CN112462763B - Mowing robot path planning method based on grid map - Google Patents
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- 238000004364 calculation method Methods 0.000 abstract description 5
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
Abstract
The invention provides a grid map-based mowing robot path planning method, which comprises the following steps: establishing an initial grid map by taking the transverse overall dimension of the robot as the side length, and solving the refined grid dimension; judging the distance between adjacent obstacles in the rasterized map, expanding the size of one grid outwards on the basis of the grid occupied by the obstacles when a preset condition is met, taking the expanded area as a buffer area, taking the expanded area as a grid refining area, and refining the grid by refining the grid size; calculating the multiple N of the transverse outline size to the grid thinning size, enabling M to be int (N/2), and expanding M grids outwards for the thinned grid occupied by the obstacles; after a starting point and an end point are specified, whether the mowing robot is in a buffer area or not is judged, and a next path is planned based on an initial grid or a refined grid map until the end point is reached. The invention saves path calculation resources, improves mowing coverage after path planning, and improves mowing efficiency and quality.
Description
Technical Field
The invention belongs to the field of intelligent mowing robot control, and particularly relates to a mowing robot path planning method based on a grid map.
Background
When the intelligent mowing robot works in an outdoor environment, a map which is used for rasterizing a working lawn is generally adopted to guide the robot to move. At present, pixel points of an image are widely adopted to create a 01 matrix of a grid map, but the 01 matrix of the grid map created by directly using the image pixel points is too large, so that the operation rate is remarkably reduced, and therefore, the 01 matrix is subjected to blocking operation by adopting a blocking matrix theory, and the grid map which can be used by a mowing robot is obtained.
In the prior art, due to the fact that lawns are different in size and shape and various obstacles exist, a high-quality grid map is difficult to build in the complex environment. The map is rasterized based on the blocking theory, and the problem of how to reasonably block the map exists. If the grid is too small, the grid map can be closer to the real environment, but the grid quantity of the map is large, so that the calculation speed of path planning is obviously reduced; if the grid is too large, the difference between the grid map and the real environment is too large, and the obtained track is not optimal. Due to unreasonable partitioning, the map has a problem in precision, so that the mowing robot cannot efficiently complete point-to-point path planning, implement a traversing planning strategy with the maximum efficiency, and also cannot give consideration to reasonable distribution of computing resources.
For example, a square lawn is preliminarily rasterized, as shown in fig. 1, wherein two shaded areas, an area a and an area B, represent obstacles, and a final grid map is created based on the principle that "an obstacle exists in a grid, i.e., the grid is deemed to be impassable", as shown in fig. 2, the identified obstacle area is C. Assuming that the lateral dimensions of the mowing robot are the same as the size of the grid, in the grid map shown in fig. 2, the mowing robot must route around the obstacle region C from the grid 1 position to the grid 2 position. However, in fig. 1, the distance between obstacle a and obstacle B is sufficient for the mowing robot to pass through, and the grid map of fig. 2 identifies this area as an obstacle as well. Similarly, when the mowing robot plans the full-coverage path of the area, the grid map identifies the area between the obstacle a and the obstacle B as the impassable area, so that the path planned by the full-coverage of the area cannot pass through the area, and the mowing robot cannot realize real full coverage on the area.
Therefore, in the mowing path planning of the intelligent robot in the prior art, the consideration of low computing resource consumption and optimal maximum efficiency traversal planning strategy cannot be realized.
Disclosure of Invention
In view of the above-mentioned defects or shortcomings in the prior art, the present invention aims to provide a method for planning a path of a mowing robot based on a grid map, which realizes optimal planning of the path, improves mowing efficiency and simultaneously realizes full coverage of an area on the premise of ensuring minimum computing resources by preliminarily rasterizing and further refining the grid of the map.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
the embodiment of the invention provides a grid map-based mowing robot path planning method, which comprises the following steps:
step S1, performing grid processing on an environment map by taking the transverse overall dimension of the mowing robot as the grid size, and establishing an initial grid map;
step S2, constructing a grid equation based on the obstacles in the initial grid map, and solving the size of the refined grid based on the grid equation;
step S3, judging the distance between adjacent obstacles in the rasterized map, expanding the size of a grid outwards on the basis of the grid occupied by the obstacles when a preset condition is met, taking the expanded area as a buffer area for path planning, taking all the areas containing the obstacles after expansion as a grid thinning area, and thinning the grid of the grid thinning area by thinning the grid size;
step S4, calculating the multiple N of the transverse outline size of the mowing robot to the grid thinning size, enabling M to be int (N/2), expanding the thinned grid occupied by the barrier after grid thinning outwards by M grids, and taking all the thinned grids after expansion as the number of the grids occupied by the barrier;
step S5, setting a starting point grid and an end point grid of the robot running track on a grid map containing an initial grid and a refined grid;
step S6, judging whether the mowing robot is in a buffer area; when not in the buffer area, the flow proceeds to step S7; when the buffer is located, the process proceeds to step S8;
step S7, planning the next path based on the initial grid map, and entering step S9;
step S8, planning the next path based on the refined grid map, and entering step S9;
step S9, judging whether the grid of the current path is the terminal point, if not, turning to step S6; and when the route is the terminal point, outputting the current planning route.
