CN111966097A - Map building method, system and terminal based on grid map regionalization exploration - Google Patents
Map building method, system and terminal based on grid map regionalization exploration Download PDFInfo
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Abstract
The invention discloses a map building method, a map building system and a map building terminal based on grid map regionalization exploration, and solves the problems that in the prior art, a mobile robot adopts a traditional exploration map building method based on a grid map, so that the robot walks slowly, an explored area is repeatedly explored, and the map building efficiency is low. The invention explores the grid map in different areas, selects the optimal target point in each area for exploration, increases the walking speed of the robot, reduces the repeated exploration rate of the explored area, and greatly improves the efficiency of exploring and establishing the map.
Description
Technical Field
The invention relates to the field of robots, in particular to a map building method, a system and a terminal based on grid map regionalization exploration.
Background
The environment map constructed by the robot is roughly divided into three types: topological maps, geometric maps, grid maps. A grid map is a product of digitally rasterizing real maps in reality. The environment is decomposed into a series of discrete grids, each grid has a value, the grids contain coordinates and whether the environment is obstructed or not, and probability values occupied by each grid are used for representing the environment information and generally identifying whether the environment is obstructed or not. Each map grid corresponds to a small area in the actual environment, so that the environment information is reflected, and the robot can easily store the map information.
The traditional exploration mapping method based on the grid map is adopted by the existing mobile robot, so that the robot walks slowly, the explored area is repeatedly explored, and the mapping efficiency is not high.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a mapping method, system and terminal for grid map based regional search, which are used to solve the problems that in the prior art, a mobile robot employs a conventional search mapping method based on a grid map, so that the robot walks slowly, and the searched area is repeatedly searched, and mapping efficiency is not high.
In order to achieve the above and other related objects, the present invention provides a mapping method based on grid map regionalization exploration, applied to a mobile robot, including: collecting laser radar data and gyroscope data; establishing a two-dimensional global grid map based on laser slam, and constructing a barrier expansion layer for the two-dimensional global grid map according to the laser radar data and gyroscope data; searching a passable point set in a two-dimensional global grid map for constructing an obstacle expansion layer based on a bsf algorithm, and selecting an optimal target point in the passable point set; calculating navigation path information from an initial point of the robot to the optimal target point, wherein the navigation path information is used for the robot to move to the optimal target point; and dividing the two-dimensional global grid map for constructing the barrier expansion layer into at least two sub-areas by taking the optimal target point at which the robot moves as a center, sequentially obtaining one or more sub-area optimal target points for completely covering each sub-area in each sub-area, and dividing the two-dimensional global grid map for constructing the barrier expansion layer by taking each sub-area optimal target point as a center to obtain an environment map covering each sub-area.
In an embodiment of the present invention, the method for searching a passable point set in a two-dimensional global grid map for constructing an obstacle inflation layer based on a bsf algorithm, and selecting an optimal target point in the passable point set includes: based on a bsf algorithm, exploring and constructing boundary point sets in a known area and an unknown area in a two-dimensional global grid map of an obstacle expansion layer, and screening the passable point set according to obstacle information at each point in the boundary point sets; based on the selection rule of the best target point, the best target point is selected in the set of passable points.
In an embodiment of the present invention, the selection rule of the optimal target point includes: selecting a point with the largest passing area breadth in the passable point set as an optimal target point; wherein the passing area popularity is related according to the known areas of all points in the passable point set and the passing area popularity of the unknown area.
In an embodiment of the present invention, the method of dividing the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas with the optimal target point at which the robot moves as a center, sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area in each sub-area, and dividing the two-dimensional global grid map for constructing the obstacle expansion layer with the optimal target point of each sub-area as a center to obtain the environment map covering each sub-area includes: dividing a two-dimensional global grid map for constructing an obstacle expansion layer into at least two sub-areas by taking the optimal target point where the robot moves as a center; sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area exploration rule, performing one or more times of segmentation on the two-dimensional global grid map for constructing the barrier expansion layer by taking the optimal target points of each area as a center, and respectively covering one or more areas formed by segmentation to obtain an environment map covering each sub-area.
In an embodiment of the present invention, the sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to the preset sub-area exploration rule, so as to perform one or more partitions on the two-dimensional global grid map for constructing the barrier expansion layer by taking the optimal target point of each area as a center, and respectively cover one or more areas formed by the partitions, so as to obtain the environment map covering each sub-area, includes: sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area exploration rule; and sequentially and respectively calculating navigation path information from the initial point of the robot to the optimal target point of each subarea, wherein the navigation path information is used for the robot to move to the optimal target point of each subarea, when the robot reaches a new optimal target point of each subarea, the previous optimal target point/optimal target point of each subarea is pressed into a stack, the two-dimensional global grid map for constructing the barrier expansion layer is respectively segmented by taking the optimal target point of each subarea reached by the robot as the center, and one or more areas formed by segmentation are respectively covered to obtain an environment map covering each subarea so as to obtain the environment map covering each subarea.
