KR101620145B1 - Method and apparatus for path search - Google Patents

Abstract

A path search method and apparatus are disclosed. The path search method includes the steps of: (a) generating all possible paths to a destination using map information; (b) calculating distances for the respective paths; (c) calculating an indivisible index reflecting whether an obstacle node has passed through each of the paths; (d) classifying each path into groups using the calculated inexecutable index; And (e) selecting, as an upper path, a path having the calculated shorter distance for the path in the classified group.

Description

[0001] The present invention relates to a path search method and apparatus,

The present invention relates to a path search, and more particularly, to a path search method and apparatus for searching for an optimal path by using map information.

A genetic algorithm (GA) is a representative technique of the path planning of a robot developed in the past. These genetic algorithms are engineering models of the mechanisms of genetics and evolution of real living things, and they are the optimization methods using the adaptive capacity in the living environment.

This genetic algorithm is an algorithm that generates an optimal path by using a fixed or variable length binary string or a real string as a chromosome and uses the fitness of each generation. The size of the population which is the number of individuals, the size of the chromosome The operation speed and the performance of the optimization depend on the parameters such as the parameter. The larger the number of populations and the size of the chromosome, the better the optimization performance, but the slower the computation speed, the more difficult it is for robots that need real-time computation.

That is, in the case of the conventional genetic algorithm, it takes a very long time to search for an appropriate path to the destination by repeating the process of selecting and reproducing the path through the path length evaluation.

The present invention relates to a route search method and a route search method capable of classifying routes generated based on a genetic algorithm into groups by using an unexecutable index and selecting an optimal route by reflecting a distance evaluation result for each group .

It is another object of the present invention to provide a path search method and apparatus for realizing application in a real time by remarkably shortening the execution time for path search.

According to an aspect of the present invention, there is provided a method of classifying a path generated based on a genetic algorithm into groups using an unexecutable index, and selecting an optimal path by reflecting a distance evaluation result for each group / RTI >

According to an embodiment of the present invention, there is provided a method of generating map information, comprising the steps of: (a) generating all possible paths to a destination using map information; (b) calculating distances for the respective paths; (c) calculating an indivisible index reflecting whether an obstacle node has passed through each of the paths; (d) classifying each path into groups using the calculated inexecutable index; And (e) selecting, as an upper path, a path having the calculated shorter distance for the path in the classified group.

The step (d) may include classifying the same or similar paths into the same group.

The method according to claim 1, wherein, in the step (d), the group is classified in a descending order of the unexecutable indices, and the step (e) Aligning in order of decreasing distance; And selecting an upper path having the shorter distance.

And may not perform the step (e) for a group whose executability index exceeds a threshold value.

The map information includes a plurality of nodes, at least a part of the nodes being obstacle nodes.

Each path includes a plurality of subpaths, and the infinitility index may be calculated using the number of subpaths passing through the obstacle node.

The step (a) may include generating a plurality of subpaths through mating and mutation operations of each node included in the map information according to a genetic algorithm; And generating a plurality of paths to the destination by connecting the sub-paths to each other.

Each of the paths includes a plurality of sub paths, and the distance may be calculated as a sum of Euclidean distances for the sub paths.

According to another aspect of the present invention, there is provided an apparatus for classifying a path generated based on a genetic algorithm into groups using an unexecutable index, and selecting an optimal path by reflecting a distance evaluation result for each group / RTI >

According to an embodiment of the present invention, there is provided an information processing apparatus including: a route generating unit generating all possible routes to a destination using map information; A path evaluator for calculating a distance to each of the paths and calculating an execution impossibility index reflecting whether the obstacle node has passed through the path; And a path selection unit for classifying each path into groups using the calculated inexecutable indices and selecting a path having a shorter calculated distance as an upper path for a path in the classified group .

Wherein the path selecting unit classifies paths having the same or similar computed inefficiency index into the same group and arranges paths included in the classified group in descending order of distance based on the calculated distance You can select an upper path with a shorter distance.

The path includes a plurality of subpaths, and the path evaluator can calculate a distance to the path by calculating a sum of Euclidean distances for the subpath.

The path includes a plurality of subpaths, and the path evaluator may calculate an unexecutability index for the path using the number of subpaths passing through the obstacle node among the subpaths included in the path.

The route search method and apparatus according to an embodiment of the present invention can classify routes generated based on genetic algorithms into groups by using the executableness index and reflect the distance evaluation results for each group An optimal path can be selected.

