KR20140145033A - Device for searching area and mapping for path of intelligent robot in unknown environments - Google Patents

Abstract

According to the present invention, the path-mapping and area-searching method in an unknown environment using a search algorithm comprises: a first step of searching for surrounding area information around an intelligent robot from the current location using a plurality of sensor modules installed in the intelligent robot; a second step of assigning state information of each cell, which forms a grid map, to a corresponding cell and generating the grid map based on the searched surrounding area information; a third step of setting up a minimum bounding rectangle (MBR) area from the generated grid map, wherein the MBR area is a minimum rectangle area that the robot can search; a fourth step of setting up a plurality of rectangle areas for the set-up MBR area using a rectangle tiling technique; a fifth step of generating a hybrid map mixed with the grid map and a topology map base on the set-up rectangle areas; a sixth step of storing the generated grid map and hybrid map; a seventh step of setting the largest area among the rectangle areas set up in the fourth step as a rectangle-based hybrid (R1) area; an eighth step of searching for new surrounding area information using the sensor modules while the robot moves around within the set R1 area; and a ninth step of updating the stored grid map and hybrid map with the new surrounding area information searched in the eighth step.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and apparatus for searching for a path in an unknown environment using a search algorithm,

The present invention relates to a path mapping and a region search method for generating a moving path of a robot in an unknown environment, and more particularly, The present invention relates to a path mapping method and a region search method in an unknown environment using a search algorithm capable of searching all areas.

In general, robots have been used to perform operations such as manipulation or movement by automatic adjustment, and in particular to carry out various tasks on behalf of humans.

In recent years, the global robot market has been growing at more than 35% annually, with service robots growing by 17% annually. In particular, the statistical results and forecasts of the robots market show that the robot moves to the service robots used in real life such as mowing robots, cleaning robots, and surveillance robots in the enterprise robot market.

A service robot that is useful for real life means a mobile robot that moves by itself and performs a given task. For example, a lawn mower should determine the extent to which lawn mowing is to be performed, and set up a self-guided mowing mission.

Mobile robots must have core technologies such as mapping, localization, path planning, obstacle avoidance and tracking in order to perform these tasks.

Especially, the most important technology that mobile robots should have is path planning. The path planning in the mobile robot can be classified into two types.

The first is to find an efficient route to the origin and destination on the map. Effective path finding is a major part of path planning research and focuses on creating short path or fast path between two points.

The second is exploration, not finding the path between two points, but finding a new one. The difference between the two path plans is the difference in the information about the environment in which the robot is operating, whether it starts with a map or starts without a map.

The state in which the robot has a map is referred to as a known environment, and the state without a map can be referred to as an unknown environment. In the known environment, the search starts after the path creation is completed based on the previously provided map, and in the unknown environment, the search starts searching without the map.

In the unknown environment, it is possible to acquire environmental information by using the sensor that the robot has, to generate a map based on the information, and to perform the search.

In particular, in order for a robot to perform an operation without prior knowledge of the environment, the robot itself must be able to generate a map. To do this, SLAM (Simultaneous Localization and Mapping) algorithm can be used.

The SLAM algorithm uses the sensor of the robot to grasp the surrounding environment information, generate the map based on the information, and estimate the position of the robot on the map at the same time.

For example, in relation to the SLAM technology, the disclosure of Japanese Patent Application Laid-Open No. 10-2010-0040234 relates to an SLAM apparatus and method, which acquires an image from the surroundings, extracts feature information from the image and extracts the extracted feature information and registered feature information And the position of the robot and the position of the feature information are matched with each other.

However, since most of the maps generated by the conventional SLAM algorithm use a high-resolution grid map, there is a problem that the update time of the map and the calculation time are often consumed for generating the route according to the size of the map.

These grid maps are difficult to use in low-end general-purpose mobile robots, and the SLAM algorithm does not provide a method of how to move the robot in order to map all the unknown environments. Therefore, it is not possible to guarantee the generation of a map that can express all unknown environments using only the SLAM algorithm.