In an embodiment of the present invention, the predetermined sub-region exploration rule includes: an exploration sequence rule and/or an exploration degree rule; wherein the heuristic order rule comprises: the search sequence of each sub-area and/or the search sequence of the area formed by the two-dimensional global grid map partition of the barrier expansion layer by the optimal target point pair of each sub-area in each sub-area; the heuristic rules include: when the search of one sub-area reaches the full coverage, the search of the next area is carried out.
In an embodiment of the present invention, the four sub-regions include an upper left region, an upper right region, a lower right region and a lower left region, and the search sequence of each sub-region is: upper left, upper right, lower right, and lower left regions.
In an embodiment of the present invention, the navigation path information includes: one or more of a navigation path, a navigation distance, and a linear distance.
In order to achieve the above and other related objects, the present invention provides a mapping system for grid map based regional search, applied to a mobile robot, the system comprising: the acquisition module is used for acquiring laser radar data and gyroscope data; the image building module is connected with the acquisition module and used for building a two-dimensional global grid map based on the laser slam and building an obstacle expansion layer on the two-dimensional global grid map according to the laser radar data and the gyroscope data; the exploration module is connected with the map building module and used for searching a passable point set in a two-dimensional global grid map for building an obstacle expansion layer based on a bsf algorithm and selecting an optimal target point in the passable point set; the navigation module is connected with the exploration module, is used for calculating navigation path information from an initial point of the robot to the optimal target point, and is used for the robot to move to the optimal target point; and the area segmentation module is connected with the search module and the navigation module, and is used for segmenting the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas by taking the optimal target point where the robot moves as a center, sequentially obtaining one or more sub-area optimal target points for completely covering each sub-area in each sub-area, and segmenting the two-dimensional global grid map for constructing the obstacle expansion layer by taking each sub-area optimal target point as a center to obtain an environment map covering each sub-area.
To achieve the above and other related objects, the present invention provides a map building terminal based on grid map regionalization exploration, including: a memory for storing a computer program; and the processor is used for executing the mapping method based on the grid map regionalization exploration.
As described above, the map building method, system and terminal based on grid map regionalization exploration according to the present invention have the following advantages: the invention explores the grid map in different areas, selects the optimal target point in each area for exploration, increases the walking speed of the robot, reduces the repeated exploration rate of the explored area, and greatly improves the efficiency of exploring and establishing the map.
Drawings
Fig. 1 is a flowchart illustrating a mapping method based on grid map regionalization exploration according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a mapping method based on grid map regionalization exploration according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a mapping system based on grid map regionalization exploration according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a map building terminal based on grid map regionalization exploration according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It is noted that in the following description, reference is made to the accompanying drawings which illustrate several embodiments of the present invention. It is to be understood that other embodiments may be utilized and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present invention. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present invention is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "over," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Throughout the specification, when a part is referred to as being "connected" to another part, this includes not only a case of being "directly connected" but also a case of being "indirectly connected" with another element interposed therebetween. In addition, when a certain part is referred to as "including" a certain component, unless otherwise stated, other components are not excluded, but it means that other components may be included.
The terms first, second, third, etc. are used herein to describe various elements, components, regions, layers and/or sections, but are not limited thereto. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the scope of the present invention.
Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," and/or "comprising," when used in this specification, specify the presence of stated features, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, operations, elements, components, items, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions or operations are inherently mutually exclusive in some way.
The embodiment of the invention provides a mapping method based on grid map regionalization exploration, and solves the problems that in the prior art, a mobile robot adopts a traditional exploration mapping method based on a grid map, so that the robot walks slowly, a searched area is repeatedly explored, and the mapping efficiency is low. The invention explores the grid map in different areas, selects the optimal target point in each area for exploration, increases the walking speed of the robot, reduces the repeated exploration rate of the explored area, and greatly improves the efficiency of exploring and establishing the map.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that those skilled in the art can easily implement the embodiments of the present invention. The present invention may be embodied in many different forms and is not limited to the embodiments described herein.
Fig. 1 is a schematic flow chart illustrating a map building method based on grid map regionalization exploration according to an embodiment of the present invention.
Applied to a mobile robot, the method comprising:
step S11: collecting laser radar data and gyroscope data.
Optionally, lidar data collected by a lidar and gyroscope data collected by a gyroscope are collected.
Optionally, data for adapting the lidar hardware and the gyroscope is collected. The types of the laser radar hardware and the gyroscope are selected according to specific requirements, and are not limited in the application.
Step S12: and establishing a two-dimensional global grid map based on the laser slam, and constructing an obstacle expansion layer on the two-dimensional global grid map according to the laser radar data and the gyroscope data.