Further, the present invention is advantageous in that the execution time for path search is remarkably shortened and can be applied in real time.

1 is a block diagram showing an internal configuration of a route search apparatus according to an embodiment of the present invention;
2 is a diagram illustrating map information according to an embodiment of the present invention.
3 is a flowchart illustrating a route search method according to an embodiment of the present invention;
FIG. 4 is a graph comparing the success rates according to the route search according to an embodiment of the present invention.
FIG. 5 is a table comparing path search execution times according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments are illustrated in the drawings and described in detail in the detailed description. It is to be understood, however, that the invention is not to be limited to the specific embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

The present invention generates all possible paths to a destination based on a genetic algorithm using pre-stored map information, and then performs an unexecutability index and a distance evaluation, and classifies each path into individual groups And then performing a distance evaluation to quickly select an optimal path.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a block diagram illustrating an internal configuration of a route search apparatus according to an embodiment of the present invention, and FIG. 2 is a diagram illustrating map information according to an embodiment of the present invention.

1, a path search apparatus 100 according to an embodiment of the present invention includes a path generation unit 110, a path evaluation unit 115, a path selection unit 120, a memory 125, and a control unit 130 ).

The route generation unit 110 is a means for generating a plurality of routes from the origin to the destination using the stored map information.

For example, the path generating unit 110 may generate a path for all paths from a source to a destination using map information including an n x n node using a known genetic algorithm (GA). At this time, at least some nodes among the plurality of nodes included in the map information may be obstacle nodes.

FIG. 2 illustrates map information according to an embodiment of the present invention. As shown in FIG. 2, the map information may include various types of obstacle nodes.

The path generation unit 110 generates a sub path with a neighboring node that can move to a single point around each node, and cross paths a plurality of paths to a destination through a method of cross- Lt; / RTI >

More specifically, the path generation unit 110 may generate a plurality of sub-paths by repeatedly performing a single-point mobility and a mutation operation according to a genetic algorithm (GA) around each node. The path generation unit 110 may generate a plurality of paths to a destination by connecting each sub-path generated around each node to each other.

The path generation unit 110 according to an embodiment of the present invention can generate all paths that can be moved to a destination centered on each node.

The path evaluating unit 115 is a means for performing the distance evaluation and the execution impossibility index evaluation for each path generated through the path generating unit 110, respectively.

For example, the path evaluating unit 115 may calculate the distance to the path by summing the distances of the sub-paths included in the respective paths.

For example, the path evaluation unit 115 may perform the distance evaluation using the following equation (1).

는 서브 경로를 나타내며,

는 서브 경로의 유클리드 거리를 나타낸다.here,

Represents a sub-path,

Represents the Euclidean distance of the subpath.

That is, the distance evaluation result for one path to the destination may be the sum of the Euclidean distances between the subpaths included in the path. Also, as already mentioned above, the subpath may be a single path to any node and to another neighboring node.

In addition, the path evaluating unit 115 may perform the executableness index evaluation using the number of subpaths passing through the obstacle node for the subpaths included in each path.

For example, the path evaluating unit 115 may perform the non-executableness index evaluation using the following equation (2).

는 장애물 노드를 통과하는 서브 경로의 수를 나타낸다.Here, Z represents each path,

Represents the number of subpaths passing through the obstacle node.

For example, the path evaluating unit 115 may perform an unexecutable index evaluation by dividing the number of subpaths passing through an obstacle node by the total number of subpaths included in each path.

As a result, if the evaluation result of the executableness index of the path evaluating unit 115 is 0, it can be seen that the path that does not pass through the obstacle node (that is, the path that can reach the destination) is. That is, it can be seen that the higher the evaluation of the unexecutable index by the path evaluating unit 115 is, the lower the probability that the route reaches the destination (that is, the route that can not reach the destination).

The path selection unit 120 is means for selecting an optimal path by using the distance evaluation and the execution impossibility index evaluation result for each path by the path evaluation unit 115. [

For example, the path selection unit 120 classifies paths having the same or similar inoperability index as a group, using the result of the nonexistence index evaluation for each path. Accordingly, the paths can be classified into a plurality of groups.

The path selecting unit 120 may sort the groups in descending order of the group having a low executableness index evaluation result. Then, the path selection unit 120 rearranges each path included in the group from the group having the low executableness index evaluation result to the shortest distance evaluation result using the distance evaluation result.

