CN113792109A - Path planning method, path planning system, robot, and storage medium - Google Patents
Path planning method, path planning system, robot, and storage medium Download PDFInfo
- Publication number
- CN113792109A CN113792109A CN202111365624.XA CN202111365624A CN113792109A CN 113792109 A CN113792109 A CN 113792109A CN 202111365624 A CN202111365624 A CN 202111365624A CN 113792109 A CN113792109 A CN 113792109A
- Authority
- CN
- China
- Prior art keywords
- dist
- point
- grid
- value
- path planning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Automation & Control Theory (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Navigation (AREA)
Abstract
The invention provides a path planning method, a path planning system, a robot and a storage medium, comprising the following steps: setting a minimum expansion distance d _ min and a maximum expansion distance d _ max; receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max; inputting a starting point and an end point in a disk, and listing the starting point as a current point P'; judging whether the adjacent grid of the current point P' can pass or not, and calculating the cost value of the adjacent grid according to a new heuristic function O (x); adding the points which can be run near P' into an openlist queue, and establishing a minimum heap according to the cost value of each point; and judging whether P' is an end point, otherwise, repeating the steps, and if so, ending the search. The invention reduces the search space of the grid map, solves the problem of too low planning speed under a large map, and adaptively selects a path which is far away from the barrier and is easier to pass and safe on the premise of ensuring the trafficability.
Description
Technical Field
The invention relates to the technical field of robot algorithms, in particular to a path planning method, a path planning system, a robot and a storage medium.
Background
The conventional service robot path planning method based on the grid map has the problems of dijstra and A, over-slow planning speed (the planning speed is exponentially reduced along with the increase of the area of the map, see fig. 1) and non-optimal planning speed (the planned path is shortest but is not optimal (the planned path is too close to an obstacle, and has potential safety hazard, see fig. 2). In addition, in the current costmap-based path planning, the static expansion range of the costmap-based path planning is in conflict with the shortest path and the too close path to the obstacle (see fig. 3).
Patent document CN110361009B discloses a path planning method, a path planning system, and a mobile robot, which are configured to obtain position information of an obstacle in an environment to be measured, establish a potential field map according to the position information of the obstacle, extract a first key point in the potential field map, screen the first key point to obtain a second key point, obtain a topological map from the second key point, and obtain an optimal path from a current position of the robot to a position of a target point. However, the patent document still has the defect that the planning speed is too slow under a large map.
Disclosure of Invention
In view of the defects in the prior art, the present invention provides a path planning method, a path planning system, a robot and a storage medium.
The invention provides a path planning method, which comprises the following steps:
step 1: setting a minimum expansion distance d _ min and a maximum expansion distance d _ max;
step 2: receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max;
and step 3: inputting a starting point and an end point in a disk, and listing the starting point as a current point P';
and 4, step 4: judging whether the adjacent grid of the current point P' can pass or not, and calculating the cost value of the adjacent grid according to a new heuristic function O (x);
and 5: adding the points which can be run near P' into an openlist queue, and establishing a minimum heap according to the cost value of each point;
step 6: judging whether P' is an end point or not, otherwise, repeating the steps 3-6, and if so, ending the search;
the step 2 comprises the following steps:
step 2.1: adding all occupied grids in the original grid map into a to-be-processed queue occ _ list, and initializing dist values of the unoccupied grids to d _ max, wherein the dist values are the minimum distance from the unoccupied grids to the occupied grids, and the d _ max is the maximum expansion distance;
step 2.2: recording each occupied grid in occ _ list as a grid point p _ i, adding peripheral unoccupied grids into dist _ expand _ list, and calculating the dist value of each unoccupied grid from p _ i;
step 2.3: if the dist value of the unoccupied grid distance p _ i is larger than d _ max, stopping adding the peripheral unoccupied grids into the dist _ expand _ list, and listing the currently processed unoccupied grids from the dist _ expand _ list; otherwise, continuously adding the unoccupied grids near the point p _ i into the dist _ expand _ list, and if the dist value of the grids in the dist _ expand _ list is changed, taking the minimum value;
step 2.4: if the dist _ expand _ list queue is not empty, repeating the step 2.3, otherwise, repeating the step 2.2;
step 2.5: if the occ _ list queue is not empty, repeating the step 2.2, otherwise ending, and generating a new map type distmap;
the step 4 comprises the following steps:
step 4.1: traversing the adjacent grid of the current grid, judging the dist value of the current grid, wherein the dist value is obtained by an algorithm for generating a distmap in the step 2, if the dist value is less than d _ min, adding no openlist queue, otherwise, adding the openlist queue;
step 4.2: the o (x) value of the grid is calculated according to the newly proposed heuristic search function o (x) = h (x) + g (x) + d (x).
