CN111966097B - Map building method, system and terminal based on grid map regional exploration - Google Patents
Map building method, system and terminal based on grid map regional exploration Download PDFInfo
- Publication number
- CN111966097B CN111966097B CN202010807195.6A CN202010807195A CN111966097B CN 111966097 B CN111966097 B CN 111966097B CN 202010807195 A CN202010807195 A CN 202010807195A CN 111966097 B CN111966097 B CN 111966097B
- Authority
- CN
- China
- Prior art keywords
- optimal target
- sub
- area
- target point
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000011218 segmentation Effects 0.000 claims description 18
- 230000015654 memory Effects 0.000 claims description 15
- 238000013507 mapping Methods 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 230000004807 localization Effects 0.000 claims 1
- 238000009826 distribution Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000003860 storage Methods 0.000 description 5
- 230000004888 barrier function Effects 0.000 description 4
- 230000009514 concussion Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008961 swelling Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Aviation & Aerospace Engineering (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Optics & Photonics (AREA)
- Electromagnetism (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a map building method, a system and a terminal based on grid map regional exploration, which solve the problems that in the prior art, a mobile robot adopts a traditional map building method based on a grid map, so that the robot moves slowly, the explored area is repeatedly explored, and the map building efficiency is low. According to the invention, the grid map is searched in the areas, the optimal target point is selected in each area for searching, the walking speed of the robot is increased, the repeated searching rate of the searched area is reduced, and the efficiency of searching and constructing the map is greatly improved.
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 regional exploration.
Background
The environment map constructed by the robot is roughly divided into three types: topological, geometric, grid maps. The grid map is a product of digital rasterization of a real map in reality. It breaks the environment into a series of discrete grids, each having a value, the grids containing basic information of the two types of coordinates, whether an obstacle is present, the probability value occupied by each grid representing the environment information, typically identified as whether an obstacle is present. Each map grid corresponds to a small area in the actual environment, reflects the information of the environment, and is easy for the robot to store map information.
The traditional exploration map building method based on the grid map is adopted by the existing mobile robot, so that the problems of slow walking of the robot, repeated exploration of an explored area, low map building efficiency and the like are caused.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present invention is directed to providing a method, a system and a terminal for map construction based on grid map regional exploration, which are used for solving the problems of slow walking of a mobile robot, repeated exploration of an explored area and low map construction efficiency caused by a traditional exploration map construction method based on a grid map adopted by the mobile robot in the prior art.
To achieve the above and other related objects, the present invention provides a mapping method based on grid map regional 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 an obstacle expansion layer for the two-dimensional global grid map according to the laser radar data and the 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 from 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; dividing a two-dimensional global grid map for constructing an obstacle expansion layer into at least two subareas by taking the optimal target point of the robot where the robot moves as the center, sequentially obtaining one or more subarea optimal target points for completely covering each subarea in each subarea, and dividing the two-dimensional global grid map for constructing the obstacle expansion layer by taking each subarea optimal target point as the center so as to obtain an environment map for covering each subarea.
In an embodiment of the present invention, the method for searching the passable point set in the two-dimensional global grid map for constructing the expansion layer of the obstacle based on the bsf algorithm, and selecting the best target point in the passable point set includes: searching and constructing a known area and a boundary point set in an unknown area in a two-dimensional global grid map of an obstacle expansion layer based on a bsf algorithm, and screening the passable point set according to obstacle information at each point in the boundary point set; and selecting the optimal target point in the passable point set based on a selection rule of the optimal target point.
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; the passing area breadth is related to the passing area breadth of the known area and the unknown area of each point in the passable point set.
In an embodiment of the present invention, the method for dividing the two-dimensional global grid map for constructing the expansion layer of the obstacle into at least two sub-areas with the optimal target point of the robot moving through the 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 expansion layer of the obstacle with each sub-area optimal target point as the center, respectively, so as to obtain the environment map for 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 an optimal target point where the robot moves as a center; according to a preset subarea exploration rule, one or more subarea optimal target points for completely covering all subareas are sequentially obtained, so that the two-dimensional global grid map for constructing the obstacle expansion layer is segmented one or more times by taking the optimal target points of all subareas as the center, and one or more areas formed by segmentation are respectively covered, so that an environment map for covering all subareas is obtained.
In an embodiment of the present invention, the method for sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region according to a preset sub-region exploration rule, so as to divide a two-dimensional global grid map for constructing an obstacle expansion layer one or more times by taking each region optimal target point as a center, and respectively covering one or more regions formed by division, so as to obtain an environment map covering each sub-region includes: sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region according to a preset sub-region exploration rule; and calculating navigation path information from an initial point of the robot to an optimal target point of each subarea respectively in sequence for the robot to move to the optimal target point of each subarea, pushing the previous optimal target point/optimal target point of the subarea into a stack when the robot reaches a new optimal target point of the subarea, dividing a two-dimensional global grid map for constructing an obstacle expansion layer by taking the optimal target point of each subarea reached by the robot as a center, and covering one or more areas formed by division respectively to obtain an environment map for covering each subarea so as to obtain the environment map for covering each subarea.
