A method for unconstrained patrol path planning of unmanned aerial vehicles with multiple target points

Abstract

The present invention relates to the field of computer graphics and geographic information science and technology, and specifically discloses a method for unconstrained patrol path planning of a drone with multiple target points, comprising the following steps: S1, obtaining patrol point sample data; S2, constructing a TIN triangle network through patrol points; S3, determining an initial reference triangle; S4, searching for triangles based on the reference triangle by the adjacent principle; S5, judging the proximity relationship between the searched triangle and the reference triangle, and determining the search result triangle; S6, when the search result triangle covers all patrol points, the search is completed; S7, merging the initial reference triangle and the search result triangle to obtain the planning result of the patrol path. The unconstrained patrol path planning method of a drone with multiple target points of the present invention can reduce the planning difficulty of the drone patrol path under unconstrained conditions and improve the planning efficiency of the drone patrol path under unconstrained conditions.

Description

Technical Field

[0001] The invention relates to the fields of computer science and geographic information science, and in particular to a method for unconstrained patrol path planning of a multi-target unmanned aerial vehicle. Background Art

[0002] The Hamiltonian path is the path to all nodes and is widely used in logistics, transportation, urban planning, travel route planning, emergency prevention and handling, and other fields. The TSP path is the traveler problem, which is the optimal solution to the Hamiltonian path problem. In the past hundred years, scholars have failed to perfectly solve the Hamiltonian path problem and the TSP path problem from a theoretical and technical level, so that the problem has become one of the classic problems in logistics, tourism, microelectronics design and other fields. The difficulty of the Hamiltonian path problem is mainly reflected in the diversity and uncertainty of path selection. Traditional scientific thinking believes that this problem is a permutation and combination in the field of mathematics, and the complexity is related to N! Therefore, its solution is also studied from the fields of mathematics, logic and traditional computer graphics.

[0003] The Hamiltonian path problem and the TSP path problem still belong to the field of natural sciences and are the concretization of space science problems. Therefore, the principle of "the more constraints there are, the more likely the problem is to be solved" in natural sciences must also be applied. Combined with the law of solving natural science problems: In solving natural science problems, necessary conditions are often easier to discover than necessary and sufficient conditions. Based on this, the solution to the problem cannot be considered only from traditional scientific fields such as logical thinking and mathematics. It is necessary to combine practical problems to find more necessary conditions to solve the problem.

[0004] 80% of the problems in human society are related to spatial location. Spatial information is reflected in points, lines and surfaces in computer graphics. Therefore, it can be considered that most human problems are related to points, lines and surfaces: spatial problems are not only affected by logical thinking, but also constrained by spatial relationships. It can be said that spatial topological relationships, which are special constraints that do not depend on image size, shape, position, and direction, are more extensive and common implicit problem constraints. When facing human spatial problems, it is even more necessary to look at the essence of the problem with spatial thinking of points, lines and surfaces, and then explore the root of the problem in order to find the key to solving the problem.

[0005] The Hamiltonian path is a typical NP problem. Its essence is to traverse all nodes, and each node is only passed once, that is, one in and one out. It is reflected in the nodes as line segments connecting in and out. This is called degree in academia, that is, the number of lines connecting nodes. A closed line with a degree of two is a Hamiltonian cycle, which is reflected in computer graphics as a single simple closed polygon. Based on this, it can be understood that the Hamiltonian cycle is essentially a point connection problem, but it is reflected in space as a closed surface. At this point, the Hamiltonian path problem integrates points, lines and surfaces in topological relationships. The Hamiltonian path is a line connection problem, but it involves two-dimensional space, that is, lines and polygons are intertwined, which is a line problem. The two-dimensionalization of a one-dimensional problem, the expansion of a one-dimensional line to a two-dimensional surface space, and the infinite dimensionality of the solution space are the fundamental reasons why the problem shows exponential growth and is difficult to solve quickly.

[0006] Unconstrained patrol path planning for multiple target points of unmanned aerial vehicles is a core content of the unmanned aerial vehicle mission planning system. The problem is solved by cluster thinking such as particle swarm optimization and multi-objective genetic algorithm. By simulating the behavior analysis of biological foraging and other multi-objective problems, it can be attributed to biological thinking algorithms. Most scholars rely on this thinking to integrate other relevant influencing factors or perform operator optimization to achieve the goal of unconstrained patrol path planning for multiple target points of unmanned aerial vehicles. However, the planning efficiency of the above algorithm is low during planning. From the basic thinking of graphics, it can be seen that the unconstrained patrol path of unmanned aerial vehicles with multiple target points meets the basic requirements of graphics. When planning, all target points need to be connected, and each target point can only be connected once, so as to ensure that the same target point will not be patrolled twice during patrol. This is the same as the above Hamiltonian path problem, but the difficulty of the above patrol path will still increase exponentially with the number of targets when planning. Therefore, it is of great significance to provide a simple and efficient planning method for the patrol path of unmanned aerial vehicles with multiple target points. Summary of the invention

