this embodiment, if there is a minimum number of initial input variable information, a single optimal solution can be obtained.

  FIG. 11 is a diagram illustrating an example in which the traveling route of each mobile object is displayed.

  In the plot showing the movement paths R1 to R4 in FIG. 11, the point with the highest concentration is the movement start point, and the concentration decreases as the traveling time of the moving bodies 1 to 4 elapses.

  In the example of FIG. 11, the input variable information corresponding to the optimal solution includes boundary coordinates (xa, ya), a movement start point P2 of the moving body 2, a movement start point P3 of the moving body 3, and a movement start point P4 of the moving body 4. It can be seen that the moving direction “1” of the moving bodies 2 to 4 is included. At this time, the moving body 1 has the movement start point P1 and the movement direction “1”.

  As described above, in the present embodiment, when searching for a traveling route of a plurality of moving objects, boundary coordinates that determine a divided region to be assigned to each moving object, a movement start point of each moving object, and a movement of each moving object Let direction be an input variable. In the present embodiment, an optimization algorithm is used to obtain a combination of input variables such that the value of the closest distance between moving objects is maximized and the value of the longest traveling time is minimized. The traveling route of each mobile unit obtained from the combination is set as the optimal traveling route of each mobile unit.

  Therefore, in the present embodiment, it is possible to search for an optimal patrol route of each moving body among a plurality of moving bodies. Further, in this embodiment, by optimizing the input variables, it is possible to obtain the optimum traveling route for each mobile object with a small amount of calculation.

Hereinafter, reduction of the calculation amount will be described according to the present embodiment. For example, when 120 places are visited by four moving bodies, even if the 120 places are simply divided into four, the search for the movement start point requires (30 × 2) ^three calculations. The reason why 30 × 2 is set is that it is necessary to consider 30 points as a movement start point and whether to rotate clockwise or counterclockwise with respect to the presented tour route. The third power is based on the assumption that a search is made for a movement start point where the other three moving bodies are not close to each other, based on the optimum traveling route on which one of the four moving bodies turns. .

  When this calculation is performed exhaustively, 216,000 calculations are required. If 100 boundary coordinate positions for dividing the divided areas are provided, 100 more calculations are required, and 21,600,000 calculations are required. This is a calculation that requires 25 days when one calculation requires 0.1 seconds.

  In the present embodiment, since a combination of input variables that has a longest closest distance between moving objects and a shortest cyclic route is obtained by an optimization algorithm, it is not necessary to perform the enormous amount of calculation described above, and processing time is reduced. It can be shortened.

  In the present embodiment, after the Pareto solution is extracted, the optimum solution is selected according to the constraint condition. However, the present invention is not limited to this. In the present embodiment, a plurality of patterns of cyclic routes obtained from the input variable information corresponding to the extracted Pareto solution may be presented to the mobile control device 200.

  In this case, the route optimizing device 100 may present a plurality of patterns of traveling routes to the user of the mobile control device 200 and select a traveling route to be adopted. When the tour route is selected, the route optimization device 100 provides input variable information corresponding to the selected tour route to the mobile control device 200.

  Moreover, the moving body control apparatus 200 of this embodiment is not limited to a scanning probe microscope or a drilling apparatus. The mobile control device 200 of the present embodiment may be a delivery control device that performs delivery by controlling a plurality of wireless transport devices, for example. In this case, the route optimization apparatus 100 presents an optimal delivery route for each wireless transport device using a plurality of wireless transport devices as a mobile object.