In an embodiment of the present invention, the preset sub-region exploration rule includes: exploring order rules and/or exploring degree rules; wherein the exploration order rule includes: the exploration sequence of each subarea and/or the exploration sequence of the area formed by dividing the two-dimensional global grid map for constructing the obstacle expansion layer by the optimal target point of each subarea in each subarea; the exploration degree rule comprises: when a sub-area search reaches full coverage, the search of the next area is performed.
In an embodiment of the present invention, the search sequence of the sub-areas is that: 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 straight line distance.
To achieve the above and other related objects, the present invention provides a mapping system based on grid map regional exploration, applied to a mobile robot, the system comprising: the acquisition module is used for acquiring laser radar data and gyroscope data; the map building module is connected with the acquisition module and used for building a two-dimensional global grid map based on laser slam and building an obstacle expansion layer for 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 is used for searching a passable point set in the two-dimensional global grid map for constructing the obstacle expansion layer based on the bsf algorithm, and selecting an optimal target point in the passable point set; the navigation module is connected with the exploration module and used for calculating navigation path information from an initial point of the robot to the optimal target point and moving the robot to the optimal target point; the region segmentation module is connected with the search module and the navigation module, and is used for segmenting a two-dimensional global grid map for constructing an obstacle expansion layer into at least two subareas by taking the optimal target point of the robot where the robot moves as a center, sequentially obtaining one or more subarea optimal target points for completely covering all subareas in each subarea, and respectively segmenting the two-dimensional global grid map for constructing the obstacle expansion layer by taking the optimal target point of each subarea as a center so as to obtain an environment map for covering all subareas.
To achieve the above and other related objects, the present invention provides a map building terminal based on grid map regional exploration, including: a memory for storing a computer program; and the processor is used for executing the map building method based on the grid map regional exploration.
As described above, the map building method, system and terminal based on grid map regional exploration have the following beneficial effects: according to the invention, the grid map is searched in the areas, the optimal target point is selected in each area for searching, the walking speed of the robot is increased, the repeated searching rate of the searched area is reduced, and the efficiency of searching and constructing the map is greatly improved.
Drawings
Fig. 1 is a flowchart of a map building method based on grid map regional exploration according to an embodiment of the invention.
Fig. 2 is a flowchart illustrating a map building method based on grid map regional exploration according to an embodiment of the invention.
Fig. 3 is a schematic structural diagram of a mapping system based on grid map regional exploration according to an embodiment of the invention.
Fig. 4 is a schematic structural diagram of a map-building terminal based on grid map regional exploration according to an embodiment of the invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
In the following description, reference is made to the accompanying drawings, which illustrate several embodiments of the 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," "above," "upper," and the like, may be used herein to facilitate a description of one element or feature as illustrated in the figures relative to another element or feature.
Throughout the specification, when a portion is said to be "connected" to another portion, this includes not only the case of "direct connection" but also the case of "indirect connection" with other elements interposed therebetween. In addition, when a certain component is said to be "included" in a certain section, unless otherwise stated, other components are not excluded, but it is meant that other components may be included.
The first, second, and third terms are used herein to describe various portions, components, regions, layers and/or sections, but are not limited thereto. These terms are only used to distinguish one portion, component, region, layer or section from another portion, component, region, layer or section. Thus, a first portion, component, region, layer or section discussed below could be termed a second portion, component, region, layer or section without departing from the scope of the present invention.
Furthermore, 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," "includes," and/or "including" specify the presence of stated features, operations, elements, components, items, categories, and/or groups, but do not preclude the presence, presence or addition of one or more other features, operations, elements, components, items, categories, and/or groups. 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, A is as follows; b, a step of preparing a composite material; c, performing operation; 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 in some way inherently mutually exclusive.
The embodiment of the invention provides a map construction method based on grid map regional exploration, which solves the problems of slow walking of a mobile robot, repeated exploration of an explored area and low map construction efficiency caused by the traditional map construction method based on the grid map adopted by the mobile robot in the prior art. According to the invention, the grid map is searched in the areas, the optimal target point is selected in each area for searching, the walking speed of the robot is increased, the repeated searching rate of the searched area is reduced, and the efficiency of searching and constructing the map is greatly improved.
The embodiments of the present invention will be described in detail below with reference to the attached drawings so that those skilled in the art to which the present invention pertains can easily implement the present invention. This invention may be embodied in many different forms and is not limited to the embodiments described herein.
As shown in fig. 1, a flowchart of a map building method based on grid map regional exploration in an embodiment of the present invention is shown.
Applied to a mobile robot, the method comprises the following steps:
Step S11: laser radar data and gyroscope data are collected.
Optionally, lidar data collected by the lidar and gyroscope data collected by the gyroscope are collected.
Optionally, data for adapting the lidar hardware as well as the gyroscope is collected. The types of the laser radar hardware and the gyroscope are selected according to specific requirements, and the laser radar hardware and the gyroscope 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 for the two-dimensional global grid map according to the laser radar data and the gyroscope data.
Optionally, based on a slam algorithm, establishing a two-dimensional global cost map expressed in a grid form, 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 an obstacle layer constructed by the two-dimensional global grid map according to the laser radar data and the gyroscope data, and expanding the obstacle layer according to the self information of the robot to obtain a two-dimensional global grid map for constructing an obstacle expansion layer; wherein the known area and the location area also comprise an obstacle swelling 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 in the passable point set.
