CN114812553A - Multi-unmanned-aerial-vehicle collaborative three-dimensional flight path planning method considering DSM (digital surface model) - Google Patents
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Abstract
The invention provides a multi-unmanned aerial vehicle collaborative three-dimensional flight path planning method which comprises the steps of firstly calculating by using a minimum outsourcing convex polygon algorithm to obtain an abstract minimum convex hull of a target operation area, generating a polygon area with a specific number of edges by using a convex polygon simplification algorithm, then dividing the area, carrying out Z-shaped coverage on each area, determining a flight mission area of each unmanned aerial vehicle, dispersing the flight mission area into abstract graph nodes, then establishing a flight path evaluation function, and solving an optimized flight path by using the evaluation function and a genetic algorithm. Finally, three-dimensional data is obtained from the DSM model and is derived as KML data. The method makes full use of the DSM model, the rapid convex hull algorithm, the region segmentation and coverage algorithm and the genetic algorithm, has the characteristics of high planning speed, low cost, high precision and good coverage effect, has obvious optimization effects on the coordinated planning and the full track coverage of the multiple unmanned aerial vehicles, and provides an effective track planning method for the field of the track planning of the multiple unmanned aerial vehicles.
Description
Technical Field
The invention relates to the field related to remote sensing science and technology, in particular to a track planning algorithm of multiple unmanned aerial vehicles, and provides a full-automatic planning method.
Background
An Unmanned Aerial Vehicle (abbreviated as UAV) is a powered, controllable, Unmanned Aerial Vehicle that can carry various task devices, perform various tasks, and be reused. Unmanned aerial vehicle and remote sensing technique's combination has all obtained the wide use in different applications such as science and technology, agriculture, traffic, medical treatment, emergent, rescue, carries the unmanned aerial vehicle of high resolution camera to be more the essential important instrument of acquireing aerial image. To realize autonomous flight of the unmanned aerial vehicle, the unmanned aerial vehicle needs to be guided to avoid obstacles and improve the efficiency of task execution by means of path planning, so that energy is saved to the maximum extent, and the waste of resources is avoided.
At present, unmanned aerial vehicle path planning algorithms are mainly classified into two types: one is to find an optimal flight path between a starting point and an end point, which can avoid obstacles; the second type is to plan an optimal path for the unmanned aerial vehicle to perform a flight mission of completely covering and scanning a specific area, and this problem is also called a "coverage path planning" problem. In some tasks requiring comprehensive scanning of a target area, such as disaster search and rescue, environmental monitoring and other tasks, an unmanned aerial vehicle is required to perform a covering flight task to comprehensively scan a target area, and therefore a second type of unmanned aerial vehicle path planning algorithm is required to be performed.
The existing unmanned aerial vehicle coverage path planning algorithm is mostly transplanted by a two-dimensional planar robot path planning algorithm in an ascending way, and when a spatial region has topographic fluctuation interference in height or encounters irregular flight-forbidden regions, a very high flight height can be set for obstacle avoidance, so that the problems of poor resolution of collected images, poor flight trajectory of the unmanned aerial vehicle and even error are caused. Furthermore, most path planning algorithms only consider the flight mission of a single drone. In large tasks such as search rescue, emergency management and the like, multiple unmanned aerial vehicles generally work in a coordinated manner, the traditional unmanned aerial vehicle path planning algorithm is poor in effect, and the cooperative advantages and efficiency of the multiple unmanned aerial vehicles cannot be fully exerted.
Therefore, in the problem of planning the coverage path of the unmanned aerial vehicle, a flight path planning algorithm capable of scheduling multiple unmanned aerial vehicles in a three-dimensional space to realize optimal coverage path planning is urgently needed. The multi-unmanned aerial vehicle collaborative three-dimensional flight path planning method considering the DSM model is an efficient and feasible solution.
Disclosure of Invention
The invention provides a multi-unmanned aerial vehicle collaborative three-dimensional flight path planning method by utilizing a DSM (digital surface model), a rapid convex hull algorithm, a region segmentation and coverage algorithm and a genetic algorithm, and fully utilizing DSM model coordinate information of a three-dimensional region aiming at the defects of the existing unmanned aerial vehicle coverage path planning algorithm.