Optionally, a two-dimensional global cost map represented in a grid form is established based on a slam algorithm, wherein the two-dimensional global cost map is divided into a known area and an unknown area according to the current position of the robot; constructing a barrier layer constructed by the two-dimensional global grid map according to the laser radar data and the gyroscope data, and expanding the barrier layer according to the self information of the robot to obtain the two-dimensional global grid map for constructing the barrier expansion layer; wherein the known area and the location area also include an obstacle expanding layer.
Step S13: searching a passable point set in a two-dimensional global grid map for constructing an obstacle expansion layer based on a bsf algorithm, and selecting an optimal target point from the passable point set.
Optionally, the searching a passable point set in a two-dimensional global grid map for constructing an obstacle expansion layer based on the bsf algorithm, and selecting an optimal target point from the passable point set includes: based on a bsf algorithm, exploring and constructing boundary point sets in a known area and an unknown area in a two-dimensional global grid map of an obstacle expansion layer, and screening the passable point set according to obstacle information at each point in the boundary point sets; based on the selection rule of the best target point, the best target point is selected in the set of passable points.
Specifically, based on a bsf algorithm, searching a boundary point set comprising boundary points in a known area and an unknown area in a two-dimensional global grid map of a constructed barrier expansion layer, and screening a plurality of boundary points according to barrier information at each boundary point obtained by the barrier expansion layer to form a passable point set; selecting a point in the set of communicable points as an optimal target point based on a selection rule of the optimal target point.
Optionally, the selection rule of the optimal target point includes: selecting a point with the largest passing area breadth in the passable point set as an optimal target point; wherein the passing area popularity is related according to the known areas of all points in the passable point set and the passing area popularity of the unknown area. The selection rule can select points with larger passing range and wider exploration range as the optimal target points.
Optionally, the popularity of the passing area is obtained by weighting and calculating the popularity of each point in the passable point set according to the known areas and the popularity of the unknown areas. The weight occupied by the traffic popularity of the known area and the traffic popularity of the unknown area can be set according to the requirement, and is not limited in the application.
Optionally, the passing area wideness is sorted from large to small, and a point in the passable point set with the highest ranking is found as the best target point.
Optionally, the traffic breadth of the known area and the traffic breadth of the unknown area of each point in the set of passable points are respectively related to the obstacle distribution situation of the known area at the passable point and the obstacle distribution situation of the unknown area at the passable point; wherein the obstacle distribution is obtained from the obstacle expanding layer.
For example, if the obstacle distribution of the known area of one point in the set of passable points forms a passable area wider than the passable area of the known area of another point, the passable area of the point has a larger passable width than the other point.
Step S14: and calculating navigation path information from the initial point of the robot to the optimal target point, wherein the navigation path information is used for the robot to move to the optimal target point.
Optionally, based on a two-dimensional global grid map for constructing the barrier expansion layer, navigable navigation path information from an initial point of the robot to the optimal target point is calculated, and the navigation path information is used for the robot to move to the optimal target point according to the robot.
Optionally, the navigation path information includes: one or more of a navigation path, a navigation distance, and a linear distance. Specifically, a navigation path from a current point to a target point is planned based on a two-dimensional global grid map for constructing an expansion layer of the barrier, and the distance of the navigation path and the linear distance between the current point and the target point are calculated.
Optionally, the smaller the ratio of the distance of the navigation path to the linear distance is, the shortest the path required to be traveled between the current point and the target point is, so as to avoid the navigation travel oscillation phenomenon, therefore, when selecting the optimal target point and the optimal sub-area target point, the ratio of the navigation distance to the linear distance needs to be calculated, and the point with the smallest ratio is selected, so as to avoid the trajectory oscillation caused by the too far path when the robot selects the point each time and selects the point next time.
Step S15: and dividing the two-dimensional global grid map for constructing the barrier expansion layer into at least two sub-areas by taking the optimal target point at which the robot moves as a center, sequentially obtaining one or more sub-area optimal target points for completely covering each sub-area in each sub-area, and dividing the two-dimensional global grid map for constructing the barrier expansion layer by taking each sub-area optimal target point as a center to obtain an environment map covering each sub-area.
Optionally, the two-dimensional global grid map for constructing the obstacle expansion layer is divided into at least two sub-regions by taking the optimal target point where the robot moves as a center, iterative exploration is performed on each region respectively, whether a point existing in a corresponding region of the two-dimensional global grid map exists in an exploration point set is searched, and if yes, the point is stored in a corresponding list and serves as a target point to be selected in the region.
Optionally, the method for dividing the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas by taking the optimal target point at which the robot moves as a center, sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area in each sub-area, and dividing the two-dimensional global grid map for constructing the obstacle expansion layer by taking the optimal target point of each sub-area as a center to obtain the environment map covering each sub-area includes: dividing a two-dimensional global grid map for constructing an obstacle expansion layer into at least two sub-areas by taking the optimal target point where the robot moves as a center; sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area exploration rule, performing one or more times of segmentation on the two-dimensional global grid map for constructing the barrier expansion layer by taking the optimal target points of each area as a center, and respectively covering one or more areas formed by segmentation to obtain an environment map covering each sub-area.