Optionally, the method for searching the passable point set in the two-dimensional global grid map for constructing the obstacle expansion layer based on the bsf algorithm and selecting the best target point in the passable point set includes: searching and constructing a known area and a boundary point set in an unknown area in a two-dimensional global grid map of an obstacle expansion layer based on a bsf algorithm, and screening the passable point set according to obstacle information at each point in the boundary point set; and selecting the optimal target point in the passable point set based on a selection rule of the optimal target point.
Specifically, searching a boundary point set comprising known areas and boundary points in unknown areas in a two-dimensional global grid map for constructing an obstacle expansion layer based on a bsf algorithm, and screening out a plurality of boundary points to form a passable point set according to obstacle information at each boundary point obtained by the obstacle expansion layer; based on a selection rule of the optimal target point, one point is selected as the optimal target point among the communicable points.
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; the passing area breadth is related to the passing area breadth of the known area and the unknown area of each point in the passable point set. By the selection rule, a point with a larger traffic range and a wider search range can be selected as an optimal target point.
Optionally, the pass area breadth is obtained by calculating the breadth weight of the known area and the unknown area of each point in the passable point set. The weight occupied by the traffic breadth of the known area and the traffic breadth of the unknown area can be set according to the requirements, and the traffic breadth of the known area and the weight occupied by the traffic breadth of the unknown area are not limited in the application.
Optionally, the passing area is ranked according to the degree of breadth from large to small, and the point in the top-ranking passable point set is found to be the best target point.
Optionally, the traffic spread of the known area and the traffic spread of the unknown area of each point in the passable point set 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 expansion layer.
For example, if the accessible area constituted by the obstacle distribution of the accessible area of one point in the accessible point set is wider than the accessible area of another point, the accessible area of the point is wider than the other point.
Step S14: and calculating navigation path information from an initial point of the robot to the optimal target point, and using the navigation path information to move the robot to the optimal target point.
Optionally, based on the two-dimensional global grid map of the expansion layer of the obstacle, the navigable navigation path information from the initial point of the robot to the optimal target point is calculated, and the navigable navigation path information is used for the robot to move 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 straight line distance. Specifically, a navigation path between a current point and a target point is planned based on a two-dimensional global grid map of an obstacle expansion layer, 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 nearest path to be walked between the current point and the target point is indicated, and the phenomenon of navigation walk concussion is avoided, so that when the optimal target point and the optimal target point of the subarea are selected, the ratio of the navigation distance to the linear distance is required to be calculated, and the point with the smallest ratio is selected, so that the track concussion caused by the fact that the robot has too far path in each time of selecting the point and the next time of selecting the point is avoided.
Step S15: dividing a two-dimensional global grid map for constructing an obstacle expansion layer into at least two subareas by taking the optimal target point of the robot where the robot moves as the center, sequentially obtaining one or more subarea optimal target points for completely covering each subarea in each subarea, and dividing the two-dimensional global grid map for constructing the obstacle expansion layer by taking each subarea optimal target point as the center so as to obtain an environment map for covering each subarea.
Optionally, the optimal target point where the robot moves is taken as the center, the two-dimensional global grid map for constructing the obstacle expansion layer is divided into at least two sub-areas, iterative exploration is carried out on each area respectively, whether points in the corresponding area of the two-dimensional global grid map exist in the exploration point set or not is searched, if yes, the points are stored in a corresponding list and serve as target points to be selected in the area.
Optionally, the method for dividing the two-dimensional global grid map for constructing the expansion barrier layer by using the optimal target point of the robot where the robot moves as a center, dividing the two-dimensional global grid map for constructing the expansion barrier layer into at least two sub-areas, 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 expansion barrier layer by using each sub-area optimal target point as a center, respectively, so as to obtain the environment map for 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 an optimal target point where the robot moves as a center; according to a preset subarea exploration rule, one or more subarea optimal target points for completely covering all subareas are sequentially obtained, so that the two-dimensional global grid map for constructing the obstacle expansion layer is segmented one or more times by taking the optimal target points of all subareas as the center, and one or more areas formed by segmentation are respectively covered, so that an environment map for covering all subareas is obtained.
Specifically, the two-dimensional global grid map for constructing the obstacle expansion layer is divided into at least two sub-areas by taking the optimal target point where the robot moves through as the center; and respectively exploring each region according to a preset sub-region exploration rule, sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region, dividing a two-dimensional global grid map for constructing an obstacle expansion layer by taking each region optimal target point as a center for one or more times, and respectively covering one or more regions formed by division to obtain an environment map for covering each sub-region.
Optionally, according to a preset sub-region exploration rule, sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region, so as to divide one or more times the two-dimensional global grid map for constructing the expansion layer of the obstacle by taking each region optimal target point as a center, and respectively covering one or more regions formed by division, so as to obtain an environment map covering each sub-region, where the method includes: sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region according to a preset sub-region exploration rule; and calculating navigation path information from an initial point of the robot to an optimal target point of each subarea respectively in sequence for the robot to move to the optimal target point of each subarea, pushing the optimal target point of the previous optimal target point/the optimal target point of the subarea into a stack when the robot reaches a new optimal target point of the subarea, dividing a two-dimensional global grid map for constructing an obstacle expansion layer by taking the optimal target point of each subarea reached by the robot as a center, and covering one or more areas formed by division respectively to obtain an environment map for covering each subarea so as to obtain the environment map for covering each subarea.
