CN116774733B - Multi-unmanned aerial vehicle coverage path planning method - Google Patents
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Abstract
The invention discloses a multi-unmanned aerial vehicle coverage path planning method. The method comprises region division and path generation, wherein a target region is divided into a plurality of exclusive regions by a region division algorithm to serve as independent operation regions of each unmanned aerial vehicle, and the coverage problem of the weighting condition is solved; therefore, the original multi-unmanned aerial vehicle optimization problem is degenerated into the CPP problem of a plurality of unmanned aerial vehicles, the explosive combination complexity is reduced, and the problems of repeated coverage and collision are avoided; each CPP problem utilizes a path planning algorithm based on a spanning tree to calculate a closed loop path in each subarea, the spanning tree shape is properly adjusted by weighting edges, the turning times are reduced, the defect that the spanning tree coverage algorithm cannot fully cover the real environment is overcome by adopting path compensation optimization, and high coverage rate and high efficiency of the weighted area can be ensured.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a multi-unmanned aerial vehicle coverage path planning method.
Background
Unmanned aerial vehicles are widely applied to daily life of people due to the advantages of unmanned aerial vehicles, and are increasingly used in the aspects of ground traffic monitoring, natural disasters, intelligent agriculture, search and rescue tasks, environmental data collection and the like. An important task for most applications is the ability to efficiently cover an area with a drone, and therefore path planning based on the covered task is a rather critical part. Coverage path planning (Coverage Path Planning, CPP) refers to planning an optimal path through which the drone can traverse the entire area to be covered while avoiding Obstacles or No Fly Zones (NFZ).
Currently, single unmanned aerial vehicle coverage path planning technology is relatively mature, but single unmanned aerial vehicle coverage is gradually not suitable for task execution in a large range and complex area due to the limitation of self performance of a single machine, and research on multi-unmanned aerial vehicle coverage path planning is gradually increased. The multi-unmanned aerial vehicle coverage path planning belongs to the NP-hard problem, and the two-step type is adopted in the most common solution at present: 1) Dividing the area to be subjected to the coverage task into a plurality of subareas; 2) Each drone performs a stand-alone overlay in a respective discrete area. The advantages are that: 1) The problem of collision and repeated coverage of multiple robots is avoided; 2) The coverage difficulty is reduced, and the problem is converted into a single-machine coverage problem. From this perspective, the multi-unmanned aerial vehicle coverage path planning problem relates to an environmental area division and path planning algorithm, and the task space division method mainly comprises the following steps: the path planning algorithm includes a spanning tree algorithm, an artificial potential field method, a sampling-based algorithm, a greedy search and graph search algorithm, a genetic algorithm, an ant colony algorithm, and the like.
However, the above solution also has some problems: different types of unmanned aerial vehicles have different maneuverability and perceptibility, and the coverage difficulty of different terrains is different, so that the balance of the workload of each unmanned aerial vehicle is difficult to control; since the area division and the single coverage are different from each other, there may be a problem in that coverage and coverage efficiency are not high enough.
Disclosure of Invention
The invention aims to provide a multi-unmanned aerial vehicle coverage path planning method aiming at the defects existing in the prior art.
