KR100955655B1 - Method for autonomous navigation of robot - Google Patents

Abstract

The present invention relates to a method of autonomous driving of a mobile robot, by using a map containing semantic information based on smart environmental technology, reliable driving is possible regardless of the area of the environment in which the robot will run at low cost. It is an object of the present invention to provide a robot autonomous driving method capable of intelligent driving even in a dynamic environment.

In order to achieve the above object, an autonomous driving method of a robot according to the present invention includes: (a) a semantic layer having information on a type and a name of a cell that divides a robot's moving space, and a position of a cell; And a metric layer with information about the size and size, a connection layer with information about connection points with other cells, and a structure including a localization information layer for determining the current position of the robot. Loading map information into the robot; (b) acquiring current location information from an external location recognition device by using the location recognition sensor provided in the robot or localization information included in the semantic map; (c) establishing a semantic driving route based on the provided destination, loaded semantic map information, and current position information of the robot; And (d) driving the robot according to a predetermined path; Include.

Mobile robot, semantic map, autonomous driving

Description

Autonomous driving of robots {METHOD FOR AUTONOMOUS NAVIGATION OF ROBOT}

The present invention relates to a method of autonomous driving of a robot, and more particularly, by loading semantic map information composed of semantic, metric, connection, object, and sensor layers in a robot, and using the map for driving of the robot, In addition, since it is possible not only to drive reliably regardless of the size of the area, but also to set a meaningful driving route through the semantic map information, the robot can autonomously operate according to the driving environment. will be.

The autonomous driving technology of the mobile robot is an important technology that is the foundation of the intelligent service robot that is recently gaining attention.

On the other hand, in order to function properly as an intelligent service robot, the robot must be able to understand the surrounding environment by using sensors and autonomously cope with the situation. Such autonomous driving technology has been actively researched since the 1980s, but the actual environment There are still many challenges to apply.

The technologies required for autonomous driving of mobile robots include 'location technology' to accurately locate the robot, 'map formation technology' to grasp the current working environment, and after creating a movement path for work execution. It is largely classified into 'route creation technology' that safely moves.

Recently, researches on SLAM (Simultaneous Localization And Mapping) in which a robot performs positioning and mapping at the same time for the purpose of autonomous driving of a mobile robot have been actively conducted.

SLAM is attractive because the robot recognizes the environment itself and performs positioning based on the information even in an unknown environment, but there is a problem that can be applied only in a specific environment. In addition, it is difficult to secure economic feasibility due to the use of precise and expensive sensors, and to date, the map created by SLAM is applicable only to the static environment where objects in the environment are not moving. It does not include the meaning of specific sub-concepts such as doors, automatic doors, elevators, stairs, etc., which makes it difficult to drive intelligently.

In this regard, Korean Patent Publication No. 10-715609 discloses an efficient space for the home service robot, which is connected to regions, the width of which can be passed by the mobile robot, but with an edge having a limited width and surrounded by obstacles. Using a node that is an area and can move to another node through the edge; The topological map forming method discloses a method of recognizing an edge using information from sensors, forming a topological map around the edges, and performing autonomous driving using the map.

However, in this method, since the map is represented by nodes and edges, the generated path does not contain semantic information. Therefore, since the portions connecting the spaces are not represented by semantic information such as doors, stairs, corridors, and the like, intelligent driving is impossible and it is difficult to apply to a real environment.

The present invention is to solve the problems of the above-described conventional autonomous driving method of the robot, by using a map containing semantic information based on smart environmental technology, the area of the environment that the robot will run at low cost It is an object of the present invention to provide a robot autonomous driving method capable of reliable driving regardless of reliability, and capable of intelligent driving even in a dynamic environment.

In order to achieve the above object, an autonomous driving method of a robot according to the present invention includes: (a) a semantic layer having information on the type and name of a cell in which a robot's moving space is divided; Meaning consisting of a structure including a metric layer having information about position and size, a connection layer having information about connection points with other cells, and a localization information layer for determining the current position of the robot. Loading enemy map information into the robot; (b) acquiring current location information from an external location recognition device by using the location recognition sensor provided in the robot or localization information included in the semantic map; (c) establishing a semantic driving route based on the provided destination, the loaded semantic map information, and the acquired current position information of the robot; And (d) driving the robot according to a predetermined path; There are constituent features to include.

