CN116257048A - Path planning method, device, equipment and storage medium - Google Patents
Path planning method, device, equipment and storage medium Download PDFInfo
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- G05D1/02—Control of position or course in two dimensions
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- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0234—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using optical markers or beacons
- G05D1/0236—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using optical markers or beacons in combination with a laser
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- 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
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- 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
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
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Abstract
The embodiment of the invention discloses a path planning method, a path planning device, path planning equipment and a storage medium. Generating a binary grid map according to probability information of each grid in the initial grid map and the forbidden area; wherein the probability information represents a probability that the grid belongs to an obstacle; determining the obstacle touch distance and the passable direction of each passable grid in the binary grid diagram; acquiring size information of the mobile robot; and determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction. According to the path planning method provided by the embodiment of the invention, the shortest path from the starting node to the target node of the mobile robot is determined according to the constraint information such as the size information, the obstacle touch distance, the passable direction and the like, so that the map construction efficiency can be improved, and the path planning reliability can be improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of mobile robots, in particular to a path planning method, a path planning device, path planning equipment and a storage medium.
Background
The map searching algorithm is one of main planning algorithms of the autonomous mobile robot, but the current map used for planning is a static grid map representing a real environment, and in an actual application scene, besides considering the factors of obstacles, special traffic rule restrictions are needed, such as that certain areas are forbidden to pass, certain channels can only run in one direction, and the like, and the existing planning map does not support the additional constraint information. In addition, the existing static grid map is not friendly to robot path planning with size change in the task process, an expansion layer of the static grid map needs to be updated along with the size change, and efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a path planning method, a device, equipment and a storage medium, wherein the map is constructed based on constraint information in the map, and a path is planned based on the size of a robot, so that the efficiency of map construction can be improved, and the reliability of path planning can be improved.
In a first aspect, an embodiment of the present invention provides a path planning method, which is characterized in that the method includes:
generating a binary grid map according to probability information of each grid in the initial grid map and the forbidden area; wherein the probability information represents a probability that the grid belongs to an obstacle;
determining the obstacle touch distance and the passable direction of each passable grid in the binary grid diagram;
acquiring size information of the mobile robot;
and determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction.
Further, generating a binary raster pattern according to probability information of each raster in the initial raster map and the marked forbidden area, including:
for each grid, if the probability information of the grid is larger than a first set threshold value, the probability information of the grid is adjusted to a first set value; otherwise, the probability information of the grid is adjusted to a second set value;
adjusting probability information of grids in the forbidden region to a first set value; wherein the first set value indicates that the grid is not passable, and the second set value indicates that the grid is passable.
Further, adjusting probability information of the grid in the forbidden region to a first set value includes:
acquiring grids positioned on the boundary of the forbidden area, and determining the grids as boundary grids;
searching grids among boundary grids in the same row or column, and determining the grids as internal grids;
and adjusting probability information of the boundary grid and the internal grid to a first set value.
Further, determining the obstacle touching distance of each passable grid in the binary grid graph includes:
and calculating the distance between the center point of the passable grid and the nearest center point of the non-passable grid, and determining the distance as the obstacle touch distance.
Further, determining a passable direction of each passable grid in the binary grid graph includes:
and determining the passable direction of the passable grid according to the passable direction of the mark.
Further, determining a shortest path of the mobile robot from a start node to a target node according to the size information, the obstacle touch distance and the passable direction includes:
and determining the shortest path from the starting node to the target node of the mobile robot by adopting a set shortest path algorithm according to the size information, the obstacle touch distance and the passable direction.
Further, the size information is an circumscribed circle radius, and a shortest path algorithm is set according to the size information, the obstacle touch distance and the passable direction to determine a shortest path from a start node to a target node of the mobile robot, including:
starting from a starting node, taking the starting node as a father node, and selecting a child node meeting the following conditions from passable grids adjacent to the father node:
the obstacle touching distance corresponding to the child node is larger than the radius of the circumscribed circle;
the included angle between the direction vector between the child node and the father node and the passable direction corresponding to the child node is smaller than a second set threshold value;
returning to the operation of selecting the child node meeting the following conditions from the passable grids adjacent to the parent node by taking the child node as a new parent node until reaching the target node, and obtaining at least one path;
selecting a shortest path from the at least one path.