In order to achieve the purpose, the technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a multi-unmanned aerial vehicle collaborative three-dimensional flight path planning method considering a DSM model, which comprises the following steps:
step 1, inputting elevation grid data of an elevation terrain of a target operation area of a plurality of unmanned aerial vehicles, and converting the elevation grid data into a Digital Surface Model (DSM);
step 2, calculating by using a minimum outsourcing convex polygon algorithm to obtain an abstract minimum convex hull of the target operation area, and generating a polygon area with a specific number of edges by using a convex polygon simplification algorithm;
step 3, segmenting the polygonal areas obtained in the step 2, performing Z-shaped coverage on each area, determining the flight mission area of each unmanned aerial vehicle, and discretizing the flight mission area into abstract graph nodes;
step 4, establishing a track evaluation function for evaluating and optimizing the unmanned aerial vehicle track of each area in the step 5;
step 5, planning and optimizing the unmanned aerial vehicle flight path in the flight task area by using a genetic algorithm and combining the flight path evaluation function established in the step 4 until an optimal solution is searched;
6, obtaining altitude information of the unmanned aerial vehicle flight path according to the optimal solution of the monitoring flight paths of the plurality of unmanned aerial vehicles obtained by the genetic algorithm in the step 5 and the DSM model of the operation area;
and 7, converting the unmanned aerial vehicle track with the elevation data obtained in the step 6 into KML (Keyhole Markup Language) data available for the unmanned aerial vehicle, and exporting the KML (Keyhole Markup Language) data.
Further, the specific implementation manner of step 2 is as follows:
formed by polygon boundariesScattered point set S ═ P 0 ,P 1 ,P 2 ,…,P n Calculating, and firstly obtaining poles Q ═ P in four directions of up, down, left and right in the scattered point set N ,P S ,P E ,P W And deleting Q from S, and connecting points in Q two by two to form an edge set T ═ E 0 ,E 1 ,E 2 ,E 3 ,…,E n And (4) forming the most initial minimum outsourcing convex polygon of the algorithm by the edge set T, and respectively calculating each point P in the scattered point set S i And each edge E in the edge set T i Distance D of i And adding the distance set R into the distance set R; screening out the maximum distance D in the distance set R max Find its corresponding edge E max And corresponding point P max A 1 is to P max Moving out the scatter set S, adding the scatter set S into the set Q, emptying the edge set T, connecting the points in the Q in pairs, recalculating to obtain a new edge set T, and repeating the process until the scatter set S consisting of the minimum outer convex polygon boundary is empty, stopping calculation, wherein the obtained edge set T is the minimum convex hull;
at this time, the number of generated convex hulls is often too many, so it is necessary to optimize the convex hulls, delete a part of vertexes, and put each vertex P in the point set Q i All go through once and apply the Helen formula:
in the formula S Delta The area of the triangle, the side length of the triangle, and the p is the half perimeter of the triangle;
the two vertexes P adjacent to the vertexes P are calculated by the above formula j ,P k And forming an area set A by the areas among the formed triangles, screening out the triangle with the smallest area in the A after calculation, considering that the triangle has the smallest contribution to the shape of the convex polygon, deleting the point with the smallest contribution degree from Q, simultaneously recalculating T, and repeating the process until the number of edges in T is less than a given threshold value epsilon.
Further, the specific implementation method of step 3 is as follows:
selecting a certain point M in the polygonal area obtained in the step 2, connecting M with each vertex of the convex polygonal area with a specific number of sides, and dividing the polygon into a plurality of triangles, wherein each triangle is a coverage area of the unmanned aerial vehicle; meanwhile, in order to ensure that the coverage area is maximized and the flight path length is shortened as much as possible, the flight path is covered in a Z shape parallel to three sides of the triangle, and each flight path is ensured to be parallel to one of three sides of the planned triangle area as much as possible.
