Method for path planning of moving objects in electromagnetic environment based on multi-domain grid

Abstract

The present invention proposes a method for path planning of moving objects in an electromagnetic environment based on multi-domain grids, including obtaining a path planning task of a target aircraft, and selecting a multi-domain grid with an appropriate grid granularity according to the path planning task; when the path planning task is static path planning, calculating the detection probability of each grid according to the electromagnetic information of the multi-domain grid; determining an adjustment factor α according to the path planning task; and using an improved A* algorithm to perform route planning of the target aircraft according to the adjustment factor α and the detection probability of each grid. Through the method proposed by the present invention, on the basis of multi-domain grids, a spatio-temporal framework model is established for electromagnetic field data, and both distance factors and safety factors are considered, so as to perform static route planning, dynamic route planning and multi-aircraft cooperative route planning for aircraft in the electromagnetic environment.

Description

Technical Field

[0001] The present invention belongs to the field of path planning, and specifically relates to methods for static route planning of a single aircraft, dynamic route planning of a single aircraft, and cooperative route planning of multiple aircraft in the electromagnetic space. Background Art

[0002] Path planning has always been a hot issue in the military field. Especially in modern wars, unmanned aerial vehicles (UAVs) are widely used, and path planning for single and multiple (cluster) UAVs has become even more important. Path planning for UAVs (clusters) needs to meet various complex environmental and coordination constraints on the battlefield in three-dimensional space and update the planned route in real time according to external information, which has always been one of the fields that scientists in various countries focus on researching.

[0003] The methods for solving path planning can be divided into four types: mathematical programming methods, artificial potential field methods, methods based on graphics, and intelligent optimization algorithms. Mathematical programming methods mainly include linear programming and nonlinear programming methods; the artificial potential field method virtualizes the gravitational force pointing to the target and the repulsive force away from obstacles in the UAV mission planning space to form an artificial force field; methods based on graphics include topological methods, Dijkstra path search algorithms, simulated annealing algorithms, grid methods, A* algorithms, Voronoi diagram methods, and probabilistic roadmap (PRM) methods, etc.; intelligent optimization algorithms include genetic algorithms, particle swarm algorithms, ant colony algorithms, and reinforcement learning, etc.

[0004] Currently, many UAV three-dimensional path planning algorithms based on the A* algorithm have been developed: In 2015, Zhan Weiwei et al. proposed a UAV trajectory planning using an improved A* algorithm, comprehensively considering factors such as flight path altitude, detection probability, and flight path length, and improving the cost function; in 2022, Bian Qiang et al. proposed a new improved A* algorithm for UAV three-dimensional path planning, adding the degree of path danger to the cost function in the form of probability, and using greedy search to optimize the path and delete redundant nodes.

[0005] Existing methods mainly consider the influence of terrain occlusion and obstacle occlusion on aircraft route planning when performing route planning, and the main purpose is to shorten the path and improve calculation efficiency. Although some methods consider the issue of safety when designing the algorithm, they do not consider the trade-off between distance factors and safety factors and lack the connection with actual battlefield requirements. In addition, existing methods do not consider the dynamic route planning when the electromagnetic environment changes and the cooperative route planning problem when multiple aircraft exist simultaneously.

[0006] The existing technology lacks consideration for electromagnetic threats during path planning, fails to weigh the importance of distance factors and safety factors according to actual needs. At the same time, the existing technology mainly focuses on static route planning problems, and there are deficiencies in the research on dynamic route planning and multi-aircraft collaborative route planning. The technical problem to be solved by the proposed solution of this application is to build a spatio-temporal framework model for electromagnetic field data based on a multi-domain grid, while considering distance factors and safety factors, and to perform static route planning, dynamic route planning, and multi-aircraft collaborative route planning for aircraft in the electromagnetic environment. Summary of the Invention

[0007] The present invention aims to solve at least one of the technical problems in the related art to some extent.

[0008] To this end, the object of the present invention is to propose a method for path planning of moving objects in an electromagnetic environment based on a multi-domain grid, which is used to perform route planning for single-aircraft static route planning, single-aircraft dynamic route planning, and multi-aircraft collaboration respectively.

[0009] To achieve the above object, an embodiment of the first aspect of the present invention proposes a method for path planning of moving objects in an electromagnetic environment based on a multi-domain grid, including:

[0010] Obtain the path planning task of the target aircraft, and select a multi-domain grid with an appropriate grid granularity according to the path planning task;

[0011] When the path planning task is static path planning, calculate the detection probability of each grid according to the electromagnetic information of the multi-domain grid;

[0012] Determine the adjustment factor α according to the path planning task;

[0013] Use the improved A* algorithm to perform route planning for the target aircraft according to the adjustment factor α and the detection probability of each grid.