Preferably, in the step 4.2, h (x) represents the euclidean distance from the current mesh point P' (xp, yp) to the starting point S (xs, ys), and h (x) = SQRT ((xs-xp) ^2 + (ys-yp) < 2 >.
Preferably, in the step 4.2, g (x) represents the euclidean distance from the current mesh point P' (xp, yp) to the end point F (xf, yf), and g (x) = SQRT ((xf-xp) ^2 + (yf-yp) ^ 2).
Preferably, in the step 4.2, d (x) represents a weight value of the current mesh at distmap, d (x) = - (p _ dist × safe _ scale);
wherein p _ dist is a dist value of the current grid obtained when the distmap is generated; safe _ scale is a parameter configured according to an actual scene.
Preferably, in the step 6, the current point P ' (xp, yp) and the end point F (xf, yf) are determined, if xp = = xf & & yp = = yf, then P ' is considered as the end point, otherwise, the grid around P ' is added to the openlist to continue the loop processing.
The invention also provides a path planning system which is realized by the path planning method of the claims and comprises the following modules:
a setting module: setting a minimum expansion distance d _ min and a maximum expansion distance d _ max;
a drawing establishing module: receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max;
an input module: inputting a starting point and an end point in a disk, and listing the starting point as a current point P';
a calculation module: judging whether the adjacent grid of the current point P' can pass or not, and calculating the cost value of the adjacent grid according to a new heuristic function O (x);
the heap building module is used for adding the passable points near the P' into an openlist queue and building a minimum heap according to the cost value of each point;
a judging module: and judging whether P' is an end point, otherwise, repeatedly executing the calculation module, the heap building module and the judgment module, and if so, ending the search.
The invention also provides a robot, which comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor implements the path planning method by executing the instructions.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the above-mentioned path planning method.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a method for generating a distmap to preprocess a dist value of each grid, so that only one preprocessing is needed to be carried out on a map. In the traditional costmap mode, the map needs to be preprocessed every time the expansion value changes, so that the preprocessing efficiency of the original map is greatly improved;
2. according to the invention, through the limitation of d _ min and d _ max, the search space in real-time path planning is reduced, and the search efficiency is greatly improved;
3. according to the invention, by introducing a new cost calculation mode of O (x) obtained from D (x), the problem of too far winding of a planned path due to overlarge expansion value is solved while an easy-to-pass and safe path far away from a barrier is planned;
4. according to the method, a safe _ scale parameter is introduced, so that preference selection can be performed on the safety of a path or the shortest path (if the safe _ scale is 0, the algorithm is degraded to A x, and if the safe _ scale is infinity, only the grid farthest from an obstacle is planned);
5. the method reduces the search space of the grid map and solves the problem of too low planning speed under a large map;
6. the invention provides a new cost calculation mode of the optimal path, which ensures the shortest route and safety;
7. the invention provides a new distmap data type, and solves the contradiction of the costmap static expansion range on the shortest path and the distance between the path and an obstacle.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is an A-algorithm search space of the present invention;
FIG. 2 is a graph of the path planned by the A-algorithm of the present invention;
FIG. 3 is a graph of the discrepancy between passability of the costmap static expansion radius and the distance from the obstacle in accordance with the present invention;
FIG. 4 shows an original map with d _ max set to 3 and d _ min set to 1 according to embodiment 2 of the present invention;
FIG. 5 is a graph one of the results of a distmap of embodiment 2 of the present invention;
FIG. 6 is a graph II of the results of distmap of embodiment 2 of the present invention;
FIG. 7 is a graph III of the results of a distmap of embodiment 2 of the present invention;
FIG. 8 is a graph showing the results of the conventional costmap method;
FIG. 9 is a graph IV of the results of example 2 of the present invention;
FIG. 10 is a graph showing the results of example 2 of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1:
the path planning method provided by the embodiment comprises the following steps:
step 1: the minimum expansion distance d _ min and the maximum expansion distance d _ max are set.