[0007] The present invention aims to solve at least one of the technical problems mentioned above and provide a method for unconstrained patrol path planning of unmanned aerial vehicles with multiple target points, which can reduce the difficulty of planning the patrol path of unmanned aerial vehicles under unconstrained conditions and improve the planning efficiency of the patrol path of unmanned aerial vehicles under unconstrained conditions.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is: a method for unconstrained patrol path planning of a multi-target UAV, characterized in that it includes the following steps:

[0009] S1, obtaining patrol point sample data, and distributing the patrol points in the same two-dimensional plane according to the relative position relationship between the patrol points;

[0010] S2, constructing a TIN triangle network through patrol points, so that all patrol points are distributed in the TIN triangle network;

[0011] S3, selecting one of the triangles in the TIN triangle network as a reference triangle;

[0012] S4, based on the reference triangle, searching for triangles by the adjacent principle;

[0013] S5, determining whether the searched triangle and the reference triangle have edges that are adjacent to each other. If the searched triangle and the reference triangle have edges that are adjacent to each other, the triangle is used as the search result triangle;

[0014] S6, taking the search result triangle as the reference triangle, repeating steps S4 to S5. If the searched triangle is only adjacent to the reference triangle at points, the triangle cannot be used as the search result triangle. If the searched triangle makes any patrol point surrounded by the search result triangle, the triangle cannot be used as the search result triangle. When the search result triangle covers all patrol points, the search is completed.

[0015] S7. Merge the reference triangle and the search result triangle, and convert the merged result into a line, which is the planning result of the patrol path.

[0016] Preferably, one of the triangles on the outermost edge line of the TIN triangle network is selected as the reference triangle.

[0017] Preferably, the reference triangle and the search result triangle are marked, and when merging, the marked triangles are directly extracted for merging.

[0018] Preferably, in step S7, the reference triangle and the result triangle are merged by deleting adjacent edges of adjacent triangles.

[0019] Preferably, the path planning method is used for planar path planning.

[0020] Beneficial effects are: compared with the prior art, the unconstrained patrol path planning method for a multi-target UAV of the present invention has the following beneficial effects:

[0021] 1. The unconstrained patrol path planning of UAVs with multiple target points is a point connection problem, but extending it to the surface area by setting reasonable constraints, that is, by merging adjacent triangles to achieve the purpose of reducing the degree of adjacent edge deletion nodes, is a specific practice of exploring problem constraints in natural sciences to accelerate problem solving.

[0022] 2. This application achieves the goal of reducing the node degree by 2 without hanging points by searching adjacent triangles, and completes the purpose of expanding to new nodes when the node degree of a simple polygon is 2. It simplifies and concretizes the constraints, achieving the effect of scientific principles first, technical implementation as the main focus, and solving engineering problems as the goal.

[0023] 3. This application is completed through the logical operation of spatial proximity query, which reduces the dimension of line connections to surface adjacency, that is, logically processing spatial multidimensional problems, which is a one-dimensional operation of two-dimensional problems and a concrete manifestation of dimensionality reduction operations, thereby achieving simple and efficient reasonable planning of drone patrol paths for multiple target points. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0025] Figure 1 This is a schematic diagram of the distribution of patrol points on a two-dimensional plane;

[0026] Figure 2 Construct a TIN triangle network diagram for patrol points;

[0027] Figure 3 A schematic diagram for selecting an initial reference triangle;

[0028] Figure 4 This is a schematic diagram of the first search for the TIN triangle network;

[0029] Figure 5 Schematic diagram of searching for adjacent edges along the periphery of a TIN triangle network;

[0030] Figure 6 A schematic diagram of a TIN triangle network continuously searching along the peripheral adjacent edges;

[0031] Figure 7 Schematic diagram of the TIN triangle network searching along the outer adjacent edges and entering the first inner ring search;

[0032] Figure 8 This is a schematic diagram of the search results for the first inner ring of the TIN triangle network;

[0033] Fig. 9 This is a schematic diagram of the search results for the TIN triangle network entering the Nth inner ring;

[0034] Fig.10 This is a schematic diagram of the TIN triangle network adjacent edge search results and patrol points overlay;

[0035] Fig.11 Schematic diagram of merging polygons for the search results of adjacent edges of TIN triangle network;

[0036] Fig.12 Schematic diagram of merging polygons into lines for the search results of adjacent edges of TIN triangle network;

[0037] Fig.13 This is a schematic diagram of the final search path and patrol points superimposed. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may be a central component at the same time. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is referred to as being "set in the middle", it does not only mean being set in the middle, as long as it is not set at both ends within the range defined by the middle. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0041] This application discloses a method for unconstrained patrol path planning of a multi-target UAV, comprising the following steps:

[0042] S1, obtaining patrol point sample data, and distributing the patrol points in the same two-dimensional plane according to the relative position relationship between the patrol points;

[0043] S2, constructing a TIN triangle network through patrol points, so that all patrol points are distributed in the TIN triangle network;

[0044] S3, selecting one of the triangles in the TIN triangle network as a reference triangle;

[0045] S4, based on the reference triangle, searching for triangles by the adjacent principle;

[0046] S5, determining whether the searched triangle and the reference triangle have edges that are adjacent to each other. If the searched triangle and the reference triangle have edges that are adjacent to each other, the triangle is used as the search result triangle;

[0047] S6, taking the search result triangle as the reference triangle, repeating steps S4 to S5. If the searched triangle is only adjacent to the reference triangle at points, the triangle cannot be used as the search result triangle. If the searched triangle makes any patrol point surrounded by the search result triangle, the triangle cannot be used as the search result triangle. When the search result triangle covers all patrol points, the search is completed.