As a preferred embodiment of the present invention, the path planning method further includes:
step S10, after the current planning path is output, whether all initial grids and refined grids are covered by all planning paths and barrier areas is judged; if not, go to step S5; when the covering is complete, the mowing operation is ended.
As a preferred embodiment of the present invention, the step S2 further includes:
in the environment map, the distance between obstacles A and B is set as d1The transverse overall dimension of the mowing robot is bcRefining the grid size of beThe distance between the nearest B end of the obstacle A and the near A end of the occupied grid is esThe distance between the nearest A end of the barrier B and the near B end of the occupied grid is exIn the transverse dimension b of the robot mowercFor the grid size, grid initialization is performed, and a grid equation is established as follows:
in the formula (1), the values of Q, W, E, R are all positive integers, and the refined grid size b is obtainede。
As a preferred embodiment of the present invention, the distance preset condition for the distance judgment in step S3 is:
1.1bc<d1<2bc (2)
in the formula (2), d1Is the distance between obstacles bcIs the transverse overall dimension of the mowing robot.
As a preferred embodiment of the present invention, the start grid and the end grid regularly set in step S5 are different, and the start grid and the end grid are the initial grid or the refinement grid.
As a preferred embodiment of the present invention, in the step S7, the path planning is performed by using a four-domain search mode of an a-star algorithm and using f (x) ═ g (x) + h (x) as a heuristic function, where f (x) represents a total cost of the initial raster, g (x) represents a cost consumed by advancing an initial raster, and h (x) represents a cost expected to be consumed by reaching a target point.
As a preferred embodiment of the present invention, in the step S8, the path planning is performed by using a four-domain search mode of a-star algorithm, and using f (x) ═ g (x) + h (x) as a heuristic function, where f (x) represents the total cost of the refined grid, g (x) represents the cost of consumption of advancing one refined grid, and g (x) represents the cost of consumption of advancing one refined grid proportionally decreases with the number of refined grids; h (x) represents the cost expected to be expended in reaching the target point.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the mowing coverage after path planning is improved and mowing efficiency and quality are improved while path calculation resources are saved by primarily rasterizing the mowing area, selecting a proper grid according to the obstacle judgment result of the primary rasterizing and refining the selected grid on the basis of the primary rasterizing, and planning the path according to the initialized grid and the refined grid.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of an obstacle area in a grid map of a mowing robot in the prior art;
FIG. 2 is a schematic view of a prior art inaccessible area identified by a mowing robot;
FIG. 3 is a flowchart of a method for planning a path of a mowing robot based on a grid map according to an embodiment of the invention;
FIG. 4 is a schematic diagram of solving for refined grid size in an embodiment of the present invention;
FIG. 5 is a diagram of a buffer according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of the expansion process in an embodiment of the present invention;
FIG. 7 is an exemplary diagram of a mowing robot movement in accordance with an embodiment of the present disclosure;
fig. 8 is an exemplary diagram of a mowing robot movement in another embodiment of the present disclosure.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The embodiment of the invention provides a grid map-based mowing robot path planning method, which comprises the steps of firstly conducting preliminary rasterization on a mowing area, selecting a proper grid according to a preliminary rasterized obstacle judgment result on the basis of the preliminary rasterization, conducting selected grid refinement, conducting path planning according to an initialized grid and a refined grid, saving path calculation resources, improving a mowing coverage after path planning, and improving mowing efficiency and quality.
Fig. 3 shows a flowchart of a method for planning a path of a lawn mowing robot based on a grid map according to an embodiment of the invention. As shown in fig. 3, the path planning method includes the following steps:
and step S1, performing grid processing on the environment map by taking the transverse overall dimension of the mowing robot as the grid size, and establishing an initial grid map.
And step S2, constructing a grid equation based on the obstacles in the initial grid map, and solving the refined grid size based on the grid equation.