[0014] In addition, a method for path planning of moving objects in an electromagnetic environment based on a multi-domain grid according to the above embodiment of the present invention may also have the following additional technical features:

[0015] Further, in an embodiment of the present invention, after selecting a multi-domain grid with an appropriate grid granularity according to the path planning task, it further includes:

[0016] When the path planning task is not static path planning, real-time simulate the detection probability of the multi-domain grid;

[0017] When it is determined to be multi-aircraft, set the detection probability value of the grid where other aircraft appear to 1;

[0018] The route planning of the target aircraft is carried out by using an improved D* algorithm.

[0019] Further, in an embodiment of the present invention, after the detection probability of the multi-domain grid is simulated in real time, it further includes:

[0020] When it is determined that there is no multi-aircraft situation, the route planning of the target aircraft is carried out by using an improved D* algorithm.

[0021] Further, in an embodiment of the present invention, the improved A* algorithm includes:

[0022] The cost function of the A* algorithm is improved to:

[0023] f improved (n)=g improved (n)+h improved (n)

[0024] g improved (n)=(1 - ln(1 - P(n))) 1-α g α (n)

[0025] h improved (n)=(1 - ln(1 - p(n))) 1-α h(n),

[0026] where p(n)=p i , which is the probability that the UAV is detected by the radar at node i; is the probability that the UAV is detected at least once from the starting point to the current node along the current path; α ∈ [0, 1], which is an adjustment factor between distance and safety.

[0027] Further, in an embodiment of the present invention, the improved D* algorithm includes:

[0028] The cost function of the improved D* algorithm is:

[0029] h improved (n)=(1 - ln(1 - p(n)))h(n).

[0030] To achieve the above object, an embodiment of the second aspect of the present invention proposes a path planning device for a moving object in an electromagnetic environment based on a multi-domain grid, including the following modules:

[0031] An acquisition module, configured to acquire a path planning task of a target aircraft, and select a multi-domain grid with an appropriate grid granularity according to the path planning task;

[0032] The first judgment module is used to calculate the detection probability of each grid according to the electromagnetic information of the multi-domain grid when the path planning task is static path planning;

[0033] The adjustment module is used to determine the adjustment factor α according to the path planning task;

[0034] The planning module is used to perform route planning for the target aircraft using the improved A* algorithm according to the adjustment factor α and the detection probability of each grid.

[0035] Furthermore, in an embodiment of the present invention, the first judgment module further includes a second judgment module, which is used for:

[0036] When the path planning task is not static path planning, it is used to simulate the detection probability of the multi-domain grid in real time;

[0037] When it is judged that there are multiple aircraft, the detection probability value of the grid where other aircraft appear is set to 1;

[0038] Use the improved D* algorithm to perform route planning for the target aircraft.

[0039] Furthermore, in an embodiment of the present invention, the second judgment module further includes a third judgment module, which is used for:

[0040] When it is judged that there are not multiple aircraft, use the improved D* algorithm to perform route planning for the target aircraft.

[0041] To achieve the above object, an embodiment of the third aspect of the present invention proposes a computer device, which is characterized by including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid as described above.

[0042] To achieve the above object, an embodiment of the fourth aspect of the present invention proposes a computer-readable storage medium, on which a computer program is stored, and is characterized in that when the computer program is executed by a processor, it implements a method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid as described above.

[0043] Compared with the existing path planning algorithms, the method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid proposed in the embodiment of the present invention models the electromagnetic field data in a spatio-temporal framework, and simultaneously considers the influence of distance factors and safety factors when performing path planning, and the weights of the two factors of distance and safety can be changed according to specific requirements. Description of the Drawings

[0044] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, where:

[0045] Figure 1 It is a schematic flow chart of a method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid provided by an embodiment of the present invention.

[0046] Figure 2 It is a schematic route diagram of a single aircraft when four radar sources are turned on and α = 1 provided by an embodiment of the present invention.

[0047] Figure 3 It is a schematic route diagram of a single aircraft when four radar sources are turned on and α = 0 provided by an embodiment of the present invention.

[0048] Figure 4 It is a schematic route diagram of a single aircraft when the fifth radar source is turned on provided by an embodiment of the present invention.

[0049] Figure 5 It is a schematic side view of the cooperative route of multiple aircraft provided by an embodiment of the present invention.

[0050] Figure 6 It is a schematic top view of the cooperative route of multiple aircraft provided by an embodiment of the present invention.