Specifically, a two-dimensional global grid map for constructing an obstacle expansion layer is divided into at least two sub-areas by taking the best target point where the robot moves as the center; respectively exploring each area according to a preset sub-area exploring rule, sequentially obtaining one or more sub-area optimal target points for completely covering each sub-area, carrying out one or more times of segmentation on the two-dimensional global grid map for constructing the barrier expansion layer by taking the optimal target points of each area as a center, and respectively covering one or more areas formed by segmentation so as to obtain an environment map covering each sub-area.
Optionally, the obtaining, according to a preset sub-region exploration rule, one or more sub-region optimal target points for completely covering each sub-region in sequence, so as to perform one or more segmentation on the two-dimensional global grid map for constructing the barrier expansion layer by taking the respective sub-region optimal target points as a center, and respectively cover one or more regions formed by the segmentation, so as to obtain an environment map covering each sub-region includes: sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area exploration rule; and sequentially and respectively calculating navigation path information from the initial point of the robot to the optimal target point of each subarea, wherein the navigation path information is used for the robot to move to the optimal target point of each subarea, when the robot reaches a new optimal target point of each subarea, the previous optimal target point/optimal target point of each subarea is pressed into a stack, the two-dimensional global grid map for constructing the barrier expansion layer is respectively segmented by taking the optimal target point of each subarea reached by the robot as the center, and one or more areas formed by segmentation are respectively covered to obtain an environment map covering each subarea so as to obtain the environment map covering each subarea.
Optionally, when each sub-area obtained through the optimal target point is used, a sub-area optimal target point used for sub-area segmentation is obtained for each sub-area, an optimal target point of a sub-area used for sub-area segmentation of each sub-area is obtained according to the sub-area optimal target point until the sub-area obtained through the optimal target point is completely covered and explored, and then the next sub-area is covered according to the above rule until each sub-area is covered. That is, the sub-area and sub-area optimal target point are not limited to corresponding to the first segmentation of the sub-area obtained by the optimal target point segmentation, which here means that for each sub-area, a search is made for a sub-area and a sub-area optimal target point covering all the segmentations that are complete.
Optionally, when one sub-area has no target point, the optimal target point of the sub-area is selected for the next sub-area according to a preset sub-area exploration rule until the area with the target point is selected.
Optionally, when the robot reaches the new optimal target point of the sub-area, the previous optimal target point/optimal target point of the sub-area is pushed into the stack, and when the subsequent sub-area is selected, the previous selected area is used as a reference, and the next sub-area is searched according to the preset sub-area searching rule of the previous sub-area.
Optionally, the preset sub-region exploration rule includes: an exploration sequence rule and/or an exploration degree rule; wherein the heuristic order rule comprises: the search sequence of each sub-area and/or the search sequence of the area formed by the two-dimensional global grid map partition of the barrier expansion layer by the optimal target point pair of each sub-area in each sub-area; the heuristic rules include: when the search of one sub-area reaches the full coverage, the search of the next area is carried out.
Preferably, the search order of the sub-regions and the search order of the region in each sub-region, which is formed by dividing the two-dimensional global grid map constructing the obstacle inflation layer by the optimal target point pair of each sub-region, are the same.
Optionally, the four sub-regions include an upper left region, an upper right region, a lower right region, and a lower left region, and an exploration sequence of each sub-region is: upper left, upper right, lower right, and lower left regions.
Specifically, the optimal target point is used as a midpoint, the two-dimensional global grid map where the robot is located is divided into four regions, namely, an upper left four region, an upper right four region, a lower right four region and a lower left four region, each region is subjected to iterative exploration according to the sequence of the upper left four region, the upper right four region, the lower right four region and the lower left four region respectively, when the exploration is performed for the first time, the upper left four region is used as an initial direction for selection, and when the current region has no target point, the upper left four region, the upper right four region, the lower right four region and the lower left four region are sequentially selected until the; and simultaneously recording the area selected by the current point and pressing the current point into the stack. The current point takes the upper left as an initial area, the current point selection area is the lower right, and when the current point is at the lower left, the current point takes the lower right as the initial area; and when the exploration of the four areas of the current point is finished, continuously exploring the unknown area by taking the previous point as a central point, and finishing the algorithm when the exploration of the four areas of which the optimal target points of all the areas are the central points is finished.
For better description of the hand-eye calibration method, the description is made with reference to an embodiment.
Example 1: a graph building method based on grid map regionalization exploration, which is applied to a mobile robot, and is shown as a graph building intention based on grid map regionalization exploration in fig. 2.