Further, the track evaluation function K established in step 4 is specifically:
K＝αS+βD
wherein, S is the total flight distance of all the drone aircrafts, and is defined as:
wherein m is the total number of unmanned aerial vehicles in the unmanned aerial vehicle group, l i The flight distance of the ith unmanned aerial vehicle;
d is an equalization function defined as:
D＝‖L‖ ∞
wherein L is the vector that the flight distance of m unmanned aerial vehicles constitutes, L ═ L 1 ,l 2 ,l 3 ,…,l m ) T ，l m Represents the flight distance of the mth drone, | · | ∞ Is an infinite norm;
α and β are customized weight adjustment parameters for adjusting the importance ratio of the two parameters in the evaluation function, and α + β is 1.
Further, in step 5, the specific method for searching for the optimal solution according to the track evaluation function established in step 4 is as follows:
setting N nodes to be accessed, setting the number of unmanned aerial vehicles to be M, and solving the problem of searching the optimal path planning by using a genetic algorithm, wherein the method comprises the following specific steps:
1. adding M-1 virtual nodes, and forming a closed loop by pairwise connection of the virtual nodes and any appointed No. 0 node, wherein the distance between different graph nodes is set to be infinite;
2. individual coding: setting a string of 1-N + M-2 continuous digital sequences, randomly scrambling the string, and encoding a chromosome;
3. generating an initial population: setting a proper initial population scale, and randomly initializing the initial population scale according to the coding mode in the step 2;
4. calculating the fitness: for each individual in the current population, calculating the fitness of the individual by using the evaluation function constructed in the step 4 as a fitness function, wherein the smaller the fitness function value is, the higher the fitness is, the better the adaptability is;
5. selecting: using a roulette selection operator as a selection operator to eliminate a plurality of individuals with low fitness in the population at a certain probability;
6. and (3) crossing: selecting a plurality of male parents from a current population according to cross probability to carry out cross operator operation, and copying one part of individuals with highest fitness in the current population in order to ensure that the optimal individuals of the population are reserved, wherein one part is directly reserved, and the other part participates in the cross operation;
7. mutation: in order to maintain the diversity of the population and prevent the algorithm from falling into the local optimal solution, a variant individual needs to be randomly determined according to the set variant probability and a variant operation is executed;
8. iteration: and repeating the steps of 4-7 until a satisfactory path is obtained, and terminating the iteration.
Further, in step 6, a specific method for obtaining the altitude information of the unmanned aerial vehicle flight path according to the obtained monitoring flight paths of the plurality of unmanned aerial vehicles and the DSM model of the working area is as follows:
obtaining the elevation information of the unmanned aerial vehicle track by using a DSM (digital model system) model of the operation area and applying the following formula:
H UAV ＝H U(DSM) +ΔH
wherein H UAV Elevation information of the drone is represented, and Δ H represents an elevation of the DSM model relative to the work area obstructions when performing the coverage mission. H U(DSM) The elevation of the obstacle and the adjacent fields thereof is represented, and the calculation method comprises the following steps:
H U(DSM) ＝H DSM +H DEM
wherein H DSM Is the elevation of the working area, H DEM The elevation of the adjacent field of the obstacle is shown, and the adjacent field is a square area which takes the obstacle as the center and has the side length of 3 meters.
Further, the adjacent field is a square area with a side length of 3 meters and centered on the obstacle.