As a result, the path selector 120 can select an upper path having a shorter distance among the paths included in the group having a low executableness index evaluation result. That is, the path selection unit 120 can classify each path into groups using the unexecutable index evaluation result, and then select the optimal path by rearranging the paths in each group using the distance evaluation result.

In other words, the route selection unit 120 can select an optimal route by performing the distance evaluation after excluding the route having a low possibility of reaching the destination node in advance by using the result of the impossibility of the index calculation, There is an advantage to navigate and plan routes.

The memory 125 stores various algorithms required for operating the path search apparatus 100 according to an embodiment of the present invention, each path generated, distance evaluation, and an unexecutable index evaluation result.

The control unit 130 may include internal components (for example, a path generating unit, a path evaluating unit 115, a path selecting unit 120, a memory 125 (not shown) of the path searching apparatus 100 according to an embodiment of the present invention ), And the like).

FIG. 3 is a flowchart illustrating a path search method according to an embodiment of the present invention. FIG. 4 is a graph comparing success rates according to a conventional path search according to an embodiment of the present invention. FIG. 4 is a table comparing path search execution times according to an embodiment of the present invention.

Each step performed below is performed by the internal components of the route search device, but will be collectively referred to as a route search device in order to facilitate understanding and explanation.

It is also assumed that the route search apparatus has previously downloaded map information or has received and stored map information. Of course, it is a matter of course that the path search apparatus may acquire the map information based on the position of the path search apparatus 100 and update it periodically or non-periodically according to the implementation method.

In step 310, the route search apparatus 100 generates all possible routes to the destination using the map information.

As described above, the map information includes a plurality of node information. At least some of each node may be an obstacle node.

Accordingly, some of the plurality of paths generated through the path search device 100 may be generated to pass through the obstacle node.

Also, each path may comprise a plurality of subpaths for paths from any node to any node.

In step 315, the path search device 100 performs distance estimation on each path.

As described above, the path search apparatus 100 may calculate the Euclidean distances of the subpaths included in the respective paths and add the calculated Euclidean distances to perform the distance evaluation on the corresponding paths. Since this is the same as that described with reference to the number 1, a duplicate description will be omitted.

In step 320, the path search device 100 performs an impossibility index evaluation on whether or not an obstacle node is included for each path. For example, the path search device 100 performs an impossibility of an executable index by using the number of sub-paths passing through an obstacle node among the sub-paths included in each path.

That is, the path search apparatus 100 performs an operation of dividing the number of sub-paths passing through the obstacle node among the sub-paths included in each path by the number of all sub-paths to perform an impossibility . Since this is the same as that described with reference to the number 2, a duplicate description will be omitted.

Accordingly, the path searching apparatus 100 can recognize a path having a high possibility of the executableness index evaluation as a path having a low possibility that the path reaches the destination.

In step 325, the path search apparatus 100 groups each path using the result of the impossibility index evaluation for each path.

For example, the path search apparatus 100 may classify the same or similar path as a group into each of the executableness index evaluation results. If the results of the impossibility index classify similar paths into a single group, the threshold value for group classification may be determined experimentally.

Then, the route search device 100 can sort the order of the group in which the executableness index evaluation result is low.

In step 330, the path search apparatus 100 rearranges the paths in the classified group using the distance evaluation results.

In step 335, the path search device 100 determines whether or not the reordering process for the classified group has been completed.

The path search apparatus 100 may classify the same or similar paths into the same group, respectively, as a result of the unexecutable index evaluation. At this time, the route search device 100 can set a route group that can not reach the destination using the result of the impossibility of the index. That is, the path search device 100 can set the path group to a path group that can not reach the destination when the executableness index evaluation result exceeds the threshold value.

Accordingly, the route search apparatus 100 can perform the reordering process using the distance evaluation for all groups below the threshold among the groups classified using the unexecutable index evaluation result.

If the reordering process using the distance evaluation is not completed for all groups below the threshold value among the classified groups, the process proceeds to step 330 and a reordering process using the distance evaluation results for the classified group can be performed.

For example, the first group, the second group, ... , And the nth group, respectively.

The path search apparatus 100 may perform the reordering process using the distance evaluation on the paths included in the first group. Then, the path search device 100 may perform a reordering process using the distance evaluation results on the paths included in the second group.