The method comprises the following steps:
collecting laser radar data and gyroscope data;
establishing a two-dimensional global map through laser slam, and constructing an obstacle expansion layer according to obstacle information;
exploring the map by using an bfs algorithm to obtain boundary point sets of all known areas and unknown areas; calculating the obstacle information of the area where each boundary point is located, and the comprehensive weight of the known area and the unknown area, sequencing all the points in the boundary point set according to the weight, and selecting the optimal target point;
calculating the shortest distance which can pass between the current point of the robot and the target point and the linear distance between the current point of the robot and the target point: the global navigation module plans a navigation path from the current point to the target point, and calculates the length of the navigation path and the linear distance between the current point and the target point;
dividing the map by taking the optimal target point where the robot moves as a central point: dividing the grid map where the robot is located into four regions of upper left, upper right, lower right and lower left by taking the current point as a midpoint, and respectively performing iterative exploration on the regions according to the sequence of upper left, upper right, lower right and lower left;
when the first exploration is carried out, selecting by taking the upper left as the starting direction, and when a current area has no target point, sequentially selecting according to the sequence of the upper left, the upper right, the lower right and the lower left until the area with the target point is selected; simultaneously recording the area selected by the current point, pressing the current point into a stack, and when the area of the subsequent point is selected, taking the area selected by the previous point as a reference, when the area selected by the previous point is upper left and upper right, taking the upper left of the current point as an initial area, taking the area selected by the current point as lower right, and when the area selected by the previous point is lower left, taking the lower right of the current point as an initial area; when the exploration of the four areas of the current point is completed, the previous point is taken as a central point, the unknown area is continuously explored, and when the exploration of the four areas of all the central points is completed, the algorithm is ended.
When the optimal target point of the sub-area and/or the optimal target point are/is selected, the ratio of the navigation distance to the linear distance needs to be calculated, and the point with the minimum ratio is selected, so that the problems that the robot is subjected to track oscillation and repeatedly searches a known area due to the fact that the robot performs point selection next time are solved.
Similar to the principle of the embodiment, the invention provides a map building system based on grid map regionalization exploration.
Specific embodiments are provided below in conjunction with the attached figures:
fig. 3 is a schematic structural diagram of a system of a map building method based on grid map regionalized exploration according to an embodiment of the present invention.
Applied to a mobile robot, the system comprising:
the acquisition module 31 is used for acquiring laser radar data and gyroscope data;
the mapping module 32 is connected with the acquisition module 31 and used for establishing a two-dimensional global grid map based on laser slam and constructing an obstacle expansion layer on the two-dimensional global grid map according to the laser radar data and the gyroscope data;
the exploration module 33 is connected with the map building module 32 and is used for searching a passable point set in a two-dimensional global grid map for building an obstacle expansion layer based on a bsf algorithm and selecting an optimal target point from the passable point set;
a navigation module 34, configured to calculate navigation path information from an initial point of the robot to the optimal target point, where the navigation path information is used for the robot to move to the optimal target point;
and an area dividing module 35, connected to the searching module 33 and connected to the navigation module 34, configured to divide the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas with the optimal target point at which the robot moves as a center, sequentially obtain one or more sub-area optimal target points for completely covering each sub-area in each sub-area, and divide the two-dimensional global grid map for constructing the obstacle expansion layer with the optimal target point of each sub-area as a center, so as to obtain an environment map covering each sub-area.
Optionally, the collecting module 31 collects lidar data collected by a lidar and gyroscope data collected by a gyroscope.
Optionally, the acquisition module 31 acquires data for adapting to hardware of the laser radar and a gyroscope. The types of the laser radar hardware and the gyroscope are selected according to specific requirements, and are not limited in the application.
Optionally, the map building module 32 builds a two-dimensional global grid map represented in a grid form based on a slam algorithm, wherein the two-dimensional global grid map is divided into a known area and an unknown area according to the current position of the robot; constructing a barrier layer constructed by the two-dimensional global grid map according to the laser radar data and the gyroscope data, and expanding the barrier layer according to the self information of the robot to obtain the two-dimensional global grid map for constructing the barrier expansion layer; wherein the known area and the location area also include an obstacle expanding layer.
Optionally, the search module 33 searches for a boundary point set in a known region and an unknown region in a two-dimensional global grid map for constructing an obstacle expansion layer based on a bsf algorithm, and screens out the passable point set according to obstacle information at each point in the boundary point set; an optimal target point is selected in the set of communicable points based on a selection rule of the optimal target point.
Specifically, based on a bsf algorithm, searching a boundary point set comprising boundary points in a known area and an unknown area in a two-dimensional global grid map of a constructed barrier expansion layer, and screening a plurality of boundary points according to barrier information at each boundary point obtained by the barrier expansion layer to form a passable point set; selecting a point in the set of communicable points as an optimal target point based on a selection rule of the optimal target point.