Optionally, when each sub-area obtained through the optimal target point is utilized, sub-area optimal target points for sub-area segmentation are obtained for each sub-area respectively, then the optimal target points of the sub-areas for sub-area segmentation are obtained according to the sub-area optimal target points, until the sub-areas obtained through the optimal target points are subjected to complete coverage exploration, and then the next sub-area is covered according to the rule above until each sub-area is completely covered. I.e. the sub-regions and sub-region optimal target points are not limited to correspond to the first segmentation of the sub-regions obtained by the optimal target point segmentation, but refer here to the exploration of sub-regions and sub-region optimal target points for each sub-region covering all segmentations completely.
Optionally, when one sub-area has no target point, selecting the sub-area optimal target point 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 sub-area optimal target point, the previous optimal target point/sub-area optimal target point is pushed onto the stack, and when the sub-area is selected, the area selected by the previous point is used as a reference, and the next sub-area is explored according to the preset sub-area exploration rule of the previous sub-area.
Optionally, the preset sub-region exploration rule includes: exploring order rules and/or exploring degree rules; wherein the exploration order rule includes: the exploration sequence of each subarea and/or the exploration sequence of the area formed by dividing the two-dimensional global grid map for constructing the obstacle expansion layer by the optimal target point of each subarea in each subarea; the exploration degree rule comprises: when a sub-area search reaches full coverage, the search of the next area is performed.
Preferably, the search order of each sub-region and the search order of the region formed by dividing the two-dimensional global grid map for constructing the obstacle expansion layer by the optimal target point of each sub-region in each sub-region are the same.
Optionally, the search sequence of the sub-areas is that: upper left, upper right, lower right, and lower left regions.
Specifically, the optimal target point is taken as a midpoint, a two-dimensional global grid map where the robot is located is divided into an upper left quarter region, an upper right quarter region, a lower right quarter region and a lower left quarter region, iterative exploration is respectively carried out on each region according to the sequence of the upper left quarter region, the upper right quarter region, the lower right quarter region and the lower left quarter region, when the first exploration is carried out, the upper left quarter region is selected as a starting direction, when the previous region does not have the target point, the upper left quarter region, the upper right quarter region, the lower right quarter region and the lower left quarter region are sequentially selected until the region with the target point is selected; and simultaneously recording the selected area of the current point, and pushing the current point into a stack. The current point takes the upper left as the initial area, the current point selection area is lower right, and when the current point is lower left, the current point takes the lower right as the initial area; when the four areas of the current point are all explored, the previous point is taken as a central point, the unknown area is explored continuously, and when the four areas of the central point with the optimal target point of all areas as the center are all explored, the algorithm is ended.
For a better description of the hand-eye calibration method, an embodiment is described.
Example 1: a diagram construction method based on grid map regional exploration is applied to a mobile robot, and is shown in fig. 2 as a diagram construction intention based on grid map regional exploration.
The method comprises the following steps:
Collecting laser radar data and gyroscope data;
building a two-dimensional global map through laser slam, and building an obstacle expansion layer according to obstacle information;
Searching the map by using bfs algorithm to obtain a boundary point set of all known areas and unknown areas; calculating the barrier information of the area where each boundary point is located, knowing the comprehensive weight of the area and the unknown area, sorting the points in the boundary point set according to the weight, and selecting the optimal target point;
calculating the shortest distance between the current point of the robot and the target point and the straight line distance between the current point of the robot and the target point: the global navigation module plans a navigation path between the current point and the target point, calculates the length of the navigation path and the linear distance between the current point and the target point;
Dividing a map by taking the optimal target point where the robot moves as a center point: dividing a grid map where the robot is located into upper left, upper right, lower right and lower left quarter areas by taking a current point as a midpoint, and respectively carrying out iterative exploration on the areas 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 the previous 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, taking the area selected by the previous point as a reference when the area of the next point is selected, taking the left upper part as a starting area when the area selected by the previous point is left upper part and right upper part, taking the right lower part as a starting area when the area selected by the current point is right lower part and taking the right lower part as the starting area when the area selected by the current point is left lower part; when the four areas of the current point are all explored, the previous point is taken as the center point, the unknown area is explored continuously, and when the four areas of all the center points are explored, the algorithm is ended.
When selecting the optimal target point of the subarea and/or the optimal target point, calculating the ratio of the navigation distance to the linear distance, and selecting the point with the minimum ratio to avoid the problems of track oscillation, repeated exploration of the known area and the like caused by the fact that the robot selects the point next time.
Similar to the principles of the above embodiments, the present invention provides a mapping system based on grid map localized exploration.
Specific embodiments are provided below with reference to the accompanying drawings:
fig. 3 shows a schematic structural diagram of a system of a map building method based on grid map regional exploration in an embodiment of the invention.