As shown in fig. 4, in the environment map, taking the existence of obstacles a and B as an example, the distance between a and B is d1The transverse overall dimension of the mowing robot is bcIdeal grid size beThe distance between the lowest end of the obstacle A and the highest end of the occupied grid is esThe distance between the uppermost end of the barrier B and the lowermost end of the occupied grid is exIn the transverse dimension b of the robot mowercFor the grid size, grid initialization is performed, and a grid equation is established as follows:
q, W, E, R is obtained through a grid equation, the numerical values of Q, W, E, R are all positive integers, and b is obtained by solvingeThe size of (c). Solving for b in the positive integer assignment to Q, W, E, ReIn the process, a decreasing set is obtained, and the maximum value in the set is selected as the final solution. beThe larger the grid area is, the smaller the grid number of the refined grid area is, the faster the operation speed is, and the least resources are consumed while reasonably avoiding obstacles.
And step S3, judging the distance between adjacent obstacles in the rasterized map, and when a preset condition is met, expanding the size of a grid outwards on the basis of the grid occupied by the obstacles, taking the expanded area as a buffer area for path planning, taking all the areas containing the obstacles after expansion as a grid thinning area, and thinning the grid of the grid thinning area by thinning the grid size.
Preferably, in this step, the preset distance condition for performing distance judgment is:
1.1bc<d1<2bc (2)
in the formula (2), d1Is the distance between obstacles bcIs the transverse overall dimension of the mowing robot.
In one embodiment of the invention, illustrated in FIG. 5, the obstacles A and B are spaced apart by a distance d1And (4) externally expanding the obtained buffer area.
And step S4, calculating the multiple N of the transverse outline size of the mowing robot to the grid thinning size, enabling M to be int (N/2), expanding the thinned grid occupied by the obstacles after the grid thinning outwards by M grids, and taking all the thinned grids after the expansion as the number of the grids occupied by the obstacles.
As shown in fig. 6, taking the mowing robot moving from the position 1 to the position 2 as an example, at this time, the mowing robot occupies 25 grids, the grid where the number 1 is located is taken as the center of the mowing robot, and the grid where the mowing robot is located is taken as a research object, in order to ensure that the mowing robot does not collide with an obstacle, the obstacle is subjected to an expansion process, that is, the obstacle expands two cells outward, as shown by a shaded portion around the obstacle in fig. 6.
In step S5, a start point and an end point of the robot trajectory are set on the grid map.
In the step, the starting point and the terminal point set each time are different, so that the aim of comprehensively covering the mowing area is fulfilled.
Step S6, judging whether the mowing robot is in a buffer area; when not in the buffer area, the flow proceeds to step S7; when the buffer is located, the process proceeds to step S8;
in step S7, a one-step route is planned based on the initial grid map, and the process proceeds to step S9.
In this step, the path planning is performed by using a four-domain search mode of an a-star algorithm and using f (x) ═ g (x) + h (x) as a heuristic function, where f (x) represents the total cost of the initial raster, g (x) represents the cost consumed by advancing one raster, and h (x) represents the cost expected to be consumed when reaching the target point.
And step S8, planning a one-step path based on the refined grid map, and entering step S9.
In this step, the route planning may use the same search mode as that of step S6, or may use a search mode different from that of step S6. Here, the same search pattern is still used as an example for explanation, but the present invention is not limited thereto.
A four-domain search mode of the a-star algorithm is adopted to perform path planning by taking f (x) ═ g (x) + h (x) as a heuristic function, wherein f (x) represents the total cost of the initial grid, g (x) represents the cost consumed by advancing one grid, and g (x) represents the cost consumed by advancing one grid which is reduced proportionally with the number of refined grids; h (x) represents the cost expected to be expended in reaching the target point. The proportional drop is proportional to the distance the moving grid has to travel. For example, as shown in fig. 7, in the grid refinement region, an initial grid is subdivided into 25 small grids, and in the initial grid map, the cost of moving one grid is 5, and in the refinement grid region, the cost of moving one small grid is 1.
Fig. 7 is a diagram illustrating a movement trajectory of the mowing robot when neither the start point nor the end point is in the dilation region according to an embodiment of the present disclosure. As shown in fig. 7, the mowing robot starts from the initial grid with the dark shading on the left side, starts to start one grid on the left side and enters the buffer area, starts to plan a path according to the refined grid, bypasses the obstacle refined grid containing the expansion area, reaches the edge of the buffer area, enters the initial grid area again, and finally ends the current path to the initial grid with the light shading on the right side. The whole path planning effectively identifies the obstacles, but also fully utilizes the effective space among the obstacles, and more completely covers the working area; meanwhile, the lawn mower can rapidly advance in a non-obstacle area, so that computing resources are saved, and mowing efficiency is improved. Fig. 8 takes position 1 and position 2 in the refined grid region as an example, and since position 1 and position 2 are both in the buffer, the whole obtained trajectory is in the refined grid map.