In addition, the robot can use a topological map to generate a movement path. If the map is represented by a grid of small divisions of the environment and each cell of the grid can represent the probability of an object in the environment or the presence or absence of an object, the topology map can represent the environment as a graph of nodes and links have. A node represents a region or a landmark. The connection between a node and a node is represented by a link.

Since the topology map maintains the environment in the form of a graph, it can express a larger environment by using a smaller amount of memory than the grid map. However, since the nodes of the graph do not represent the correct environment of the area, It is not possible to move within the node, and thus it is not suitable for performing the accurate mission of the robot.

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide a hybrid map including characteristics of a topology map having characteristics of a grid map, And to provide a path mapping method and an area search method in an unknown environment using a search algorithm that enables the robot to search all areas for an unknown environment by using the generated hybrid map.

However, the object of the present invention is not limited to the above-mentioned objects, and other objects not mentioned can be clearly understood by those skilled in the art from the following description.

According to another aspect of the present invention, there is provided a method of searching for a path in an unknown environment using a search algorithm according to an embodiment of the present invention, A second step of generating a grid map by allocating state information of each cell constituting a grid map to the corresponding cell based on the searched peripheral area information; A fourth step of setting a plurality of rectangular areas using a Rectangle Tiling technique for the set MBR area, and a fourth step of setting a plurality of rectangular areas using the Rectangle Tiling technique, A fifth step of generating a hybrid map in which a grid map and a topology map are mixed based on the generated grid map, A sixth step of storing the hybrid map, a seventh step of setting the largest area among the plurality of rectangular areas set in the fourth step as R1 (Rectangle-Based Hybrid) area, An eighth step of searching for new peripheral information using the plurality of sensor modules, and a ninth updating step of updating the new peripheral information detected in the eighth step to the stored grid map and the hybrid map. .

In addition, the path mapping and area search method in the unknown environment using the search algorithm according to the present invention may further include checking whether the MBR area has been changed based on the updated grid map and the hybrid map, The method comprising the steps of: setting an R2 region adjacent to the R1 region as a new search region based on a new MBR region; and searching the surrounding region information of the robot based on the set R2 region .

Also, the path mapping and area search method in the unknown environment using the search algorithm according to the present invention is characterized in that the MBR area set in the third step includes a blank area and an obstacle.

In addition, the path mapping and area search method in the unknown environment using the search algorithm according to the present invention is characterized in that each rectangular area set in the fourth step is defined as one node and includes coordinate information of the corresponding area do.

In addition, the path mapping and area searching method in the unknown environment using the search algorithm according to the present invention is characterized in that, in the eighth step, the movement of the robot is moved to Wall following in the set R1 area, do.

In addition, the path mapping and area search method in the unknown environment using the search algorithm according to the present invention is characterized in that in the eighth step, the movement of the robot is shifted to a boustrophedon using a coverage algorithm in the set R1 area So that the search is performed.

In addition, the path mapping and area search method in the unknown environment using the search algorithm according to the present invention is characterized in that, in the tenth step, a new R1 area is set using a Rectangle Tiling technique for a new MBR area.

In addition, the path mapping and area search method in the unknown environment using the search algorithm according to the present invention is characterized in that, in the tenth step, the size of the R1 area is compared with the size of the R2 area, And the R2 region is set as the search region.

Also, the path mapping and the area searching method in the unknown environment using the search algorithm of the present invention is characterized in that, in the tenth step, the size of the R1 area is compared with the size of the R2 area, And sets the R2 region as the search region when the movement distance in the R1 region is smaller than the movement distance to the R2 region from the current position of the robot.