The invention has the beneficial effects that: by utilizing a DSM model, a rapid convex hull algorithm, a region segmentation and coverage algorithm and a genetic algorithm, aiming at the defects of the existing unmanned aerial vehicle coverage path planning algorithm, the coordinate information of the DSM model in a three-dimensional region is fully utilized, and the multi-unmanned aerial vehicle collaborative three-dimensional flight path planning method is provided: firstly, calculating by using a minimum outsourcing convex polygon algorithm to obtain an abstract minimum convex hull of a target operation area, generating a polygon area with a specific number of edges by using a convex polygon simplification algorithm, then dividing the area, carrying out Z-shaped coverage on each area, determining a flight mission area of each unmanned aerial vehicle, discretizing the flight mission area into abstract graph nodes, establishing a flight path evaluation function, and solving an optimized flight path by using the evaluation function in combination with a genetic algorithm. Finally, three-dimensional data is obtained from the DSM model and is derived as KML data. The method makes full use of the DSM model, the rapid convex hull algorithm, the region segmentation and coverage algorithm and the genetic algorithm, makes full use of the DSM model coordinate information of the three-dimensional region, can obtain the excellent multi-unmanned aerial vehicle collaborative path planning track in the whole planning process only in a very short time, has the characteristics of high planning speed, low cost, high precision and good coverage effect, simultaneously has an obvious optimization effect on multi-unmanned aerial vehicle coordinated planning and full track coverage, and provides an effective track planning method for the field of track planning of multi-unmanned aerial vehicles.
In conclusion, the method is reliable and practical, has better speed and practicability for the flight path planning of multiple unmanned aerial vehicles, is specifically implemented in the actual flight path planning of the multiple unmanned aerial vehicles, obtains excellent effects, and has better practicability and feasibility.
Drawings
Fig. 1 is a flowchart for generating a convex polygon area with a specific number of sides, fig. 1(a) - (e) show the process of generating a minimum outsourcing convex polygon from a scatter diagram by the algorithm of this step, and fig. 1(f) - (h) show the process of deleting a specific side to obtain a convex polygon area with a specific number of sides.
Fig. 2 is a schematic diagram of implementing zigzag coverage on a target area, fig. 2(a) is a schematic diagram of dividing a polygon into a plurality of triangles, and fig. 2(b) is a schematic diagram of adopting zigzag coverage parallel to three sides of a triangle.
Fig. 3 is a schematic diagram of acquiring elevation information of a track of an unmanned aerial vehicle by using a DSM model, fig. 3(a) is a schematic diagram of a drone swarm flying at a lower altitude difference Δ H, ABCD is a cross-sectional diagram of the DSM model, and EF is an actual flight path of the unmanned aerial vehicle; FIG. 3(b) is a schematic view of an obstacle in the flight path; fig. 3(c) is a schematic view of the flight path of the drone after correcting the elevation information of the obstacle.
FIG. 4 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in more detail with reference to the accompanying drawings and reference signs, so that those skilled in the art can implement the present invention after studying the description. It should be understood that the specific embodiments described herein are illustrative only and are not limiting upon the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The embodiment provides a multi-unmanned aerial vehicle collaborative three-dimensional flight path planning method considering a DSM model, which comprises the following steps:
step 1, inputting elevation grid data of an elevation terrain of a target operation area of a plurality of unmanned aerial vehicles, and converting the elevation grid data into a Digital Surface Model (DSM);
step 2, calculating by using a minimum outsourcing convex polygon algorithm to obtain an abstract minimum convex hull of the target operation area, and generating a polygon area with a specific number of edges by using a convex polygon simplification algorithm;
step 3, segmenting the polygonal areas obtained in the step 2, performing Z-shaped coverage on each area, determining the flight mission area of each unmanned aerial vehicle, and discretizing the obtained flight path into abstract graph nodes;
step 4, establishing a track evaluation function for evaluating and optimizing the unmanned aerial vehicle track of each area in the step 5;
step 5, planning and optimizing the unmanned aerial vehicle flight path in the flight mission area by using a genetic algorithm and combining the flight path evaluation function established in the step 4 until an optimal solution is searched;
6, obtaining altitude information of the unmanned aerial vehicle flight path according to the optimal solution of the monitoring flight paths of the plurality of unmanned aerial vehicles obtained by the genetic algorithm in the step 5 and the DSM model of the operation area;
and 7, converting the unmanned aerial vehicle track with the elevation data obtained in the step 6 into KML (Keyhole Markup navigation) data available for the unmanned aerial vehicle, and exporting the KML data.