In this way, the route search apparatus 100 can perform a reordering process using the distance evaluation results for the lower groups according to the unexecutable index evaluation result. At this time, if the group set to the group that can reach the destination by the threshold among the classified groups is up to the fifth group, the route search device 100 can perform the reordering process using the distance evaluation results up to the fifth group , And after the sixth group, the reordering process using the distance evaluation result may not be performed.

In this way, the route search apparatus 100 classifies the routes that are likely to reach the destination and the routes that are difficult to reach to the destination node through the evaluation of the executability index, The distance estimation result may be used to perform the reordering process.

When the reordering process using the distance evaluation is completed for all groups below the threshold value among the classified groups, the path searching apparatus 100 selects an optimal path by using the distance evaluation result for each group in step 340.

That is, the path search apparatus 100 can recognize a path having a short distance evaluation result as a path suitable for path planning or search.

FIG. 4 is a graph illustrating a success rate of searching a route based on a conventional route search method based on a genetic algorithm and a route search method according to an embodiment of the present invention. As shown in FIG. 4, it can be seen that the success rate for searching the route of the route search method according to an embodiment of the present invention is high.

In addition, FIG. 5 is a table comparing the execution time for searching a path according to a conventional path search method based on a genetic algorithm and a path search method according to an embodiment of the present invention. As shown in FIG. 5, it can be seen that the execution time according to the path search method according to an embodiment of the present invention is much faster than the conventional genetic algorithm.

Meanwhile, the path search method according to an embodiment of the present invention may be implemented in a form of a program command that can be executed through a variety of means for electronically processing information, and may be recorded in a storage medium. The storage medium may include program instructions, data files, data structures, and the like, alone or in combination.

Program instructions to be recorded on the storage medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of software. Examples of storage media include magnetic media such as hard disks, floppy disks and magnetic tape, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, magneto-optical media and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as devices for processing information electronically using an interpreter or the like, for example, a high-level language code that can be executed by a computer.

The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the appended claims. It will be understood that the invention may be varied and varied without departing from the scope of the invention.

110: Path generation unit
115: path evaluation unit
120: Path selection unit
125: Memory
130:

Claims (13)

A method for a path search device to search for a path,
(a) generating all possible routes to a destination using map information;
(b) calculating distances for the respective paths;
(c) calculating an execution impossibility index reflecting whether or not an obstacle node has passed through each of the paths;
(d) classifying the groups in the order of decreasing the executableness index, classifying the same or similar paths into the same group, calculating each path as a group ; And
(e) sorting the paths included in each group from the group having the lowest executable indices calculated in descending order of distances using the calculated distances, and selecting the upper path as the path having the shortest distance
, ≪ / RTI &
The step (e)
And the executableness index is not performed for a group exceeding a predetermined threshold. delete delete delete The method according to claim 1,
Wherein the map information includes a plurality of nodes,
Wherein at least some of the nodes are obstacle nodes. 6. The method of claim 5,
Each path including a plurality of sub-paths,
Wherein the executableness index is calculated using the number of sub-paths passing through the obstacle node. The method according to claim 1,
The step (a)
Generating a plurality of subpaths through mating and mutation operations of each node included in the map information according to a genetic algorithm; And
And generating a plurality of paths to a destination by connecting the sub paths to each other. The method according to claim 1,
Each path including a plurality of sub-paths,
Wherein the distance is calculated as a sum of Euclidean distances for the sub-paths. 9. A recording medium product having recorded thereon a program code for performing the method according to any one of claims 1 to 8. A route generation unit for generating all possible routes to a destination using map information;
A path evaluator for calculating a distance to each of the paths and calculating an execution impossibility index reflecting whether the obstacle node has passed through the path; And
Classifying the groups into groups in the order of decreasing the computed unexecutability index, classifying the same or similar paths into the same group as the calculated unexecutable indices, Sorting the routes in descending order of distance using the calculated distances for the paths included in each group from the group having the lowest executable indices calculated, part
, ≪ / RTI &
The path selector
Wherein the step of arranging the groups in the order of short distance using the calculated distances is not performed for the groups whose executability index exceeds a predetermined threshold value, Search device. delete 11. The method of claim 10,
The path including a plurality of sub-paths,
Wherein the path evaluator calculates a distance to the path by calculating a sum of Euclidean distances for the sub path. 11. The method of claim 10,
The path including a plurality of sub-paths,
Wherein the route evaluator calculates an executableness index for the route using the number of sub-paths that pass through the obstacle node among the sub-paths included in the route.

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