In a second aspect, an embodiment of the present invention further provides a path planning apparatus, including:
the binary raster pattern generation module is used for generating a binary raster pattern according to probability information of each grid in the initial raster map and the forbidden area; wherein the probability information represents a probability that the grid belongs to an obstacle;
the obstacle touch distance and passable direction determining module is used for determining the obstacle touch distance and passable direction of each passable grid in the binary grid graph;
the size information acquisition module is used for acquiring the size information of the mobile robot;
and the shortest path determining module is used for determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction.
In a third aspect, an embodiment of the present invention further provides a computer apparatus, including: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the path planning method according to the embodiments of the invention when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processing device implements a path planning method according to an embodiment of the present invention.
The embodiment of the invention provides a path planning method, a path planning device, path planning equipment and a storage medium. Generating a binary grid map according to probability information of each grid in the initial grid map and the forbidden area; wherein the probability information indicates a probability that the grid belongs to an obstacle; determining the touch distance and the passable direction of the obstacle of each passable grid in the binary grid diagram; acquiring size information of the mobile robot; and determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction. According to the path planning method provided by the embodiment of the invention, the shortest path from the starting node to the target node of the mobile robot is determined according to the constraint information such as the size information, the obstacle touch distance, the passable direction and the like, so that the map construction efficiency can be improved, and the path planning reliability can be improved.
Drawings
FIG. 1 is a flow chart of a path planning method according to a first embodiment of the present invention;
FIG. 2 is an exemplary diagram of generating a binary raster pattern in accordance with a first embodiment of the present invention;
FIG. 3 is a diagram illustrating an example of determining a touch distance of an obstacle according to a first embodiment of the invention;
FIG. 4 is an exemplary diagram of determining a passable direction of a passable grid in accordance with a first embodiment of the invention;
FIG. 5 is an exemplary diagram of determining a shortest path in a first embodiment of the present invention;
fig. 6 is a schematic structural diagram of a path planning apparatus according to a second embodiment of the present invention; .
FIG. 7 is a schematic diagram of a computer device according to a third embodiment of the present invention;
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a path planning method according to an embodiment of the present invention, where the method may be applied to a case of planning a travel path of a mobile robot, and the method may be performed by a path planning apparatus, as shown in fig. 1, and specifically includes the following steps:
and 110, generating a binary raster pattern according to probability information of each raster in the initial raster map and the forbidden area.
Wherein the probability information indicates the probability that the grid belongs to the obstacle, and the probability information can be a value between 0 and 1, and the larger the value is, the larger the probability that the grid belongs to the obstacle is. The initial grid map (Occupancy Grid Map, OGP) may be obtained by scanning the target environment with a laser slam technique or based on a mapping technique. The initial grid map may be represented as om= { p i,j … … }, where p i,j The probability that the (i, j) th grid is an obstacle is represented. The forbidden region can be a forbidden region marked by a user selecting one or more sub-regions R= { R1, R2, & gtRn } in the barrier-free region according to actual requirements. Binary grid patterns it is understood that the probability information of a pattern grid is represented by two values, either "0" or "1".
Specifically, fig. 2 is an exemplary diagram of generating a binary raster pattern in the present embodiment, and as shown in fig. 2, the process of generating the binary raster pattern according to probability information of each grid in the initial raster map and the marked forbidden region may be: for each grid, if the probability information of the grid is larger than a first set threshold value, adjusting the probability information of the grid to a first set value; otherwise, adjusting the probability information of the grid to a second set value; and adjusting the probability information of the grids in the forbidden area to a first set value.
Wherein the first set value indicates that the grid is not passable, and the second set value indicates that the grid is passable. The first set value may be "1" and the second set value may be "0". The first set threshold may be set to any value between 0.5 and 0.8, for example to 0.5. In this embodiment, if the probability information of the grid is greater than 0.5, the probability information of the grid is adjusted to 1; otherwise, the probability information of the grid is adjusted to 0.
Optionally, the process of adjusting the probability information of the grid in the forbidden region to the first set value may be: acquiring grids positioned on the boundary of the forbidden area, and determining the grids as boundary grids; searching grids among boundary grids in the same row or column, and determining the grids as internal grids; the probability information of the boundary grid and the internal grid is adjusted to a first set value.