In order to achieve the above object, the present invention provides a method for planning a coverage path of a multi-unmanned aerial vehicle, comprising:
step 1, carrying out area division, wherein the area division specifically comprises the following steps:
step 1.1, acquiring a target area needing to be subjected to coverage path planning and an obstacle or a disabled area needing to be avoided in the target area, discretizing the target area to represent the target area, the obstacle or the disabled area by a series of unit grid nodes respectively, and assigning a state and a weight of each unit grid;
step 1.2, calculating each unmanned aerial vehicleDistance from each mesh node in the target area, and generates +/for each drone>Is>，/>，/>For the distance calculation function,is unmanned plane->Is>Coordinates of nodes of the unit grid;
step 1.3, constructing an allocation matrix for determining the attribution relation of each unit grid in the regional division stage：
；
Step 1.4, introducing a balance correction factorAnd connectivity correction factor->Evaluation matrix->Make corrections to balance the allocation target area and ensure allocation to each drone +.>Is connected, modified evaluation matrix +.>The method comprises the following steps: />，/>Representing multiplication of elements and then according to the modified evaluation matrix +.>Updating allocation matrixThe method comprises the steps of performing iterative circulation until a region division target to be achieved is met;
step 1.5 by assigning matricesGet every unmanned aerial vehicle +.>A divided cell grid:；
by usingRepresenting +.>The number of cells currently allocated is then allocated to each drone +.>Is>The method comprises the following steps:
；
step 2, for each unmanned aerial vehicleThe cell grid obtained in this way->The internal generation path specifically comprises the following steps:
step 2.1, each unmanned aerial vehicleIs expressed as +.>, wherein ,/>Is->Cell grid node number on the unmanned aerial vehicle path;
step 2.2 dividing the cell gridThe corresponding target area is discretized into square grids formed by four unit grids, each square grid is used as a free node, two adjacent free nodes are connected into one side of a spanning tree, and each side is subjected to preset weight according to different weight combinations;
2.3, constructing a minimum spanning tree by using a Kruskal algorithm, obtaining different minimum spanning trees according to different weight combinations, constructing a closed path between adjacent square grids around the minimum spanning tree, and selecting the closed path with the minimum turning number and the corresponding weight combination;
and 2.4, carrying out path compensation optimization on the closed path with the minimum turning number according to the selected weight combination so as to obtain a path planning result.
Further, in the step 1.1, the number of rows after discretizing the target areaAnd column number->The method comprises the following steps of:
wherein , and />Maximum and minimum values of the target area in the X direction, respectively, < >>Andmaximum and minimum values of the target area in the Y direction, respectively, < >>Is the cell grid size.
Further, in the step 1.1, the target area, the obstacle or the area where the flying is forbidden is represented by a series of unit grid nodes respectively:
wherein ,for the target area +.>Is an obstacle or a region of forbidden flight, +.>For the coordinates of the cell grid nodes, the set of cell grids to be covered +.>Expressed as:
。
further, in the step 1.1, the state and the weight of each unit grid are assigned as follows:
using oneRepresenting the state of a cell grid of a target area, the state of the cell grid of the target area being:
wherein ,status of cell grid for target area, +.>Is the total number of unmanned aerial vehicles;
using another oneMatrix of->Assigning a weight to each cell gridTotal weight of whole grid map>The method comprises the following steps:
。
further, the balance correction factorExpressed as:
wherein ,for the balance correction factor before the iterative loop, +.>For correction of the adjustable parameter->Is->The weight fraction required to be covered by the unmanned aerial vehicle is set;
the balance correction factorTo achieve a minimum target value +.>, wherein ,
for the ratio of the sub-regions after allocation.
Further, the connectivity correction factorExpressed as:
wherein ,is->Is +.>A set of connected unit grids +.>For allocation to unmanned aerial vehicle->But is +.>Non-connected set of cell grids +.>，/>。
Further, in the step 2.3, the manner of constructing the closed path is specifically as follows:
step 2.3.1, judging the accessibility of four unit grids adjacent to the current position of the unmanned aerial vehicle and the surrounding through the current position and direction of the unmanned aerial vehicle and the edges of the spanning tree, wherein the accessibility is specifically as follows:
if the unit grid where the current position of the unmanned aerial vehicle is located and a certain unit grid belong to one square or belong to two squares with a spanning tree sharing edge, and the unit grid does not belong to an obstacle or a no-fly area, judging that the unmanned aerial vehicle is reachable, otherwise judging that the unmanned aerial vehicle is not reachable;
step 2.3.2, for the unit grids judged to be reachable, carrying out path coverage according to the action priority sequence corresponding to the action direction, judging whether the covered paths have intersection with the edges of the spanning tree, and adding the unit grids into a path point set if the covered paths have no intersection;
and 2.3.3, taking the unit grids currently added into the path point set as the current position of the unmanned aerial vehicle, and returning to the step 1 until all four adjacent unit grids around the current position of the unmanned aerial vehicle are boundaries of the covered or obstacle or no-fly area or the target area.
