CN114545923A - Robot mapping method, electronic device and computer storage medium - Google Patents

Abstract

The application discloses a robot mapping method, electronic equipment and a computer storage medium. The graph establishing method comprises the following steps: determining nodes in the skeleton based on the skeleton of a grid map, wherein the grid map is constructed based on an environment area where the robot is located, and the grid map comprises a boundary to be explored; determining a root node based on the position of the robot, and determining a node tree according to the root node and nodes in the skeleton; and controlling the robot to explore and build a graph according to the position relation between the leaf nodes in the node tree and the boundary to be explored. Through the mode, the robot can automatically build the image, the intelligent level is improved, and the safety is improved.

Description

Robot mapping method, electronic device and computer storage medium

technical field

The present application relates to the technical field of intelligent mapping, and in particular, to a mapping method for a robot, an electronic device and a computer storage medium.

Background technique

Robots operate in different unknown environments, involving robot positioning, mapping and planning technology. First, the robot is required to complete the construction of the environment map, so as to perform subsequent navigation and other operations.

Robot mapping technology is that the robot realizes the perception of the external environment through sensors and completes its own positioning. The traditional mapping method uses manual control or follow-up to control the robot to move to complete the mapping. This method requires human control, and the level of intelligence needs to be improved.

SUMMARY OF THE INVENTION

The present application provides a robot mapping method, electronic equipment and computer storage medium, so as to realize the robot automatic mapping, improve the intelligence level, and improve the safety.

In order to solve the above technical problems, the present application proposes a method for constructing a map of a robot. The robot's mapping method includes: determining the nodes in the skeleton based on the skeleton of the grid map, wherein the grid map is constructed based on the environmental area where the robot is located, and the grid map includes the boundary to be explored; determining the position based on the robot The root node determines the node tree according to the root node and the nodes in the skeleton; according to the positional relationship between the leaf nodes in the node tree and the boundary to be explored, the robot is controlled to explore and build a map.

Wherein, determining the nodes in the skeleton includes: determining the grids corresponding to the corners, the grids corresponding to the branches, and/or the grids corresponding to the ends in the skeleton as nodes.

Wherein, determining the nodes in the skeleton includes: traversing the grid on the skeleton, and judging whether the grid is a node according to the positional relationship between the neighboring grid of the grid and the skeleton.

Wherein, determining whether the grid is a node or not is based on the relationship between the grid's neighborhood grid and the skeleton, including: only one of the grid-based neighborhood grids is on the skeleton, and determining that the grid is a leaf node; Only two of the neighborhood grids of the grid are on the skeleton, and the two neighborhood grids and the grid are not in a straight line, and the grid is determined to be a turning node; at least three of the grid-based neighborhood grids are on the skeleton. above, determine that the grid is a branch node.

Wherein, the above-mentioned grid map includes grids to be explored; the above-mentioned determining the positional relationship between leaf nodes in the node tree and the boundary to be explored includes: based on at least three of the neighborhood grids of the grid where the leaf nodes are located are grids to be explored, It is determined that the positional relationship between the leaf node and the boundary to be explored is that the leaf node is in the boundary to be explored.

Wherein, according to the positional relationship between the leaf nodes in the node tree and the boundary to be explored, controlling the mobile robot to explore and build a map includes: taking the parent node of the leaf node in the boundary to be explored as the node to be explored; controlling the robot to move to the node to be explored for Explore Mapping.

Wherein, the above-mentioned mapping method further includes: taking the position of the robot as the current node, judging whether the leaf node of the current node has a node to be explored; if so, controlling the robot to move to the node to be explored; if not, controlling the robot to move to the parent node of the current node.

Wherein, the above-mentioned mapping method further includes: calculating the length of the boundary to be explored, including: starting from the leaf node located at the boundary to be explored, and moving along the skeleton to determine the length of the boundary to be explored; For the leaf nodes of the boundary to be explored, move the robot to perform the steps of exploring and mapping.

In order to solve the above technical problems, the present application proposes an electronic device. The electronic device includes a memory and a processor coupled to each other, and the processor is used for executing program data stored in the memory, so as to implement the above-mentioned method for constructing a robot.