A boundary grid is understood here to mean a grid containing forbidden regions. After the boundary grids are obtained, two boundary grids positioned in the same row or the same column are obtained row by row or column by column, then the grids positioned between the two boundary grids in the same row or the same column are used for determining the internal grids, the boundary grids and the internal grids form grids in a forbidden area, and finally the probability information of the boundary grids and the internal grids is adjusted to be 1.
Step 120, determining the obstacle touching distance and the passable direction of each passable grid in the binary grid diagram.
The obstacle touch distance is understood to be the distance of the nearest obstacle from the passable grid. The passable direction is understood to mean the direction in which the robot can travel, as a vector, for example
Indicates the 0 direction, & lt + & gt>
Indicating a 45 deg. direction,/->
90 °, is indicated>
Indicating that any direction is accessible.
Specifically, the method for determining the obstacle touch distance of each passable grid in the binary grid graph may be: and calculating the distance between the center point of the passable grid and the nearest center point of the non-passable grid, and determining the distance as the obstacle touch distance.
Exemplary, FIG. 3 is an exemplary diagram of determining the obstacle touch distance according to the present embodimentAs shown in fig. 3, the distance between each trafficable grid and the nearest obstacle is calculated, and the trafficable direction of each trafficable grid is initialized to
I.e. any direction can be passed through.
The manner of determining the passable direction of each passable grid in the binary grid graph may be: and determining the passable direction of the passable grid according to the passable direction of the mark.
In many application scenarios, it is necessary to limit the traveling direction of the robots, so as to avoid the robots from facing each other in a narrow passage. Illustratively, FIG. 4 is an exemplary diagram of determining a passable direction of a passable grid in the present implementation. As shown in fig. 4, the passable direction of the passable grid is determined according to the passable direction of the sign.
Step 130, obtaining size information of the mobile robot.
The size information of the robot may be a size of the robot after loading the cargo. The size information may be the radius of the circumscribed circle of the robot after loading the cargo.
And 140, determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction.
In this embodiment, the method for determining the shortest path between the mobile robot and the target node from the start node according to the size information, the obstacle touch distance and the passable direction may be: and determining the shortest path between the starting node and the target node of the mobile robot by adopting a set shortest path algorithm according to the size information, the obstacle touch distance and the passable direction.
The shortest path setting algorithm may be Dijkstra algorithm or a-algorithm.
Specifically, the process of determining the shortest path of the mobile robot from the starting node to the target node by adopting a set shortest path algorithm according to the size information, the obstacle touch distance and the passable direction may be: starting from a starting node, taking the starting node as a father node, and selecting a child node meeting the following conditions from passable grids adjacent to the father node: the touching distance of the obstacle corresponding to the child node is larger than the radius of the circumscribed circle; the included angle between the direction vector between the child node and the father node and the passable direction corresponding to the child node is smaller than a second set threshold value; returning to execute the operation of selecting the child node meeting the following conditions from the passable grids adjacent to the parent node by taking the child node as a new parent node until reaching the target node, and obtaining at least one path; the shortest path is selected from the at least one path.
The trafficable grids adjacent to the parent node can be understood as trafficable grids adjacent to the grid where the parent node is located in the up, down, left and right directions. The obstacle touch distance corresponding to the child node can be understood as the obstacle touch distance of the grid where the child node is located, and the passable direction corresponding to the child node can be understood as the passable direction of the grid where the child node is located. Let the parent node be Np (i p ,j p ) The child node is Nc (i c ,j c ). The conditions that child nodes can pass are: (1) s (i) c ,j c )>The radius of the robot circumscribed circle; (2)
Or a travel direction vector (i) c -i p ,j c -j p ) And set traveling direction->
The included angle of (2) is smaller than the second set threshold. Wherein s (i) c ,j c ) And representing the obstacle touch distance corresponding to the child node. Fig. 5 is an exemplary diagram of determining the shortest path in the present embodiment, and as shown in fig. 5, the shortest path between the starting node and the target node of the mobile robot is determined according to the size information, the obstacle touch distance and the passable direction. />
According to the technical scheme of the embodiment, a binary raster pattern is generated according to probability information of each raster in an initial raster map and a forbidden area; wherein the probability information indicates a probability that the grid belongs to an obstacle; determining the touch distance and the passable direction of the obstacle of each passable grid in the binary grid diagram; acquiring size information of the mobile robot; and determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction. According to the path planning method provided by the embodiment of the invention, the shortest path from the starting node to the target node of the mobile robot is determined according to the constraint information such as the size information, the obstacle touch distance, the passable direction and the like, so that the map construction efficiency can be improved, and the path planning reliability can be improved.