Optionally, the selection rule of the optimal target point includes: selecting a point with the largest passing area breadth in the passable point set as an optimal target point; wherein the passing area popularity is related according to the known areas of all points in the passable point set and the passing area popularity of the unknown area. The selection rule can select points with larger passing range and wider exploration range as the optimal target points.
Optionally, the popularity of the passing area is obtained by weighting and calculating the popularity of each point in the passable point set according to the known areas and the popularity of the unknown areas. The weight occupied by the traffic popularity of the known area and the traffic popularity of the unknown area can be set according to the requirement, and is not limited in the application.
Optionally, the passing area wideness is sorted from large to small, and a point in the passable point set with the highest ranking is found as the best target point.
Optionally, the traffic width of the known area and the traffic width of the unknown area of each point in the set of passable points are respectively related to the obstacle distribution situation of the known area at the passable points and the obstacle distribution situation of the unknown area at the passable points; wherein the obstacle distribution is obtained from the obstacle expanding layer.
For example, if the obstacle distribution of the known area of one point in the set of passable points forms a passable area wider than the passable area of the known area of another point, the passable area of the point has a larger passable width than the other point.
Optionally, the navigation module 34 calculates, based on a two-dimensional global grid map for constructing the obstacle expansion layer, passable navigation path information from an initial point of the robot to the optimal target point, so that the robot moves to the optimal target point according to the navigation path information.
Optionally, the navigation path information includes: one or more of a navigation path, a navigation distance, and a linear distance. Specifically, a navigation path from a current point to a target point is planned based on a two-dimensional global grid map for constructing an expansion layer of the barrier, and the distance of the navigation path and the linear distance between the current point and the target point are calculated.
Optionally, the smaller the ratio of the distance of the navigation path to the linear distance is, the shortest the path required to travel between the current point and the target point is, so as to avoid the navigation travel oscillation phenomenon, therefore, when selecting the optimal target point and the optimal target point in the sub-area, the ratio of the navigation distance to the linear distance needs to be calculated, and the point with the smallest ratio is selected, so as to avoid the trajectory oscillation caused by the next point selection of the robot.
Optionally, the area segmentation module 35 uses the optimal target point where the robot moves as a center, segments the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas, performs iterative search on each sub-area, finds whether a point existing in a corresponding area of the two-dimensional global grid map exists in the search point set, and stores the point in a corresponding list if the point exists, so as to serve as a target point to be selected in the area.
Optionally, the area segmentation module 35 segments the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas by taking the optimal target point where the robot moves as a center; sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area exploration rule, performing one or more times of segmentation on the two-dimensional global grid map for constructing the barrier expansion layer by taking the optimal target points of each area as a center, and respectively covering one or more areas formed by segmentation to obtain an environment map covering each sub-area.
Specifically, the area segmentation module 35 segments the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas with an optimal target point where the robot moves as a center; respectively exploring each area according to a preset sub-area exploring rule, sequentially obtaining one or more sub-area optimal target points for completely covering each sub-area, carrying out one or more times of segmentation on the two-dimensional global grid map for constructing the barrier expansion layer by taking the optimal target points of each area as a center, and respectively covering one or more areas formed by segmentation so as to obtain an environment map covering each sub-area.
Optionally, the region segmentation module 35 sequentially obtains one or more optimal target points of the sub-regions for completely covering each sub-region according to a preset sub-region exploration rule; and sequentially and respectively calculating navigation path information from the initial point of the robot to the optimal target point of each subarea, wherein the navigation path information is used for the robot to move to the optimal target point of each subarea, when the robot reaches a new optimal target point of each subarea, the previous optimal target point/optimal target point of each subarea is pressed into a stack, the two-dimensional global grid map for constructing the barrier expansion layer is respectively segmented by taking the optimal target point of each subarea reached by the robot as the center, and one or more areas formed by segmentation are respectively covered to obtain an environment map covering each subarea so as to obtain the environment map covering each subarea.
Optionally, the region segmentation module 35 obtains the optimal sub-region target points for sub-region segmentation for each sub-region by using each sub-region obtained through the optimal target point, obtains the optimal sub-region target points for re-segmenting the sub-regions of each sub-region according to the optimal sub-region target points until the sub-region obtained through the optimal target point is completely covered and explored, and then covers the next sub-region according to the above rule until each sub-region is covered. That is, the sub-area and sub-area optimal target point are not limited to corresponding to the first segmentation of the sub-area obtained by the optimal target point segmentation, which here means that for each sub-area, a search is made for a sub-area and a sub-area optimal target point covering all the segmentations that are complete.
Optionally, when there is no target point in one sub-area, the area segmentation module 35 selects the optimal target point in the sub-area for the next sub-area according to a preset sub-area exploration rule until the area with the target point is selected.