Applied to a mobile robot, the system comprises:
the acquisition module 31 is used for acquiring laser radar data and gyroscope data;
The map building module 32 is connected with the acquisition module 31 and is used for building a two-dimensional global grid map based on laser slam and building an obstacle expansion layer for the two-dimensional global grid map according to the laser radar data and the gyroscope data;
The exploration module 33 is connected with the mapping module 32 and is used for searching a passable point set in the two-dimensional global grid map for constructing the obstacle expansion layer based on the bsf algorithm and selecting an optimal target point in the passable point set;
A navigation module 34 for calculating navigation path information from an initial point of the robot to the optimal target point for the robot to move to the optimal target point;
The region dividing module 35 is connected to the searching module 33 and to the navigation module 34, and is configured to divide the two-dimensional global grid map for constructing the expansion layer of the obstacle into at least two sub-regions with the optimal target point where the robot moves as a center, sequentially obtain one or more sub-region optimal target points for completely covering each sub-region in each sub-region, and divide the two-dimensional global grid map for constructing the expansion layer of the obstacle with the optimal target point of each sub-region as a center, so as to obtain the environment map for covering each sub-region.
Optionally, the acquisition module 31 acquires laser radar data acquired by a laser radar and gyroscope data acquired by a gyroscope.
Optionally, the acquisition module 31 acquires data for adapting the lidar hardware and the gyroscope. The types of the laser radar hardware and the gyroscope are selected according to specific requirements, and the laser radar hardware and the gyroscope 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 an obstacle layer constructed by the two-dimensional global grid map according to the laser radar data and the gyroscope data, and expanding the obstacle layer according to the self information of the robot to obtain a two-dimensional global grid map for constructing an obstacle expansion layer; wherein the known area and the location area also comprise an obstacle swelling layer.
Optionally, the searching module 33 searches for a known region and a boundary point set in an unknown region in the two-dimensional global grid map for constructing the obstacle expansion layer based on the bsf algorithm, and screens out the passable point set according to the obstacle information at each point in the boundary point set; the optimal target point is selected among the set of communicable points based on a selection rule of the optimal target point.
Specifically, searching a boundary point set comprising known areas and boundary points in unknown areas in a two-dimensional global grid map for constructing an obstacle expansion layer based on a bsf algorithm, and screening out a plurality of boundary points to form a passable point set according to obstacle information at each boundary point obtained by the obstacle expansion layer; based on a selection rule of the optimal target point, one point is selected as the optimal target point among the communicable points.
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; the passing area breadth is related to the passing area breadth of the known area and the unknown area of each point in the passable point set. By the selection rule, a point with a larger traffic range and a wider search range can be selected as an optimal target point.
Optionally, the pass area breadth is obtained by calculating the breadth weight of the known area and the unknown area of each point in the passable point set. The weight occupied by the traffic breadth of the known area and the traffic breadth of the unknown area can be set according to the requirements, and the traffic breadth of the known area and the weight occupied by the traffic breadth of the unknown area are not limited in the application.
Optionally, the passing area is ranked according to the degree of breadth from large to small, and the point in the top-ranking passable point set is found to be the best target point.
Optionally, the traffic spread of the known area and the traffic spread of the unknown area of each point in the passable point set are related to the obstacle distribution of the known area at the passable point and the obstacle distribution of the unknown area at the passable point, respectively; wherein the obstacle distribution is obtained from the obstacle expansion layer.
For example, if the accessible area constituted by the obstacle distribution of the accessible area of one point in the accessible point set is wider than the accessible area of another point, the accessible area of the point is wider than the other point.
Optionally, the navigation module 34 calculates navigable navigation path information from an initial point of the robot to the optimal target point based on a two-dimensional global grid map of the expansion layer of the obstacle, for the robot to move 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 straight line distance. Specifically, a navigation path between a current point and a target point is planned based on a two-dimensional global grid map of an obstacle expansion layer, 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 nearest path to be walked between the current point and the target point is indicated, and the phenomenon of navigation walk concussion is avoided, so that when the optimal target point and the optimal target point of the subarea are selected, the ratio of the navigation distance to the linear distance is required to be calculated, and the point with the minimum ratio is selected, so that the track concussion caused by the next point selection of the robot is avoided.
Optionally, the region segmentation module 35 segments the two-dimensional global grid map for constructing the expansion layer of the obstacle into at least two sub-regions with the optimal target point where the robot moves through as a center, and performs iterative exploration on each region to find out whether points exist in the corresponding region of the two-dimensional global grid map in the exploration point set, if yes, the points are stored in the corresponding list and serve as the target points to be selected in the region.
Optionally, the region segmentation module 35 segments the two-dimensional global grid map for constructing the expansion layer of the obstacle into at least two sub-regions with the optimal target point where the robot moves through as a center; according to a preset subarea exploration rule, one or more subarea optimal target points for completely covering all subareas are sequentially obtained, so that the two-dimensional global grid map for constructing the obstacle expansion layer is segmented one or more times by taking the optimal target points of all subareas as the center, and one or more areas formed by segmentation are respectively covered, so that an environment map for covering all subareas is obtained.
Specifically, the region segmentation module 35 segments the two-dimensional global grid map for constructing the expansion layer of the obstacle into at least two sub-regions with the optimal target point where the robot moves through as a center; and respectively exploring each region according to a preset sub-region exploration rule, sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region, dividing a two-dimensional global grid map for constructing an obstacle expansion layer by taking each region optimal target point as a center for one or more times, and respectively covering one or more regions formed by division to obtain an environment map for covering each sub-region.