In addition, in the path mapping and area search method in the unknown environment using the search algorithm of the present invention, the state information allocated to each cell may be any one of a free area, an unkown area, and an obstacle Characterized in that

According to the path mapping and the area search method in the unknown environment using the search algorithm of the present invention, a hybrid map is generated by using a search algorithm, and all areas where the robot operates in an unknown environment using the generated hybrid map There is an advantage to be able to navigate.

According to the path mapping and area search method in the unknown environment using the search algorithm of the present invention, by using the hybrid map combining the features of the grid map and the features of the topology map, the expression power of the environment is increased, Use and fast path search can be performed. Moreover, utilizing the search algorithm according to the present invention has an advantage that it can be applied to various ranges.

FIG. 1 is a block diagram schematically illustrating the configuration of a path mapping and area searching apparatus for implementing a path mapping and area searching method in an unknown environment using a searching algorithm according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating a path mapping and a region searching method in an unknown environment using a search algorithm according to an embodiment of the present invention.
FIG. 3 is an exemplary diagram illustrating a grid map generated through the grid map generation unit. FIG.
4 is an exemplary diagram showing an example of obtaining the largest rectangle by using Cache
5 is an exemplary diagram illustrating a grid map and a hybrid map generated in accordance with the present invention.
6 is an exemplary diagram illustrating the relationship between an adjacent node and an adjacent center point (ACP) according to the present invention.
7 is an exemplary diagram illustrating an exemplary search method for an unknown environment using an algorithm according to the present invention.
FIG. 8 is an example of applying an MBR (Minimum Bounding Rectangle) area to a grid map according to the present invention.
Figure 9 is an exemplary diagram of performing coverage using a coverage algorithm in the R1 region according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a detailed description of preferred embodiments of the present invention will be given with reference to the accompanying drawings. In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Embodiments in accordance with the concepts of the present invention can make various changes and have various forms, so that specific embodiments are illustrated in the drawings and described in detail in this specification or application. It is to be understood, however, that it is not intended to limit the embodiments according to the concepts of the present invention to the particular forms of disclosure, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ",or" having ", or the like, specify that there is a stated feature, number, step, operation, , Steps, operations, components, parts, or combinations thereof, as a matter of principle.

FIG. 1 is a block diagram schematically showing the configuration of a region searching and mapping apparatus for generating a movement path of an intelligent robot according to an embodiment of the present invention.

3, the region searching and mapping apparatus 10 includes a sensor unit 110, a grid map generating unit 120, a hybrid map generating unit 140, a map storing unit 150, an area searching unit 130, A movement path generation unit 160, and a map update unit 170. [

The sensor unit 110 includes a plurality of sensor modules 111. The sensor unit 110 may include a sensor such as a laser scanner, an ultrasonic sensor, or the like, which can be mounted on the intelligent robot and can search for a blank area in which the robot can move, Sensor or the like.

The grid map generation unit 120 may generate a grid map based on the peripheral area information of the robot detected by the sensor unit 110. [ In particular, the grid map generation unit 120 may generate a grid map using a SLAM (Simultaneous Localization and Mapping) algorithm. The SLAM used in the grid map generation unit 120 refers to the robot itself generating a map in a state in which the robot has no prior information on the environment and estimating its position based on the generated map

The region searching unit 130 can set a minimum bounding rectangle (MBR) region, which is a minimum rectangular region that the robot can search, from the generated grid map. This MBR area may contain blank areas and obstacles.

In addition, the area searching unit 130 can set a plurality of rectangular areas using the Rectangle Tiling technique for the set MBR area.

The hybrid map generator 140 may generate a hybrid map using a plurality of rectangular regions extracted from the grid map generated by the grid map generator 120 through the region searching unit 130. [ For example, the rectangular area may be divided into a first rectangular area including the position of the current robot and a second rectangular area to an n-th rectangular area adjacent to the first rectangular area, respectively. Here, n is a natural number.

The map storage unit 150 may store the grid map generated by the grid map generation unit 120 and the hybrid map generated by the hybrid map generation unit 140.

The movement path generation unit 160 can generate the movement path of the robot based on the hybrid map.

