KR100347191B1 - Operation management control device and method - Google Patents

Abstract

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to an operation management control apparatus and method for determining a driving route of an unmanned vehicle in an unmanned transportation system such as a factory. The operation management control for obtaining a driving route to efficiently move a plurality of unmanned vehicles. The present invention has been made for the purpose of providing an apparatus and a method thereof, and for this purpose, the present invention examines the reverse section of the driving route of each unmanned vehicle provided by the path arranging unit 107 and the path searching unit 108, and After limiting the direction to a specific section of the driving route based on the cost, the route search section 108 calculates the driving route of each driverless vehicle again and performs the above operation until there is no reverse driving section.

Description

Operation control device and method

BACKGROUND OF THE INVENTION Field of the Invention [0001] The present invention relates to an operation control apparatus and method for determining a driving route of an unmanned transport vehicle in an unmanned transport system such as a factory.

10 is a system configuration diagram of an unmanned conveyance system having a plurality of unmanned vehicles.

In the figure, the driving management control apparatus 102 manages the unmanned transportation system, the traveling path 101 is of the sub-type, and # 1, # 2 --- # 5 are unmanned vehicles. Also, 1, 2, 3, ---, 28 are nodes interspersed on the driving path 101, and the driverless cars # 1 to # 5 are stops, turns and Carrying out and lowering of a conveyed object is performed.

In addition, each of the driverless cars # 1 to # 5 has a function of determining a driving route to the target node, and moves to the target node given by the driving management control device 102 in the path determined by the driver. Moreover, the determination of this route is described by an optimum route determination device (Japanese Patent Application Laid-Open No. 5-77244).

An operation example of this unmanned transfer system will be described below.

First, it is assumed that the movement instruction shown in FIG. 10 is transmitted from the driving management control apparatus 102 to the driverless cars # 1 to # 5. Then, the driverless cars # 1 to # 5 prepare an optimum driving route to the indicated moving target. However, in the preparation of this driving route, the driving route of another unmanned vehicle is not considered, and the optimum driving route is obtained only when no other driverless vehicle exists. FIG. 11 is a diagram showing driving paths of each of the unmanned vehicles # 1 to # 5, wherein (a) shows the driving paths of each of the driverless vehicles # 1 to # 5 in a solid line, a dotted line, a dashed line, a dashed-dotted line, It is the operation | movement figure shown by the two-dot dashed line, respectively, (b) is the figure which integrated their path | route.

Next, the unmanned vehicles # 1 to # 5 send nodes on their driving route to the driving management control apparatus 102 in the order of movement, and reserves the nodes. The operation management control device 102 examines the requested route (node example) in order from the beginning, and permits the reservation if no driverless vehicle is reserved. Driverless cars # 1 to # 5 move to authorized nodes. These controls prevent collisions between driverless cars.

Now, for example, it is assumed that the driverless cars # 1 to # 5 have progressed to nodes 4, 6, 20, 22, and 3, respectively. 12 is a driving diagram showing the current position of each of the unmanned vehicles # 1 to # 5 at this time and the driving route thereafter. If the next movement is performed as it is, the contention occurs that the unmanned vehicle # 1 and the unmanned vehicle # 2 move in the opposite direction to each other. In this case, the destination cannot be reached unless either side changes the route. At this time, the reservation of the node of the moving line is not permitted in the driverless cars # 1 and # 2.

Here, the detour (node 4 → 18 → 19 → 20 → 21 ----) of the driverless car # 1 is found and the node 18 is reserved. This allows the driverless car # 2 to move in the original path, but this time there is a raceway between the driverless cars # 1 and # 3. FIG. 13 is a diagram illustrating a route of each of the driverless cars # 1 to # 5. This time, driverless car # 3 finds a detour (node 20 → 6 → 5 → 4 → 3 → 16 → 15), reserves node 6, and moves to that node.

In this way, the reverse contention and the detour search are repeated, and the driverless vehicles # 1 to # 5 move to the destination point.

However, the above-described method may occur due to an increase in useless movement and waiting, such as repeating the detour to change the driving route after the race of the driving route occurs. In addition, as the number of driverless cars increases, there is a problem that useless movement and air greatly reduce the conveyance efficiency.

The present invention has been made under such a background, and an object thereof is to provide an operation management control apparatus and method capable of efficiently moving a plurality of unmanned vehicles to a target point.