In order to solve the above technical problems, the present application proposes a computer storage medium. Program data is stored on the computer storage medium, and the program data can be executed to realize the above-mentioned method for constructing a robot.

The mapping method of the robot of the present application first determines the nodes in the skeleton based on the skeleton of the grid map, and the grid map includes the boundary to be explored, so the skeleton extends to the boundary to be explored, that is, the skeleton includes the skeleton of the boundary to be explored Further, the application establishes the node tree of the nodes in the skeleton, and based on the positional relationship between the leaf nodes in the node tree and the boundary to be searched, controls the mobile robot to explore and build a map; in this way, not only can the robot automatically build a map, improve the construction The intelligent level of the map; and the application calculates the exploration point based on the skeleton of the grid map, so that the exploration point can be far away from the obstacle, and the safety can be improved.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the drawings that are used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, under the premise of no creative work, other drawings can also be obtained from these drawings, wherein:

Fig. 1 is a schematic flowchart of an embodiment of a method for constructing a robot of the present application;

Fig. 2 is the structural representation of the grid map constructed by the embodiment of Fig. 1;

Fig. 3 is the structural representation of the grid map of the embodiment of Fig. 2 and its skeleton;

Fig. 4 is the structural representation of the partial structure of the embodiment of Fig. 3;

Fig. 5 is the structural representation of the partial structure of the embodiment of Fig. 3;

Fig. 6 is the structural representation of the partial structure of the embodiment of Fig. 3;

Fig. 7 is a specific flow chart of step S15 in the embodiment of Fig. 1;

Fig. 8 is a specific flow chart of step S12 in the embodiment of Fig. 1;

9 is a schematic flowchart of an embodiment of a mapping method for a robot of the present application;

10 is a schematic structural diagram of an embodiment of the mapping device of the present application;

11 is a schematic structural diagram of an embodiment of an electronic device of the present application;

FIG. 12 is a schematic structural diagram of an embodiment of a computer storage medium of the present application.

Detailed ways

The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is particularly pointed out that the following examples are only used to illustrate the present application, but do not limit the scope of the present application. Likewise, the following embodiments are only some of the embodiments of the present application but not all of the embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

In the description of the embodiments of the present application, it should be noted that, unless otherwise expressly specified and limited, the terms "connected" and "connected" should be understood in a broad sense, for example, it may be a fixed connection or a detachable connection, Or integral connection; it can be mechanical connection or electrical connection; it can be directly connected or indirectly connected through an intermediate medium. Those of ordinary skill in the art can understand the specific meanings of the above terms in the embodiments of the present application in specific situations.

In the embodiments of the present application, unless otherwise expressly specified and limited, the first feature "on" or "under" the second feature may be in direct contact with the first and second features, or the first and second features pass through the middle indirect contact with the media. Also, the first feature being "above", "over" and "above" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is level higher than the second feature. The first feature being "below", "below" and "below" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature has a lower level than the second feature.

In the description of this specification, description with reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples", etc., mean specific features described in connection with the embodiment or example , structures, materials, or features are included in at least one example or example of the embodiments of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

The robot, its mapping method, and the computer storage medium provided by the present application will be described in detail below with reference to the embodiments.

The present application first proposes a method for constructing a map of a robot, as shown in FIG. 1 , which is a schematic flowchart of an embodiment of the method for constructing a robot in the present application. The mapping method of this embodiment specifically includes the following steps:

Step S11: Determine the nodes in the skeleton based on the skeleton of the grid map, wherein the grid map is constructed based on the environment area where the robot is located, and the grid map includes boundaries to be explored.

The robot is provided with a detection device capable of detecting space obstacles and a moving device for movement. The robot is also equipped with a processor, which can send and receive instructions and process data information.

In this embodiment, the robot may construct a grid map of the environment area through Simultaneous Localization And Mapping (SLAM), where the grid map includes the boundary to be explored. Based on the information of the grid map, the robot moves to the boundary to be explored to explore and map the boundary to be explored, and expand the constructed grid map.