When the intelligent robot moves along the movement path, the map update unit 160 updates the m-th rectangular area newly extracted by the area search unit 130 to the hybrid map stored in the map storage unit 150 (Where m is a natural number, m > n).

FIG. 2 is a flowchart illustrating a path mapping and a region searching method in an unknown environment using a search algorithm according to an embodiment of the present invention.

Referring to FIGS. 1 and 2, peripheral region information is searched at a current position of the robot from a sensor unit 110 including a plurality of sensor modules 111 mounted on an intelligent robot (not shown) (S101) .

When the current coordinate of the robot is X _t (x, y), and the distance measurement value of one point of the sensor is d and the corresponding angle is θ, the sensor measurement value of t time is Zt (x, y, d, Can be expressed.

When the sensor value d is smaller than the maximum measurement value r of the sensor, a probability of 0.7 is given, and the probability value of the current cell plus the value is updated to the latest value. If the next sensor value d and r are the same, a probability value of 0.05 is assigned and the cell is updated. Finally, if d is greater than r, the probability value is treated as an error of the sensor value, Do not update the value of.

The grid map generator 120 allocates state information of each cell forming the grid map to the corresponding cell based on the peripheral area information searched by the sensor unit 110 (S102), and generates a grid map (S103). In particular, the grid map generation unit 120 may generate a grid map using a SLAM (Simultaneous Localization and Mapping) algorithm.

In the grid map, the environment is represented by a set of cells of constant size.

As shown in Fig. 3, cells of constant size can be used to display objects such as blank spaces and obstacles. In addition, each cell has a state value to represent an object. This state value can have a binary number value or a probability value. The grid map can be used to represent space in a coverage algorithm.

Generally, when using the resources the robot has, the following errors may occur. The first is the sensor error that the robot uses to acquire environmental information. When acquiring environmental information (mostly distance information) using a sensor, it is possible to obtain an erroneous value depending on the characteristics of the sensor and the surrounding environment. For example, when an ultrasonic sensor is used, it is possible to measure an accurate value depending on the material to be reflected by the ultrasonic wave, to not measure the value very much, or to obtain a wrong value. Therefore, an error in the sensor measurement value can lead to an incorrect map.

The second is an error in the running information of the robot. The traveling robot moves using a motor. When the robot moves, it is possible to measure the moving distance of the robot by measuring the number of rotations of the motor using an encoder. However, there is a problem in the measurement due to the mechanical problem of the motor. The robot is slipping due to the unevenness of the floor of the environment where the robot operates, or the rotation of the left and right wheels is not constant. There may be a problem in figuring out what to measure. Also, as the robot moves more and more time, error information is accumulated in the driving information obtained by using the encoder.

Therefore, SLAM can solve the problems occurring in SLAM by introducing probability technique. The problem occurring in this SLAM can be expressed as a probability distribution that can be calculated at time k as follows.

[Formula 1]

Here, x _k is the k time of the robot state vector, m is any landmark set, Z _{0: k} is recorded observations to k time, U _{0: k} is up to k times the control vector, x ₀ is a robot Lt; / RTI >

), 제어 벡터(u_k), 관찰값(z_k)을 Bayes정리를 사용하여 계산될 수 있다. 이 계산은 상태 전이 모델(State transition model)과 관찰 모델(Observation model)이 필요하다.
A recursive method is needed to find the probability distribution of [Equation 1] above. In order to obtain x _k in a recursive way, the joint probability at k-1

), The control vector (u _k ), and the observation value (z _k ) can be calculated using Bayes' theorem. This calculation requires a state transition model and an observational model.

Observation model means the probability value of observation value (z _k ) when knowing the position of the robot and the landmark position. This observation model can be expressed by the following relation.

[Formula 2]

If the position and the map of the robot are defined, the observation value can be assumed to be independent of the state of the current map and the current robot. The motion model of the robot can be defined as a probability model of state transition as the following relation.

