Flight path planning method applied to low-altitude airspace

Abstract

The invention relates to the technical field of air traffic management, in particular to a flight path planning method applied to a low-altitude airspace, and the method comprises the following steps: obtaining airspace data and flight plan data which represent an air region and flight time and position; the air area is divided into a plurality of grids, each edge of each grid is provided with an entrance point and an exit point, the entrance point is an entrance for the unmanned aerial vehicle to enter the current grid, and the exit point is an exit for the unmanned aerial vehicle to leave the current grid; determining all entry points and exit points as network nodes of the air area; generating a directed graph according to the network nodes and the flight plan data; searching the directed graph according to the flight plan data to obtain a flight path meeting the flight plan, and taking the flight path as a final planned flight path; the flight path planned by the method can reduce complex flight and conflicts, improve flight safety and improve flight path planning efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of air traffic management, and in particular to a trajectory planning method applied to low-altitude airspace. Background Art

[0002] Due to the complex operation scenarios and high operation density of low-altitude drones, a digital drone air traffic management (UTM) system is urgently needed to apply advanced artificial intelligence technology. The digital low-altitude airspace model is the prerequisite for applying advanced artificial intelligence technology in UTM. The trajectory planning method based on this affects the operation efficiency of the airspace.

[0003] The currently widely used low-altitude airspace model is the gridded airspace model, which divides the airspace plane into square grids arranged in a matrix. The current airspace operation mode is to enable the drone to use the center points of adjacent grids as waypoints to form a flight path. In one operation mode, the eight grids at the top, bottom, left, right, lower left, lower right, upper left and upper right positions around the grid are determined as adjacent grids, and the waypoints of adjacent grids are connected to form a planned track; in another mode, the four grids at the top, bottom, left and right positions around the grid are determined as adjacent grids, and the waypoints of adjacent grids are connected to form a planned track.

[0004] The current trajectory planning method usually uses the center point of the grid as the waypoint, which usually results in a flight path with severe head-on conflicts. In addition, the exit point of the grid is also the entry point of the grid, which leads to complex flight conflicts at the entry and exit points of the grid. And when the centers of two diagonal grids are used as adjacent trajectory path points, since this trajectory happens to pass through the common vertex of these two grids, if the positioning is slightly deviated during the actual flight, the drone will fly into a third grid that is adjacent to both grids. Therefore, the current trajectory planning method of the low-altitude airspace model affects the operating efficiency of the airspace. Summary of the invention

[0005] In view of the above problems, the present invention provides a trajectory planning method applied to low-altitude airspace, which solves the technical problems of head-on conflict and entry and exit point flight conflict in the trajectory planning path in the prior art.

[0006] The present invention provides a trajectory planning method applied to low-altitude airspace, comprising the following steps:

[0007] Step S1, obtaining airspace data and flight plan data, wherein the airspace data is information about the air area in which the drone can fly; the flight plan data is information about the time and location of the drone's planned flight;

[0008] Step S2: based on the airspace data, the aerial area is divided into a plurality of grids, and an entry point and an exit point are set on each edge of each grid, wherein the entry point is the entrance for the drone to enter the current grid, and the exit point is the exit for the drone to leave the current grid; all the entry points and exit points are determined as network nodes of the aerial area;

[0009] Step S3: Generate a directed graph representing the flight path of the UAV based on the network nodes and the flight plan data; search the directed graph based on the flight plan data to obtain a flight path that satisfies the flight plan as the final planned track.

[0010] Preferably, in step S1, the airspace data includes the position and size of the air area; the flight plan data includes the flight speed, starting position, departure time, end position and arrival time.

[0011] Preferably, step S2 specifically includes:

[0012] Step S2-1, divide the airspace plane into square grids arranged in a matrix. The grid with grid coordinates (m, n) represents the grid of the mth row and nth column in the square grid arranged in a matrix. An entry point and an exit point are set on each edge of each grid as nodes that the drone flies through. The node set expression is:

[0013]

[0014] in, is the set of nodes on the grid (m,n), Indicates the position coordinates of the nodes marked as φ1, φ2, and φ3 on the grid (m, n). When the values ​​of (φ1, φ2) are (0, 0), (0, 1), (1, 0), and (1, 1), they indicate that the nodes are on the left, right, bottom, and top of the current grid, respectively. When φ3 is 0, the node is the entrance, and when it is 1, the node is the exit.