In the case of the autonomous driving method according to the present invention, a method of loading in advance the map information about the environment in which the robot is to travel to the robot through the smart environment is established. Therefore, it is possible to provide autonomous driving at low cost compared to a method in which a robot prepares a map by providing expensive sensor equipment.

In addition, the above-described hierarchical information of the map provided according to the present invention has the following functions.

First, in the semantic map, a space that can be distinguished from an entire environment is represented by a basic unit called a cell, and the entire environment is represented by an aggregate of each unit cell.

In addition, since the semantic layer has information on the type and name of each cell, the semantic layer can semantically know where the robot is currently located. Accordingly, unlike the conventional mobile robot instructing the position and direction (x, y, q) in the form, according to the map according to the present invention to direct the robot to the destination in the semantic name, such as '101 laboratory' It becomes possible.

In addition, since the metric layer has position and size information of each cell, the information of the metric layer may include information about which cell (x, y) of the destination is located when the robot sets the route plan. ), It can be used to calculate the actual distance after setting the route plan.

In addition, the connection layer has information about connection points connected to one or more other cells in each cell, and has topological information indicating the type of connection points in addition to simple connection information. In this way, the robot knows which connection point it is currently passing through, and can perform intelligent processing accordingly. For example, when a robot passes an automatic door, it waits for the door to open in the front, passes through it, or if it is a normal door, it recognizes the handle to open the door, or requests to open the door by sound and passes through the door. Intelligent autonomous driving, which was difficult to implement on a map, is made possible according to the present invention.

In addition, the localization layer is a layer that holds information on the means by which the robot can obtain its current location information from the outside in addition to its own sensor. As such, in the map according to the present invention, by providing information of the position recognition means of the robot installed in the external environment, the error of the position recognition by its own sensor installed in the robot can be corrected through the position recognition means of the robot installed in the external environment. In this way, it is possible to reliably recognize the position and autonomously in large areas.

On the other hand, the position recognition means of the robot may be a position recognition sensor of the robot installed in the environment, in this case, the localization information is a robot in addition to the information on the position recognition sensor (position installed position, direction (x, y, q) information) Network address for connecting to a location sensor via the sensor network. Through this information, the robot can receive the location information by intelligently determining the location recognition sensor to be connected or disconnected while moving, and the reliable location information with reference to the map information according to the present invention even in a large space where many sensors are installed. You can get it.

In addition, the semantic map may further include an object layer having information about a fixed object located in each cell, wherein the object layer includes information about the position and orientation (x, y, q) of the object. Recognition information of an object (eg, an RFID unique number of up to 128 bits) may be stored. Through this information, the movement destination of the robot can be given by the ID or name of the object, and even when the robot is first entered, if the map information is given to the present invention from the server, autonomous driving is possible without additional exploration work.

In addition, in the autonomous driving method of the robot according to the present invention, the setting of the semantic driving route of step (c) comprises: (e) identifying a hierarchy of information of a provided destination; (f) converting information provided to the semantic layer or the object layer into information of the metric layer by utilizing the metric layer information of the semantic map if the information of the provided destination is not a metric layer but a semantic layer or object layer; (g) establishing a connection layer level path through the metric layer information of the provided destination through the connection layer information of the semantic map; (h) adding metric information to the paths of the connection layer level through the metric hierarchy information of the semantic map; And (i) adding semantic information to each path through semantic layer information of the semantic map to the path to which the metric information is added.

Through the algorithm described above, the mobile robot can use the provided semantic map information to set up a semantic route, thereby enabling intelligent autonomous driving.

In addition, in the autonomous driving method of the robot according to the present invention, the loading of the semantic map information in the step (a) is preferably performed in an XML file format, and standardization of the map information is possible by expressing the map in XML format. Done.

In addition, in the autonomous driving method of the robot according to the present invention, the loading of the semantic map information of the step (a) is to be performed only when the robot does not have a semantic map of the environment, which is already If you have a map of the environment, you do not need to repeatedly load the map information even if you enter the environment again.