[Formula 3]

The state transition assumes a Markov process. The current position (x _k ) of the robot can be obtained from the previous position (x _{k -1} ) and the control vector (u _k ). It is also independent of observation and map.

SLAM is a recursive two-step process that can be expressed as a time-update and a measurement-update through the following relation.

[Formula 4]

[Formula 5]

)을 구하기 위한 재귀적인 단계를 제공할 수 있다.[Equation 4] and [Equation 5]

) Can be provided in a recursive step.

[Equation 4] is a prediction step. The motion model of Equation 3 can be used by predicting the position of the robot when the control vector u _k is transmitted to the robot at time k-1.

[Equation 5] is a correction step, and the position and the map of the robot are simultaneously corrected using the observation value at time k, and can be calculated using the observation model of the above-mentioned [Equation 2].

After generating the grid map, the area searching unit 130 sets a minimum bounding rectangle (MBR) area including a blank area and an obstacle as a minimum rectangular area that can be searched by the robot in step S104, and sets Rectangle Tiling A first rectangular area including a position of a current robot and a second rectangular area to an n-th rectangular area adjacent to the first rectangular area are set (S105). Here, n is a natural number.

Rectangle Tiling means finding a sub-rectangle N (m, n) inside the rectangle divided by m × n cells. If the rectangle is represented by two coordinates, the upper right coordinate (m, n) and the lower left coordinate (i, j) can be displayed. At this time, the number of sub-squares of the rectangle can be obtained by the following relation.

[Formula 6]

In the present invention, the concept of Cache is introduced and a large square is selected when using the Rectangle Tiling technique. Cache stores the number of free cells in the current row based on the current row. The size of the rectangle can be compared using the number of consecutive free cells and the value stored in the cache while searching in each column in the vertical direction.

After this process is performed on all columns, the largest rectangle in the target space can be found. 4 (a) and 4 (b) are diagrams showing an example of finding the largest rectangle using Cache. In FIG. 4 (a), a square composed of 16 × 12 cells is composed of a free cell (blue, True) which can be composed of a sub-rectangle and an Occupied cell (white, false) which can not be composed of a sub-rectangle.

At this time, Cache means a column of fire type, and each cell of the cache contains Free cells from the current column to the end column.

4 (b) shows the contents of the cache in the fourth column of the 16 × 12 square. To find the largest rectangle, find the current column's cache, move the cell of the cache from bottom to top, find the largest rectangle by using the coordinates of the current cell and the size of the cache to find the rectangle width.

The hybrid map generation unit 140 may generate a hybrid map by changing the rectangular area into a new data structure based on a plurality of rectangular areas set by the area search unit 130. [ This hybrid map is a combination of the characteristics of the grid map and the characteristics of the topology map.

The map storage unit 150 stores the hybrid map generated by the hybrid map generation unit 140 and the grid map generated by the grid map generation unit 120 (S107).

Thereafter, the largest area among the plurality of rectangular areas constituting the hybrid map is set as R (Rectangle-Based Hybrid) 1 area through the area searching unit 130 (S108) In operation S109, the robot moves along the movement path of the robot, that is, within the set R1 area, and searches for new peripheral information using a plurality of sensor modules.

The movement path generation unit 160 can generate a robot movement path based on the hybrid map stored in the map storage unit 150.

In addition, the new peripheral area information that is searched while the robot is moved is updated at step S110 by the map updating unit 170, and the grid map and the hybrid map stored in the map storing unit 150 are updated.

The area searching unit 130 of the present invention checks whether the MBR area has been changed based on the updated grid map and the hybrid map (S111). At this time, when the MBR area is changed, the R2 area adjacent to the R1 area is set as a m-th rectangular area as a new search area based on the new MBR area (S112). In addition, the mth rectangular area can be updated to the hybrid map stored in the map storage unit 150 (where m is a natural number, m > n).