Example two
Fig. 6 is a schematic structural diagram of a path planning apparatus according to a second embodiment of the present invention. As shown in fig. 6, the apparatus includes:
the binary raster pattern generation module 210 is configured to generate a binary raster pattern according to probability information of each grid in the initial raster map and the forbidden region; wherein the probability information indicates a probability that the grid belongs to an obstacle;
the obstacle touch distance and passable direction determining module 220 is configured to determine an obstacle touch distance and a passable direction of each passable grid in the binary grid map;
a size information obtaining module 230, configured to obtain size information of the mobile robot;
the shortest path determining module 240 is configured to determine a shortest path between the mobile robot and the target node according to the size information, the obstacle touch distance and the passable direction.
Optionally, the binary raster graphics generating module 210 is further configured to:
for each grid, if the probability information of the grid is larger than a first set threshold value, adjusting the probability information of the grid to a first set value; otherwise, adjusting the probability information of the grid to a second set value;
adjusting probability information of grids in the forbidden region to a first set value; wherein the first set value indicates that the grid is not passable, and the second set value indicates that the grid is passable.
Optionally, the binary raster graphics generating module 210 is further configured to:
acquiring grids positioned on the boundary of the forbidden area, and determining the grids as boundary grids;
searching grids among boundary grids in the same row or column, and determining the grids as internal grids;
the probability information of the boundary grid and the internal grid is adjusted to a first set value.
Optionally, the obstacle touching distance and passable direction determining module 220 is further configured to:
and calculating the distance between the center point of the passable grid and the nearest center point of the non-passable grid, and determining the distance as the obstacle touch distance.
Optionally, the obstacle touching distance and passable direction determining module 220 is further configured to:
and determining the passable direction of the passable grid according to the passable direction of the mark.
Optionally, the shortest path determining module 240 is further configured to:
and determining the shortest path between the starting node and the target node of the mobile robot by adopting a set shortest path algorithm according to the size information, the obstacle touch distance and the passable direction.
Optionally, the size information is an circumscribed circle radius, and the shortest path determining module 240 is further configured to:
starting from a starting node, taking the starting node as a father node, and selecting a child node meeting the following conditions from passable grids adjacent to the father node:
the touching distance of the obstacle corresponding to the child node is larger than the radius of the circumscribed circle;
the included angle between the direction vector between the child node and the father node and the passable direction corresponding to the child node is smaller than a second set threshold value;
returning to execute the operation of selecting the child node meeting the following conditions from the passable grids adjacent to the parent node by taking the child node as a new parent node until reaching the target node, and obtaining at least one path;
the shortest path is selected from the at least one path.
The device can execute the method provided by all the embodiments of the invention, and has the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in this embodiment can be found in the methods provided in all the foregoing embodiments of the invention.
Example III
Fig. 7 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. FIG. 7 illustrates a block diagram of a computer device 312 suitable for use in implementing embodiments of the present invention. The computer device 312 shown in fig. 7 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. Device 312 is a computing device for typical path planning functions.
As shown in FIG. 7, the computer device 312 is in the form of a general purpose computing device. Components of computer device 312 may include, but are not limited to: one or more processors 316, a storage device 328, and a bus 318 that connects the different system components (including the storage device 328 and the processor 316).
The storage 328 may include computer system-readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 330 and/or cache memory 332. The computer device 312 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 334 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 7, commonly referred to as a "hard disk drive"). Although not shown in fig. 7, a disk drive for reading from and writing to a removable nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from and writing to a removable nonvolatile optical disk (e.g., a Compact Disc-Read Only Memory (CD-ROM), digital versatile Disc (Digital Video Disc-Read Only Memory, DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 318 through one or more data medium interfaces. Storage 328 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
The computer device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing device, camera, display 324, etc.), one or more devices that enable a user to interact with the computer device 312, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 312 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 322. Moreover, the computer device 312 may also communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network Wide Area Network, a WAN) and/or a public network such as the internet via the network adapter 320. As shown, network adapter 320 communicates with other modules of computer device 312 via bus 318. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 312, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk array (Redundant Arrays of Independent Disks, RAID) systems, tape drives, data backup storage systems, and the like.