Further, the concrete method for calculating the abstract minimum convex hull of the target operation area in step 2 and generating the polygon area with a specific number of edges by using the convex polygon simplification algorithm comprises the following steps:
for the scatter set S ═ { P ] composed of polygon boundaries 0 ,P 1 ,P 2 ,…,P n Calculating, and firstly obtaining poles Q ═ P in four directions, namely up, down, left and right, in the scattered point set N ,P S ,P E ,P W And deleting Q from S, and connecting points in Q two by two to form an edge set T ═ E 0 ,E 1 ,E 2 ,E 3 ,…,E n And (4) the edge set T forms the most initial minimum outsourcing convex polygon of the algorithm. Respectively calculating each point P in the scattered point set S i And each edge E in the edge set T i Distance D of i And added to the distance set R. Screening out the maximum distance D in the distance set R max Find its corresponding edge E max And corresponding point P max . Will P max Moving out a scattered point set S, adding the scattered point set S into a set Q, emptying an edge set T, and connecting points in Q pairwiseAnd then, recalculating to obtain a new edge set T. And repeating the iteration process until the scatter set S formed by the minimum outer convex polygon boundary is empty, and stopping the calculation to obtain an edge set T which is the minimum convex hull.
In this case, the number of generated convex hulls is often too large, and therefore, the convex hulls need to be optimized and part of the vertexes need to be deleted. Each vertex P in the set of points Q i All traverse once and apply the Helen formula:
in the formula S Delta Is the area of the triangle, a, b, c are the side lengths of the triangle, and p is the half perimeter of the triangle.
Calculating two vertexes P adjacent to the two vertexes by the formula j ,P k The areas between the formed triangles form an area set A. And after the calculation is finished, screening out the triangle with the smallest area in the A, considering that the triangle has the smallest contribution to the shape of the convex polygon, deleting the point with the smallest contribution degree from the Q, and recalculating the T. The above process is repeated until the number of edges in T is less than a given threshold e.
Fig. 1(a) - (e) show the process of generating the minimum outsourcing convex polygon from the scatter diagram by the algorithm of the step.
Fig. 1(f) - (h) show the process of deleting a specific edge, thereby obtaining a specific number of convex polygon areas.
Further, the specific method for dividing the area, implementing the Z-shaped coverage and discretizing the flight path into the abstract graph nodes in the step 3 of the invention comprises the following steps:
selecting a certain point M in the polygonal area obtained in the step 2, connecting M with each vertex of the convex polygonal area with a specific number of sides, and dividing the polygon into a plurality of triangles, wherein each triangle is a coverage area of the unmanned aerial vehicle as shown in fig. 2 (a). Meanwhile, in order to ensure that the coverage area is maximized and the flight path length is shortened as much as possible, the invention adopts the zigzag coverage parallel to three sides of the triangle, and ensures that each flight path is parallel to one of three sides of the planned triangle area as much as possible, as shown in fig. 2 (b).
The generated paths are then discretized into several abstract graph nodes.
Further, the track evaluation function K established in step 4 of the present invention is specifically:
K＝αS+βD
wherein, S is the total flight distance of all the drone aircrafts, and is defined as:
wherein m is the total number of unmanned aerial vehicles in the unmanned aerial vehicle group, l i Is the flight distance of the ith unmanned aerial vehicle.
D is an equalization function defined as:
D＝‖L‖ ∞
wherein L is the flight distance L of m unmanned aerial vehicles i Formed vector, L ═ L 1 ,l 2 ,l 3 ,…,l m ) T 。‖·‖ ∞ Is an infinite norm.
Alpha and beta are self-defined weight value adjusting parameters and are used for adjusting the importance ratio of the two parameters in the evaluation function. α + β ═ 1 is always true. In the present method, α ═ β ═ 0.5 is set because it is considered that the degrees of influence of the two factors are the same.
It is easy to find from the definition of the evaluation function that the smaller K is, the better the established track path is.
The construction principle of the evaluation function in the step 4 is as follows:
to evaluate the flight path planned by the algorithm, it is first required to ensure that the resources are not wasted. Considering the total route of the unmanned aerial vehicle cluster, obviously, the shorter the total route, the less resources such as fuel oil are consumed by the flight path, and therefore, the shorter the total route, the better.