The surrounding area information of the robot is continuously searched based on the set R2 region (S113), and if there is no new search area, the search operation through the robot is terminated. The path mapping and the area mapping in the unknown environment using the search algorithm,

In the hybrid map of the present invention, one square information can be represented by one node. In particular, the hybrid map has a structure of a node and an adjacent center point (ACP). A node represents a blank rectangle in a grid map, and an ACP represents a center point of two adjoining nodes.

6 is an example of a relationship between a node and an ACP in the hybrid map, and nodes A and B can be defined as follows.

A (A _x1 , A _y1 , A _x2 , A _y2 )

B ( _Bx1 , _By1 , _Bx2 , _By2 )

Therefore, when the nodes A and B are adjacent to each other as shown in FIG. 6A, the ACP can be obtained through the following relational expression.

[Equation 7]

6B, when the nodes A and B are vertically adjacent to each other, the ACP can be obtained through the following relational expression.

[Equation 8]

Generally, in a topology map, a node maintains only basic information of an area to be expressed. However, in the hybrid map according to the present invention, a rectangle is referred to as a node and the coordinate information of the area is maintained. .

In addition, ACP is used to indicate the connection between nodes and can be used to generate the movement path between nodes.

The hybrid map according to the present invention can be expressed as the following expression.

[Equation 9]

Here, V is a list of nodes, and can be displayed by the ID of the node, the coordinate information of the rectangle R, the list between nodes, and the adjacent node list as shown in the following expression.

[Equation 10]

A is a list of nodes adjacent to the V node and can be displayed to include the V node ID, the ACP, the distance between the V node and the ACP, and the A node list as shown in the following equation.

[Equation 11]

In particular, when the robot operates and the V-node is updated when the hybrid map is updated, a new V-node is added to the stored V-node list, and at this time, the A-node list is updated can do. The map updating unit 170 may perform the update through the following update algorithm.

According to the present invention, as shown in FIG. 7A, when a robot is located in an unknown environment, the robot uses its own sensor to grasp the environment, generates a map using the information, It is necessary to be able to grasp the position of the user and create the movement path so that the map can be expanded.

The area searching unit 130 can set a minimum bounding rectangle (MBR) area as shown in FIG. 7B based on the generated map.

The MBR area can be obtained using the following algorithm.

The MBR area obtained by the algorithm has a characteristic that it can be continuously changed when the environment changes to the minimum area representing the current environmental condition.

The MBR area shown in FIG. 7 (b) is represented by a grid map as shown in FIG. When the MBR area is set, the movement path of the robot is generated through the movement path generation unit 160, and new information is acquired along the generated movement path to extend the map. In particular, to select a new path, the grid map can be decomposed into rectangular regions using the Rectangle Tiling technique for the MBR region.

As shown in Fig. 8, the largest area not including the obstacle among the resolved rectangles is selected as R1 area, and this area can be selected as the search area of the robot. In FIG. 8, a dotted rectangle (green) is selected as the R1 area in the center of the robot, and the hybrid map can be updated using the information of the selected R1 area. At this time, the V node information shown in [Equation 10] can be stored in the hybrid map.

When the R1 area is selected to allow the robot to freely move to the free area free from obstacles, the robot expands its surroundings through the search of the corresponding area, and moves the selected R1 area rapidly as shown in FIG. 7 (c) You can move to Wall following to acquire. Accordingly, it is possible to acquire new peripheral information while moving, and update the information obtained through the map updating unit 170. [

When the R1 area is searched and the map is updated, the robot checks whether the MBR area has been changed based on the newly updated map. If the MBR area is changed as shown in FIG. 7 (d), the robot performs Rectangle tiling To select the R2 area as a new search area.

The R1 area that is completed is the free area without obstacle but it is considered as the obstacle area because it is the area where the search is completed and is not included in the free area when a new R2 area is sought. R1 area can be obtained by using the following algorithm.