The invention described in claim 1 is a driving control management apparatus for controlling the driving of a plurality of unmanned vehicles traveling on a traveling path consisting of a plurality of nodes at a stop position and a connection path connecting the nodes, wherein the position of the node is provided. And calculating a driving cost in a possible driving section connecting the current node and the target node based on the connection between the nodes, thereby searching for an optimal driving route connecting the current node having the minimum cost to the target node in each driverless vehicle. Path search means; And a reverse path including a driving path having directions facing each other from the plurality of optimum driving paths in each of the unmanned vehicles based on the result obtained by the path searching means, and the reverse section of each unmanned vehicle driving path. A path alignment means for calculating the total cost for each driverless vehicle by adding the costs and then limiting the moving direction of the driving route of the unmanned vehicle having the largest total cost to the reverse section; Wherein the route search means and the route alignment means are optimal driving paths without collision of the driverless vehicle by repeating the reverse section exclusion action through the command of the total cost of the reverse section until all the reverse sections are removed. It is characterized by determining.

The invention as set forth in claim 2 is a driving management control device for controlling a driving that runs on a traveling path consisting of a plurality of nodes at a stop position and a connection path for connecting the nodes, wherein each optimum of the plurality of unmanned vehicles is optimal. A first step of obtaining a traveling route, a second step of finding a reverse section which is a reverse traveling route with each other in the plurality of optimum driving routes obtained by the first step, and ending the processing if there is no reverse section, In other cases, the third step of integrating the cost of the reverse section, the fourth step of reorienting the reverse section having the largest cost in one direction, and the optimum driving again for all unmanned vehicles in the directioned traveling path Having a fifth step of obtaining a route, the optimum running route without contention is obtained by repeating the third to fifth steps. There.

According to the invention as set forth in claim 1, the route alignment means restricts the conveying direction to a specific section of the driving route based on the route and the cost of the plurality of unmanned vehicles created by the route searching means, and searches the route again in the restricted route. The means creates a path for each driverless vehicle. For this reason, the path | route which has little useless movement of a driverless vehicle and a path with little air | atmosphere can be calculated | required, and the effect that the movement efficiency of a driverless vehicle can be improved is acquired.

According to the invention as set forth in claim 2, in the first step, each optimal driving route of the plurality of driverless vehicles is obtained, the reverse section is obtained in the second step in those paths, and the reverse cost is calculated in the third step, When the cost of the preceding result is maximized in 4 steps in one direction of the reverse section, the fifth step obtains and corrects all the paths in the redirected driving path, and the above third to fifth steps have no reverse section. Is repeated until. For this reason, the effect that several path | routes can be calculated which do not have a reverse direction section, and is less expensive.

EXAMPLE

Next, an embodiment of the present invention will be described with reference to the drawings. 1 is a block diagram showing the configuration of the operation management control apparatus 102 according to the present embodiment. In this figure, the transportation instruction table memory 103 stores the position of the conveyed object, the conveying ship and the like. The unmanned vehicle data memory 104 stores states such as the current position and the moving direction of each unmanned vehicle. The traveling route data memory 105 stores coordinates of each node on the traveling route, its connection control, and cost. The cost here is an index relating to running between nodes, that is, a value indicating ease of running.

In addition, the route determination unit 106 determines the most suitable driving route of the driverless vehicle. The path determining unit 106 is constituted by a CPU or the like and can be functionally divided into a path sorting unit 107 and a path searching unit 108. These path sorter 107 and path searcher 108 are described in detail below.

A: Description of processing performed by the path search unit 108

When the path search instruction comes from the path sorter 107, the path search unit 108 first obtains a path from the starting node to the target node. Next, the costs of each route are accumulated from the costs stored in the traveling data memory 105, and the route whose minimum is the cost is selected as the optimum route.

2 is a diagram showing the cost of each mark of the traveling path 101. FIG. In this figure, () denotes the cost of each mark. For example, the cost between nodes 1 and 2 is "3000". Here, when the starting node is 1 and the target nerd is 4, the paths of nodes 1 → 2 → 3 → 4 are minimized to the accumulated cost of "7500", and the optimal path is selected. However, in the case where the direction information described later is included in the route search instruction, the route in the opposite direction to the set direction is not selected.

The path search unit 108 outputs the path obtained by the above method and its cost to the path sorter 107. However, the route created here becomes an optimal route only when there is no raceway competition.

B: Description of the Process Performed by the Path Sorter 107

The path sorter 107 obtains a final driving path by using a tree search means. The tree referred to here takes the configuration of branching across the bottom as shown in FIG. Here, N1 and N2 --- are branching points containing branching conditions, and double branching point N1 is a root branching point at which branching starts. For example, when branching point N2 is made into a current branching point, branching point N1 becomes the parent branching point of branching point N2, and branching points N3 and N4 become the child branching point of branching point N2. The search is basically performed from the upper branch point to the lower branch point. However, in the case of inability to search, it returns to the parent branch point (hereinafter referred to as a back track) and branches to another branch point.