Simultaneously, because many unmanned aerial vehicles's dispatch has simultaneity (a plurality of unmanned aerial vehicles are set out simultaneously and are carried out the track and cover), cooperativity, therefore when a plurality of unmanned aerial vehicles' cruise time is approximate, represent that the work load that each unmanned aerial vehicle distributed is unanimous, can accomplish to set out simultaneously, return simultaneously, be favorable to next dispatch. Therefore, considering a plurality of paths having equal total routes, it is obvious that the path having the shortest longest route is the best. When the path lengths of all drones are equal in a journey, the equality function D reaches its minimum value.
The evaluation function K shows that good track route planning should ensure that the flight paths and times of all unmanned aerial vehicles are balanced as much as possible under the condition that the total route is as short as possible.
Further, the specific method for searching the optimal solution according to the track evaluation function established in the step 4 is as follows:
assuming that N graph nodes need to be accessed and the number of unmanned aerial vehicles is M, the optimal path searching planning problem is solved by using a genetic algorithm, and the method comprises the following specific steps:
1. adding M-1 virtual nodes, and forming a closed loop by pairwise connection of the virtual nodes and any appointed No. 0 node, wherein the distance between different graph nodes is set to be infinite;
2. individual coding: setting a string of 1-N + M-2 continuous digital sequences, randomly disordering, and encoding chromosomes;
3. generating an initial population: setting a proper initial population scale, and randomly initializing the initial population scale according to the coding mode in the step 2;
4. calculating the fitness: and (4) calculating the fitness of each individual in the current population by using the evaluation function constructed in the step (4) as a fitness function. The smaller the fitness function value of the individual is, the higher the fitness is, the better the adaptability is;
5. selecting: using a roulette selection operator as a selection operator to eliminate a plurality of individuals with low fitness in the population at a certain probability;
6. and (3) crossing: and selecting a plurality of male parents for carrying out cross operator operation on the current population according to the cross probability. Meanwhile, in order to ensure that the optimal individuals of the population are reserved, the individuals with the highest fitness in the current population are copied, wherein one of the individuals is directly reserved, and the other one participates in the cross operation;
7. mutation: in order to maintain the diversity of the population and prevent the algorithm from falling into the local optimal solution, a variant individual needs to be randomly determined according to the set variant probability and a variant operation is executed;
8. iteration: and repeating the steps of 4-7 until a satisfactory path is obtained, and terminating the iteration.
The "suitable point M" described in step 2 can be determined by solving using the above genetic algorithm with the convex polygon boundary of the specific number of edges obtained in step 2 as the boundary limiting condition.
Further, in step 6, the specific method for obtaining the altitude information of the flight path of the unmanned aerial vehicle according to the previously obtained monitored flight paths of the plurality of unmanned aerial vehicles and the DSM model of the working area includes:
obtaining the elevation information of the unmanned aerial vehicle track by using a DSM (digital model system) model of the operation area and applying the following formula:
H UAV ＝H U(DSM) +ΔH
wherein H UAV Elevation information of the drone is represented, and Δ H represents an elevation of the DSM model relative to the work area obstructions when performing the coverage mission. H U(DSM) The elevation of the obstacle and the adjacent fields thereof is represented, and the calculation method comprises the following steps:
H U(DSM) ＝H DSM +H DEM
wherein H DSM Is the elevation of the working area, H DEM The elevation of the adjacent field of the obstacle is shown, and the adjacent field is a square area which takes the obstacle as the center and has the side length of 3 meters.