Further, in the step 2.4, the method for performing path compensation optimization on the closed path with the minimum turning number is specifically as follows: re-surrounding the spanning tree construction path according to the selected weight combination, if the idle unit grids are detected outside the action direction, moving towards the unit grids, and also moving according to the action priority sequence corresponding to the action direction, in the process, if the grid where a certain unit grid is detected to be provided with the edge of the spanning tree, ignoring the unit grids, and continuously detecting until all four adjacent unit grids around are all covered or obstacle or the boundary of a disabled area or target area; and then returning along the completely opposite direction, if the idle cell grid is detected, continuing to branch a new path, detecting according to the process, after finishing the detection and returning to the closed path with the minimum turning number, continuing to cover along the spanning tree, and if the idle cell grid is detected, detecting according to the process until the cover is finished.
The beneficial effects are that: according to the method, a calculation process is divided into two stages of target area balance division and subarea spanning tree planning, a target area is divided into a plurality of exclusive areas through an area division algorithm to serve as independent operation areas of each unmanned plane, and the coverage problem of weighting conditions (different difficulties or exploration priorities) is solved; therefore, the original multi-unmanned aerial vehicle optimization problem is degenerated into the CPP problem of a plurality of unmanned aerial vehicles, the explosive combination complexity is reduced, and the problems of repeated coverage and collision are avoided; each CPP problem utilizes a path planning algorithm based on a spanning tree to calculate a closed loop path in each subarea, the spanning tree shape is properly adjusted by weighting edges, the turning times are reduced, the defect that the spanning tree coverage algorithm cannot fully cover the real environment is overcome by adopting path compensation optimization, and high coverage rate and high efficiency of the weighted area can be ensured.
Drawings
Fig. 1 is a flow chart of a multi-unmanned aerial vehicle coverage path planning method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a discretized target area;
FIG. 3 is a schematic diagram of constructing a spanning tree from different weight combinations;
fig. 4 is a schematic diagram of a path planned from a spanning tree.
Detailed Description
The invention will be further illustrated by the following drawings and specific examples, which are carried out on the basis of the technical solutions of the invention, it being understood that these examples are only intended to illustrate the invention and are not intended to limit the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides a method for planning a coverage path of a multi-unmanned aerial vehicle, including:
step 1, carrying out region division, wherein the region division specifically comprises the following steps:
step 1.1, acquiring a target area needing to be subjected to coverage path planning and an obstacle or a disabled area needing to be avoided in the target area, discretizing the target area to represent the target area, the obstacle or the disabled area by a series of unit grid nodes respectively, and assigning a state and a weight of each unit grid. Specifically, the number of rows after discretization of the target areaAnd column number->The method comprises the following steps of:
wherein , and />Maximum and minimum values of the target area in the X direction, respectively, < >>Andmaximum and minimum values of the target area in the Y direction, respectively, < >>Is the cell grid size.
The target area, obstacle or no-fly area is represented by a series of cell grid nodes, respectively:
wherein ,for the target area +.>Is an obstacle or a region of forbidden flight, +.>For the coordinates of the cell grid node (center), the set of cell grids to be covered +.>Expressed as:
。
the state and weight of each cell grid are assigned as follows:
using oneTo represent the state of the cell grid of the target area, the state of the cell grid of the target area being:
wherein ,status of cell grid for target area, +.>Is the total number of unmanned aerial vehicles. Referring specifically to fig. 2, the irregular heptagon-shaped shaded area in fig. 2 is the target area,the left trapezoid and the right circle represent different obstacles, respectively, and the number in each cell grid is the state thereof.
Using another oneMatrix of->Assigning a weight to each cell gridTotal weight of whole grid map>The method comprises the following steps:
。
step 1.2, calculating each unmanned aerial vehicleDistance from each mesh node in the target area, and generates +/for each drone>Is>，/>，/>For the distance calculation function,is unmanned plane->Is used for the initial position of (a). The distance calculation function may be Euclidean distance calculation function or Mannich distance calculation functionThe Hadun distance calculation function or other distance calculation functions can be selected according to requirements.