3 is a floor chart showing a process performed by the path alignment unit 107, and the following description will be given based on this diagram.

When the processing is started (step SP1), in step SP2, a search instruction is issued to the route search unit 108, and the driving route of each driverless vehicle is obtained. Here, the route search unit 108, which has received the search instruction, searches the route by the above-described method, and outputs the result to the route sorter 107. Further, this search instruction includes an unmanned target node determined by the data stored in the transport instruction table memory 103.

In step SP3, the root branch point of the tree is made empty.

In step SP4, a section (reverse section) in which any two unmanned vehicles move in the opposite direction to each other based on the driving route of each driverless vehicle supplied from the path search unit 108 is obtained, and this is performed for all combinations of the driverless vehicles. do.

In step SP5, in the result of step SP4, the process ends if there is no reverse section (step SP16). If there is a reverse section, the process proceeds to the next step SP6. If there is no reverse section, the driving route at that time becomes the final driving route.

In step SP6, the cost of the reverse section of the path of each driverless vehicle is integrated. Here, the cost of the reverse section is read from the traveling road data memory 105. In addition, when there is contention between the plurality of paths in the reverse direction in one reverse section, the cost for the number of times of the contention is accumulated.

In step SP7, the symbols attached to the driverless vehicle are sorted in the order of the running route cost, and a competitive driverless vehicle set is created.

In step SP8, a branching point having this contentionless drone set is added below the mother branching point. However, when this step SP8 is processed for the first time, the contention drone set is set at the root branch point.

In step SP9, the driverless driver of interest is determined from the contention driverless vehicle set. Attention driverless cars are selected in order from the first driverless cars in a set of competing driverless vehicles set in order of cost. In addition, when there is no next driverless vehicle, it carries out without attention driverless vehicle.

In step SP10, if there is no driverless difference of interest in the processing of the previous step SP9, the process proceeds to the next depth SP11, and if there is an attentionless driverless difference, it branches to step SP13.

In step SP11, it is checked whether the current branching point is before the root branching point. If the branching point is not the root branching point, the process proceeds to the next step SP12. Then, all processing is terminated due to the routing failure (step SP17).

In step SP12, the process of the current branch point is moved to the mother branch point (back track), and the process returns to the process of step SP9. The direction setting made when branching to the current branch point is released at this time.

In step SP13, the reverse section of the path of the driverless vehicle of interest is set in the reverse direction of the moving direction of the driverless vehicle on the traveling road (to be one-way) and added to the direction information.

In step SP14, a search instruction is issued to the route search unit 108, and all the unmanned vehicle routes on the set travel direction are obtained and corrected.

In step SP15, it is checked whether there is a path not found in the path search of the previous step SP14, and if it exists, the process proceeds to the next step SP16, and when it does not exist, the process returns to step SP14.

In step SP16, after canceling the direction determination of the traveling route made in step SP13, the process returns to step SP9.

In the above process of the path alignment unit 107, when there are no paths connecting two points adjacent to each other on the traveling road, the section is not included in the reverse section.

C: Example 1

An operation example of the travel management control device 102 will be described below. FIG. 4 is a driving diagram showing a conveyance path of each driverless vehicle, in which parts corresponding to those of FIG. 11 are denoted by the same reference numerals and description thereof will be omitted. In this operation example, the start nodes and the target nodes of each of the unmanned vehicles # 1 to # 5 are also the same as in FIG.

First, the path sorting unit 107 determines the moving target nodes of the unmanned vehicles # 1 to # 5 based on the conveying instructions stored in the conveying instruction table memory 103, and issues a search instruction to the route searching unit 108. FIG. . The route search section 108 searches for each driving route of the driverless cars # 1 to # 5 according to this search instruction, and outputs the result to the route sorting section 107 (FIG. 4 (a)).

The route sorting unit 107 searches for the aforementioned reverse section and its contention partner from the traveling route (initial route) supplied from the route search section 108, and integrates the costs of the reverse section. 6 is a diagram showing the result of integration of the reverse section and the cost. Therefore, the integration results are sorted in order of increasing cost, and a contention drone set described below is created.