Step S9, judging whether the grid of the current path is the terminal point, if not, turning to step S6; when the end point is reached, the flow proceeds to step S10.
Step S10, outputting the current planning path, and judging whether all the planning paths and the barrier area cover all the initial grids and the refined grids; if not, go to step S5; when the covering is complete, the mowing operation is ended.
Preferably, in this step, when not fully covered, the current end point is designated as the start point grid of step S5.
According to the technical scheme, the mowing robot path planning method based on the grid map provided by the embodiment of the invention has the advantages that firstly, a mowing area is subjected to preliminary rasterization, then on the basis of the preliminary rasterization, a proper grid is selected according to a barrier judgment result of the preliminary rasterization, the selected grid is refined, and then the path planning is carried out according to the initialized grid and the refined grid, so that path calculation resources are saved, the mowing coverage after path planning is improved, and the mowing efficiency and the mowing quality are improved.
The foregoing description is only exemplary of the preferred embodiments of the invention and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features and (but not limited to) features having similar functions disclosed in the present invention are mutually replaced to form the technical solution.
Claims (6)
1. A mowing robot path planning method based on a grid map is characterized by comprising the following steps:
step S1, performing grid processing on an environment map by taking the transverse overall dimension of the mowing robot as the grid size, and establishing an initial grid map;
step S2, constructing a grid equation based on the obstacles in the initial grid map, and solving the size of the refined grid based on the grid equation; the method specifically comprises the following steps:
in the environment map, the distance between obstacles A and B is set as d1The transverse overall dimension of the mowing robot is bcRefining the grid size of beThe distance between the nearest B end of the obstacle A and the near A end of the occupied grid is esThe distance between the nearest A end of the barrier B and the near B end of the occupied grid is exIn the transverse dimension b of the robot mowercFor the grid size, grid initialization is performed, and a grid equation is established as follows:
in the formula (1), the values of Q, W, E, R are all positive integers, and the refined grid size b is obtainede；
Step S3, judging the distance between adjacent obstacles in the rasterized map, expanding the size of a grid outwards on the basis of the grid occupied by the obstacles when a preset condition is met, taking the expanded area as a buffer area for path planning, taking all the areas containing the obstacles after expansion as a grid thinning area, and thinning the grid of the grid thinning area by thinning the grid size;
step S4, calculating the multiple N of the transverse outline size of the mowing robot to the grid thinning size, enabling M to be int (N/2), expanding the thinned grid occupied by the barrier after grid thinning outwards by M grids, and taking all the thinned grids after expansion as the number of the grids occupied by the barrier;
step S5, setting a starting point grid and an end point grid of the robot running track on a grid map containing an initial grid and a refined grid;
step S6, judging whether the mowing robot is in a buffer area; when not in the buffer area, the flow proceeds to step S7; when the buffer is located, the process proceeds to step S8;
step S7, planning the next path based on the initial grid map, and entering step S9;
step S8, planning the next path based on the refined grid map, and entering step S9;
step S9, judging whether the grid of the current path is the terminal point, if not, turning to step S6; and when the route is the terminal point, outputting the current planning route.
2. The lawn mowing robot path planning method according to claim 1, further comprising:
step S10, after the current planning path is output, whether all initial grids and refined grids are covered by all planning paths and barrier areas is judged; if not, go to step S5; when the covering is complete, the mowing operation is ended.
3. The robot lawnmower path planning method according to claim 1 or 2, wherein the preset conditions for the distance determination in step S3 are:
1.1bc<d1<2bc (2)
in the formula (2), d1Is the distance between obstacles bcIs the transverse overall dimension of the mowing robot.
4. The robot lawnmower path planning method according to claim 1 or 2, wherein the start grid and the end grid set by the path rule in step S5 are different, and the start grid and the end grid are an initial grid or a refined grid.
5. The lawn mowing robot path planning method according to claim 1 or 2, wherein in the path planning in step S7, a four-domain search mode of an a-star algorithm is adopted, and path planning is performed with f (x) ═ g (x) + h (x) as a heuristic function, where f (x) represents a total cost of the initial grid, g (x) represents a cost of consumption of advancing an initial grid, and h (x) represents a cost of consumption expected to reach a target point.
6. The robot lawnmower path planning method according to claim 1 or 2, wherein the path planning in step S8 is performed using a four-domain search mode of a-star algorithm with f (x) ═ g (x) + h (x) as a heuristic function, wherein f (x) represents a total cost of the refined grid, g (x) represents a cost of consumption of advancing one refined grid, and g (x) represents a cost of consumption of advancing one refined grid that decreases in proportion to the number of refined grids; h (x) represents the cost expected to be expended in reaching the target point.
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