Step 1.3, constructing an allocation matrix for determining the attribution relation of each unit grid in the regional division stage：
。
Step 1.4, introducing a balance correction factorAnd connectivity correction factor->Evaluation matrix->Make corrections to balance the allocation target area and ensure allocation to each drone +.>Is connected, modified evaluation matrix +.>The method comprises the following steps: />，/>Representing multiplication of elements and then according to the modified evaluation matrix +.>Update allocation matrix->And (5) iterating the loop until the region division target to be achieved is met.
The balance correction factorExpressed as:
wherein ,for the balance correction factor before the iterative loop, +.>For correction of the adjustable parameter->Is->The weight fraction required to be covered by the unmanned aerial vehicle is set;
the balance correction factorTo achieve a minimum target value +.>, wherein ,
for the ratio of the sub-regions after allocation.
The above-mentioned communicationSex correction factorExpressed as:
wherein ,is->Is +.>A set of connected unit grids +.>For allocation to unmanned aerial vehicle->But is +.>Non-connected set of cell grids +.>，/>. If all allocated areas of all unmanned aerial vehicles are connected, then +.>Is an all-matrix.
Step 1.5 by assigning matricesGet every unmanned aerial vehicle +.>A divided cell grid:；
by usingRepresenting +.>The number of cells currently allocated is then allocated to each drone +.>Is>The method comprises the following steps:
。
updating an evaluation matrix by multiple iterative loopsAnd allocation matrix->Meets the region division target to be achieved: the subareas to be covered by each unmanned aerial vehicle are communicated; the union of all the subareas forms an initial area; each drone has equal (or proportional) sub-regions; each unmanned aerial vehicle is in its own sub-area. Expressed by the formula:
step 2, for each unmanned aerial vehicleThe cell grid obtained in this way->The internal generation path specifically comprises the following steps:
step 2.1, each unmanned aerial vehicleIs expressed as +.>, wherein ,/>Is->Cell grid node number on the path of the unmanned aerial vehicle. If node-> and />The conditions can be satisfied: />Description-> and />Is two adjacent nodes.
Step 2.2 dividing the cell gridThe corresponding target area is discretized into square grids formed by four unit grids, each square grid is used as a free node, two adjacent free nodes are connected into one side of the spanning tree, and each side is subjected to preset weight according to different weight combinations. Referring to FIG. 2, it can be seen that the square is formed with side length +.>. Similarly, the square has corresponding states, namely, completely free and incapable of covering (completely occupied or partially occupied).
And 2.3, constructing a minimum spanning tree by using a Kruskal algorithm, obtaining different minimum spanning trees according to different weight combinations, surrounding the minimum spanning tree, constructing a closed path between adjacent grids, and selecting the closed path with the minimum turning number and the corresponding weight combination thereof. Referring specifically to fig. 3, fig. 3 illustrates the structure of a spanning tree obtained under four different weight combinations a, b, c, d, and fig. 4 illustrates a path constructed by the spanning tree obtained according to the weight combination b. The four different weight combination modes of a, b, c, d are shown in the following table:
the above-mentioned way of constructing the closed path is specifically as follows:
step 2.3.1, judging the accessibility of four unit grids adjacent to the current position of the unmanned aerial vehicle and the surrounding through the current position and direction of the unmanned aerial vehicle and the edges of the spanning tree, wherein the accessibility is specifically as follows:
if the unit grid where the current position of the unmanned aerial vehicle is located and a certain unit grid belong to one square or belong to two squares with the same side of the spanning tree, and the unit grid does not belong to an obstacle or a disabled area, judging that the unmanned aerial vehicle is reachable, otherwise judging that the unmanned aerial vehicle is not reachable.
And 2.3.2, carrying out path coverage on the unit grids judged to be reachable according to the action priority sequence corresponding to the action direction, judging whether the covered paths intersect with the edges of the spanning tree, and adding the unit grids into the path point set if the covered paths do not intersect. Taking the above-mentioned action direction as an example of anticlockwise direction, the corresponding action priority order is right turn > straight line > left turn.