Step 2: receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max, wherein the step 2 comprises the following steps:
step 2.1: adding all occupied grids in the original grid map into a to-be-processed queue occ _ list, and initializing dist values of the unoccupied grids to d _ max, wherein the dist values are the minimum distance from the unoccupied grids to the occupied grids, and the d _ max is the maximum expansion distance;
step 2.2: recording each occupied grid in occ _ list as a grid point p _ i, adding peripheral unoccupied grids into dist _ expand _ list, and calculating the dist value of each unoccupied grid from p _ i; example (c): if the coordinates of p _ i are (p _ i _ x, p _ i _ y) and the unoccupied grid is (x _, y _), dist is the distance between two grids ((p _ i _ x-x _) 2 + (p _ i _ y-y _) 2) (1/2);
step 2.3: if the dist value of the unoccupied grid distance p _ i is larger than d _ max, stopping adding the peripheral unoccupied grids into the dist _ expand _ list, and listing the currently processed unoccupied grids from the dist _ expand _ list; otherwise, continuously adding the unoccupied grids near the point p _ i into the dist _ expand _ list, and if the dist value of the grids in the dist _ expand _ list is changed, taking the minimum value;
step 2.4: if the dist _ expand _ list queue is not empty, repeating the step 2.3, otherwise, repeating the step 2.2;
step 2.5: if occ _ list queue is not empty, repeat step 2.2, otherwise end, generate a new map type distmap.
And step 3: the start point and the end point are input in the disk, and the start point is listed as the current point P'.
And 4, step 4: judging whether the adjacent grid of the current point P' can pass or not, and calculating the cost value of the adjacent grid according to a new heuristic function O (x), wherein the step 4 comprises the following steps:
step 4.1: traversing the adjacent grid of the current grid, judging the dist value of the current grid, wherein the dist value is obtained by an algorithm for generating a distmap in the step 2, if the dist value is less than d _ min, adding no openlist queue, otherwise, adding the openlist queue;
step 4.2: calculating the o (x) value of the grid according to a newly proposed heuristic search function o (x) = h (x) + g (x) + d (x); h (x) represents the Euclidean distance from the current mesh P' (xp, yp) point to the starting point S (xs, ys), H (x) = SQRT ((xs-xp) ^2 + (ys-yp) ^ 2); g (x) represents the Euclidean distance from the current mesh P' (xp, yp) point to the end point F (xf, yf), G (x) = SQRT ((xf-xp) ^2 + (yf-yp) ^ 2); d (x) represents a weight value of the current mesh under the distmap, d (x) = - (p _ dist × safe _ scale), where p _ dist is a dist value of the current mesh obtained when the distmap is generated; safe _ scale is a parameter configured according to an actual scene.
And 5: and adding the feasible points near P' into the openlist queue, and establishing a minimum heap according to the cost value of each point.
Step 6: judging whether P 'is an end point, otherwise, repeating the steps 3-6, if so, ending the search, judging the current point P' (xp, yp) and the end point F (xf, yf), if xp = = xf & & yp = = yf, considering P 'as the end point, and if not, adding the grid around P' into the openlist to continue the circulation processing.
The invention also provides a robot, which comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor realizes the path planning method by executing the instructions.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the above-mentioned path planning method.
Example 2:
on the basis of embodiment 1, this embodiment provides a path planning system, which adopts the path planning method provided in embodiment 1, and includes the following modules:
a setting module: setting a minimum expansion distance d _ min and a maximum expansion distance d _ max;
a drawing establishing module: receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max;
an input module: inputting a starting point and an end point in a disk, and listing the starting point as a current point P';
a calculation module: judging whether the adjacent grid of the current point P' can pass or not, and calculating the cost value of the adjacent grid according to a new heuristic function O (x);
the heap building module is used for adding the passable points near the P' into an openlist queue and building a minimum heap according to the cost value of each point;
a judging module: and judging whether P' is an end point, otherwise, repeatedly executing the calculation module, the heap building module and the judgment module, and if so, ending the search.
Example 3:
those skilled in the art will understand this embodiment as a more specific description of embodiments 1 and 2.
As shown in fig. 1 to 3, the path planning method provided in this embodiment includes the following steps:
step 1: setting a minimum expansion distance d _ min and a maximum expansion distance d _ max;
step 2: receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max (the search space of path planning is dynamically reduced in the step);
and step 3: inputting a starting point and an end point, and listing the starting point as a current point P';
and 4, step 4: judging whether the adjacent grid of the current point P' can pass or not, and calculating a cost value of the adjacent grid according to a new heuristic function O (x) (wherein O (x) is the key for ensuring the shortest route and the safety of the path);
step 6: and judging whether P' is an end point, otherwise, repeating the steps 3-6, and if so, ending the search.