[0051] Figure 7 It is a technical flow chart of the present invention provided by an embodiment of the present invention.

[0052] Figure 8 It is a schematic flow chart of a device for path planning of a moving object in an electromagnetic environment based on a multi-domain grid provided by an embodiment of the present invention. Detailed Embodiments

[0053] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0054] The following describes the method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid according to an embodiment of the present invention with reference to the accompanying drawings.

[0055] Embodiment 1

[0056] Figure 1 It is a schematic flow chart of a method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid provided by an embodiment of the present invention.

[0057] As Figure 1As shown in the figure, the method for path planning of moving objects in an electromagnetic environment based on multi-domain grids includes the following steps:

[0058] S101: Obtain the path planning task of the target aircraft, and select a multi-domain grid with an appropriate grid granularity according to the path planning task;

[0059] S102: When the path planning task is static path planning, calculate the detection probability of each grid according to the electromagnetic information of the multi-domain grid;

[0060] S103: Determine the adjustment factor α according to the path planning task;

[0061] S104: Use the improved A* algorithm to plan the route of the target aircraft according to the adjustment factor α and the detection probability of each grid.

[0062] Furthermore, in an embodiment of the present invention, after selecting a multi-domain grid with an appropriate grid granularity according to the path planning task, it further includes:

[0063] When the path planning task is not static path planning, the detection probability of the multi-domain grid is simulated in real time;

[0064] When there are multiple aircraft, set the detection probability value of the grid where other aircraft appear to 1;

[0065] Use the improved D* algorithm to plan the route of the target aircraft.

[0066] Furthermore, in an embodiment of the present invention, after simulating the detection probability of the multi-domain grid in real time, it further includes:

[0067] When it is determined that there are not multiple aircraft, use the improved D* algorithm to plan the route of the target aircraft.

[0068] Furthermore, in an embodiment of the present invention, the improved A* algorithm includes:

[0069] Improve the cost function of the A* algorithm to:

[0070] f improved (n) = g improved (n) + h improved (n)

[0071] g improved (n) = (1 - ln(1 - P(n))) 1-α g α (n)

[0072] h improved (n) = (1 - ln(1 - p(n))) 1-α h(n),

[0073] where p(n) = p i , which is the probability that the UAV is detected by the radar at node i; is the probability that the UAV is detected at least once from the starting point to the current node along the current path; α ∈ [0, 1] is the adjustment factor between distance and safety.

[0074] Furthermore, in an embodiment of the present invention, the improved D* algorithm includes:

[0075] The cost function of the improved D* algorithm is:

[0076] h improved (n) = (1 - ln(1 - p(n)))h(n).

[0077] Embodiment 2

[0078] A path planning technology for moving objects in an electromagnetic environment based on multi-domain grids proposed by the present invention mainly improves the cost function and update method of the A* algorithm and the D* algorithm, and uses the electromagnetic information of the multi-domain grids to perform single-aircraft static route planning, single-aircraft dynamic route planning, and multi-aircraft cooperative route planning respectively.

[0079] 1) Single-aircraft static route planning

[0080] The main key technology involved in single-aircraft static route planning is the design of the cost function. The original cost function of the A* algorithm for a certain node f(n) = g(n) + h(n). It can be clearly seen that this equation contains two parts. One part is the actual cost g(n) from the starting point to the current node, and the other part is the heuristic function h(n) from the current node to the end point. In order to make the cost function take into account the influence of both distance and safety at the same time, the cost function of the A* algorithm is improved to:

[0081] f improved (n) = g improved (n) + h improved (n)

[0082] g improved (n) = (1 - ln(1 - P(n))) 1-α g α (n)

[0083] h improved (n) = (1 - ln(1 - p(n))) 1-α h(n),

[0084] where p(n) = p i , which refers to the probability that the UAV is detected by the radar at node (grid) i; refers to the probability that the UAV is found at least once from the starting point to the current node along the current path; α ∈ [0, 1] is the adjustment factor between "distance" and "safety". It can be seen that when α = 1, f improved (n) = g(n) + h(n), and the cost function degenerates into the A* algorithm that only considers distance; when α = 0,

[0085] f improved (n) = 1 - ln(1 - P(n)) + (1 - ln(1 - p(n)))h(n),

[0086] In the original A* algorithm, the g(n) term responsible for finding the shortest distance is replaced by the 1 - ln(1 - P(n)) term for finding the minimum probability. By gradually adjusting the value of α from 1 to 0, the algorithm can gradually shift from focusing on distance to focusing on safety, and plan the most suitable path for UAVs performing different tasks in different environments and conditions. As Figure 2 、 Figure 3 shown, it is the single - vehicle route schematic diagram when α = 1 with four radar sources turned on.