Driverless set = (# 2, # 1, # 5, # 3, # 4)

Next, the path arranging unit 107 sets this contention drone set at the root branch point N1 of the tree shown in FIG. 7, and selects the unmanned vehicle # 2 at the head as the unmanned driver of interest. Therefore, the direction of the moving direction of the driverless vehicle # 2 and the direction of the reverse direction (that is, one-way) opposite to the reverse section of the path of the driverless vehicle # 2 is performed, and in the subsequent path search, the nodes 2 → 3 → 4 → 5 → Movement is limited to the direction of 6 → 7.

Thus, the path sorting unit 107 outputs a search instruction including direction information, and the path search unit 108 performs path search again based on the direction information. 4 (b) is a diagram showing the route results. In this figure, the set section is indicated by a thick arrow. Based on this result, the path alignment unit 107 again performs cost integration of each path, and a contention driverless set (# 1, # 3, # 4, # 5) is created. Therefore, the branch point N2 is added under the branch point N1 of the tree (FIG. 7), and the contention driverless set is set at the branch point N2. In addition, tie the drone # 2 of N1 and the branch point N2.

This time, the unmanned vehicle # 1 at the head of the set of branching points N2 is selected as the unmanned driver of interest, the direction of the reverse section is set, and added to the previous direction setting. Thus, when the path searcher 108 obtains and corrects the path, the search result of FIG. 4C is obtained. Here, nodes 9 → 8 and 13 → 12 are newly one-way. In addition, the path alignment unit 107 again integrates the costs, creates the contentionless driverless set = (# 5, # 1, # 4, # 2, # 3), and the branch point N3 below the branch point N2 of the contention tree. Set this contention driverless set to.

Driverless car # 5 becomes a driverless driverless car this time, and the same process is performed. As a result, the current direction setting (node 24 → 23 → 22 → 21 → 20 → 19 → 18 → 17 → 16 → 15) is added to the previous direction setting. It becomes impossible to get. This state means that the path alignment has failed (indicated by x in FIG. 7), and the direction setting of the set path is canceled at this time.

Therefore, the driverless driverless car is changed to the next driverless car # 1 of the contention driverless car set at the branch point N3, and the same processing is performed, but the path of the driverless car # 1 is not obtained here. Therefore, in the competitive driverless vehicle set, the driverless vehicle is sequentially selected as the driverless driverless vehicle and subjected to the same process, but all path alignments fail.

For this reason, it backtracks to the mother branch point N2, and cancels direction setting (node 9-> 8, node 13-> 12) based on the unmanned vehicle # 1 of the last time of the branch point 2. As shown in FIG. Thus, if the next unmanned vehicle # 3 of the competitive unmanned vehicle set is the unmanned driver of interest, and the same processing is performed, the search result shown in Fig. 5A is obtained. Then, a contention driverless set (# 1, # 4) is set at the branch point N4 of the branch point N2. This time, driverless car # 1 is selected as the unmanned driverless car, the search result of FIG. 5 (b) is obtained, and the competitive driverless car set (# 1, # 4, # 3, # 2) is the branch point N4. It is set to N5. Moreover, unmanned vehicle # 1 is selected for the unmanned vehicle of interest, and direction determination and route search are performed (FIG. 5 (c)). Since the reverse section does not exist in the route this time, the route alignment is successful (Fig. 7), and each route at this time becomes the final driving route. The route driving is sent from the driving management control device 102 to the driverless cars # 1 to # 5, and each driverless vehicle # 1 to # 5 moves along the route.

D: Operation Example 2

Next, an example of operation on the traveling route 101 (FIG. 8) will be described. The traveling route 101 is a traffic prohibition between the nodes 20 and 21 of the traveling route 101 (FIG. 11). In this operation example, the starting node and the target node of each of the unmanned vehicles # 1 to # 5 are the same as those of the operation example 1 described above (Fig. 11). In this case, between the nodes 6, 7, between the nodes 7, 8, and the nodes 21, 22 are not included in the reverse section because there are no paths that circumscribe this and bypass other than the path.

Here, the same processing as in operation example 1 is performed, and an initial path is first obtained as shown in FIG. 8 (a). Based on this, cost integration is performed, and a contention driverless set (# 1, # 2, # 4, # 5, # 3) is created, and the contention driverless set is set at the root branch point N6 of the tree shown in FIG. Is set. Therefore, after the direction determination (nodes 13 → 12 → 11 → 10 → 9 → 8, nodes 6 → 5 → 4 → 3 → 2) is performed using the leading driverless car # 1 of this competitive driverless vehicle set as the attentionless driverless vehicle, the vehicle is driven. Get the route and fix it. Here, there is an unmanned vehicle traveling in the reverse direction even between nodes 8 and 7, and between nodes 7 and 6, but the reverse section is not included for the above reason.