Wherein, the step 2 comprises the following steps:
step 2.1: adding all occupied grids in the original grid map into a to-be-processed queue occ _ list, and initializing dist of the unoccupied grids to d _ max;
step 2.2: for each black grid point p _ i in occ _ list, adding the peripheral unoccupied grid into dist _ expanded _ list, and calculating the distance dist of each point from p _ i;
step 2.3: if the distance dist of the point from p _ i is larger than the maximum expansion distance d _ max, stopping dist diffusion and discharging from dist _ expand _ list; if the dist value of the point in the dist _ expand _ list is changed, taking the minimum value;
step 2.4: if the dist _ expand _ list queue is not empty, repeating for 2.3, otherwise repeating for 2.2;
step 2.5: repeat 2.2 if occ _ list queue is not empty, else end.
Wherein, the step 4 comprises the following steps:
4.1 traversing the adjacent grid of the current grid, judging the dist value of the current grid (obtained by the distmap generation algorithm in the step 2), and if the dist value is less than d _ min, not adding openlist (the step reduces the search space of the whole map); otherwise, adding the openlist queue;
4.2 calculate the o (x) value of the grid according to the new proposed heuristic search function o (x) = h (x) + g (x) + d (x), where h (x), g (x) are the same as in the a algorithm, and respectively represent the distance from the current point to the starting point and the distance from the current point to the end point, d (x) = - (p _ dist _ safe _ scale), p _ dist represents the dist value of the grid, safe _ scale represents the safety factor, and the larger the coefficient, the larger the influence of dist on the overall o (x) cost value, the more the grid tends to move away from the obstacle.
The a path planned by the a-x algorithm in fig. 2 is too close to the obstacle, while the O path is safer and easier to pass through. FIG. 3 is a graph of the discrepancy between passability of the costmap static expansion radius and the distance from the obstacle.
For example: d _ max is set to be 3, d _ min is 1, the original map is as shown in fig. 4, after the processing is performed by the dist _ expand algorithm, the result of the distmap is as shown in fig. 5 after O1 in the first occ _ list, a point O1 is a first point added to the processing of the dist _ expand _ list, a gray area is a grid point with dist information generated by the dist _ expand algorithm, and grid dist values respectively represented by three grays from dark to light are 1, 2 and 3 < = d _ max respectively. After processing the second point O2 in occ _ list, the result of distmap is shown in fig. 6, where U point is the updated dist value added with O2 point, and the processing result of final dist _ map is shown in fig. 7, and dis _ max = 3.
If the conventional costmap method is adopted, as shown in fig. 8 and 9, the starting point and the end point are S and F, respectively, and the expansion distance is 1, a path ■ in the mesh is planned; planning a path if the expansion distance is 2; if the expansion distance is 3, the path cannot be planned.
And repeating the steps 3-6 to obtain a path searching process which is ● part in the disk map, as shown in FIG. 10.
The invention provides a method for generating a distmap to preprocess a dist value of each grid, so that only one preprocessing is needed to be carried out on a map. And the traditional costmap mode needs to preprocess the map when the expansion value changes, so that the preprocessing efficiency of the original map is greatly improved.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (8)
1. A path planning method is characterized by comprising the following steps:
step 1: setting a minimum expansion distance d _ min and a maximum expansion distance d _ max;
step 2: receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max;
and step 3: inputting a starting point S (xs, ys) and an end point F (xf, yf) in the distmap, and listing the starting point as a current point P' (xp, yp);
and 4, step 4: judging whether the adjacent grid of the current point P' can pass or not, and calculating the cost value of the adjacent grid according to a new heuristic function O (x);
and 5: adding the points which can be run near P' into an openlist queue, and establishing a minimum heap according to the cost value of each point;
step 6: judging whether P' is an end point or not, otherwise, repeating the steps 3-6, and if so, ending the search;
the step 2 comprises the following steps:
step 2.1: adding all occupied grids in the original grid map into a to-be-processed queue occ _ list, and initializing dist values of the unoccupied grids to d _ max, wherein the dist values are the minimum distance from the unoccupied grids to the occupied grids, and the d _ max is the maximum expansion distance;
step 2.2: recording each occupied grid in occ _ list as a grid point p _ i, adding peripheral unoccupied grids into dist _ expand _ list, and calculating the dist value of each unoccupied grid from p _ i;
step 2.3: if the dist value of the unoccupied grid distance p _ i is larger than d _ max, stopping adding the peripheral unoccupied grids into the dist _ expand _ list, and listing the currently processed unoccupied grids from the dist _ expand _ list; otherwise, continuously adding the unoccupied grids near the point p _ i into the dist _ expand _ list, and if the dist value of the grids in the dist _ expand _ list is changed, taking the minimum value;
step 2.4: if the dist _ expand _ list queue is not empty, repeating the step 2.3, otherwise, repeating the step 2.2;
step 2.5: if the occ _ list queue is not empty, repeating the step 2.2, otherwise ending, and generating a new map type distmap;
the step 4 comprises the following steps:
step 4.1: traversing the adjacent grid of the current grid, judging the dist value of the current grid, wherein the dist value is obtained by an algorithm for generating a distmap in the step 2, if the dist value is less than d _ min, adding no openlist queue, otherwise, adding the openlist queue;
step 4.2: the o (x) value of the grid is calculated according to the newly proposed heuristic search function o (x) = h (x) + g (x) + d (x).