Optionally, the area dividing module 35 sequentially obtains one or more sub-area optimal target points for completely covering each sub-area according to a preset sub-area exploration rule; and calculating navigation path information from an initial point of the robot to an optimal target point of each subarea respectively in sequence for the robot to move to the optimal target point of each subarea, pushing the optimal target point of the previous optimal target point/the optimal target point of the subarea into a stack when the robot reaches a new optimal target point of the subarea, dividing a two-dimensional global grid map for constructing an obstacle expansion layer by taking the optimal target point of each subarea reached by the robot as a center, and covering one or more areas formed by division respectively to obtain an environment map for covering each subarea so as to obtain the environment map for covering each subarea.
Optionally, when using each sub-area obtained by the optimal target point, the area dividing module 35 obtains the sub-area optimal target point for sub-area division for each sub-area, obtains the optimal target point for sub-area division for each sub-area according to the sub-area optimal target point, until the sub-area obtained by the optimal target point is completely covered and explored, and then covers the next sub-area according to the rule above until each sub-area is completely covered. I.e. the sub-regions and sub-region optimal target points are not limited to correspond to the first segmentation of the sub-regions obtained by the optimal target point segmentation, but refer here to the exploration of sub-regions and sub-region optimal target points for each sub-region covering all segmentations completely.
Optionally, when one sub-area has no target point, the area dividing module 35 selects the sub-area optimal target point 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 sub-area optimal target point, the area dividing module 35 pushes the previous optimal target point/sub-area optimal target point onto the stack, and when the following sub-area is selected, the area selected by the previous point is used as a reference, and the next sub-area is explored according to the preset sub-area exploration rule of the previous sub-area.
Optionally, the preset sub-region exploration rule includes: exploring order rules and/or exploring degree rules; wherein the exploration order rule includes: the exploration sequence of each subarea and/or the exploration sequence of the area formed by dividing the two-dimensional global grid map for constructing the obstacle expansion layer by the optimal target point of each subarea in each subarea; the exploration degree rule comprises: when a sub-area search reaches full coverage, the search of the next area is performed.
Preferably, the search order of each sub-region and the search order of the region formed by dividing the two-dimensional global grid map for constructing the obstacle expansion layer by the optimal target point of each sub-region in each sub-region are the same.
Optionally, the search sequence of the sub-areas is that: upper left, upper right, lower right, and lower left regions.
Specifically, the region segmentation module 35 segments the two-dimensional global grid map where the robot is located into upper left, upper right, lower right and lower left quarter regions with the optimal target point as a midpoint, and performs iterative search on each region in the order of upper left, upper right, lower right and lower left, when performing the first search, selects the region with the upper left as the starting direction, and when the previous region has no target point, sequentially selects the region with the target point in the order of upper left, upper right, lower right and lower left until the region with the target point is selected; and simultaneously recording the selected area of the current point, and pushing the current point into a stack. The current point takes the upper left as the initial area, the current point selection area is lower right, and when the current point is lower left, the current point takes the lower right as the initial area; when the four areas of the current point are all explored, the previous point is taken as a central point, the unknown area is explored continuously, and when the four areas of the central point with the optimal target point of all areas as the center are all explored, the algorithm is ended.
Fig. 4 shows a schematic structural diagram of a map-building terminal 40 based on grid map regional exploration in an embodiment of the present invention.
The map-building terminal 40 based on grid map regional exploration includes: a memory 41 and a processor 42, the memory 41 for storing a computer program; the processor 42 runs a computer program to implement the grid map based regional search mapping method as described in fig. 1.
Alternatively, the number of the memories 41 may be one or more, and the number of the processors 42 may be one or more, and one is taken as an example in fig. 4.
Optionally, the processor 42 in the map-building terminal 40 based on grid map regional exploration loads one or more instructions corresponding to the process of the application program into the memory 41 according to the steps as shown 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 map-building method based on grid map regional exploration as shown in fig. 1.
Optionally, the memory 41 may include, but is not limited to, high speed random access memory, nonvolatile memory. Such as one or more 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 (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, 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.
Alternatively, the processor 42 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), and the like; but may also be a digital signal processor (DIGITAL SIGNAL Processing, 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.
The invention also provides a computer readable storage medium storing a computer program which realizes the map building method based on grid map regional exploration as shown in fig. 1 when running. The computer-readable storage medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disk-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 an article of manufacture that is not accessed by a computer device or may be a component used by an accessed computer device.
In summary, the method, the system and the terminal for constructing the map based on the regional exploration of the grid map are used for solving the problems that in the prior art, the mobile robot adopts the traditional method for constructing the map based on the grid map, so that the robot moves slowly, the explored region is repeatedly explored, and the map construction efficiency is low. According to the invention, the grid map is searched in the areas, the optimal target point is selected in each area for searching, the walking speed of the robot is increased, the repeated searching rate of the searched area is reduced, and the efficiency of searching and constructing the map is greatly improved. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. It is therefore intended that all equivalent modifications and changes made by those skilled in the art without departing from the spirit and technical spirit of the present invention shall be covered by the appended claims.