The path mapping and the area search method in the unknown environment using the search algorithm according to the present invention can be represented by the following algorithm.

As described above, there is a need to pass all areas of the environment in which the robot operates, in addition to searching for the unknown environment.

9, the movement path generation unit 160 can perform coverage using a Beoustrophedon algorithm so as to perform coverage on the R1 area selected in FIG. 7 (c) .

For example, in the case of a cleaning robot, it is possible to perform cleaning for all areas by performing a coverage algorithm. Particularly, through the above-described coverage algorithm, it is possible to achieve the best efficiency when performing coverage in an obstacle-free area.

In the path mapping and area search method in the unknown environment using the search algorithm according to the present invention, the coverage algorithm operating in the unknown environment can be expressed as follows.

As described above, according to the path mapping and the area search method in the unknown environment using the search algorithm of the present invention, the characteristics of the topology map having features of grid map, low memory usage, and fast path search, By using the hybrid map including the feature, it is possible to operate in an environment of low memory and low specification.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. will be. Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.

10: Path mapping and area search device
110:
111: Sensor module
120: grid map generating unit
130:
140: Hybrid map generator
150: map storage unit
160:
170: map update section

Claims (11)

A first step of searching for peripheral region information of the robot at a current position from a plurality of sensor modules mounted on the intelligent robot;
A second step of generating a grid map by allocating state information of each cell constituting a grid map to a corresponding cell based on the searched peripheral area information;
A third step of setting an MBR (Minimum Bounding Rectangle) region, which is a minimum rectangular region that can be searched by the robot, from the generated grid map;
A fourth step of setting a plurality of rectangular areas using the Rectangle Tiling technique for the set MBR area;
A fifth step of generating a hybrid map in which a grid map and a topology map are mixed based on a plurality of rectangular areas set;
A sixth step of storing the generated grid map and the hybrid map;
A seventh step of setting the largest area among the plurality of rectangular areas set in the fourth step as a Rectangle-Based Hybrid area;
An eighth step of searching for new peripheral area information using the plurality of sensor modules while the robot is moving within a set R1 area; And
And updating the new peripheral region information found in the eighth step to the stored grid map and the hybrid map.
The method according to claim 1,
After the ninth step,
Checking whether the MBR area is changed based on the updated grid map and the hybrid map, and setting the R2 area adjacent to the R1 area as a new search area based on the new MBR area when the MBR area is changed ; And
And searching the surrounding area information of the robot based on the set R2 area. The method of claim 1, further comprising:
The method according to claim 1,
Wherein the MBR area set in the third step includes a blank area and an obstacle.
The method according to claim 1,
Wherein each of the rectangular areas set in the fourth step is defined as one node and includes coordinate information of the corresponding area.
The method according to claim 1,
In the eighth step, the movement of the robot is moved to Wall following in the set R1 area, and the searching is performed. In this way, the path mapping and the area searching method in the unknown environment using the search algorithm.
The method according to claim 1,
Wherein, in the eighth step, the movement of the robot is moved to a bustrophedon using a coverage algorithm in a set R1 area to perform a search. In the eighth step, Way.
3. The method of claim 2,
In the tenth step,
And a new R1 area is set using a Rectangle Tiling technique for a new MBR area.
3. The method of claim 2,
In the tenth step,
Comparing the size of the R1 area with the size of the R2 area, and setting the R2 area as a search area when the R1 area is larger than the R2 area.
3. The method of claim 2,
In the tenth step,
If the R1 area and the R2 area are equal to each other and the moving distance in the R1 area is smaller than the moving distance to the R2 area from the current position of the robot, Wherein the step of searching for a path in the unknown environment using the search algorithm is performed.
The method according to claim 1,
Wherein the grid map is generated using a SLAM (Simultaneous Localization and Mapping) algorithm.
The method according to claim 1,
Wherein the state information allocated to each cell is one of a free area, an unkown area, and an obstacle.

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