2. The path planning method according to claim 1, wherein in step 4.2, h (x) represents the euclidean distance from the current mesh P' (xp, yp) point to the starting point S (xs, ys), and h (x) = SQRT ((xs-xp) < 2 + (ys-yp) < 2 >.
3. The path planning method according to claim 1, wherein in step 4.2, g (x) represents the euclidean distance from the current mesh point P' (xp, yp) to the end point F (xf, yf), and g (x) = SQRT ((xf-xp) < 2 + (yf-yp) < 2 >.
4. A path planning method according to claim 1, characterized in that in step 4.2, d (x) represents a weight value of the current mesh at distmap, d (x) = - (p _ dist × safe _ scale);
wherein p _ dist is a dist value of the current grid obtained when the distmap is generated; safe _ scale is a parameter configured according to an actual scene.
5. The path planning method according to claim 1, wherein in step 6, the current point P ' (xp, yp) and the end point F (xf, yf) are determined, if xp = = xf & & yp = = yf, then P ' is considered as the end point, otherwise, the mesh around P ' is added to the openlist to continue the loop processing.
6. A path planning system, which is implemented by the path planning method of claim 1, comprising the following modules:
a setting module: setting a minimum expansion distance d _ min and a maximum expansion distance d _ max;
a drawing establishing module: receiving an original grid map needing planning, and generating a new map type distmap according to d _ min and d _ max;
an input module: inputting a starting point and an end point in a disk, and listing the starting point as a current point P';
a calculation module: judging whether the adjacent grid of the current point P' can pass or not, and calculating the cost value of the adjacent grid according to a new heuristic function O (x);
the heap building module is used for adding the passable points near the P' into an openlist queue and building a minimum heap according to the cost value of each point;
a judging module: and judging whether P' is an end point, otherwise, repeatedly executing the calculation module, the heap building module and the judgment module, and if so, ending the search.
7. A robot, characterized in that the robot comprises a processor and a memory, the memory stores a plurality of instructions, and the processor implements the path planning method according to any one of claims 1 to 5 by executing the plurality of instructions.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the path planning method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111365624.XA CN113792109B (en) | 2021-11-18 | 2021-11-18 | Path planning method, path planning system, robot, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111365624.XA CN113792109B (en) | 2021-11-18 | 2021-11-18 | Path planning method, path planning system, robot, and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113792109A true CN113792109A (en) | 2021-12-14 |
| CN113792109B CN113792109B (en) | 2022-02-11 |
Family
ID=78877340
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111365624.XA Active CN113792109B (en) | 2021-11-18 | 2021-11-18 | Path planning method, path planning system, robot, and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113792109B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111310984A (en) * | 2020-01-21 | 2020-06-19 | 成都智库二八六一信息技术有限公司 | Path planning method and system based on two-dimensional map grid division |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110035087A1 (en) * | 2009-08-10 | 2011-02-10 | Samsung Electronics Co., Ltd. | Method and apparatus to plan motion path of robot |
| CN107345815A (en) * | 2017-07-24 | 2017-11-14 | 东北大学 | A kind of home-services robot paths planning method based on improvement A* algorithms |
| CN109443364A (en) * | 2018-11-13 | 2019-03-08 | 国网浙江宁波市鄞州区供电有限公司 | Paths planning method based on A* algorithm |
| CN110502006A (en) * | 2019-07-22 | 2019-11-26 | 中国矿业大学 | A kind of Mine Abandoned Land mobile robot complete coverage path planning method |
| CN110595482A (en) * | 2019-10-28 | 2019-12-20 | 深圳市银星智能科技股份有限公司 | Path planning method and device with obstacle avoidance weight and electronic equipment |
-
2021