Optionally, when the robot reaches the new optimal target point of the sub-area, the area segmentation module 35 pushes the previous optimal target point/optimal target point of the sub-area into the stack, and when the next sub-area is selected, the next sub-area is searched according to the preset sub-area searching rule of the previous sub-area with reference to the previous selected area.
Optionally, the preset sub-region exploration rule includes: an exploration sequence rule and/or an exploration degree rule; wherein the heuristic order rule comprises: the search sequence of each sub-area and/or the search sequence of the area formed by the two-dimensional global grid map partition of the barrier expansion layer by the optimal target point pair of each sub-area in each sub-area; the heuristic rules include: when the search of one sub-area reaches the full coverage, the search of the next area is carried out.
Preferably, the search order of the sub-regions and the search order of the region in each sub-region, which is formed by dividing the two-dimensional global grid map constructing the obstacle inflation layer by the optimal target point pair of each sub-region, are the same.
Optionally, the four sub-regions include an upper left region, an upper right region, a lower right region, and a lower left region, and an exploration sequence of each sub-region is: upper left, upper right, lower right, and lower left regions.
Specifically, the area segmentation module 35 segments the two-dimensional global grid map where the robot is located into four regions, namely, an upper left four region, an upper right four region, a lower right four region and a lower left four region, and performs iterative exploration on the regions respectively according to the order of the upper left four region, the upper right four region, the lower right four region and the lower left four region, when the exploration is performed for the first time, the upper left four region is used as the starting direction for selection, and when there is no target point in the current region, the selection is performed sequentially according to the order of the upper left four region, the upper right four region, the lower right four region and the lower left four region until the region with; and simultaneously recording the area selected by the current point and pressing the current point into the stack. The current point takes the upper left as an initial area, the current point selection area is the lower right, and when the current point is at the lower left, the current point takes the lower right as the initial area; and when the exploration of the four areas of the current point is finished, continuously exploring the unknown area by taking the previous point as a central point, and finishing the algorithm when the exploration of the four areas of which the optimal target points of all the areas are the central points is finished.
Fig. 4 shows a schematic structural diagram of a map building terminal 40 based on grid map regionalization exploration in the embodiment of the present invention.
The map building terminal 40 based on grid map regionalization exploration comprises: a memory 41 and a processor 42, the memory 41 being for storing computer programs; the processor 42 runs a computer program to implement the mapping method based on grid map regionalization exploration as described in fig. 1.
Alternatively, the number of the memories 41 may be one or more, the number of the processors 42 may be one or more, and fig. 4 illustrates one example.
Optionally, the processor 42 in the mapping terminal 40 based on grid map regionalized exploration loads one or more instructions corresponding to the processes of the application program into the memory 41 according to the steps described in fig. 1, and the processor 42 runs the application program stored in the first memory 41, so as to implement various functions in the mapping method based on grid map regionalized exploration as described in fig. 1.
Optionally, the memory 41 may include, but is not limited to, a high speed random access memory, a non-volatile memory. Such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state storage devices; the Processor 42 may include, but is not limited to, a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
Optionally, the Processor 42 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
The present invention further provides a computer-readable storage medium storing a computer program, which when running implements the mapping method based on grid map regionalized exploration as shown in fig. 1. The computer-readable storage medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disc-read only memories), magneto-optical disks, ROMs (read-only memories), RAMs (random access memories), EPROMs (erasable programmable read only memories), EEPROMs (electrically erasable programmable read only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. The computer readable storage medium may be a product that is not accessed by the computer device or may be a component that is used by an accessed computer device.
In summary, the method, the system and the terminal for establishing the map based on the grid map regionalization exploration are used for solving the problems that in the prior art, the mobile robot adopts the traditional exploration map establishing method based on the grid map, so that the robot walks slowly, the searched area is repeatedly explored, and the map establishing efficiency is low. The invention explores the grid map in different areas, selects the optimal target point in each area for exploration, increases the walking speed of the robot, reduces the repeated exploration rate of the explored area, and greatly improves the efficiency of exploring and establishing the map. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A map building method based on grid map regionalization exploration is applied to a mobile robot and comprises the following steps:
collecting laser radar data and gyroscope data;
establishing a two-dimensional global grid map based on laser slam, and constructing a barrier expansion layer for the two-dimensional global grid map according to the laser radar data and gyroscope data;
searching a passable point set in a two-dimensional global grid map for constructing an obstacle expansion layer based on a bsf algorithm, and selecting an optimal target point in the passable point set;
calculating navigation path information from an initial point of the robot to the optimal target point, wherein the navigation path information is used for the robot to move to the optimal target point;
and dividing the two-dimensional global grid map for constructing the barrier expansion layer into at least two sub-areas by taking the optimal target point at which the robot moves as a center, sequentially obtaining one or more sub-area optimal target points for completely covering each sub-area in each sub-area, and dividing the two-dimensional global grid map for constructing the barrier expansion layer by taking each sub-area optimal target point as a center to obtain an environment map covering each sub-area.