Further, in the autonomous driving method of the robot according to the present invention, the step (b), the information about the nearest position recognition sensor from the information on the position recognition sensor, and receives the position information from the nearest position recognition sensor To determine the magnetic position of the robot. Through this configuration, it is possible to obtain reliable position information even if the robot is not equipped with an expensive position recognition sensor, and it is possible to prevent the error of the position information from accumulating through the position information acquired from the position recognition sensor installed in the environment. This allows the robot to reliably determine its current position in large areas.

In addition, the information on the connection point includes information on the passing method according to each connection point, it characterized in that the robot can correspond to the type of connection point.

In the robot autonomous driving method according to the invention, the robot is provided with a distance measuring means for measuring the distance to the object disposed in each cell, the three or more fixed objects through the distance measuring means The magnetic position may be determined by calculating information of a distance between the robot and the robot and information of three or more fixed objects obtained from the object layer. Thus, even when the robot cannot obtain its location information from the outside due to an environment in which the location sensor is not provided or a malfunction or failure of the location sensor, the object information provides a backup of the current location information of the robot. It can be a means.

According to the driving method of the robot according to the present invention, by using a method of loading a map including semantic information based on smart environmental technology to the robot from the outside, the robot can drive intelligently and efficiently at low cost .

In addition, according to the driving method of the robot according to the present invention, it is possible to reliably run in a variety of areas and spaces, such as homes, offices, buildings, public institution buildings.

In addition, according to the driving method of the robot according to the present invention, a semantic path including a meaning of a specific concept such as a door, an automatic door, a room, a corridor, etc. is not simply recognized as a node and an edge. It is possible to intelligently implement the operation of the robot even in a complicated and dynamic environment because of the driving by.

Hereinafter, the present invention will be described in more detail with reference to Examples of the present invention. However, the following examples are merely examples and should not be construed as limiting the present invention.

First, in the driving method of the robot according to an embodiment of the present invention, a semantic map loaded on the robot will be described.

In the semantic map according to the embodiment of the present invention, the minimum unit of space that can be distinguished from the environment is set as a cell, and the unit cell is a rectangle as shown in FIG. 1. It is expressed in the space of the inside, and it is a fixed point that can be used as a landmark, and a position recognition sensor that recognizes the current location of the robot by recognizing the connection point connected to another space, the indicator provided in the robot. It is defined as being composed.

The unit cell was modeled to have five layers of data structures of semantic, metric, connection points, sensors, and objects. 1 was performed.

Data structure of the semantic map according to the embodiment of the present invention hierarchy Information meaning Concrete example Semantic Type Type of current cell Room, corridor Name The name of the cell No. 102 Metric Position Center position of the cell x, y Size Cell size (width, height) w, h connection Number Number of connection points in the cell n ≥ 1  Unit connection point [n] Type of connection point Location, direction of connection point ID of the linked cell sensor Number Number of installed sensors m ≥ 0 Unit sensor [m] Position and orientation of the installed sensor Network address of the installed sensor object Number Number of objects in a unit cell j ≥ 0 Unit object [j] The position and orientation of the object RFID code of the object

As shown in Table 1, of the semantic map data structure according to the embodiment of the present invention, the semantic layer has information on the type and name of the cell, which allows the robot to know where the space is currently located. . It also makes it possible to instruct the robot in a semantic name, such as "Lab 101," as opposed to conventional locations and directions (x, y, q).

The metric layer is a layer having cell position and size information. Since a cell according to an embodiment of the present invention is a unit cell modeled as a rectangle, the position is x, y coordinates, and the size is width and width. Expressed in height. As such, the metric hierarchy allows the robot to search which cell the location (x, y) of the destination belongs to when planning the route and is used to calculate the actual distance after the route planning.

The connection layer has information on connection points connected to other cells, which means that it has topological information between each cell. There is more than one connection point in the connection layer. In addition to the simple connection information, the connection point type information (for example, open doors, general doors, swing doors, automatic doors, elevators, etc.) can be used to determine which connection points the robot is currently passing through. According to the type of connection point, for example, when the robot passes through the automatic door, it can wait until the front door is opened and pass through it.If it is a normal door, it recognizes the handle to open the door or opens the door by sound. It enables intelligent autonomous driving, such as asking for a request through a door.