When the route search shown in FIG. 7 (b) is obtained by the route search, the cost of the driving route is accumulated, and the contention driverless vehicle sets # 1, # 3, # 4, # 5 are generated. In addition, the contention driverless vehicle set is set at the branch point N7 of the branch point N6. Therefore, the direction setting (nodes 6 → 20 → 19 → 18 → 17 → 16 → 15) is performed as the unmanned driver of the driverless car # 1 and added to the previous direction setting. However, in this direction limitation, since the path of the driverless vehicle cannot be obtained, the previous direction setting is canceled, and the next driverless vehicle # 3 of the contention driverless vehicle set at the branch point N7 is the attentionless driverless vehicle. After setting the direction of the unmanned vehicle # 3, the paths of the unmanned vehicle # 1 to # 5 are obtained again. As a result, the path shown in FIG. 7C is obtained, and since there is no reverse section in the path, this is the final alignment result. Therefore, each of the driverless cars # 1 to # 5 moves along this travel path.

As described above, according to this embodiment, since the driving route of each driverless vehicle is searched and aligned so that there is no reverse section, it is possible to reduce unnecessary movement or waiting, and to improve the efficiency of driving the driverless vehicle. Obtained.

As described above, according to the present invention, since the driving route of a plurality of unmanned vehicles is searched so that there is no reverse driving section and the cost is further reduced, it is possible to obtain a driving route with little useless movement and less waiting of the driverless vehicle. The effect that the moving efficiency of a car can be improved is acquired.

1 is a block diagram of an operation management control apparatus according to an embodiment of the present invention;

2 is a cost display of each mark in the driving degree,

3 is a flowchart showing processing performed by the cost alignment unit of FIG. 1,

4 is a driving diagram showing a driving route of the driverless vehicle in operation example 1 of the embodiment,

5 is a driving diagram showing a driving route of an unmanned vehicle in Operation Example 1 of the embodiment;

6 is a result of integration of costs in the path of FIG.

7 is a tree diagram in the operation example 1;

8 is a driving diagram in operation example 2 of the embodiment,

9 is a tree diagram in operation example 2;

10 is a system configuration diagram of the unmanned transport system,

FIG. 11 is a driving diagram showing the driving route of the driverless vehicle in the conventional example (FIG. 10),

12 is a driving diagram showing a driving route in a conventional example,

13 is a driving diagram showing a traveling route in the conventional example.

* Description of the main parts of the drawings

# 1, # 2 --- Drone 101 --- Driveway

102 --- Operation control system 103 --- Transfer order table memory

104 --- driverless data memory

105 --- Driving data memory

106 --- route determination section 108 --- route search section

107 --- path alignment

Claims (2)

In the operation management control device for controlling the operation of a plurality of unmanned vehicles running on a traveling path consisting of a plurality of nodes in the stop position and a connection path connecting the nodes, Optimum driving for connecting the current node having the minimum cost in each unmanned vehicle to the target node by calculating the driving cost in a possible driving section connecting the current node and the target node based on the position of the node and the connection between the nodes. Route search means for searching a route; And On the basis of the result obtained by the route searching means, in each unmanned vehicle, a reverse section including a driving route having a direction facing each other from the plurality of optimal driving routes is searched, and the cost of the reverse section of each unmanned vehicle driving route is determined. Calculating a total cost for each unmanned vehicle by summing, and then restricting a moving direction of the driving route of the unmanned vehicle having the largest total cost to a reverse section of the unmanned vehicle; Including, Here, the route search means and the route alignment means determine the optimal driving route without collision of the driverless vehicle by repeating the reverse section exclusion action through the command of the total cost of the reverse section until all the reverse sections are removed. Operation management control device characterized in that. In the operation management control device for controlling the operation of a plurality of unmanned vehicles running on a traveling path consisting of a plurality of nodes in the stop position and a connection path connecting the nodes, A first step of obtaining optimal driving paths of the plurality of unmanned vehicles; In the plurality of optimum running paths obtained by the first step, A second step of obtaining a reverse section that is a driving path in a reverse direction to each other, If the reverse section does not exist, terminating the process; otherwise, integrating the cost of the reverse section; A fourth step of redirecting the reverse section having the largest cost in one direction; A fifth step of obtaining the optimum driving route again for all unmanned vehicles in the oriented driving route; And an optimum running route without contention by repeating the third to fifth steps.