[0048] S7. Merge the reference triangle and the search result triangle, and convert the merged result into a line, which is the planning result of the patrol path.

[0049] In one embodiment of the present application, the path planning method of the present application can be performed on a computer, and the specific steps are as follows:

[0050] S1. Obtain patrol point sample data and distribute the patrol points in the same two-dimensional plane according to the relative position relationship between the patrol points, such as Figure 1 As shown;

[0051] S2. Construct a TIN triangle network through patrol points so that all patrol points are distributed in the TIN triangle network. At the same time, the TIN triangle network can be converted into a surface through topological surface construction, such as Figure 2 As shown;

[0052] S3. Select one of the triangles in the TIN triangle network as the reference triangle, such as Figure 3 As shown;

[0053] S4. Based on the reference figure, search for triangles by the adjacent principle, such as Figure 4 As shown;

[0054] S5. Determine whether the searched triangle and the reference triangle have edges that are adjacent. If the searched triangle and the reference triangle have edges that are adjacent, that is, the searched triangle and the reference triangle share a common edge, then the triangle is used as the search result triangle. Figure 4 As shown;

[0055] S6, taking the search result triangle as the reference triangle, repeating S4 to S5, if the search triangle is adjacent to the reference triangle only by points, then the triangle cannot be used as the search result triangle, if the search triangle makes any patrol point surrounded by the search result triangle, then the triangle cannot be used as the search result triangle, such as Figures 5 to 8 As shown in , when the search result triangle covers all patrol points, the search is completed, as Fig. 9 and Fig.10As shown; the patrol point mentioned in the present application is surrounded by the search result triangle, which means that the patrol point is located in the middle of the polygon formed by the search triangle and the search result triangle; preferably, one of the triangles on the outermost edge line of the TIN triangle network can be selected as the reference triangle, and when searching, it can be preferred to search along the outer edge line of the TIN triangle network, so that the search triangle surrounds the TIN triangle network from the outside to the inside, which will be more conducive to the subsequent merging of triangles;

[0056] S7, merge the initial reference triangle and the search result triangle, and convert the merged result into a line, which is the planning result of the patrol path, such as Fig.12 and Fig.13 Preferably, during the triangle search process, the reference triangle and the search result triangle can be marked and stored, and when merging, the computer can directly extract the marked triangles for merging, thereby improving the processing efficiency of the computer, and when merging, the reference triangle and the result triangle can be merged by deleting the adjacent edges of the adjacent triangles.

[0057] In another embodiment of the present application, the path planning method of the present application can also be used for planar path planning.

[0058] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be included in the scope of the technical solution of the present invention.

Claims

1. A method for unconstrained patrol path planning of a multi-target UAV, characterized in that: The following steps are involved: S1, obtaining patrol point sample data, and distributing the patrol points in the same two-dimensional plane according to the relative position relationship between the patrol points; S2, constructing a TIN triangle network through patrol points, so that all patrol points are distributed in the TIN triangle network; S3, selecting one of the triangles on the outermost edge line of the TIN triangle network as a reference triangle, and during the search, searching along the outer edge line of the TIN triangle network so that the searched triangle surrounds the TIN triangle network from the outside to the inside; S4, starting with the reference triangle, searching for triangles by the adjacent principle; S5, determining whether the searched triangle and the reference triangle have edges that are adjacent to each other. If the searched triangle and the reference triangle have edges that are adjacent to each other, the triangle is used as the search result triangle; S6, taking the search result triangle as the reference triangle, repeating steps S4 to S5. If the searched triangle is only adjacent to the reference triangle at points, the triangle cannot be used as the search result triangle. If the searched triangle makes any patrol point surrounded by the search result triangle, the triangle cannot be used as the search result triangle. When the search result triangle covers all patrol points, the search is completed. S7. Merge the reference triangle and the search result triangle, and convert the merged result into a line, which is the planning result of the patrol path.

2. The method for unconstrained patrol path planning of a multi-target UAV according to claim 1 is characterized in that: The reference triangle and the search result triangle are marked, and when merging, the marked triangles are directly extracted for merging.

3. The method for unconstrained patrol path planning of a multi-target UAV according to claim 1 is characterized in that: In step S7, the reference triangle and the result triangle are merged by deleting the adjacent edges of the adjacent triangles.

4. The method for unconstrained patrol path planning of a multi-target UAV according to claim 1 is characterized in that: The path planning method is used for planar path planning.

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