[0087] The original update method of the A* algorithm is: for a certain node n, check its 26 - neighborhood nodes. If a certain neighborhood node is already in the Open list, but the distance from the starting point through node n to this neighborhood node is shorter than the "shortest distance" already recorded from the starting point to this neighborhood node, then update the parent node of this neighborhood node to n, and update the "shortest distance" from the starting point to this neighborhood node to the distance from the starting point through node n to this neighborhood node. Since the improved A* algorithm needs to consider both distance and safety factors, the update method that only uses distance is no longer applicable. The improved algorithm selects g improved (n) = (1 - ln(1 - P(n))) 1-α g α (n) for update. g α (n) is a term that records the shortest - distance information, and (1 - ln(1 - P(n))) 1-α is a term that records the lowest detection probability. The product of the two represents the comprehensive influence of the two on path update. Figure 5 It is the single - vehicle route schematic diagram when the fifth radar source is turned on.

[0088] 2) Single - vehicle dynamic path planning

[0089] There are two key technologies involved in single - vehicle dynamic route planning:

[0090] First, the cost function of the D* algorithm is improved so that the cost function considers not only the influence of distance but also the influence of safety. The original h(n) only contains distance information, making it impossible for the D* algorithm to consider safety factors during updates. Therefore, a penalty factor term 1 - ln(1 - p(n)) is multiplied before h(n), making the cost of grids with a high detection probability large, and the algorithm will preferentially search grids with a low detection probability. The improved cost function of the D* algorithm is:

[0091] h improved (n) = (1 - ln(1 - p(n)))h(n).

[0092] Second, the update method of the D* algorithm is improved. The initial three-dimensional Euclidean distance is replaced by a distance definition method that includes detection probability information, and h improved (n) is used for updating.

[0093] 3) Multi-aircraft cooperative route planning

[0094] Other aircraft appearing within the scope of the aircraft domain are regarded as non-flyable grid points, the detection probability value is set to 1, and the penalty factor term multiplied before h(n) approaches +∞, so that the algorithm will not select this grid for search. Then, the single-aircraft dynamic route planning algorithm is called for each aircraft to perform real-time path planning. Figure 5 is a side view schematic diagram of multi-aircraft cooperative route, Figure 6 is a top view schematic diagram of multi-aircraft cooperative route.

[0095] The above is the complete process of the path planning method for moving objects in the electromagnetic environment based on multi-domain grids, Figure 7 which is the technical route schematic diagram of the present invention.

[0096] Compared with the existing path planning algorithms, the path planning method for moving objects in the electromagnetic environment based on multi-domain grids proposed in the embodiments of the present invention models the electromagnetic field data in a spatio-temporal framework, considers the influence of both distance factors and safety factors during path planning, and can change the weights of the two factors of distance and safety according to specific requirements.

[0097] To implement the above embodiments, the present invention also proposes a path planning device for moving objects in the electromagnetic environment based on multi-domain grids.

[0098] Figure 8 is a structural schematic diagram of a path planning device for moving objects in the electromagnetic environment based on multi-domain grids provided by an embodiment of the present invention.

[0099] As Figure 8As shown in the figure, the path planning device for moving objects in an electromagnetic environment based on a multi-domain grid includes: an acquisition module 100, a first judgment module 200, an adjustment module 300, and a planning module 400. Among them,

[0100] The acquisition module is used to obtain the path planning task of the target aircraft and select a multi-domain grid with an appropriate grid granularity according to the path planning task;

[0101] The first judgment module is used to calculate the detection probability of each grid according to the electromagnetic information of the multi-domain grid when the path planning task is a static path planning;

[0102] The adjustment module is used to determine the adjustment factor α according to the path planning task;

[0103] The planning module is used to perform route planning for the target aircraft using the improved A* algorithm according to the adjustment factor α and the detection probability of each grid.

[0104] Furthermore, in an embodiment of the present invention, the first judgment module further includes a second judgment module, which is used for:

[0105] When the path planning task is not a static path planning, the detection probability of the multi-domain grid is simulated in real time;

[0106] When it is judged that there are multiple aircraft, the detection probability value of the grid where other aircraft appear is set to 1;

[0107] The improved D* algorithm is used to perform route planning for the target aircraft.

[0108] Furthermore, in an embodiment of the present invention, the second judgment module further includes a third judgment module, which is used for:

[0109] When it is judged that there are not multiple aircraft, the improved D* algorithm is used to perform route planning for the target aircraft.