2. The method for map creation based on grid map regionalized exploration according to claim 1, wherein said bsf algorithm searches a navigable point set in a two-dimensional global grid map for constructing an obstacle inflation layer, and selects an optimal target point in said navigable point set comprises:
based on a bsf algorithm, exploring and constructing boundary point sets in a known area and an unknown area in a two-dimensional global grid map of an obstacle expansion layer, and screening the passable point set according to obstacle information at each point in the boundary point sets;
an optimal target point is selected in the set of communicable points based on a selection rule of the optimal target point.
3. The method of claim 2, wherein the selection rule of the optimal target point comprises:
selecting a point with the largest passing area breadth in the passable point set as an optimal target point;
wherein the passing area popularity is related according to the known areas of all points in the passable point set and the passing area popularity of the unknown area.
4. The method of claim 1, wherein the dividing the two-dimensional global grid map for constructing the obstacle inflation layer into at least two sub-regions with the optimal target point at which the robot moves as a center, sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region in each sub-region, and dividing the two-dimensional global grid map for constructing the obstacle inflation layer with the sub-region optimal target points as a center to obtain the environment map covering each sub-region comprises:
dividing a two-dimensional global grid map for constructing an obstacle expansion layer into at least two sub-areas by taking the optimal target point where the robot moves as a center;
sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area exploration rule, performing one or more times of segmentation on the two-dimensional global grid map for constructing the barrier expansion layer by taking the optimal target points of each area as a center, and respectively covering one or more areas formed by segmentation to obtain an environment map covering each sub-area.
5. The method of claim 4, wherein the sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area search rule, and the obtaining the environment map covering each sub-area by performing one or more segmentation on the two-dimensional global grid map for constructing the expanded barrier layer with the optimal target points of each area as a center and covering each segmented one or more areas respectively comprises:
sequentially obtaining one or more optimal target points of the sub-areas for completely covering each sub-area according to a preset sub-area exploration rule;
sequentially and respectively calculating navigation path information from the initial point of the robot to the optimal target point of each subarea, wherein the navigation path information is used for the robot to move to the optimal target point of each subarea,
when the robot reaches a new sub-area optimal target point, the previous optimal target point/sub-area optimal target point is pressed into a stack, the two-dimensional global grid map for constructing the barrier expansion layer is divided by taking each sub-area optimal target point reached by the robot as a center, and one or more areas formed by division are covered respectively to obtain an environment map covering each sub-area so as to obtain the environment map covering each sub-area.
6. The method for map creation based on grid map regionalized exploration according to claim 4 or 5, wherein said preset sub-region exploration rules comprise: an exploration sequence rule and/or an exploration degree rule;
wherein the content of the first and second substances,
the exploration sequence rule comprises the following steps: the search sequence of each sub-area and/or the search sequence of the area formed by the two-dimensional global grid map partition of the barrier expansion layer by the optimal target point pair of each sub-area in each sub-area;
the heuristic rules include: when the search of one sub-area reaches the full coverage, the search of the next area is carried out.
7. The method of claim 6, wherein the four sub-regions are upper left, upper right, lower right, and lower left, and the search order of the sub-regions is: upper left, upper right, lower right, and lower left regions.
8. The method of mapping based on grid map regionalized exploration according to claim 1, wherein said navigation path information comprises: one or more of a navigation path, a navigation distance, and a linear distance.
9. A mapping system based on grid map regionalization exploration is characterized in that the mapping system is applied to a mobile robot, and the system comprises:
the acquisition module is used for acquiring laser radar data and gyroscope data;
the image building module is connected with the acquisition module and used for building a two-dimensional global grid map based on the laser slam and building an obstacle expansion layer on the two-dimensional global grid map according to the laser radar data and the gyroscope data;
the exploration module is connected with the map building module and used for searching a passable point set in a two-dimensional global grid map for building an obstacle expansion layer based on a bsf algorithm and selecting an optimal target point in the passable point set;
the navigation module is connected with the exploration module, is used for calculating navigation path information from an initial point of the robot to the optimal target point, and is used for the robot to move to the optimal target point;
and the area segmentation module is connected with the search module and the navigation module, and is used for segmenting the two-dimensional global grid map for constructing the obstacle expansion layer into at least two sub-areas by taking the optimal target point where the robot moves as a center, sequentially obtaining one or more sub-area optimal target points for completely covering each sub-area in each sub-area, and segmenting the two-dimensional global grid map for constructing the obstacle expansion layer by taking each sub-area optimal target point as a center to obtain an environment map covering each sub-area.
10. A map building terminal based on grid map regionalization exploration is characterized by comprising:
a memory for storing a computer program;
a processor for performing the mapping method of grid map based regionalized exploration according to any of claims 1 to 8.
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