Claims (8)
1. The map construction method based on the grid map regional exploration is characterized by being applied to a mobile robot and comprising the following steps of:
collecting laser radar data and gyroscope data;
Establishing a two-dimensional global grid map based on laser slam, and constructing an obstacle expansion layer for the two-dimensional global grid map according to the laser radar data and the 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 from 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;
Dividing a two-dimensional global grid map for constructing an obstacle expansion layer into at least two subareas by taking the optimal target point of the robot where the robot moves as a center, sequentially obtaining one or more subarea optimal target points for completely covering each subarea in each subarea, and dividing the two-dimensional global grid map for constructing the obstacle expansion layer by taking each subarea optimal target point as a center so as to obtain an environment map for covering each subarea;
Wherein, include:
dividing a two-dimensional global grid map for constructing an obstacle expansion layer into at least two sub-areas by taking an optimal target point where the robot moves as a center;
Sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region according to a preset sub-region exploration rule, so as to divide one or more times a two-dimensional global grid map for constructing an obstacle expansion layer by taking each region optimal target point as a center, and respectively covering one or more regions formed by division so as to obtain an environment map for covering each sub-region, wherein the method comprises the following steps: sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region according to a preset sub-region exploration rule; and calculating navigation path information from an initial point of the robot to an optimal target point of each sub-area respectively in sequence for the robot to move to the optimal target point of each sub-area, pushing the optimal target point of the previous optimal target point/sub-area into a stack when the robot reaches a new optimal target point of each sub-area, dividing a two-dimensional global grid map for constructing an obstacle expansion layer by taking the optimal target point of each sub-area reached by the robot as a center, and covering one or more areas formed by division respectively to obtain an environment map for covering each sub-area.
2. The map construction method based on grid map regional exploration according to claim 1, wherein the bsf algorithm searches a passable point set in a two-dimensional global grid map for constructing an obstacle expansion layer, and the manner of selecting the best target point in the passable point set comprises:
searching and constructing a known area and a boundary point set in an unknown area in a two-dimensional global grid map of an obstacle expansion layer based on a bsf algorithm, and screening the passable point set according to obstacle information at each point in the boundary point set;
And selecting the optimal target point in the passable point set based on a selection rule of the optimal target point.
3. The mapping method based on grid map localized exploration according to 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;
The passing area breadth is related to the passing area breadth of the known area and the unknown area of each point in the passable point set.
4. The mapping method based on grid map regional exploration according to claim 1, wherein the preset sub-region exploration rule comprises: exploring order rules and/or exploring degree rules;
Wherein the exploration order rule includes: the exploration sequence of each subarea and/or the exploration sequence of the area formed by dividing the two-dimensional global grid map for constructing the obstacle expansion layer by the optimal target point of each subarea in each subarea;
the exploration degree rule comprises: when a sub-area search reaches full coverage, the search of the next area is performed.
5. The method for mapping based on grid map regional exploration according to claim 4, wherein the subareas comprise four areas of upper left, upper right, lower right and lower left, and the exploration sequence of each subarea is as follows: upper left, upper right, lower right, and lower left regions.
6. The mapping method based on grid map localized exploration of claim 1, wherein said navigation path information comprises: one or more of a navigation path, a navigation distance, and a straight line distance.
7. A mapping system based on grid map localized exploration, applied to a mobile robot, the system comprising:
the acquisition module is used for acquiring laser radar data and gyroscope data;
the map building module is connected with the acquisition module and used for building a two-dimensional global grid map based on laser slam and building an obstacle expansion layer for 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 is used for searching a passable point set in the two-dimensional global grid map for constructing the obstacle expansion layer based on the bsf algorithm, and selecting an optimal target point in the passable point set;
the navigation module is connected with the exploration module and used for calculating navigation path information from an initial point of the robot to the optimal target point and moving the robot to the optimal target point;
The region segmentation module is connected with the exploration module and the navigation module and is used for segmenting a two-dimensional global grid map for constructing an obstacle expansion layer into at least two subareas by taking the optimal target point of the robot where the robot moves as a center, sequentially obtaining one or more subarea optimal target points for completely covering all subareas in each subarea, and respectively segmenting the two-dimensional global grid map for constructing the obstacle expansion layer by taking the optimal target point of each subarea as a center so as to obtain an environment map for covering all subareas;
The area segmentation module is used for segmenting a two-dimensional global grid map for constructing an obstacle expansion layer into at least two sub-areas by taking an optimal target point where the robot moves through as a center; sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region according to a preset sub-region exploration rule, so as to divide one or more times a two-dimensional global grid map for constructing an obstacle expansion layer by taking each region optimal target point as a center, and respectively covering one or more regions formed by division so as to obtain an environment map for covering each sub-region, wherein the method comprises the following steps: sequentially obtaining one or more sub-region optimal target points for completely covering each sub-region according to a preset sub-region exploration rule; and calculating navigation path information from an initial point of the robot to an optimal target point of each sub-area respectively in sequence for the robot to move to the optimal target point of each sub-area, pushing the optimal target point of the previous optimal target point/sub-area into a stack when the robot reaches a new optimal target point of each sub-area, dividing a two-dimensional global grid map for constructing an obstacle expansion layer by taking the optimal target point of each sub-area reached by the robot as a center, and covering one or more areas formed by division respectively to obtain an environment map for covering each sub-area.