In the disclosed technology, forms such as the following additional notes are possible.
(Appendix 1)
The closest distance between a plurality of moving bodies and the longest time required for the patrol when each of the plurality of moving bodies visits a plurality of points are output variables, the movement start point of each moving body, and each movement A process of obtaining the output variable that is an optimal solution by optimizing the input variable, with the moving direction of the body and area information specifying the area that each moving body circulates as input variables;
A route optimization program that causes a computer to execute a process of outputting a cyclic route for each of the moving bodies calculated using the input variable when the output variable becomes an optimal solution.
(Appendix 2)
The process of acquiring the output variable is as follows:
Calculating the traveling route of each of the moving bodies so that the closest distance between the plurality of moving bodies is maximized and the longest time required for the traveling is minimized;
The route optimization program according to supplementary note 1, wherein the output variable is calculated based on the calculated cyclic route of each mobile object.
(Appendix 3)
After executing the process of obtaining the output variable,
The route optimization program according to supplementary note 2, which causes the computer to execute a process of extracting an output variable satisfying a preset constraint condition from the group of output variables serving as the optimal solution.
(Appendix 4)
The constraint condition is that the closest distance is a predetermined value or more,
The route optimization program according to supplementary note 3, which causes the computer to execute a process of selecting the output variable with the shortest longest time from the output variables that satisfy the constraint condition.
(Appendix 5)
From the group of output variables that are the optimal solution,
The route optimization program according to supplementary note 2, which causes the computer to execute a process of selecting an output variable in which the closest distance is equal to or greater than a predetermined value and the longest time is shortest.
(Appendix 6)
The process of outputting the traveling route for each mobile unit is as follows:
6. The method according to any one of appendices 1 to 5, including a process of outputting the input variable when the output variable becomes an optimal solution, or a process of outputting screen data for displaying a traveling route for each moving body. Route optimization program.
(Appendix 7)
The closest distance between a plurality of moving bodies and the longest time required for the patrol when each of the plurality of moving bodies visits a plurality of points are output variables, the movement start point of each moving body, and each movement An output variable acquisition unit that obtains the output variable that is an optimal solution by optimizing the input variable, with the moving direction of the body and region information that identifies the region that each moving body circulates as input variables;
A route optimization device comprising: a route presentation unit that outputs a cyclic route for each of the moving bodies calculated using the input variable when the output variable becomes an optimal solution.
(Appendix 8)
A computer-aided route optimization method comprising:
The closest distance between a plurality of moving bodies and the longest time required for the patrol when each of the plurality of moving bodies visits a plurality of points are output variables, the movement start point of each moving body, and each movement The movement direction of the body and the area information specifying the area that each moving body circulates are input variables, the input variables are optimized to obtain the output variable that is the optimal solution,
A route optimization method for outputting a cyclic route for each moving object calculated using the input variable when the output variable becomes an optimal solution.

  The present invention is not limited to the specifically disclosed embodiments, and various modifications and changes can be made without departing from the scope of the claims.

DESCRIPTION OF SYMBOLS 100 Path | route optimization apparatus 110 Path | route optimization program 110A Path | route optimization process part 112 Initial input variable setting part 113 Output variable acquisition part 114 Input variable reset part 115 Pareto solution extraction part 116 Optimal solution selection part 117 Path | route presentation part 200 Mobile body Control device

Claims (5)

The closest distance between a plurality of moving bodies and the longest time required for the patrol when each of the plurality of moving bodies visits a plurality of points are output variables, the movement start point of each moving body, and each movement A process of obtaining the output variable that is an optimal solution by optimizing the input variable, with the moving direction of the body and area information specifying the area that each moving body circulates as input variables;
A route optimization program that causes a computer to execute a process of outputting a cyclic route for each of the moving bodies calculated using the input variable when the output variable becomes an optimal solution. The process of acquiring the output variable is as follows:
Calculating the traveling route of each of the moving bodies so that the closest distance between the plurality of moving bodies is maximized and the longest time required for the traveling is minimized;
The route optimization program according to claim 1, wherein the output variable is calculated based on the calculated cyclic route of each mobile object. From the group of output variables that are the optimal solution,
The route optimization program according to claim 2, wherein the computer executes a process of selecting an output variable in which the closest distance is not less than a predetermined value and the longest time is shortest. The closest distance between a plurality of moving bodies and the longest time required for the patrol when each of the plurality of moving bodies visits a plurality of points are output variables, the movement start point of each moving body, and each movement An output variable acquisition unit that obtains the output variable that is an optimal solution by optimizing the input variable, with the moving direction of the body and region information that identifies the region that each moving body circulates as input variables;
A route optimization device comprising: a route presentation unit that outputs a cyclic route for each of the moving bodies calculated using the input variable when the output variable becomes an optimal solution. A computer-aided route optimization method comprising:
The closest distance between a plurality of moving bodies and the longest time required for the patrol when each of the plurality of moving bodies visits a plurality of points are output variables, the movement start point of each moving body, and each movement The movement direction of the body and the area information specifying the area that each moving body circulates are input variables, the input variables are optimized to obtain the output variable that is the optimal solution,
A route optimization method for outputting a cyclic route for each moving object calculated using the input variable when the output variable becomes an optimal solution.

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