And 2.3.3, taking the unit grids currently added into the path point set as the current position of the unmanned aerial vehicle, and returning to the step 1 until all four adjacent unit grids around the current position of the unmanned aerial vehicle are boundaries of the covered or obstacle or no-fly area or the target area.
And 2.4, carrying out path compensation optimization on the closed path with the minimum turning number according to the selected weight combination so as to obtain a path planning result. The method comprises the following steps: and re-surrounding the spanning tree construction path according to the selected weight combination, if the idle unit grids are detected outside the action direction, moving towards the unit grids, and also moving according to the action priority sequence corresponding to the action direction, in the process, if the grid where one unit grid is detected to be provided with the edges of the spanning tree, ignoring the unit grids, and continuously detecting until all four adjacent unit grids around are all covered or obstacle or no-fly areas or boundaries of target areas. And then returning along the completely opposite direction, if the idle cell grid is detected, continuing to branch a new path, detecting according to the process, after finishing the detection and returning to the closed path with the minimum turning number, continuing to cover along the spanning tree, and if the idle cell grid is detected, detecting according to the process until the cover is finished.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that other parts not specifically described are within the prior art or common general knowledge to a person of ordinary skill in the art. Modifications and alterations may be made without departing from the principles of this invention, and such modifications and alterations should also be considered as being within the scope of the invention.
Claims (8)
1. The multi-unmanned aerial vehicle coverage path planning method is characterized by comprising the following steps of:
step 1, carrying out area division, wherein the area division specifically comprises the following steps:
step 1.1, acquiring a target area needing to be subjected to coverage path planning and an obstacle or a disabled area needing to be avoided in the target area, discretizing the target area to represent the target area, the obstacle or the disabled area by a series of unit grid nodes respectively, and assigning a state and a weight of each unit grid;
step 1.2, calculating each unmanned aerial vehicleDistance from each mesh node in the target area, and generates +/for each drone>Is>，/>，/>For calculating the function of distance>Is unmanned plane->Is>Coordinates of nodes of the unit grid;
step 1.3, constructing an allocation matrix for determining the attribution relation of each unit grid in the regional division stage：
；
Wherein L is the set of unit grids to be covered,is the total number of unmanned aerial vehicles;
step 1.4, introducing a balance correction factorAnd connectivity correction factor->Evaluation matrix->Make corrections to balance the allocation target area and ensure allocation to each drone +.>Is connected, modified evaluation matrix +.>The method comprises the following steps:，/>representing multiplication of elements and then according to the modified evaluation matrix +.>Updating allocation matrixThe method comprises the steps of performing iterative circulation until a region division target to be achieved is met;
the area division targets are as follows: the subareas to be covered by each unmanned aerial vehicle are communicated; the union of all the subareas forms an initial area; each unmanned aerial vehicle has equal or proportional subregions; each unmanned aerial vehicle is located in a sub-area of the unmanned aerial vehicle;
step 1.5 by assigning matricesGet every unmanned aerial vehicle +.>A divided cell grid:；
by usingRepresenting +.>The number of cells currently allocated is then allocated to each droneIs>The method comprises the following steps:
；
wherein ,a weight for each cell grid;
step 2, for each unmanned aerial vehicleThe cell grid obtained in this way->The internal generation path specifically comprises the following steps:
step 2.1, each unmanned aerial vehicleIs expressed as +.>, wherein ,is->Cell grid node number on the unmanned aerial vehicle path;
step 2.2 dividing the cell gridThe corresponding target area is discretized into square grids formed by four unit grids, each square grid is used as a free node, two adjacent free nodes are connected into one side of a spanning tree, and each side is subjected to preset weight according to different weight combinations;
2.3, constructing a minimum spanning tree by using a Kruskal algorithm, obtaining different minimum spanning trees according to different weight combinations, constructing a closed path between adjacent square grids around the minimum spanning tree, and selecting the closed path with the minimum turning number and the corresponding weight combination;
and 2.4, carrying out path compensation optimization on the closed path with the minimum turning number according to the selected weight combination so as to obtain a path planning result.