SLAM to build a map refers to that the robot creates a map in a completely unknown environment under the condition that its own position is uncertain, and uses the map for autonomous positioning and navigation at the same time. The SLAM problem can be described as: the robot starts moving from an unknown location in an unknown environment, localizes itself according to the position estimate and sensor data during the movement, and builds an incremental map at the same time.

When a robot builds a map, the robot needs to know its position in the environment and record the position of the features in the environment. The robot builds an environment map while positioning.

The grid map is composed of grids, each grid can represent the state of the surrounding environment at its location, which includes three states: the occupancy state representing the presence of obstacles, and the idle state where there are no obstacles and the robot can pass freely. Status and unknown status that the robot has not yet detected (whether there is an obstacle in the grid is unknown). The robot can plan the forward route according to the grid map, and the grid map is continuously detected and updated during the progress of the robot.

The robot can determine the size of the grid according to parameters such as environmental resolution, environmental information storage capacity, and decision-making speed. For example, if the environmental resolution is large, the storage capacity of environmental information is large, and the decision-making speed is slow, a small grid can be selected; if the environmental resolution is small, the storage capacity of environmental information is small, and the decision-making speed is fast, a large grid can be selected, but The ability to find paths in dense obstacle environments is weak.

Of course, in other embodiments, the robot may also use other technologies to construct a grid map of the environment area.

In an application scenario, the grid map constructed in this embodiment is shown in FIG. 2 . Among them, the area within the dotted rectangle is the environment area of the robot; the solid line is the obstacle area (the state corresponding to the grid is occupied); the area enclosed by the solid line is the free area (the state corresponding to the grid is free) ; The area other than the obstacle area and the free area in the dashed rectangular frame is the unknown area (the state of the corresponding grid is unknown); the boundary between the free area and the unknown area is the boundary to be explored (part of it has been dashed dotted line inside the rectangle).

The robot uses the skeleton extraction method to extract the skeleton of the grid map, and can obtain the center position of the free area in the grid map, which can ensure that the skeleton is located in the middle of the obstacles on both sides, that is, the grid on the skeleton is in the best position away from the obstacles. Location.

Of course, in other embodiments, in order to shorten the length of the skeleton of the grid map, the skeleton in the free area may not be located in the central area, as long as the robot moves along the skeleton without touching two obstacles, namely Can.

Specifically, the robot can first project the initial position of the robot, the obstacle area and the boundary to be explored into the grid map, determine the passable area of the robot in the grid map, and then use the Zhang-Suen algorithm to skeletonize the passable area. extract.

The skeleton of the extracted raster map shown in Figure 2 is shown in Figure 3 with the bold black line.

Further, in this embodiment, a grid corresponding to a corner, a grid corresponding to a branch, and/or a grid corresponding to an end of the skeleton can be determined as a node of the skeleton, because the corners, branches, and ends of the skeleton can be It reflects the change of the robot's motion direction.

Optionally, the robot can traverse the grid on the skeleton, and judge whether the grid is a node according to the positional relationship between the neighboring grid of the grid and the skeleton, such as whether the neighborhood grid is on the skeleton.

The robot takes its initial position (starting position) as the root node, traverses each grid on the skeleton, and determines whether each grid point is a node (including leaf nodes, turning nodes and branch nodes).

In some embodiments, the neighborhood grid includes an eight-neighborhood grid.

Specifically, in this embodiment, step S11 can be implemented by the following method:

(1) Only one of the grid-based neighborhood grids is on the skeleton, and the grid is determined as a leaf node.

If only one of the neighboring grids of the grid is on the skeleton, the grid is considered as the node located at the end of the skeleton, and the grid is determined as the leaf node of the skeleton.

For example, as shown in Figure 4, if there is one and only one neighbor grid C1 on the skeleton B, and the other neighbor grids are not on the skeleton B, then the grid A1 is determined. It is the leaf node of the skeleton.

(2) Only two of the grid-based neighborhood grids are on the skeleton, and the two neighborhood grids and the grid are not in a straight line, and the grid is determined to be a turning node.