8. The utility model provides a build drawing terminal based on grid map regional exploration which characterized in that includes:
A memory for storing a computer program;
a processor for performing the grid map localization exploration-based mapping method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010807195.6A CN111966097B (en) | 2020-08-12 | 2020-08-12 | Map building method, system and terminal based on grid map regional exploration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010807195.6A CN111966097B (en) | 2020-08-12 | 2020-08-12 | Map building method, system and terminal based on grid map regional exploration |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111966097A CN111966097A (en) | 2020-11-20 |
| CN111966097B true CN111966097B (en) | 2024-06-14 |
Family
ID=73364897
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010807195.6A Active CN111966097B (en) | 2020-08-12 | 2020-08-12 | Map building method, system and terminal based on grid map regional exploration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111966097B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113050632B (en) * | 2021-03-11 | 2022-06-14 | 珠海一微半导体股份有限公司 | Map exploration method and chip for robot to explore unknown area and robot |
| CN112884894B (en) * | 2021-04-28 | 2021-09-21 | 深圳大学 | Scene reconstruction data acquisition method and device, computer equipment and storage medium |
| CN114281076B (en) * | 2021-12-13 | 2024-02-09 | 烟台杰瑞石油服务集团股份有限公司 | Covering and moving operation method of robot |
| CN114545923A (en) * | 2021-12-28 | 2022-05-27 | 美的集团(上海)有限公司 | Robot mapping method, electronic device and computer storage medium |
| CN115145270B (en) * | 2022-06-17 | 2023-07-14 | 上海景吾酷租科技发展有限公司 | Autonomous exploration drawing target selection method and system |
| CN114812539B (en) * | 2022-06-21 | 2022-11-18 | 杭州涂鸦信息技术有限公司 | Map searching method, map using method, map searching device, map using device, robot and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108344414A (en) * | 2017-12-29 | 2018-07-31 | 中兴通讯股份有限公司 | A kind of map structuring, air navigation aid and device, system |
| CN110703747A (en) * | 2019-10-09 | 2020-01-17 | 武汉大学 | Robot autonomous exploration method based on simplified generalized Voronoi diagram |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2787856A1 (en) * | 2012-05-12 | 2013-11-12 | University Of Florida Research Foundation, Inc. | Systems and methods for estimating the geographic location at which image data was captured |
| CN107480638B (en) * | 2017-08-16 | 2020-06-30 | 北京京东尚科信息技术有限公司 | Vehicle obstacle avoidance method, controller, device and vehicle |
| CN111026131B (en) * | 2019-12-30 | 2023-03-14 | 达闼机器人股份有限公司 | Expansion region determining method and device, robot and storage medium |
-
2020
- 2020-08-12 CN CN202010807195.6A patent/CN111966097B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108344414A (en) * | 2017-12-29 | 2018-07-31 | 中兴通讯股份有限公司 | A kind of map structuring, air navigation aid and device, system |
| CN110703747A (en) * | 2019-10-09 | 2020-01-17 | 武汉大学 | Robot autonomous exploration method based on simplified generalized Voronoi diagram |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111966097A (en) | 2020-11-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111966097B (en) | Map building method, system and terminal based on grid map regional exploration | |
| CN112000754B (en) | Map construction method, device, storage medium and computer equipment | |
| CN104615138A (en) | Dynamic indoor region coverage division method and device for mobile robot | |
| CN110008941B (en) | Method and device for detecting travelable area, computer equipment and storage medium | |
| CN109059924A (en) | Adjoint robot Incremental Route method and system for planning based on A* algorithm | |
| US20240192702A1 (en) | Navigation method of robot, chip and robot | |
| CN107092978B (en) | Shortest path layered planning method for virtual earth | |
| CN113805590A (en) | Indoor robot autonomous exploration method and system based on boundary driving | |
| CN101821732A (en) | Nearest-neighbor geographic search | |
| CN114440916B (en) | Navigation method, device, equipment and storage medium | |
| CN109271467B (en) | Direction-aware road network moving object k neighbor query method | |
| CN112526993A (en) | Grid map updating method and device, robot and storage medium | |
| CN109341698B (en) | Path selection method and device for mobile robot | |
| CN114295119B (en) | Map construction method and device | |
| CN114779779A (en) | Path planning method, path planning device, computer equipment and storage medium | |
| JP2020042793A (en) | Obstacle distribution simulation method, device, and terminal based on probability plot | |
| CN114690753A (en) | Hybrid strategy-based path planning method, autonomous traveling equipment and robot | |
| CN116501064A (en) | Path planning and obstacle avoidance method for photovoltaic power station cleaning robot | |
| CN115308770A (en) | Dynamic obstacle detection method based on fitting graph | |
| US20210325196A1 (en) | Vector Tile Navigation | |
| CN113375686A (en) | Path planning method and device and intelligent conveying system | |
| KR101743072B1 (en) | Method and apparatus of path planning | |
| CN114442642B (en) | Path planning method, path planning device, computer equipment and storage medium | |
| CN111742242A (en) | Point cloud processing method, system, device and storage medium | |
| CN112446953B (en) | Point cloud processing method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |