CN114993308A - Navigation path planning method and device and storage medium - Google Patents
Navigation path planning method and device and storage medium Download PDFInfo
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- CN114993308A CN114993308A CN202111673891.3A CN202111673891A CN114993308A CN 114993308 A CN114993308 A CN 114993308A CN 202111673891 A CN202111673891 A CN 202111673891A CN 114993308 A CN114993308 A CN 114993308A
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Abstract
The application discloses a navigation path planning method, a navigation path planning device and a storage medium, relates to the technical field of artificial intelligence, traverses a travelable grid by combining a Chinese character bow-shaped navigation algorithm and an A star path finding algorithm, and plans a navigation path based on a traversed route. The navigation path obtained based on the method has less repeated paths and high coverage rate, so that the working efficiency of the mobile equipment can be improved, and the condition that partial areas are not covered can be avoided. The method comprises the following steps: acquiring a grid map of a task area; the grid map is composed of candidate grids, and the candidate grids comprise a drivable grid and a non-drivable grid; and traversing the travelable grids in the grid map by taking the initial grids in the candidate grids as starting points and combining the Chinese character bow-shaped navigation algorithm and the A-star road finding algorithm, and planning the navigation path of the mobile equipment based on the sequence of traversing the travelable grids.
Description
Technical Field
The embodiment of the application relates to the technical field of artificial intelligence, in particular to a navigation path planning method, a navigation path planning device and a storage medium.
Background
With the rapid development of the artificial intelligence technology, at present, the mowing robot can replace manual operation to carry out tasks such as lawn trimming and the like, and the labor cost is greatly reduced. Currently, mowing robots can generally perform lawn mowing tasks based on random paths.
However, when performing a task of trimming a lawn based on a random path, the work efficiency of the lawn mowing robot is low, and there is often a case where a partial area is not covered.
Disclosure of Invention
The application provides a method and a device for planning a navigation path and a storage medium, which can improve the working efficiency of mobile equipment and avoid the situation that partial areas are not covered by the navigation path for the mobile equipment.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a method for planning a navigation path, including: acquiring a grid map of a task area; the grid map is composed of candidate grids, and the candidate grids comprise a drivable grid and a non-drivable grid; and traversing the travelable grids in the grid map by taking the initial grids in the candidate grids as starting points and combining the Chinese character bow-shaped navigation algorithm and the A-star road finding algorithm, and planning the navigation path of the mobile equipment based on the sequence of traversing the travelable grids.
According to the technical scheme, after the grid map formed by the candidate grids is obtained, the drivable grids in the grid map can be traversed based on the zigzag navigation algorithm, and the navigation path is planned based on the traversed route. The travelable grid is traversed based on the zigzag navigation algorithm, and the number of times of repeated traversal of the candidate grid can be reduced, so that the repeated paths in the navigation path planned by the method are fewer, and the working efficiency of the mobile device can be improved. In addition, since an obstacle needs to be avoided in the process of planning the navigation path, in the process of traversing the drivable grid, in order to bypass the non-drivable grid, after the traversal of the grid map is completed once, a part of the drivable grid may not be traversed. Therefore, in the application, the traversable grids which are not traversed can be searched based on the A-star road finding algorithm, and all traversable grids in the grid map can be ensured to be traversed by combining the Chinese character bow type navigation algorithm and the A-star road finding algorithm. Therefore, the navigation path obtained based on the technical scheme provided by the application has fewer repeated paths and high coverage rate, so that the working efficiency of the mobile equipment can be improved, and the condition that partial areas are not covered can be avoided.
Optionally, in a possible design manner, the "traversing the travelable grid in the grid map by using the starting grid of the candidate grids as a starting point and combining the zigzag navigation algorithm and the a-star road finding algorithm" may include:
step A: taking the initial grid as a starting point, searching a drivable grid based on a Chinese character bow-shaped navigation algorithm until a stopping grid is searched;
and B: judging whether the driving grids which are not searched exist;
and C: determining a target grid from the non-searched travelable grids in the case that the non-searched travelable grids are determined to exist; searching a travelable grid based on an A star routing algorithm by taking the stop grid as a starting point until a target grid is searched, and taking the target grid as a new starting grid;
and C, repeatedly executing the step A to the step C until no driving grid which is not searched exists, and determining that the driving grid in the grid map is completely traversed.
Optionally, in another possible design manner, the "searching for a drivable grid based on a zigzag navigation algorithm using the starting grid as a starting point until the ending grid is found" may include:
traversing the candidate grids of the row to which the initial grid belongs along the task execution direction by taking the initial grid as a starting point until the boundary grid is found; the boundary grid is the previous grid of the searched first non-driving grid;
step D: judging whether a drivable grid exists in the next grid of the boundary grid along the path traversal direction;
step E: traversing the next row of grids based on the task execution direction by taking the current boundary grid as a starting point under the condition that a drivable grid exists in the next row of grids until a new boundary grid is found; traversing the next row of grids along the reverse direction of the task execution direction by taking the new boundary grid as a starting point until the new boundary grid is found again; taking the opposite direction of the task execution direction as a new task execution direction;
and D, repeating the steps D to E until no drivable grid exists in the next row of grids, and determining the boundary grid found for the last time as the termination grid.
Optionally, in another possible design manner, before "traversing the candidate grids in the row to which the starting grid belongs along the task execution direction with the starting grid as a starting point until the boundary grid is found", the application may further include:
acquiring the length of each boundary of the grid map;
the task execution direction is determined based on the length of each bar boundary and the position of the starting grid.
Alternatively, in another possible design, the "determining a target grid from the unsearched travelable grids" includes:
and determining a target grid from the non-searched travelable grids based on the distance between the non-searched travelable grids and the stop grid.
Optionally, in another possible design, the acquiring a grid map of a task area includes:
acquiring real-time position coordinates of the mobile equipment in the process that the mobile equipment circles along the outer boundary of the task area and the obstacle boundary in the task area respectively;
determining position information of the outer boundary and position information of the obstacle boundary based on the real-time position coordinates;
and establishing a grid map based on the position information of the outer boundary and the position information of the obstacle boundary, and determining a drivable grid and a non-drivable grid in the grid map.
Optionally, in another possible design, the "establishing a grid map based on the position information of the outer boundary and the position information of the obstacle boundary, and determining a drivable grid and a non-drivable grid in the grid map" includes:
determining the number of grids extending along the outside boundary and the outside obstacle boundary based on the geometric parameters of the mobile device;
and establishing a grid map based on the position information of the outer boundary, the position information of the obstacle boundary and the expanded grid number, and determining a drivable grid and a non-drivable grid in the grid map.
Optionally, in another possible design, after the "determining to traverse the travelable grid in the grid map" is completed, the method may further include:
traversing the non-driving grids in the grid map by taking the found last termination grid as a starting point based on an A-star road finding algorithm, the position of the outer boundary of the task area and the position of the obstacle boundary in the task area;
planning a navigation path for a mobile device based on a sequence of traversing a traversable grid, comprising: and planning a navigation path based on the sequence of traversing the drivable grids, the sequence of traversing the non-drivable grids and the position of the starting grid.
Optionally, in another possible design manner, the non-driving grids include obstacle grids and non-obstacle grids, and the "traversing the non-driving grids in the grid map based on the a-star road finding algorithm, the position of the outer boundary of the task area, and the position of the obstacle boundary in the task area, with the found last termination grid as a starting point" includes:
traversing barrier grids in the grid map by taking the found last termination grid as a starting point based on the A star path finding algorithm and the position of the barrier boundary;
and traversing the non-obstacle grids in the grid map by taking the traversed last obstacle grid as a starting point based on the A star road finding algorithm and the position of the outer boundary.
Optionally, in another possible design manner, the path finding rule of the a-star path finding algorithm is as follows: the path is shortest and the number of direction adjustments is minimal.
In a second aspect, the present application provides an acquisition module and a planning module;
specifically, the acquisition module is used for acquiring a grid map of the task area; the grid map is composed of candidate grids, and the candidate grids comprise a driving grid and a non-driving grid;
and the planning module is used for traversing the travelable grids in the grid map acquired by the acquisition module by taking the initial grids in the candidate grids as starting points and combining the Chinese character bow-shaped navigation algorithm and the A-star road finding algorithm, and planning the navigation path of the mobile equipment based on the sequence of traversing the travelable grids.
Optionally, in a possible design manner, the planning module includes a search sub-module, a judgment sub-module, and a determination sub-module;
a search submodule, configured to perform step a: taking the initial grid as a starting point, searching a drivable grid based on a Chinese character bow-shaped navigation algorithm until a stopping grid is searched;
a judgment submodule for executing the step B: judging whether the driving grids which are not searched exist;
a determination submodule, configured to perform step C: determining a target grid from the non-searched travelable grids in the case that the non-searched travelable grids are determined to exist; searching a travelable grid based on an A-star path finding algorithm by taking the stop grid as a starting point until a target grid is searched, and taking the target grid as a new initial grid;
and the determining sub-module is further used for determining that the drivable grids in the grid map are traversed when the steps A to C are repeatedly executed until the drivable grids which are not searched are determined to be not existed.
Optionally, in another possible design, the search submodule is specifically configured to:
traversing the candidate grids of the row to which the initial grid belongs along the task execution direction by taking the initial grid as a starting point until the boundary grid is found; the boundary grid is the previous grid of the searched first non-driving grid;
step D: judging whether a drivable grid exists in the next grid of the boundary grid along the path traversal direction;
step E: traversing the next row of grids based on the task execution direction by taking the current boundary grid as a starting point under the condition that a drivable grid exists in the next row of grids until a new boundary grid is found; traversing the next grid in the reverse direction of the task execution direction by taking the new boundary grid as a starting point until the new boundary grid is searched again; taking the opposite direction of the task execution direction as a new task execution direction;
and D, repeating the steps D to E until no drivable grid exists in the next row of grids, and determining the boundary grid found for the last time as the termination grid.
Optionally, in another possible design, the planning apparatus for a navigation path provided by the present application may further include a determining module;
the acquisition module is also used for acquiring the length of each boundary of the grid map before the search module finds the boundary grid;
and the determining module is used for determining the task execution direction based on the length of each boundary and the position of the starting grid.
Optionally, in another possible design, the determining submodule is specifically configured to:
and determining a target grid from the non-searched travelable grids based on the distance between the non-searched travelable grids and the stop grid.
Optionally, in another possible design manner, the obtaining module is specifically configured to:
acquiring real-time position coordinates of the mobile equipment in the process that the mobile equipment circles along the outer boundary of the task area and the obstacle boundary in the task area respectively;
determining position information of the outer boundary and position information of the obstacle boundary based on the real-time position coordinates;
and establishing a grid map based on the position information of the outer boundary and the position information of the obstacle boundary, and determining a drivable grid and a non-drivable grid in the grid map.
Optionally, in another possible design manner, the obtaining module is further specifically configured to:
determining the number of grids extending along the outside boundary and the outside obstacle boundary based on the geometric parameters of the mobile device;
and establishing a grid map based on the position information of the outer boundary, the position information of the obstacle boundary and the expanded grid number, and determining a drivable grid and a non-drivable grid in the grid map.
Optionally, in another possible design manner, the planning module is specifically configured to:
after the drivable grids in the grid map are determined to be traversed, taking the found last termination grid as a starting point, and traversing the non-drivable grids in the grid map based on the A-star road finding algorithm, the position of the outer boundary of the task area and the position of the obstacle boundary in the task area;
and planning a navigation path based on the sequence of traversing the drivable grids, the sequence of traversing the non-drivable grids and the position of the starting grid.
Optionally, in another possible design, the non-driving grid includes an obstacle grid and a non-obstacle grid, and the planning module is further specifically configured to:
traversing barrier grids in the grid map by taking the found last termination grid as a starting point based on the A star path finding algorithm and the position of the barrier boundary;
and traversing the non-obstacle grids in the grid map by taking the traversed last obstacle grid as a starting point based on the A star road finding algorithm and the position of the outer boundary.
Optionally, in another possible design manner, the path finding rule of the a star path finding algorithm is as follows: the path is shortest and the number of directional adjustments is minimal.
In a third aspect, the present application provides a navigation path planning apparatus, including a memory, a processor, a bus, and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the apparatus for planning a navigation path is operating, the processor executes the computer-executable instructions stored in the memory to cause the apparatus for planning a navigation path to perform the method for planning a navigation path as provided in the first aspect above.
In a fourth aspect, the present application provides a computer-readable storage medium having instructions stored therein, which when executed by a computer, cause the computer to perform the method for planning a navigation path as provided in the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the method of planning a navigation path as provided in the first aspect.
It should be noted that the computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged with a processor of the navigation path planning apparatus, or may be packaged separately from the processor of the navigation path planning apparatus, which is not limited in this application.
For the descriptions of the second, third, fourth and fifth aspects in this application, reference may be made to the detailed description of the first aspect; in addition, for the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to beneficial effect analysis of the first aspect, and details are not repeated here.
In the present application, the names of the planning devices of the navigation path do not limit the devices or the function modules themselves, and in practical implementation, the devices or the function modules may appear by other names. Insofar as the functions of the respective devices or functional modules are similar to those of the present application, they fall within the scope of the claims of the present application and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
Fig. 1 is a schematic flowchart of a navigation path planning method according to an embodiment of the present disclosure;
fig. 2 is a scene schematic diagram of a task area according to an embodiment of the present application;
fig. 3 is a schematic diagram of a grid map according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another navigation path planning method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a method for planning a navigation path according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a navigation path planning apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of another navigation path planning apparatus according to an embodiment of the present application.
Detailed Description
The following describes in detail a navigation path planning method, an apparatus, and a storage medium provided in an embodiment of the present application with reference to the accompanying drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
With the rapid development of the artificial intelligence technology, at present, the mowing robot can replace manual operation to carry out tasks such as lawn trimming and the like, and the labor cost is greatly reduced. Currently, mowing robots can generally perform lawn mowing tasks based on random paths.
However, when performing a task of trimming a lawn based on a random path, the work efficiency of the robot lawnmower is low, and it is often the case that a partial area is not covered.
In view of the problems in the prior art, the embodiments of the present application provide a navigation path planning method, which traverses a travelable grid by combining a zigzag navigation algorithm and an a-star road finding algorithm, and plans a navigation path based on a traversed route. The navigation path obtained based on the method has fewer repeated paths and high coverage rate, so that the working efficiency of the mobile equipment can be improved, and the condition that partial areas are not covered can be avoided.
The navigation path planning method provided by the embodiment of the application can be applied to a navigation path planning device. In a possible implementation manner, the planning device for the navigation path may be the mobile device itself or a chip system in the mobile device, where the chip system is used to support the planning device for the navigation path to implement the planning function for the navigation path. The system-on-chip may include a chip or may include other discrete devices or circuit structures.
Wherein the mobile device may be a lawn mowing robot. Of course, in practical applications, the mobile device may also be other movable artificial intelligence devices.
In another possible implementation manner, the planning apparatus for the navigation path may be a physical machine (e.g., a background server of the mobile device), or may be a Virtual Machine (VM) deployed on the physical machine.
The navigation path planning method provided by the embodiment of the application can be applied to planning the navigation path of the lawn mowing robot for executing the task of trimming the lawn, and certainly, in practical application, the navigation path planning method can also be applied to planning the navigation paths of other mobile devices for executing the task, and the navigation path planning method is not limited by the embodiment of the application. In the following description of the embodiments of the present application, a description will be given taking a navigation path for a mowing robot to perform a task of trimming a lawn as an example. The following describes in detail a navigation path planning method provided in an embodiment of the present application with reference to the accompanying drawings.
Referring to fig. 1, a navigation path planning method provided in the embodiment of the present application includes S101-S102:
s101, acquiring a grid map of the task area.
The grid map is composed of candidate grids, and the candidate grids comprise a driving grid and a non-driving grid. The drivable grid corresponds to a location in the mission area where the mobile device is drivable, and the non-drivable grid corresponds to a location in the mission area where the mobile device is not drivable, e.g., the non-drivable grid may correspond to a location of an obstacle in the mission area.
Optionally, in the embodiment of the present application, the grid map of the task area may be obtained in the following manner: acquiring real-time position coordinates of the mobile equipment in the process that the mobile equipment circles along the outer boundary of the task area and the obstacle boundary in the task area respectively; determining position information of the outer boundary and position information of the obstacle boundary based on the real-time position coordinates; and establishing a grid map based on the position information of the outer boundary and the position information of the obstacle boundary, and determining a drivable grid and a non-drivable grid in the grid map.
In order to avoid collision between the mobile device and the obstacle in the process of executing the task, the embodiment of the application can acquire the real-time position coordinate of the mobile device in the process of circling along the obstacle boundary in the task area when the grid map is established. Therefore, the navigation path avoiding the obstacle can be determined according to the position information of the obstacle boundary. In addition, in order to avoid that the area of the mobile device executing the task exceeds the task area too much, the embodiment of the application can also acquire the real-time position coordinate of the mobile device in the process of circulating along the outer boundary of the task area when the grid map is established. Therefore, the navigation path which is less beyond the task area can be planned based on the position information of the outer boundary, and the working efficiency of the mobile equipment can be further improved.
Optionally, in a possible implementation manner, the number of grids extending along the outside boundary and the obstacle boundary may also be determined based on the geometric parameter of the mobile device in the embodiment of the present application; a drivable grid and a non-drivable grid in the grid map are then determined based on the position of the outer boundary, the position of the obstacle boundary, and the number of expanded grids.
Since different lawn mowing robots may differ in size, the safe distance (i.e., the distance at which no collision occurs) between different mowing robots and an obstacle may differ, and the size of the coverage area of different mowing robots when performing a task of mowing a lawn may also differ. Therefore, when determining the drivable grids and the non-drivable grids in the grid map, the number of grids extending outside the outer boundary and the obstacle boundary may be determined based on the geometric parameters of the lawn mowing robot. The obstacle boundary is extended outwards based on the expanded grid number, so that the mowing robot with a larger size can be prevented from colliding with the obstacle. And the outer boundary is extended outwards based on the expanded grid number, so that the lawn on the outer boundary can be covered, and the coverage rate of the mowing robot in the process of executing the task of trimming the lawn is further improved.
In one possible implementation, the number of grids that extend beyond the outer boundary and beyond the obstacle boundary may be determined based on a proportional relationship of the size of the grid map to the actual size of the task area, as well as geometric parameters of the mobile device. For example, the expanded number of grids may be one grid.
Illustratively, referring to FIG. 2, a scene diagram of a task area is provided. As shown in fig. 2, the inside of the outer boundary is the task area, and the inside of the obstacle boundary is the area where the obstacle in the task area is located. In practical application, the mowing robot can be controlled to respectively circle around the outer boundary and the obstacle boundary shown in fig. 2, and then a grid map is established based on the obtained real-time position coordinates in the circle process. Then, the outer boundary and the obstacle boundary may be expanded outward by one grid as shown in fig. 2, a grid inside the expanded outer boundary and outside the expanded obstacle boundary may be determined as a drivable grid, and the remaining grids may be determined as non-drivable grids.
And S102, traversing the drivable grids in the grid map by taking the initial grid in the candidate grids as a starting point and combining the Chinese character bow-shaped navigation algorithm and the A star road finding algorithm, and planning the navigation path of the mobile equipment based on the sequence of traversing the drivable grids.
The starting grid of the first traversal may be a grid determined according to the starting position of the task area, and the starting position may be a position determined by a human in advance according to the outer boundary of the task area. For example, the grid corresponding to the start position shown in fig. 2 may be determined as the start grid.
Optionally, in the embodiment of the present application, the travelable grid in the grid map may be traversed in the following manner: step A: using the initial grid as a starting point, searching a travelable grid based on a Chinese character 'gong' navigation algorithm until a stop grid is searched; and B: judging whether the driving grids which are not searched exist; and C: determining a target grid from the non-searched travelable grids under the condition that the non-searched travelable grids are determined to exist; searching a travelable grid based on an A star routing algorithm by taking the stop grid as a starting point until a target grid is searched, and taking the target grid as a new starting grid; and C, repeatedly executing the step A to the step C until no driving grid which is not searched exists, and determining that the driving grid in the grid map is completely traversed.
In order to reduce the number of times that the candidate grid is repeatedly traversed, and thus determine a navigation path with a shorter path, the navigable grid may be traversed based on a zigzag navigation algorithm in the embodiment of the present application.
In addition, in the process of traversing the drivable grid based on the zigzag navigation algorithm, in order to bypass the non-drivable grid, after completing one traversal, there may be a case where a part of the drivable grid is not traversed. Therefore, in the embodiment of the present application, after completing one traversal, if there is a drivable grid that is not traversed, the drivable grid that is not traversed may be found based on the a-star routing algorithm with the end grid of the previous traversal as the starting point, and the drivable grid is traversed based on the zigzag navigation algorithm again with the grid as the starting point. By switching between the A star road finding algorithm and the zigzag navigation algorithm, the embodiment of the application can ensure that the travelable grids in the grid map are traversed, so that the situation that partial areas are not covered when the task is executed can be avoided.
In one possible implementation manner, starting from the end grid, the travelable grid is searched based on the a-star routing algorithm, and the routing rule until the target grid is found may be: and searching an optimal path from the termination grid to the target grid in the grid map based on a heuristic function, wherein in order to obtain a navigation path with a shorter path, the heuristic function during searching can be the sum of the distance from the search point to the termination grid and the distance from the search point to the target grid, and each step of the search path is determined according to the minimum value of the sum of the distances.
Optionally, in another possible implementation manner, the path finding rule of the a-star path finding algorithm in the embodiment of the present application is: the path is shortest and the number of direction adjustments is minimal.
Under the condition that the path searching rule of the A star path searching algorithm is the shortest path, the determined navigation path needs to be adjusted in direction for many times, and the path is stepped, so that the stable operation of the mowing robot is not facilitated. Therefore, the principle of the minimum number of directional adjustments can be added to the routing rule with the shortest path in the embodiment of the present application. That is, the adding cost value for reducing the number of times of direction adjustment is added in the heuristic function, and the more the number of times of direction adjustment is, the larger the cost value is. Therefore, the navigation path with less direction adjustment times can be determined, and the stable operation of the mowing robot is facilitated.
Illustratively, referring to FIG. 3, a schematic diagram of a grid map is provided. As shown in fig. 3, the grid with the number 1 may be a starting grid, and when the grid is used as a starting point and a drivable grid is found based on the bow navigation algorithm, and a terminating grid with the number 2 is found, the first traversal is completed. As can be seen in fig. 3, to avoid the non-travel grids, there are travel grids that are not looked up after the first traversal is complete (i.e., the grids within the dashed box in fig. 3). The target grid, which may be, for example, the grid numbered 3, may be determined from the travelable grids that have not been sought. Then, a route for searching for a traversable grid can be determined based on the a-star road finding algorithm with the grid numbered 2 as a starting point and the grid numbered 3 as an end point. As shown in fig. 3, in the case where the routing rule is that the path is shortest and the number of times of direction adjustment is the minimum, the path for searching the travelable grid based on the a-star routing algorithm may be determined by searching along a straight line to the grid No. 5 starting from the grid No. 2, and then searching along a straight line to the grid No. 3 starting from the grid No. 5. And then, taking the grid with the number of 3 as a new initial grid, searching the travelable grid based on the zigzag navigation algorithm, and completing the second traversal when finding the ending grid with the number of 4. As can be seen from fig. 3, after the second traversal is completed, no travelable grid that is not searched exists, and it can be determined that the travelable grid in the grid map has been traversed.
Optionally, in order to reduce the length of the determined navigation path, thereby further improving the working efficiency of the mobile device, the embodiment of the present application may determine the target grid from the unsearched travelable grids by: determining a target grid from the non-sought travelable grids based on the distance of the non-sought travelable grids from the termination grid.
For example, as shown in fig. 3, if the grid numbered 2 is the current termination grid and the grids in the dashed box are travelable grids that are not currently searched, the one of the grids in the dashed box with the smallest distance from the grid numbered 2 (i.e., the grid numbered 3 in fig. 3) may be determined as the target grid.
Optionally, in the embodiment of the present application, the termination grid may be found in the following manner: traversing the candidate grids of the row to which the initial grid belongs along the task execution direction by taking the initial grid as a starting point until the boundary grid is found; the boundary grid is the previous grid of the searched first non-driving grid; step D: judging whether a drivable grid exists in the next grid of the boundary grid along the path traversal direction; step E: traversing the next row of grids based on the task execution direction by taking the current boundary grid as a starting point under the condition that a drivable grid exists in the next row of grids until a new boundary grid is found; traversing the next row of grids along the reverse direction of the task execution direction by taking the new boundary grid as a starting point until the new boundary grid is found again; taking the opposite direction of the task execution direction as a new task execution direction; and D, repeating the steps D to E until no drivable grid exists in the next row of grids, and determining the boundary grid found for the last time as the termination grid.
Optionally, in the embodiment of the present application, the initial task execution direction may be determined as follows: acquiring the length of each boundary of the grid map; the task execution direction is determined based on the length of each bar boundary and the position of the starting grid.
In order to further reduce the number of times of direction adjustment in the navigation path, and thus further improve the working efficiency of the mowing robot, in the embodiment of the application, the task execution direction may be determined along the straight line where the longer boundary is located in the grid map and the position of the starting grid.
For example, referring to fig. 3, if the starting grid is the grid numbered 1, the candidate grids of the row to which the grid numbered 1 belongs may be traversed in the initial task execution direction shown in fig. 3 until the first non-driving grid (i.e., the grid numbered 6 in fig. 3) is found, and the previous grid to the found grid numbered 6 (i.e., the grid numbered 7 in fig. 3) is determined as the boundary grid. Then, a new boundary grid (i.e. the grid numbered 8 in fig. 3) can be found based on the initial task execution direction shown in fig. 3, starting from the grid numbered 7 (it should be noted that, when the search is performed based on the initial task execution direction, the first grid searched is the first grid searched by rotating counterclockwise with the initial task execution direction as the starting point). Then, the candidate grid in the row to which the grid numbered 8 belongs may be traversed from the grid numbered 8 as the starting point in the direction opposite to the initial task execution direction shown in fig. 3 until a new boundary grid (i.e., the grid numbered 9 in fig. 3) is found again, and the direction opposite to the initial task execution direction shown in fig. 3 is taken as the new task execution direction. And repeating the steps until the grid with the number of 2 is found. Specifically, the grid numbered 1 can be searched to the grid numbered 2 according to the following numbering sequence: 1. 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 28, 30, 31, 32, 33, 2.
Optionally, after the drivable grid in the grid map is determined to be traversed, the non-drivable grid in the grid map can be traversed based on the a-star road finding algorithm, the position of the outer boundary of the task area and the position of the obstacle boundary in the task area by taking the found last termination grid as a starting point; a navigation path is then planned based on the order of traversing the drivable grid, the order of traversing the non-drivable grid, and the position of the starting grid.
When the navigation path is planned, the path for executing the lawn trimming task along the drivable grid can be planned, the non-drivable grids can be traversed along the outer boundary and the obstacle boundary respectively after the drivable grids in the grid map are traversed, and then the path for executing the lawn trimming task respectively by going around the inner part of the outer boundary and by going around the outer part of the obstacle boundary is planned based on the sequence of traversing the non-drivable grids. Thus, the coverage rate of the mowing robot when the mowing robot carries out the task of mowing the lawn can be further improved,
optionally, the non-driving grids may further include an obstacle grid and a non-obstacle grid, and when traversing the non-driving grids, the obstacle grids in the grid map may be traversed based on the a-star road finding algorithm and the position of the obstacle boundary, with the last found termination grid as a starting point; and then, traversing the non-obstacle grids in the grid map by taking the traversed last obstacle grid as a starting point based on the A star road finding algorithm and the position of the outer boundary.
The obstacle grid may be a non-driving grid within the obstacle boundary, and the non-obstacle grid may correspond to the non-driving grid that makes a turn within the outer boundary.
The initial position is generally arranged at a position close to the charging pile of the mowing robot, so that the mowing robot can return to the initial position again after executing a lawn mowing task, and the mowing robot can be charged in time conveniently. To further shorten the length of the planned navigation path, the obstacle grid in the grid map may be traversed first, followed by the non-obstacle grids in the grid map. In this way, the mobile device can quickly reach the starting position after performing the lawn mowing task.
For example, if the last end grid found is the grid numbered 4 in fig. 3, the grid numbered 4 may be used as a starting point, the grid numbered 34 closest to the grid numbered 4 in the obstacle boundary is used as an end point, and the number sequence of the traversal grids is determined based on the a-star routing algorithm, where the sequence may be: 4. 35, 36, 34. Then, the obstacle grid of one circle within the obstacle boundary may be traversed clockwise or counterclockwise starting with the grid numbered 34. The edging path of the mowing robot to the obstacle boundary may be determined based on an order of traversing the obstacle grid of a week within the obstacle boundary. The number order of the traversed grids may then be determined based on the a-star routing algorithm starting with the grid numbered 34 and ending with a non-obstacle grid (such as the grid numbered 37) within the outer boundary. The non-obstacle grid shown in fig. 3 may then be traversed clockwise or counterclockwise starting with the grid numbered 37. A closeout path of the lawn mowing robot to the outer boundary may be determined based on the order of traversing the non-obstacle grid.
According to the technical scheme, after the grid map formed by the candidate grids is obtained, the drivable grids in the grid map can be traversed based on the zigzag navigation algorithm, and the navigation path is planned based on the traversed route. The travelable grid is traversed based on the zigzag navigation algorithm, and the number of times of repeated traversal of the candidate grid can be reduced, so that the navigation path planned by the embodiment of the application has fewer repeated paths, and the working efficiency of the mobile equipment can be improved. In addition, since an obstacle needs to be avoided in the process of planning the navigation path, in the process of traversing the drivable grid, in order to bypass the non-drivable grid, after the traversal of the grid map is completed once, a part of the drivable grid may not be traversed. Therefore, in the embodiment of the application, the traversable grids which are not traversed can be searched based on the a-star road finding algorithm, and all traversable grids in the grid map can be ensured to be traversed by combining the Chinese character bow type navigation algorithm and the a-star road finding algorithm. It can be seen that the navigation path obtained based on the technical scheme provided by the embodiment of the application has fewer repeated paths and high coverage rate. Therefore, the embodiment of the application can improve the working efficiency of the mobile equipment and can avoid the situation that partial area is not covered.