In step 6, H is calculated UAV The principle of (1) is as follows:
in order to fit a two-dimensional plane model of a flight path and reduce the height as much as possible to obtain a higher-resolution image, the drone swarm should fly with a lower height difference Δ H based on the elevation information of the working area, as shown in fig. 3(a), ABCD is a cross-sectional view of a DSM model, and EF is an actual flight path of the drone. However, when an obstacle is encountered, the unmanned aerial vehicle cannot directly raise or lower the height due to the flight inertia thereof, and thus may collide with the obstacle, resulting in a danger, as shown in fig. 3 (b). In order to solve the problem, the area near the obstacle is also included in the elevation information, and the elevation information of the obstacle is corrected, so that the unmanned aerial vehicle can better avoid the obstacle. After the altitude information of the obstacle is corrected, the flight path of the drone is shown in fig. 3 (c).
The above steps are merely embodiments of the present invention, and various changes and modifications can be made by those skilled in the art without departing from the scope of the present invention. The scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention.
Claims (7)
1. A multi-unmanned aerial vehicle collaborative three-dimensional flight path planning method considering DSM model is characterized by comprising the following steps:
step 1, inputting elevation grid data of an elevation terrain of a target operation area of a plurality of unmanned aerial vehicles, and converting the elevation grid data into a Digital Surface Model (DSM);
step 2, calculating by using a minimum outsourcing convex polygon algorithm to obtain an abstract minimum convex hull of the target operation area, and generating a polygon area with a specific number of edges by using a convex polygon simplification algorithm;
step 3, segmenting the polygonal areas obtained in the step 2, performing Z-shaped coverage on each area, determining the flight mission area of each unmanned aerial vehicle, and discretizing the flight mission area into abstract graph nodes;
step 4, establishing a track evaluation function for evaluating and optimizing the unmanned aerial vehicle track of each area in the step 5;
step 5, planning and optimizing the unmanned aerial vehicle flight path in the flight task area by using a genetic algorithm and combining the flight path evaluation function established in the step 4 until an optimal solution is searched;
6, obtaining altitude information of the unmanned aerial vehicle flight path according to the optimal solution of the monitoring flight paths of the plurality of unmanned aerial vehicles obtained by the genetic algorithm in the step 5 and the DSM model of the operation area;
and 7, converting the unmanned aerial vehicle track with the elevation data obtained in the step 6 into KML (Keyhole Markup Language) data available for the unmanned aerial vehicle, and exporting the KML (Keyhole Markup Language) data.
2. The method of claim 1, wherein the method comprises: the specific implementation manner in the step 2 is as follows:
for the scatter set S ═ { P ] composed of polygon boundaries 0 ,P 1 ,P 2 ,…,P n Calculating, and firstly obtaining poles Q ═ P in four directions of up, down, left and right in the scattered point set N ,P S ,P E ,P W And deleting Q from S, and connecting points in Q two by two to form an edge set T ═ E 0 ,E 1 ,E 2 ,E 3 ,…,E n And (4) forming the most initial minimum outsourcing convex polygon of the algorithm by the edge set T, and respectively calculating each point P in the scattered point set S i And each edge E in the edge set T i Distance D of i And adding the distance into the distance set R; screening out the maximum distance D in the distance set R max Find its corresponding edge E max And corresponding point P max From P to P max Moving out the scatter set S, adding the scatter set S into the set Q, emptying the edge set T, connecting points in the Q in pairs, recalculating to obtain a new edge set T, and repeating the iteration process until the scatter set S consisting of the minimum outer convex polygon boundary is empty, and stopping calculation to obtain the edge set T which is the minimum convex hull;
in this case, the number of generated convex hulls is often too large, so that optimization is needed to delete a part of vertices, and each vertex P in the point set Q needs to be checked i All traverse once and apply the Helen formula:
in the formula S Delta The area of the triangle, the side length of the triangle, and the p is the half perimeter of the triangle;
the two vertexes P adjacent to the vertexes P are calculated by the above formula j ,P k The areas among the formed triangles form an area set AAnd after the calculation is finished, screening out the triangle with the smallest area in the A, considering that the triangle has the smallest contribution to the shape of the convex polygon, deleting the point with the smallest contribution degree from the Q, simultaneously recalculating the T, and repeating the process until the number of edges in the T is smaller than a given threshold value epsilon.