- 2021-11-18 CN CN202111365624.XA patent/CN113792109B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110035087A1 (en) * | 2009-08-10 | 2011-02-10 | Samsung Electronics Co., Ltd. | Method and apparatus to plan motion path of robot |
| CN107345815A (en) * | 2017-07-24 | 2017-11-14 | 东北大学 | A kind of home-services robot paths planning method based on improvement A* algorithms |
| CN109443364A (en) * | 2018-11-13 | 2019-03-08 | 国网浙江宁波市鄞州区供电有限公司 | Paths planning method based on A* algorithm |
| CN110502006A (en) * | 2019-07-22 | 2019-11-26 | 中国矿业大学 | A kind of Mine Abandoned Land mobile robot complete coverage path planning method |
| CN110595482A (en) * | 2019-10-28 | 2019-12-20 | 深圳市银星智能科技股份有限公司 | Path planning method and device with obstacle avoidance weight and electronic equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111310984A (en) * | 2020-01-21 | 2020-06-19 | 成都智库二八六一信息技术有限公司 | Path planning method and system based on two-dimensional map grid division |
| CN111310984B (en) * | 2020-01-21 | 2023-09-26 | 成都智库二八六一信息技术有限公司 | Path planning method and system based on two-dimensional map grid division |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113792109B (en) | 2022-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dutta et al. | Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments | |
| Sallam et al. | A two-stage multi-operator differential evolution algorithm for solving resource constrained project scheduling problems | |
| CN109724612A (en) | A kind of AGV paths planning method and equipment based on topological map | |
| CN113792109B (en) | Path planning method, path planning system, robot, and storage medium | |
| CN114815802A (en) | Unmanned overhead traveling crane path planning method and system based on improved ant colony algorithm | |
| CN110763247A (en) | Robot path planning method based on combination of visual algorithm and greedy algorithm | |
| CN113408988B (en) | Scheduling optimization method for warehouse system, electronic equipment and storage medium | |
| CN115454062A (en) | Robot dynamic path planning method and system based on Betz curve | |
| CN115195706A (en) | Parking path planning method and device | |
| CN117271101B (en) | Operator fusion method and device, electronic equipment and storage medium | |
| CN114330887A (en) | Task arranging method, task arranging device and computer readable storage medium | |
| CN116518977A (en) | Unmanned aerial vehicle path planning method, system and equipment | |
| Zamuda et al. | Improving constrained glider trajectories for ocean eddy border sampling within extended mission planning time | |
| CN112764413B (en) | Robot path planning method | |
| CN115534998A (en) | Automatic driving integrated decision-making method and device, vehicle and storage medium | |
| CN109389350A (en) | A kind of generation method and system of merger task | |
| Kargin et al. | Planning and control method based on fuzzy logic for intelligent machine | |
| CN111912407B (en) | Path planning method of multi-robot system | |
| CN113627646B (en) | Path planning method, device, equipment and medium based on neural network | |
| Chen | Conflict-free scheduling algorithm for multiple AGVs in smart factories | |
| CN116540743B (en) | Centralized scheduling real-time path planning method and device for high-speed sorting robot | |
| CN115096313B (en) | Data processing method and device for regional obstacle avoidance | |
| CN111033532B (en) | Training method and system for generating countermeasure network, electronic device and storage medium | |
| Lee et al. | Time varied self-reliance aerial ground traffic monitoring system with pre-recognition collision avoidance | |
| CN116451592B (en) | Extensible intelligent shelter layout optimization method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 311231 building 3, No. 477, Hongxing Road, Qiaonan block, economic and Technological Development Zone, Xiaoshan District, Hangzhou City, Zhejiang Province Patentee after: Hangzhou Jingwu Intelligent Technology Co.,Ltd. Address before: Room 12, 3rd floor, No.2 Lane 1446, Yunguan Road, Lingang New District, Pudong New Area pilot Free Trade Zone, Shanghai, 201306 Patentee before: Shanghai Jingwu Intelligent Technology Co.,Ltd. |
|
| CP03 | Change of name, title or address |