In summary, as shown in fig. 4, an embodiment of the present application further provides a navigation path planning method, which includes S401 to S405:
s401, acquiring a grid map of the task area.
S402, taking the initial grid as a starting point, searching a travelable grid based on a zigzag navigation algorithm until the ending grid is searched.
And S403, judging whether the travelable grids which are not searched exist.
In a case where it is determined that there is a travelable grid that has not been searched for, step S404 is executed; in the case where it is determined that there is no travelable grid that has not been searched for, step S405 is executed.
S404, determining a target grid from the unsearched travelable grids; and searching the travelable grid based on the A-star routing algorithm by taking the stop grid as a starting point until the target grid is searched, and taking the target grid as a new starting grid.
After step S404, execution returns to step S402.
S405, determining a travelable grid in the traversed grid map, and planning a navigation path of the mobile device based on the sequence of traversing the travelable grid.
Optionally, as shown in fig. 5, an embodiment of the present application further provides a navigation path planning method, including the following steps:
s501, acquiring a grid map of the task area.
And S502, traversing the candidate grids of the row to which the initial grid belongs along the task execution direction by taking the initial grid as a starting point until the boundary grid is found.
S503, judging whether a travelable grid exists in the next row of grids of the boundary grid along the path traversing direction.
In a case where it is determined that there is a travelable grid in the next row of grids, executing step S504; in a case where it is determined that the travelable grid does not exist in the next line of grids, step S505 is executed.
S504, traversing the next row of grids based on the task execution direction by taking the current boundary grid as a starting point until a new boundary grid is found; traversing the next row of grids along the reverse direction of the task execution direction by taking the new boundary grid as a starting point until the new boundary grid is found again; and taking the reverse direction of the task execution direction as a new task execution direction.
After step S504, the flow returns to step S503.
And S505, determining the boundary grid found for the last time as a termination grid.
And S506, judging whether the driving grid which is not searched exists or not.
In a case where it is determined that there is a travelable grid that has not been searched for, executing step S507; in the case where it is determined that there is no travelable grid that has not been searched for, step S508 is performed.
S507, determining a target grid from the unsearched travelable grids; and searching the travelable grid based on the A star routing algorithm by taking the stop grid as a starting point until the target grid is searched, and taking the target grid as a new starting grid.
After step S507, execution returns to step S503.
And S508, determining a travelable grid in the grid map after the travelable grid is traversed, and planning a navigation path of the mobile equipment based on the sequence of traversing the travelable grid.
As shown in fig. 6, an embodiment of the present application further provides a navigation path planning apparatus, which may include: an acquisition module 11 and a planning module 12.
The obtaining module 11 executes S101 in the above method embodiment, and the planning module 12 executes S102 in the above method embodiment.
Specifically, the obtaining module 11 is configured to obtain a grid map of the task area; the grid map is composed of candidate grids, and the candidate grids comprise a driving grid and a non-driving grid;
and the planning module 12 is configured to traverse the travelable grid in the grid map acquired by the acquisition module 11 by using a starting grid of the candidate grids as a starting point and combining the zigzag navigation algorithm and the a-star road finding algorithm, and plan a navigation path of the mobile device based on a sequence of traversing the travelable grid.
Optionally, in a possible design manner, the planning module 12 includes a searching sub-module, a judging sub-module and a determining sub-module;
a search submodule, configured to perform step a: taking the initial grid as a starting point, searching a drivable grid based on a Chinese character bow-shaped navigation algorithm until a stopping grid is searched;
a judgment submodule for executing the step B: judging whether the driving grids which are not searched exist;
a determination submodule, configured to perform step C: determining a target grid from the non-searched travelable grids under the condition that the non-searched travelable grids are determined to exist; searching a travelable grid based on an A star routing algorithm by taking the stop grid as a starting point until a target grid is searched, and taking the target grid as a new starting grid;
and the determining sub-module is further used for determining that the drivable grids in the grid map are traversed when the steps A to C are repeatedly executed until the drivable grids which are not searched are determined to be not existed.
Optionally, in another possible design manner, the search submodule is specifically configured to:
traversing the candidate grids of the row to which the initial grid belongs along the task execution direction by taking the initial grid as a starting point until the boundary grid is found; the boundary grid is the previous grid of the searched first non-driving grid;
step D: judging whether a drivable grid exists in the next grid of the boundary grid along the path traversal direction;
step E: traversing the next line of grids based on the task execution direction by taking the current boundary grid as a starting point under the condition of determining that the next line of grids has the travelable grid until a new boundary grid is found; traversing the next row of grids along the reverse direction of the task execution direction by taking the new boundary grid as a starting point until the new boundary grid is found again; taking the opposite direction of the task execution direction as a new task execution direction;
and D, repeating the step D to the step E until determining that no travelable grid exists in the next line of grids, and determining the boundary grid searched for the last time as the termination grid.
Optionally, in another possible design, the planning apparatus for a navigation path provided by the present application may further include a determining module;
the obtaining module 11 is further configured to obtain lengths of boundaries of the grid map before the searching module finds the boundary grid;
and the determining module is used for determining the task execution direction based on the length of each boundary and the position of the starting grid.
Optionally, in another possible design, the determining submodule is specifically configured to:
determining a target grid from the non-sought travelable grids based on the distance of the non-sought travelable grids from the termination grid.
Optionally, in another possible design, the obtaining module 11 is specifically configured to:
acquiring real-time position coordinates of the mobile equipment in the process that the mobile equipment circles along the outer boundary of the task area and the obstacle boundary in the task area respectively;
determining position information of the outer boundary and position information of the obstacle boundary based on the real-time position coordinates;
and establishing a grid map based on the position information of the outer boundary and the position information of the obstacle boundary, and determining a drivable grid and a non-drivable grid in the grid map.