The processor 316 executes various functional applications and data processing by running programs stored in the storage 328, for example, to implement the path planning method provided by the above-described embodiments of the present invention.
Example IV
The embodiment of the invention provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium, and when the program is executed by a processing device, the path planning method as in the embodiment of the invention is realized. The computer readable medium of the present invention described above may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: generating a binary grid map according to probability information of each grid in the initial grid map and the forbidden area; wherein the probability information represents a probability that the grid belongs to an obstacle; determining the obstacle touch distance and the passable direction of each passable grid in the binary grid diagram; acquiring size information of the mobile robot; and determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A method of path planning, comprising:
generating a binary grid map according to probability information of each grid in the initial grid map and the forbidden area; wherein the probability information represents a probability that the grid belongs to an obstacle;
determining the obstacle touch distance and the passable direction of each passable grid in the binary grid diagram;
acquiring size information of the mobile robot;
and determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction.
2. The method of claim 1, wherein generating a binary raster pattern from probability information for each raster in an initial raster map and the marked forbidden regions comprises:
for each grid, if the probability information of the grid is larger than a first set threshold value, the probability information of the grid is adjusted to a first set value; otherwise, the probability information of the grid is adjusted to a second set value;
adjusting probability information of grids in the forbidden region to a first set value; wherein the first set value indicates that the grid is not passable, and the second set value indicates that the grid is passable.
3. The method of claim 2, wherein adjusting probability information of the grid in the forbidden region to the first set point comprises:
acquiring grids positioned on the boundary of the forbidden area, and determining the grids as boundary grids;
searching grids among boundary grids in the same row or column, and determining the grids as internal grids;
and adjusting probability information of the boundary grid and the internal grid to a first set value.
4. The method of claim 1, wherein determining the obstacle touch distance for each trafficable grid in the binary grid map comprises:
and calculating the distance between the center point of the passable grid and the nearest center point of the non-passable grid, and determining the distance as the obstacle touch distance.
5. The method of claim 1, wherein determining the passable direction of each passable grid in the binary grid map comprises:
and determining the passable direction of the passable grid according to the passable direction of the mark.
6. The method of claim 1, wherein determining a shortest path of the mobile robot from a start node to a target node based on the size information, the obstacle touch distance, and the passable direction comprises:
and determining the shortest path from the starting node to the target node of the mobile robot by adopting a set shortest path algorithm according to the size information, the obstacle touch distance and the passable direction.
7. The method of claim 6, wherein the size information is an circumscribed circle radius, and determining a shortest path of the mobile robot from a start node to a target node using a set shortest path algorithm based on the size information, the obstacle touch distance, and the passable direction comprises:
starting from a starting node, taking the starting node as a father node, and selecting a child node meeting the following conditions from passable grids adjacent to the father node:
the obstacle touching distance corresponding to the child node is larger than the radius of the circumscribed circle;
the included angle between the direction vector between the child node and the father node and the passable direction corresponding to the child node is smaller than a second set threshold value;
returning to the operation of selecting the child node meeting the following conditions from the passable grids adjacent to the parent node by taking the child node as a new parent node until reaching the target node, and obtaining at least one path;
selecting a shortest path from the at least one path.
8. A path planning apparatus, comprising:
the binary raster pattern generation module is used for generating a binary raster pattern according to probability information of each grid in the initial raster map and the forbidden area; wherein the probability information represents a probability that the grid belongs to an obstacle;
the obstacle touch distance and passable direction determining module is used for determining the obstacle touch distance and passable direction of each passable grid in the binary grid graph;
the size information acquisition module is used for acquiring the size information of the mobile robot;
and the shortest path determining module is used for determining the shortest path between the starting node and the target node of the mobile robot according to the size information, the obstacle touch distance and the passable direction.
9. A computer device, comprising: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the path planning method according to any of claims 1-7 when said program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processing device, implements a path planning method according to any one of claims 1-7.
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