If two neighboring grids of the grid are on the skeleton, the grid is considered to be in the middle of the skeleton, and it is further judged whether the two neighboring grids and the grid are on a straight line; if so, determine The grid is not a node of the skeleton; if not, it is determined that the grid is the turning node of the skeleton, and the turning node is located at the turning of the skeleton.

For example, as shown in Figure 5, if there are only two neighboring grids C2 and C3 in the eight-neighbor grid of grid A2, and the neighboring grids C2 and C3 are not on the skeleton B, and the neighboring grids C2 and C3 are different from grid A2 If it is located on a straight line, the grid A2 is determined as the turning node of the skeleton B.

(3) Based on at least three of the grid's neighborhood grids on the skeleton, determine that the grid is a branch node.

If at least three of the neighboring grids of the grid are on the skeleton, the grid is considered to be in the middle of the skeleton, and the grid is determined as the branch node at the branch of the skeleton.

For example, as shown in FIG. 6 , if there are three neighboring grids C4 , C5 , and C6 on the skeleton B in the eight neighborhood grids of the grid A3 , then the grid A3 is determined as the branch node at the branch of the skeleton B.

The nodes in the skeleton of the grid map can be determined by the above method.

Step S12: Determine the root node based on the position of the robot, and determine the node tree according to the root node and the nodes in the skeleton.

The position of the robot can be the initial position of the robot or the current position of the robot (the robot is always moving).

Taking the position of the robot as the root node, the nodes of the skeleton obtained in step S12 are stored in a tree structure to form a node tree.

Specifically, starting from the root node, move along the grid on the skeleton to the next-level node of the root node, and then move to the next-level node of the next-level node, until the last-level node, that is, the leaf node; wherein , the next-level node and the previous-level node have a parent-child relationship, that is, the next-level node is the child node of its previous-level node, and the previous-level node is the parent node of its next-level node.

In the above moving process, the distance between the child node and its parent node can be obtained. A node tree is established based on the hierarchical relationship and distance before the above nodes.

Based on the above node tree, by marking the leaf nodes, a series of leaf nodes of the node tree can be obtained; the node tree not only includes the root node, but also includes nodes at all levels between the root node and the leaf nodes, and the node tree can reflect each node parent-child relationship.

Step S13: Control the robot to explore and build a map according to the positional relationship between the leaf nodes in the node tree and the boundary to be explored.

The grid map further includes grids to be explored, that is, grids whose grid status is unknown, including grids corresponding to boundaries to be explored and grids corresponding to unknown areas.

Optionally, in this embodiment, if at least three neighboring grids of the grid where the leaf node is located are grids to be explored, it is determined that the positional relationship between the leaf node and the boundary to be explored is that the leaf node is on the boundary to be explored.

The robot traverses each leaf node, obtains the state of its eight-neighborhood grid for the grid where each leaf node is located, and makes statistics; if the state of at least three grids in the eight-neighborhood grid of the grid where the leaf node is located is If it is unknown, that is, at least three of the neighboring grids of the grid where the leaf node is located are grids to be explored, then it is determined that the leaf node is at the boundary to be explored.

After traversing each leaf node in the node tree according to the above method, the robot obtains a series of leaf nodes in the boundary to be explored.

The mapping method of the robot in this embodiment first determines the nodes in the skeleton based on the skeleton of the grid map, and the grid map includes the boundary to be explored, so the skeleton extends to the boundary to be explored, that is, the skeleton includes the boundary to be explored. skeleton; further, in this embodiment, a node tree of nodes in the skeleton is established, and based on the positional relationship between the leaf nodes in the node tree and the boundary to be searched, the mobile robot is controlled to explore and build a map; in this way, not only can the robot automatically build a map, but also The intelligent level of mapping is improved; and the application is based on the skeleton of the grid map to calculate the exploration points, so that the exploration points can be far away from obstacles, and the safety can be improved; at the same time, the exploration points are arranged in a certain order to ensure that the exploration points are to be explored. Exploratory ordering of points.

Optionally, in this embodiment, step S13 may be implemented by the method shown in FIG. 7 . The method of this embodiment includes step S71 and step S72.