Optionally, in another possible design, the obtaining module 11 is further specifically configured to:
determining the number of grids extending along the outside boundary and the outside obstacle boundary based on the geometric parameters of the mobile device;
and establishing a grid map based on the position information of the outer boundary, the position information of the obstacle boundary and the expanded grid number, and determining a drivable grid and a non-drivable grid in the grid map.
Optionally, in another possible design, the planning module 12 is specifically configured to:
after the drivable grids in the grid map are determined to be traversed, taking the found last termination grid as a starting point, and traversing the non-drivable grids in the grid map based on the A-star road finding algorithm, the position of the outer boundary of the task area and the position of the obstacle boundary in the task area;
and planning a navigation path based on the sequence of traversing the drivable grids, the sequence of traversing the non-drivable grids and the position of the starting grid.
Optionally, in another possible design, the non-driving grids include an obstacle grid and a non-obstacle grid, and the planning module 12 is further specifically configured to:
traversing barrier grids in the grid map by taking the found last termination grid as a starting point based on the A star path finding algorithm and the position of the barrier boundary;
and traversing the non-obstacle grids in the grid map by taking the traversed last obstacle grid as a starting point based on the A star road finding algorithm and the position of the outer boundary.
Optionally, in another possible design manner, the path finding rule of the a-star path finding algorithm is as follows: the path is shortest and the number of directional adjustments is minimal.
Optionally, the device for planning a navigation path may further include a storage module, where the storage module is configured to store program codes of the device for planning a navigation path, and the like.
As shown in fig. 7, an embodiment of the present application further provides a navigation path planning apparatus, which includes a memory 41, processors 42(42-1 and 42-2), a bus 43, and a communication interface 44; the memory 41 is used for storing computer execution instructions, and the processor 42 is connected with the memory 41 through a bus 43; when the navigation path planning device is operating, the processor 42 executes the computer-executable instructions stored in the memory 41 to cause the navigation path planning device to perform the navigation path planning method provided in the above embodiment.
In particular implementations, processor 42 may include one or more Central Processing Units (CPUs), such as CPU0 and CPU1 shown in FIG. 7, as one embodiment. And as an example, the means for planning a navigation path may comprise a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 7. Each of the processors 42 may be a single-Core Processor (CPU) or a multi-Core Processor (CPU). Processor 42 may refer herein to one or more devices, circuits, and/or processing cores that process data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be self-contained and coupled to the processor 42 via a bus 43. The memory 41 may also be integrated with the processor 42.
In a specific implementation, the memory 41 is used for storing data in the present application and computer-executable instructions corresponding to software programs for executing the present application. Processor 42 may navigate various functions of the path planner by running or executing software programs stored in memory 41, as well as invoking data stored in memory 41.
The communication interface 44 is any device, such as a transceiver, for communicating with other devices or communication networks, such as a control system, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), and the like. The communication interface 44 may include a receiving unit implementing a receiving function and a transmitting unit implementing a transmitting function.
The bus 43 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended ISA (enhanced industry standard architecture) bus, or the like. The bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
As an example, in conjunction with fig. 6, the function implemented by the planning module in the navigation path planning apparatus is the same as the function implemented by the processor in fig. 7, and the function implemented by the storage module in the navigation path planning apparatus is the same as the function implemented by the memory in fig. 7.
For the explanation of the related content in this embodiment, reference may be made to the above method embodiment, which is not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the computer executes the instructions, the computer is enabled to execute the method for planning a navigation path provided in the foregoing embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, 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.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (13)
1. A method for planning a navigation path, comprising:
acquiring a grid map of a task area; the grid map is composed of candidate grids including a drivable grid and a non-drivable grid;
and traversing the traversable grids in the grid map by taking an initial grid in the candidate grids as a starting point and combining a Chinese bow-shaped navigation algorithm and an A star road finding algorithm, and planning a navigation path of the mobile equipment based on the sequence of traversing the traversable grids.
2. The method for planning a navigation path according to claim 1, wherein traversing the traversable grid in the grid map by using a bow-shaped navigation algorithm and an a-star road finding algorithm with a starting grid of the candidate grids as a starting point comprises:
step A: searching the drivable grid based on the zigzag navigation algorithm by taking the initial grid as a starting point until a final grid is searched;
and B, step B: judging whether the driving grids which are not searched exist or not;
and C: determining a target grid from the non-searched travelable grids if it is determined that the non-searched travelable grid exists; searching the drivable grid based on the A star path finding algorithm by taking the termination grid as a starting point until the target grid is searched, and taking the target grid as a new starting grid;
and repeatedly executing the steps A to C until the travelable grids which are not searched do not exist, and determining that the travelable grids in the grid map are traversed.
3. The method for planning a navigation path according to claim 2, wherein the searching for the traversable grid based on the zigzag navigation algorithm starting from the start grid until the end grid is found comprises:
traversing the candidate grids of the row to which the initial grid belongs along the task execution direction by taking the initial grid as a starting point until the boundary grid is found; the boundary grid is the previous grid of the searched first non-driving grid;
step D: judging whether the travelable grids exist in the next row of grids of the boundary grids along the path traversing direction;
step E: traversing the next row of grids based on the task execution direction by taking the current boundary grid as a starting point until a new boundary grid is found under the condition that the travelable grid exists in the next row of grids; traversing the next row of grids along the reverse direction of the task execution direction by taking the new boundary grid as a starting point until the new boundary grid is found again; taking the opposite direction of the task execution direction as a new task execution direction;
and D, repeating the step D to the step E until the next row of grids is determined to have no drivable grid, and determining the boundary grid found for the last time as the termination grid.
4. The method for planning a navigation path according to claim 3, wherein the starting grid is used as a starting point, the candidate grids in the row to which the starting grid belongs are traversed along the task execution direction until the boundary grid is found, and the method further comprises:
acquiring the length of each boundary of the grid map;
determining the task execution direction based on the lengths of the respective boundaries and the position of the start grid.
5. The method for planning a navigation path according to claim 2, wherein the determining a target grid from the non-searched travelable grids comprises:
determining the target grid from the non-sought travelable grids based on the distance of the non-sought travelable grid from the termination grid.