The sensor layer has information of a location sensor installed in a cell, and the information of the sensor includes the number of location sensors installed in a unit cell, the location and direction in which the sensor is installed, as well as the location of the robot through the sensor network. The network address of the sensor for the communication connection is included to receive the information. Through the information of the sensor layer, the robot can intelligently determine the location recognition sensor to create or break a connection while moving, and thus receive the location information, even in a large space where many sensors are installed. The location can be recognized accurately by referring to the information.

The object hierarchy has information of fixed objects located in each cell. The information includes the number of stationary objects located within each cell, the location and orientation of each object, and up to 128 bits of RFID code attached to each stationary object. Through this information, the robot can obtain the position and direction of the object that matches the RFID code recognized by the RFID reader provided from the map information, so that even if the environment is entered for the first time, autonomous driving without additional exploration work is required. This becomes possible. In addition, since the RFID code is unique information for each object, it can be a key value that can receive appropriate map information through the server when there is no map information. If the robot cannot use the position sensor from the position sensor due to a failure or no installation, and there are at least three fixed objects in the unit cell, the robot measures the distance information between itself and each object, thereby determining the position of each object. By working with information, you can also determine the exact current location. That is, the information of the object according to the present invention can also be used for the backup of the sensor layer.

Next, an algorithm for establishing a semantic path by the robot using the aforementioned semantic map will be described.

Since the semantic map according to the embodiment of the present invention is configured for each layer, it is possible to request a route plan according to the information level of each layer. The final route plan is made at the connection layer, but the route plan request in the metric hierarchy of the general (x, y, q) format where the destination is presented in terms of location and direction, the destination being "Lab.101", "small Various types of route planning requests are possible, such as a route planning request of a semantic layer of the form "room", or a route planning request of an object layer of the form of "00FF312310FC-small room table".

In addition, the requested route plan includes all the information, semantics, metrics, and connection information of all layers, which are added in turn as they pass through each layer. The general flow of setting the requested path plan as a semantic path is as follows.

For example, when requesting a path plan of the semantic level, as indicated by arrows in FIG. 3, the semantic layer is converted into position information suitable for the metric level, and the metric layer converts the position to fit the connection layer again. The topological path is calculated at the connection layer. The calculated topological path is composed of simple connection information. In addition, path information such as location information and actual distance between connection points is added through the metric layer, and finally, semantic path is added through the semantic layer. Is derived.

Similarly, as shown in FIG. 3, when the route plan is requested at the object layer level, the semantic path is set through the object layer → metric layer → connection layer → metric layer → semantic layer in the same manner as the semantic layer. If a path plan is requested at the metric layer level, a semantic path is established through the metric layer → connection layer → metric layer → semantic layer.

A method of setting up a semantic route using the semantic map according to the present invention will be described in more detail with reference to FIG. 4.

First, when the robot loads a semantic map of the environment, and then requests a route plan (destination) from the robot (S101), it is determined whether the destination is object information (S102), and the object information. In step S103, metric information is extracted from the object layer information of the semantic map.

If the destination is not object information, it is determined whether the destination is semantic information (S104). If the destination is semantic information, the metric information is extracted through the semantic information layer of the semantic map. (S105).

If the destination is not semantic information, it is determined whether the information is metric information (S106). If the destination is not metric information, the message " Destination of unknown format " is output and processed as an error (S107).

When the metric information (x, y) of the destination is obtained through the above steps, the robot extracts cell information by using the metric layer information of the loaded semantic map (S108).

Subsequently, the robot recognizes its current location through a sensor held in the robot, extracts cell information by using the metric hierarchy information of the semantic map, and initializes the starting point, that is, the starting point for setting the robot's semantic path. And the setting for the destination is completed (S109).

In the initialized state, it is determined whether the last cell of the semantic path is the destination cell (S110), and if it is not the destination cell, the cell connected to the last cell is added using the connection layer information of the semantic map (S111). If the destination cell is correct, the search using the connection layer information is terminated (S112).