Step S71: Take the parent node of the leaf node at the boundary to be explored as the node to be explored.

Step S72: Control the robot to move to the node to be explored to explore and build a map.

Since the entire boundary to be explored may be a long straight line, leaf nodes will appear at both ends of the boundary. Therefore, the robot obtains the parent node of the leaf node of the boundary to be explored as the node to be explored, and can explore the entire boundary to be explored, improving mapping accuracy and performance. efficiency.

In another embodiment, step S13 may be implemented by the method shown in FIG. 8 . The method of this embodiment includes steps S81 to S85.

Step S81: Take the parent node of the leaf node at the boundary to be explored as the node to be explored.

Step S82: Taking the position where the robot is located as the current node, determine whether there is a node to be explored in the leaf node of the current node.

In some application scenarios, the position of the robot may not be located on the node of the skeleton, and the node closest to the position of the robot can be selected as the current node of the robot.

Step S83: If there is, control the robot to move to the node to be explored.

If there are nodes to be explored in the leaf nodes of the current node of the robot, the robot is controlled to preferentially explore the nodes to be explored at the current node, which can shorten the movement path of the robot and improve the efficiency of mapping.

Step S84: If it does not exist, control the robot to move to the parent node of the current node.

If the leaf node of the current node of the robot does not have a leaf node at the boundary to be explored, it is considered that the robot will not move to its leaf node along the current node in order to map the unknown area; at this time, it can be traced back to the current node. After the parent node of , the exploration priority of the nodes to be explored is sorted.

Finally, the node to be explored with the highest priority is obtained as the next exploration point position, and the robot is controlled to reach the specified position. That is, it can be ensured that the robot will preferentially explore in the current direction.

Step S85: Control the robot to explore and build a map at the designated position.

In this embodiment, the robot is controlled to preferentially perform exploration and mapping on the nodes to be explored at the current node, which can shorten the movement path of the robot and improve the efficiency of mapping.

The present application further proposes a method for building a map of a robot according to another embodiment, as shown in FIG. 9 . FIG. 9 is a schematic flowchart of an embodiment of the method for building a map for a robot according to the present application. The mapping method of this embodiment specifically includes the following steps:

Step S91: Determine the nodes in the skeleton based on the skeleton of the grid map, wherein the grid map is constructed based on the environment area where the robot is located, and the grid map includes boundaries to be explored.

Step 91 is similar to the above-mentioned step S11 and will not be repeated here.

Step S92: Determine the root node based on the position of the robot, and determine the node tree according to the root node and the nodes in the skeleton.

Step S92 is similar to the above-mentioned step S12, and is not repeated here.

Step S93: According to the positional relationship between the leaf node in the node tree and the boundary to be explored.

Step S93 is similar to the above-mentioned step S13, and is not repeated here.

Step S94: Calculate the length of the boundary to be explored.

Optionally, this embodiment starts from a leaf node located at the boundary to be explored and moves along the skeleton to determine the length of the boundary to be explored.

Specifically, starting from the leaf nodes located on the boundary to be explored, along the nodes on the skeleton to find the node at the boundary to be explored, the boundary to be explored is formed; when the length of the boundary to be explored (the adjacent nodes of the skeleton can be calculated when constructing the node tree When the distance between them) is greater than a certain threshold, it means that the unexplored area is large and needs to be explored. At this time, the parent node of the leaf node is added to the set of nodes to be explored.

Step S95: If the length is greater than the threshold, control the robot to explore and build a map.

Step S95 is similar to the above-mentioned step S13, and is not repeated here.

If the length is less than or equal to the threshold, it means that the unexplored area is small and no exploration is required, and the leaf node is discarded.

In one embodiment, as shown in FIG. 10 , a mapping apparatus is provided, and the apparatus can use software modules or hardware modules, or a combination of the two to become a part of computer equipment, the apparatus specifically includes: skeleton node extraction The module 111, the node tree determination module 112 and the exploration mapping module 113, the node tree determination module 112 is respectively connected with the skeleton node extraction module 111 and the exploration mapping module 113; wherein: the skeleton node extraction module 111 is used for the skeleton based on the grid map , determine the nodes in the skeleton, the grid map is constructed based on the environment area where the robot is located, and the grid map includes the boundary to be explored; the node tree determination module 112 is used to determine the root node based on the position of the robot, according to the root node and the skeleton in the skeleton. The node of the node determines the node tree; the exploration and mapping module 113 is used to control the robot to explore and construct a map according to the positional relationship between the leaf nodes in the node tree and the boundary to be explored.