6. The method for planning a navigation path according to claim 1, wherein the obtaining of the grid map of the task area comprises:
acquiring real-time position coordinates of the mobile equipment in the process that the mobile equipment respectively circles along the outer boundary of the task area and the obstacle boundary in the task area;
determining position information of the outer boundary and position information of the obstacle boundary based on the real-time position coordinates;
and establishing the grid map based on the position information of the outer boundary and the position information of the obstacle boundary, and determining the drivable grid and the non-drivable grid in the grid map.
7. The method for planning a navigation path according to claim 6, wherein the establishing the grid map based on the position information of the outer boundary and the position information of the obstacle boundary and determining the drivable grid and the non-drivable grid in the grid map comprises:
determining a number of grids extending along the outside boundary and along the obstacle boundary based on a geometric parameter of the mobile device;
building the grid map based on the position information of the outer boundary, the position information of the obstacle boundary and the expanded grid number, and determining the drivable grid and the non-drivable grid in the grid map.
8. The method for planning a navigation path according to claim 2, wherein after determining that the traversable grid in the grid map has been traversed, the method further comprises:
traversing the non-driving grids in the grid map by taking the found last termination grid as a starting point based on the A-star road finding algorithm, the position of the outer boundary of the task area and the position of the obstacle boundary in the task area;
the planning a navigation path for a mobile device based on the sequence of traversing the traversable grid includes: planning the navigation path based on an order of traversing the drivable grid, an order of traversing the non-drivable grid, and a position of the starting grid.
9. The method for planning a navigation path according to claim 8, wherein the non-driving grids include an obstacle grid and a non-obstacle grid, and traversing the non-driving grids in the grid map based on the a-star road finding algorithm, the position of the outer boundary of the task area, and the position of the obstacle boundary in the task area starting from the found last termination grid comprises:
traversing the obstacle grids in the grid map based on the A-star road finding algorithm and the position of the obstacle boundary by taking the found last termination grid as a starting point;
traversing the non-obstacle grids in the grid map based on the A-star road finding algorithm and the positions of the outer boundaries, starting from the last obstacle grid traversed.
10. The method for planning a navigation path according to any one of claims 1 to 9, wherein the routing rule of the a-star routing algorithm is: the path is shortest and the number of directional adjustments is minimal.
11. A navigation path planning apparatus, comprising:
the acquisition module is used for acquiring a grid map of the task area; the grid map is composed of candidate grids including a drivable grid and a non-drivable grid;
and the planning module is used for traversing the travelable grids in the grid map acquired by the acquisition module by taking the initial grids in the candidate grids as starting points and combining a Chinese character bow navigation algorithm and an A star road finding algorithm, and planning the navigation path of the mobile equipment based on the sequence of traversing the travelable grids.
12. The planning device of a navigation path is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
when the navigation path planning device is operated, the processor executes the computer-executable instructions stored in the memory to cause the navigation path planning device to execute the navigation path planning method according to any one of claims 1 to 10.
13. A computer-readable storage medium having stored therein instructions which, when executed by a computer, cause the computer to perform a method of planning a navigation path according to any one of claims 1 to 10.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023125512A1 (en) * | 2021-12-31 | 2023-07-06 | 丰疆智能(深圳)有限公司 | Navigation path planning method and apparatus, and storage medium |
| WO2024092728A1 (en) * | 2022-11-04 | 2024-05-10 | 苏州宝时得电动工具有限公司 | Autonomous working apparatus and system, and working method |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111773717A (en) * | 2020-06-30 | 2020-10-16 | 完美世界(北京)软件科技发展有限公司 | Object control method and apparatus, storage medium, and electronic apparatus |
| CN117128974B (en) * | 2023-09-21 | 2024-02-23 | 北京华如科技股份有限公司 | Navigation route searching method and device |
| CN117073688B (en) * | 2023-10-16 | 2024-03-29 | 泉州装备制造研究所 | Coverage path planning method based on multi-layer cost map |
| CN117593900B (en) * | 2024-01-16 | 2024-05-14 | 浙江爱特电子技术股份有限公司 | Pre-hospital first-aid scheduling method and system |
| CN117948978A (en) * | 2024-01-17 | 2024-04-30 | 中国南方航空股份有限公司 | Route planning method, system, equipment and medium based on B spline curve equation |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101372062B1 (en) * | 2012-06-29 | 2014-03-07 | 인텔렉추얼디스커버리 주식회사 | Moving robot and method for online complete coverage path planning |
| US10353399B2 (en) * | 2017-07-21 | 2019-07-16 | AI Incorporated | Polymorphic path planning for robotic devices |
| CN107843262A (en) * | 2017-10-30 | 2018-03-27 | 洛阳中科龙网创新科技有限公司 | A kind of method of farm machinery all standing trajectory path planning |
| CN108981710B (en) * | 2018-08-07 | 2019-10-11 | 北京邮电大学 | A kind of complete coverage path planning method of mobile robot |
| CN109540155A (en) * | 2019-02-01 | 2019-03-29 | 西安全志科技有限公司 | A kind of path planning and navigation method, computer installation and the computer readable storage medium of sweeping robot |
| CN110955262B (en) * | 2019-12-10 | 2023-04-07 | 河海大学常州校区 | Control method and system for path planning and tracking of photovoltaic module cleaning robot |
| CN114993308A (en) * | 2021-12-31 | 2022-09-02 | 丰疆智能(深圳)有限公司 | Navigation path planning method and device and storage medium |
-
2021
- 2021-12-31 CN CN202111673891.3A patent/CN114993308A/en active Pending
-
2022
- 2022-12-27 WO PCT/CN2022/142257 patent/WO2023125512A1/en unknown
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023125512A1 (en) * | 2021-12-31 | 2023-07-06 | 丰疆智能(深圳)有限公司 | Navigation path planning method and apparatus, and storage medium |
| WO2024092728A1 (en) * | 2022-11-04 | 2024-05-10 | 苏州宝时得电动工具有限公司 | Autonomous working apparatus and system, and working method |
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| WO2023125512A1 (en) | 2023-07-06 |
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