After the search is completed, the connection information uses the metric layer information of the semantic map, adds location information of each connection point (S113), and adds semantic information using the metric layer information (S114). By completing the semantic path (S115), the operation is terminated.

After programming the semantic route planning setting logic using the semantic map according to the present invention as described above, as shown in Figures 2a and 2b, various types of cells (cells), general doors, automatic doors, corridors, elevators It is applied to a real environment composed of various connection points.

First, as shown in Table 2, the space shown in FIG. 2B was modeled by dividing the space into six cells, and a total of three position recognition sensors, one each in an elevator corridor, a central hall, and a corridor, were installed.

Modeling of semantic maps in accordance with an embodiment of the present invention Cell shape Position / size Number of sensors Outside porch hall x, y = 0, -1194 w, h = 640, 200 0 Indoor strings hall x, y = 0, -977 w, h = 440, 234 0 Elevator corridor hallway x, y = 0, -430 w, h = 440, 860 One Concourse hall x, y = 0, 432 w, h = 1872, 865 One hallway hallway x, y = 1400, 125 w, h = 928, 250 One Room 101 room x, y = 1400, 557 w, h = 928, 615 0

The starting point and the arrival point were selected as the location with the largest number of waypoints. The current position of the robot, which is the starting point, was set to "Lab 101", and the destination was designated as "outdoor entrance", requesting route planning at a semantic level. It was.

FIG. 5 illustrates route information calculated using a semantic map according to an embodiment of the present invention through a program capable of implementing the logic of the above-described flowchart.

As shown, in order to get from the starting point "Lab 101" to the end point "outward porch", you will pass five waypoints to your final destination and the first and fifth waypoints will be "doors" and the fourth waypoint. Notice that the dot is an "automatic door." Therefore, through the semantic map according to the embodiment of the present invention, the robot can pass through the other waypoints as they are open spaces, and the first, fourth, and fifth waypoints can pass through the points only when appropriate measures are taken. It will be recognized. In addition, if you look at the route information, you can see that there is a cell to pass through, the actual location and distance information of the waypoint.

6 is a diagram illustrating a figure map and calculated route information using only semantic map information according to an exemplary embodiment of the present invention. From this, the actual information is simple as shown in Table 2, but it can be seen that the configured map is very similar to FIG. 2B, which is the actual drawing of the environment.

Next, autonomous driving in the real environment was carried out through the route planning using the semantic map as described above.

In the autonomous driving of a mobile robot, a known reactive (active) algorithm was used. In order to implement the algorithm, seven ultrasonic sensor modules were installed in front of the mobile robot to cope with a dynamic environment.

Regarding the position recognition of the robot, the robot searches for the optimal sensor from the position sensor information of the sensor hierarchy and measures the current position as follows.

First, the robot determines the cell where it is currently located, and then puts the position recognition sensor disposed in all cells adjacent to the current cell into the candidate set for the optimal sensor search. In the candidate set S = S ₁ , S ₂ , ..., S _N which contains all the position sensors in the cell where the robot is currently located and adjacent cells, Select the position sensor.

[Equation 1]

(Xs: position of candidate sensor in space, Xr: position of robot in absolute space)

FIG. 7 is a diagram schematically illustrating an example in which a robot searches for an optimal sensor using a semantic map. As shown in FIG. 7, the current position of the robot is cell C3. Therefore, C2, C3, C4, and C5, which are adjacent to C3, are to be searched, and the optimal sensor is determined by the above equation [1] in the position recognition sensor candidate set S = S1, S2, S3 disposed in the cell. At this time, the robot compares the address of the determined optimal sensor with the address of the currently connected sensor. If it is different from the current sensor, the robot disconnects the current sensor and attempts to connect to the optimal sensor. Update the existing location information through the information.

Accordingly, according to the driving method of the robot according to the present invention, even if the robot travels in various areas, the error of the position information is not accumulated and reliable magnetic position recognition is possible regardless of the size of the area in which the robot travels. . Therefore, driving in various environments can be performed without difficulty.