The mapping device of this embodiment is also used to implement the above-mentioned method for mapping a robot.

For the specific limitation of the mapping device, reference may be made to the limitation of the robot mapping method above, which will not be repeated here. Each module in the above-mentioned mapping apparatus may be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

The present application further proposes an electronic device, as shown in FIG. 11 , which is a schematic structural diagram of an embodiment of the electronic device of the present application. The electronic device 100 in this embodiment includes a processor 101 , a memory 102 coupled to the processor 101 , an input and output device 103 , and a bus 104 .

The processor 101, the memory 102, and the input/output device 103 are respectively connected to the bus 104, the memory 102 stores program data, and the processor 101 is used for executing the program data to implement the above-mentioned method for constructing a robot.

The controllers in the above-described embodiments may be integrated in the processor 101 .

In this embodiment, the processor 101 may also be called a CPU (Central Processing Unit, central processing unit). The processor 101 may be an integrated circuit chip with signal processing capability. The processor 101 may also be a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components . A general purpose processor may be a microprocessor or the processor 101 may be any conventional processor or the like.

The electronic device 100 in this embodiment may be a robot, and is further provided with a detection device and a moving device capable of detecting space obstacles.

The robot mapping method and device provided by the present application extract the skeleton from the grid map of the current environment area, and extend the skeleton to the map boundary; at the same time, the to-be-explored point is calculated based on the skeleton, and the obtained to-be-explored point is far away from obstacles, and the maximum The security is improved to a certain extent; at the same time, the exploration points are arranged in a certain order, which ensures the orderliness of the exploration of the points to be explored.

The present application further proposes a computer-readable storage medium. As shown in FIG. 12 , the computer-readable storage medium 160 of this embodiment is used to store the program data 161 of the above-mentioned embodiment, and the program data 161 can be executed to realize the construction of the above-mentioned robot. graph method. The program data 161 has been described in detail in the above method embodiments, and will not be repeated here.

The computer-readable storage medium 160 in this embodiment may be, but is not limited to, a U disk, an SD card, a PD optical drive, a mobile hard disk, a large-capacity floppy drive, a flash memory, a multimedia memory card, a server, and the like.

In one embodiment, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the foregoing method embodiments.

Different from the prior art, the present application first determines the nodes in the skeleton based on the skeleton of the grid map, and the grid map includes the boundary to be explored, so the skeleton extends to the boundary to be explored, that is, the skeleton includes the boundary of the boundary to be explored. skeleton; further, the present application establishes a node tree of nodes in the skeleton, and controls the mobile robot to explore and build a map based on the positional relationship between the leaf nodes in the node tree and the boundary to be searched; in this way, not only can the robot automatically build a map, improve the The intelligent level of mapping; and the application is based on the skeleton of the grid map to calculate the exploration points, so that the exploration points can be far away from obstacles, which can improve the safety; at the same time, the exploration points are arranged in a certain order to ensure that the points to be explored are arranged. order of exploration.

In addition, if the above functions are implemented in the form of software functions and sold or used as independent products, they can be stored in a storage medium readable by a mobile terminal, that is, the present application also provides a storage device storing program data, so The program data can be executed to implement the methods of the above embodiments, and the storage device can be, for example, a USB flash drive, an optical disc, a server, or the like. That is to say, the present application can be embodied in the form of a software product, which includes several instructions to make an intelligent terminal execute all or part of the steps of the methods described in the various embodiments.

In addition, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In the description of the present application, "plurality" means at least two, such as two, three, etc., unless expressly and specifically defined otherwise.