[0110] To achieve the above object, an embodiment of the third aspect of the present invention proposes a computer device, which is characterized in that it includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for path planning of moving objects in an electromagnetic environment based on a multi-domain grid as described above is implemented.

[0111] To achieve the above object, an embodiment of the fourth aspect of the present invention proposes a computer-readable storage medium, on which a computer program is stored. The computer program is characterized in that when it is executed by a processor, the method for path planning of moving objects in an electromagnetic environment based on a multi-domain grid as described above is implemented.

[0112] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0113] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0114] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A path planning method for moving objects in an electromagnetic environment based on a multi-domain grid, characterized in that It includes the following steps: Obtain the path planning task of the target aircraft, and select a multi-domain grid with an appropriate grid granularity according to the path planning task; When the path planning task is a static path planning, calculate the detection probability of each grid according to the electromagnetic information of the multi-domain grid; Determine the adjustment factor α according to the path planning task; Perform route planning for the target aircraft using the improved A* algorithm according to the adjustment factor α and the detection probability of each grid; Among them, the improved A* algorithm includes: Improve the cost function of the A* algorithm to: f improved f(n) = g improved g(n) + h improved h(n) g improved g(n) = (1 - ln(1 - P(n))) 1-α g α g(n) h improved (n) = (1 - ln(1 - p(n))) 1-α h(n), where p(n) = p i , is the probability that the UAV is detected by the radar at node i; g α (n) is a term that records the shortest distance information, h(n) is the heuristic function from the current node to the end point, is the probability that the UAV is detected at least once from the starting point to the current node along the current path, represents the probability that the UAV is not detected at all from the starting point to the current node along the current path; α ∈ [0, 1], is the adjustment factor between distance and safety.

2. The method according to claim 1, characterized in that, After selecting a multi-domain grid with an appropriate grid granularity according to the path planning task, it further includes: When the path planning task is not a static path planning, simulate the detection probability of the multi-domain grid in real time; When it is determined to be a multi-aircraft situation, set the detection probability value of the grid where other aircraft appear to 1; Perform route planning for the target aircraft using the improved D* algorithm.

3. The method according to claim 2, wherein After simulating the detection probability of the multi-domain grid in real time, it further includes: When it is determined not to be a multi-aircraft situation, perform route planning for the target aircraft using the improved D* algorithm.

4. The method according to claim 2, characterized in that, The improved D* algorithm includes: Improve the cost function of the D* algorithm to: h improved (n) = (1 - ln(1 - p(n)))h(n).

5. An apparatus for path planning of a moving object in an electromagnetic environment based on a multi-domain grid, characterized in that, It includes the following modules: An acquisition module, configured to obtain the path planning task of the target aircraft, and select a multi-domain grid with an appropriate grid granularity according to the path planning task; A first judgment module, configured to calculate the detection probability of each grid according to the electromagnetic information of the multi-domain grid when the path planning task is a static path planning; An adjustment module, configured to determine the adjustment factor α according to the path planning task; A planning module, configured to perform route planning for the target aircraft using the improved A* algorithm according to the adjustment factor α and the detection probability of each grid; Among them, the improved A* algorithm includes: Improve the cost function of the A* algorithm to: f improved f(n) = g improved f(n) + h improved f(n) g improved g(n) = (1 - ln(1 - P(n))) 1-α g α g(n) h improved (n) = (1 - ln(1 - p(n))) 1-α h(n), where p(n) = p i , is the probability that the UAV is detected by the radar at node i; g α (n) is a term that records the shortest distance information, h(n) is the heuristic function from the current node to the end point, is the probability that the UAV is detected at least once from the starting point to the current node along the current path, represents the probability that the UAV is not detected at all from the starting point to the current node along the current path; α ∈ [0, 1], is the adjustment factor between distance and safety.

6. The device according to claim 5, characterized in that, The first judgment module further includes a second judgment module, configured to: When the path planning task is not a static path planning, simulate the detection probability of the multi-domain grid in real time; When it is determined to be a multi-aircraft situation, set the detection probability value of the grid where other aircraft appear to 1; Perform route planning for the target aircraft using the improved D* algorithm.

7. The device according to claim 6, characterized in that, The second judgment module further includes a third judgment module, configured to: When it is determined not to be a multi-aircraft situation, perform route planning for the target aircraft using the improved D* algorithm.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid as described in any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for path planning of a moving object in an electromagnetic environment based on a multi-domain grid as described in any one of claims 1-4.

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