2. The method for planning a coverage path of a multi-unmanned aerial vehicle according to claim 1, wherein in step 1.1, the number of rows after discretization of the target areaAnd column number->The method comprises the following steps of:
；
；
wherein , and />Maximum and minimum values of the target area in the X direction, respectively, < >> and />Maximum and minimum values of the target area in the Y direction, respectively, < >>Is the cell grid size.
3. The method of claim 2, wherein in step 1.1, the target area, the obstacle, or the area where the flying is forbidden are represented by a series of cell grid nodes respectively:
；
；
wherein ,for the target area +.>For barriers or areas to be protected against flying, the set of cell grids to be covered is +.>Expressed as:
。
4. a multi-unmanned aerial vehicle coverage path planning method according to claim 3, wherein in step 1.1, the state and the weight of each cell grid are assigned as follows:
using oneRepresenting the state of a cell grid of a target area, the state of the cell grid of the target area being:
；
wherein ,a state of a cell grid that is a target area;
using another oneMatrix of->A weight is assigned to each cell grid>Total weight of whole grid map>The method comprises the following steps:
。
5. the multi-unmanned aerial vehicle coverage path planning method according to claim 1, which comprises the following steps ofCharacterized in that the balance correction factorExpressed as:
；
wherein ,for the balance correction factor before the iterative loop, +.>For correction of the adjustable parameter->Is->The weight fraction required to be covered by the unmanned aerial vehicle is set;
the balance correction factorTo achieve a minimum target value +.>, wherein ,
；
；
；
for the ratio of the sub-regions after allocation.
6. The multi-unmanned aerial vehicle coverage path planning method of claim 1, wherein the connectivity correction factorExpressed as:
；
wherein ,is->Is +.>A set of connected unit grids +.>For allocation to unmanned aerial vehiclesBut is +.>Non-connected set of cell grids +.>，/>。
7. The method for planning a coverage path of a multi-unmanned aerial vehicle according to claim 1, wherein in the step 2.3, the manner of constructing the closed path is specifically as follows:
step 2.3.1, judging the accessibility of four unit grids adjacent to the current position of the unmanned aerial vehicle and the surrounding through the current position and direction of the unmanned aerial vehicle and the edges of the spanning tree, wherein the accessibility is specifically as follows:
if the unit grid where the current position of the unmanned aerial vehicle is located and a certain unit grid belong to one square or belong to two squares with a spanning tree sharing edge, and the unit grid does not belong to an obstacle or a no-fly area, judging that the unmanned aerial vehicle is reachable, otherwise judging that the unmanned aerial vehicle is not reachable;
step 2.3.2, for the unit grids judged to be reachable, carrying out path coverage according to the action priority sequence corresponding to the action direction, judging whether the covered paths have intersection with the edges of the spanning tree, and adding the unit grids into a path point set if the covered paths have no intersection;
and 2.3.3, taking the unit grids currently added into the path point set as the current position of the unmanned aerial vehicle, and returning to the step 1 until all four adjacent unit grids around the current position of the unmanned aerial vehicle are boundaries of the covered or obstacle or no-fly area or the target area.
8. The method for planning a coverage path of a multi-unmanned aerial vehicle according to claim 7, wherein in the step 2.4, the path compensation optimization method for the closed path with the smallest number of turns is specifically as follows: re-surrounding the spanning tree construction path according to the selected weight combination, if the idle unit grids are detected outside the action direction, moving towards the unit grids, and also moving according to the action priority sequence corresponding to the action direction, in the process, if the grid where a certain unit grid is detected to be provided with the edge of the spanning tree, ignoring the unit grids, and continuously detecting until all four adjacent unit grids around are all covered or obstacle or the boundary of a disabled area or target area; and then returning along the completely opposite direction, if the idle cell grid is detected, continuing to branch a new path, detecting according to the process, after finishing the detection and returning to the closed path with the minimum turning number, continuing to cover along the spanning tree, and if the idle cell grid is detected, detecting according to the process until the cover is finished.
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