Any description of a process or method in a flowchart or otherwise described herein may be understood to represent a mechanism, segment or portion of code comprising one or more executable instructions for implementing a specified logical function or step of the process , and the scope of the preferred embodiments of the present application includes alternative implementations in which the functions may be performed out of the order shown or discussed, including performing the functions substantially concurrently or in the reverse order depending upon the functions involved, which should It is understood by those skilled in the art to which the embodiments of the present application belong.

The logic and/or steps represented in flowcharts or otherwise described herein, for example, may be considered an ordered listing of executable instructions for implementing the logical functions, may be embodied in any computer-readable medium, For use by an instruction execution system, apparatus or device (which may be a personal computer, server, network device or other system that can fetch instructions from and execute instructions from an instruction execution system, apparatus or apparatus) or in conjunction with such instruction execution system, apparatus or apparatus and use. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or apparatus. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections with one or more wiring (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), Read Only Memory (ROM), Erasable Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, followed by editing, interpretation, or other suitable medium as necessary process to obtain the program electronically and then store it in computer memory.

The above description is only an embodiment of the present application, and is not intended to limit the scope of the patent of the present application. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present application, or directly or indirectly applied to other related technologies Fields are similarly included within the scope of patent protection of this application.

Claims (10)

1. A robot mapping method is characterized by comprising the following steps:

determining nodes in a skeleton based on the skeleton of a grid map, wherein the grid map is constructed based on an environment area where a robot is located, and the grid map comprises a boundary to be explored;

determining a root node based on the position of the robot, and determining a node tree according to the root node and nodes in the skeleton;

and controlling the robot to explore and build a graph according to the position relation between the leaf nodes in the node tree and the boundary to be explored.

2. The method of claim 1, wherein the determining the nodes in the skeleton comprises:

and determining grids corresponding to corners, branches and/or grids corresponding to tail ends in the skeleton as nodes.

3. The method of claim 1, wherein the determining the nodes in the skeleton comprises:

and traversing the grids on the framework, and judging whether the grids are nodes or not according to the position relation between the neighborhood grids of the grids and the framework.

4. The mapping method according to claim 3, wherein the determining whether the grid is a node according to the position relationship between the grid in the neighborhood of the grid and the skeleton includes:

determining that the grid is a leaf node based on only one of the neighborhood grids of the grid being on the skeleton;

determining that the grid is a turning node based on that only two of the neighborhood grids of the grid are on the framework and the two neighborhood grids and the grid are not on the same straight line;

and judging and determining the grid as a branch node based on that at least three of the neighborhood grids of the grid are on the framework.

5. The mapping method of claim 1, wherein the grid map comprises a grid to be explored; determining the position relation between the leaf nodes in the node tree and the boundary to be explored, wherein the step of determining the position relation comprises the following steps:

and based on that at least three grids in the neighborhood of the grid where the leaf node is located are grids to be explored, judging the position relation between the leaf node and the boundary to be explored to be that the leaf node is located at the boundary to be explored.

6. The mapping method according to claim 1, wherein the controlling the robot exploration mapping according to the position relationship between the leaf nodes in the node tree and the boundary to be explored comprises:

taking a father node of the leaf node positioned on the boundary to be explored as a node to be explored;

and controlling the robot to move to the node to be explored for exploring and building a graph.

7. The mapping method according to claim 6, wherein the mapping method further comprises:

taking the position of the robot as a current node, and judging whether a leaf node of the current node has a node to be explored or not;

if the node exists, controlling the robot to move to the node to be explored;

and if the current node does not exist, controlling the robot to move to the father node of the current node.

8. The mapping method according to claim 1, wherein the mapping method further comprises:

calculating the length of the boundary to be explored, comprising: starting from a leaf node located on the boundary to be explored, moving along the skeleton to determine the length of the boundary to be explored;

and if the length is larger than the threshold value, executing a step of controlling the robot to explore and establish a map.

9. An electronic device, comprising: comprises a memory and a processor which are coupled with each other, wherein the processor is used for executing the program data stored in the memory so as to realize the mapping method of the robot in any one of the claims 1 to 8.

10. A computer storage medium having stored thereon program data executable to implement the method of mapping a robot of any of claims 1 to 8.

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