8 shows the result of the autonomous driving performed by the mobile robot under the conditions of the starting point and the arrival point. Each time you try to drive, you can see that the obstacles and the experimenter block the front of the robot to create a dynamic environment.

1 is a diagram illustrating a structure of a unit cell modeled in a semantic map according to an embodiment of the present invention.

2A and 2B are plan views of photographs and environments of an environment of a semantic map according to an embodiment of the present invention, respectively.

3 is a schematic diagram of a process for setting a route plan based on a semantic map according to the present invention.

4 is a flowchart illustrating a process of setting a route plan using a semantic map according to an embodiment of the present invention.

FIG. 5 is a computer screen showing a route plan from the "101 lab" to the "outdoor hall" from which a robot is derived by utilizing a semantic map according to an embodiment of the present invention.

6 is a computer screen showing a graphical map created using a semantic map and a set route plan according to an embodiment of the present invention.

FIG. 7 is a diagram schematically illustrating an example in which a robot searches for an optimal sensor using a semantic map.

8 is a computer screen showing a result of a robot actually traveling by utilizing a semantic map according to an embodiment of the present invention.

Claims (10)

As a robot autonomous driving method, (a) a semantic layer having information on the type and name of a cell that divides the robot's movement space, a metric layer having information on the position and size of the cell, and other cells Loading semantic map information into a robot, the structure including a connection layer having information about a connection point of and a localization information layer for determining a current location of the robot; (b) acquiring the current location information by an external location recognition device by using the location recognition sensor provided in the robot or localization information included in the semantic map; (c) establishing a semantic driving route based on the provided destination, the loaded semantic map information, and the acquired current position information of the robot; And (d) driving the robot according to a set route; Including, Setting of the semantic driving route of step (c), (e) identifying a hierarchy of information of the provided destination; (f) converting information provided to the semantic layer into information of the metric layer by utilizing the metric layer information of the semantic map if the information of the provided destination is a semantic layer rather than a metric layer; (g) establishing a connection layer level path through the metric layer information of the provided destination through the connection layer information of the semantic map; (h) adding metric information to the paths of the connection layer level through the metric hierarchy information of the semantic map; And (i) adding semantic information to each path through the semantic layer information of the semantic map to the path to which the metric information is added. The method of claim 1, wherein the semantic map further includes an object hierarchy having information about fixed objects located in each cell. delete The method of claim 2, wherein the setting of the semantic driving path of the step (c), (e) identifying a hierarchy of information of the provided destination; (f) converting information provided to the semantic layer or the object layer into information of the metric layer by utilizing the metric layer information of the semantic map if the information of the provided destination is not a metric layer but a semantic layer or object layer; (g) establishing a connection layer level path through the metric layer information of the provided destination through the connection layer information of the semantic map; (h) adding metric information to the paths of the connection layer level through the metric hierarchy information of the semantic map; And (i) adding semantic information to each path through the semantic layer information of the semantic map to the path to which the metric information is added. According to claim 1 or 2, wherein the localization information is installed in the environment, characterized in that the information on the position recognition sensor that can derive the current position of the robot by recognizing the indicator (indicator) provided in the robot. Autonomous driving method of the robot. The method of claim 1 or 2, wherein the loading of the semantic map information of step (a) is performed in an XML file format. The method of claim 1 or 2, wherein the loading of the semantic map information in step (a) is performed only when the robot does not have a semantic map of the environment. The method of claim 5, wherein the step (b) comprises: obtaining information on the nearest position recognition sensor from the information on the position recognition sensor and receiving position information from the nearest position recognition sensor to determine the magnetic position of the robot. The autonomous driving method of the robot which makes it possible to reliably determine a magnetic position even in a large area | region by making a decision. The method according to claim 1 or 2, wherein the information on the connection point includes information on a passing method according to each connection point, so that the robot can respond according to the type of connection point. . According to claim 2, wherein the robot is provided with a distance measuring means for measuring the distance to the object disposed in each cell, the distance between the three or more fixed objects and the robot through the distance measuring means The autonomous driving method of the robot, characterized in that the magnetic position can be determined by calculating the measured information and the information of the three or more fixed objects obtained from the object layer.

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