3. The method of claim 1, wherein the method comprises: the specific implementation method of the step 3 is as follows:
selecting a certain point M in the polygonal area obtained in the step 2, connecting M with each vertex of the convex polygonal area with a specific number of sides, and dividing the polygon into a plurality of triangles, wherein each triangle is a coverage area of the unmanned aerial vehicle; meanwhile, in order to ensure that the coverage area is maximized and the flight path length is shortened as much as possible, the flight path is covered in a Z shape parallel to three sides of the triangle, and each flight path is ensured to be parallel to one of three sides of the planned triangle area as much as possible.
4. The method of claim 1, wherein the method comprises: the track evaluation function K established in the step 4 is specifically:
K＝αS+βD
wherein, S is the total flight distance of all the drone aircrafts, and is defined as:
wherein m is the total number of unmanned aerial vehicles in the unmanned aerial vehicle group, l i The flight distance of the ith unmanned aerial vehicle is taken as the flight distance of the ith unmanned aerial vehicle;
d is an equalization function defined as:
D＝‖L‖ ∞
wherein L is the vector that the flight distance of m unmanned aerial vehicles constitutes, L ═ L 1 ,l 2 ,l 3 ,…,l m ) T ，l m Represents the flight distance of the mth drone, | · | ∞ Is an infinite norm;
α and β are customized weight adjustment parameters for adjusting the importance ratio of the two parameters in the evaluation function, and α + β is 1.
5. The method of claim 1, wherein the method comprises: in step 5, the specific method for searching the optimal solution according to the track evaluation function established in step 4 is as follows:
setting N nodes to be accessed, setting the number of unmanned aerial vehicles to be M, and solving the problem of searching the optimal path planning by using a genetic algorithm, wherein the method comprises the following specific steps:
1. adding M-1 virtual nodes, and forming a closed loop by pairwise connection of the virtual nodes and any appointed No. 0 node, wherein the distance between different graph nodes is set to be infinite;
2. individual coding: setting a string of 1-N + M-2 continuous digital sequences, randomly disordering, and encoding chromosomes;
3. generating an initial population: setting a proper initial population scale, and randomly initializing the initial population scale according to the coding mode in the step 2;
4. calculating the fitness: for each individual in the current population, calculating the fitness of the individual by using the evaluation function constructed in the step 4 as a fitness function, wherein the smaller the fitness function value is, the higher the fitness is, the better the adaptability is;
5. selecting: using a roulette selection operator as a selection operator to eliminate a plurality of individuals with low fitness in the population at a certain probability;
6. and (3) crossing: selecting a plurality of male parents from the current population according to cross probability to carry out cross operator operation, and copying one part of the individual with the highest fitness in the current population to ensure that the optimal individual of the population is reserved, wherein one part is directly reserved, and the other part participates in the cross operation;
7. mutation: in order to maintain the diversity of the population and prevent the algorithm from falling into the local optimal solution, randomly determining variant individuals according to the set variant probability and executing the variant operation;
8. iteration: and repeating the steps of 4-7 until a satisfactory path is obtained, and terminating the iteration.
6. The method of claim 1, wherein the method comprises: in step 6, the specific method for obtaining the altitude information of the unmanned aerial vehicle flight path according to the obtained monitoring flight paths of the plurality of unmanned aerial vehicles and the DSM model of the working area comprises the following steps:
obtaining the elevation information of the unmanned aerial vehicle track by using a DSM (digital model system) model of the operation area and applying the following formula:
H UAV ＝H U(DSM) +ΔH
wherein H UAV Indicating elevation information of the drone, Δ H indicating elevation of the DSM model with respect to the obstacles of the working area when performing the covering mission, H U(DSM) The elevation of the obstacle and the adjacent fields thereof is represented, and the calculation method comprises the following steps:
H U(DSM) ＝H DSM +H DEM
wherein H DSM Is the elevation of the working area, H DEM Elevation of adjacent areas of the obstacle.
7. The method of claim 6, wherein the method comprises: the adjacent field is a square area with the side length of 3 meters and taking the barrier as the center.
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