[0015] Step S2-2, setting the entry point and exit point on the common edge of two adjacent grids to coincide, that is, the exit point of one grid is exactly the entry point of another grid, and the entry point of one grid is exactly the exit point of another grid;

[0016] Step S2-3: Determine a set of all nodes of all grids in the aerial area as the network nodes of the aerial area.

[0017] Preferably, the node The position coordinates are expressed as:

[0018]

[0019] Among them, L Unit represents the side length of the grid, α and β are the ratios of the distance from the node to the vertex of the grid edge to the length of the grid edge, and α+β=1 and α<β;

[0020] The corresponding relationship between the entry points and exit points that coincide on the common edge of two adjacent grids is:

[0021]

[0022] in, The coordinates are The nodes on the grid are labeled φ1, (1-φ2), (1-φ3).

[0023] Preferably, step S3 specifically includes:

[0024] Step S3-1, generating a directed graph according to the network nodes based on energy constraints and arrival time constraints;

[0025] Step S3-2, determining the destination node of the drone according to the flight plan data, and searching the directed graph;

[0026] If the destination node exists in the directed graph, the flight path between the start node and the destination node is determined as the planned track;

[0027] If the destination node does not exist in the directed graph, the midway departure time of the flight plan data is postponed by a preset time interval;

[0028] Step S3-3, return to step S3-1 until the planned track is determined. If the departure time is postponed to the point where the arrival time constraint cannot be met, it is determined that a track that meets the requirements cannot be provided.

[0029] Preferably, step S3-1 specifically includes:

[0030] Step S3-1-1, determining a starting node according to a starting position in the flight plan data;

[0031] Step S3-1-2, determining an energy constraint and an arrival time constraint from the flight plan data, wherein the energy constraint is used to ensure that the UAV has sufficient energy to complete the flight path, and the arrival time constraint is used to ensure that the UAV arrives before the arrival time;

[0032] Step S3-1-3: According to the network node, based on the energy constraint and the arrival time constraint, generate a directed graph with the starting node.

[0033] Preferably, in step S3-1-2, the energy constraint is expressed as:

[0034]

[0035] in, Indicates the node that the drone currently plans to pass through The moment of entering the grid (m,n), Indicates i drone slave node The flight time in grid (m,n) when flying to grid (m′,n′), It represents the minimum time required for UAV i to reach the destination node after flying from grid (m,n) to grid (m′,n′); Indicates the moment when UAV i enters the target airspace, It represents the flight time that the energy capacity of UAV i can sustain in the target airspace;

[0036] The arrival time constraint is expressed as:

[0037]

[0038] in, represents the latest acceptable arrival time of drone i, Determined by the arrival time in the flight plan data.

[0039] Preferably, The calculation method is:

[0040]

[0041] Among them, V i Pre is the flight speed in the flight plan data of UAV i, is the flight distance of UAV i in the grid (m,n), φ1, φ2, φ 1′ ,φ 2′ are the entry and exit node marks of the grid (m, n) when the i-th UAV flies from the grid (m, n) to the grid (m′, n′), L Unit Indicates the side length of the grid;

[0042] The calculation method is:

[0043]

[0044] in, is the terminal node reached by UAV i, which is determined by the end position in the flight plan data; Δx and Δy are and The lateral and longitudinal distances in the airspace plane.

[0045] Preferably, step S3-1-3 specifically includes:

[0046] A breadth-first strategy is adopted starting from the starting node in the graph, and all the network nodes are traversed according to energy constraints and arrival time constraints to generate a directed graph representing all possible flight paths of the UAV under energy constraints and arrival time constraints.

[0047] Compared with the prior art, the present invention has at least the following beneficial effects:

[0048] (1) The present invention optimizes the inlet and outlet separation of the grid model of low-altitude airspace, and on this basis provides a trajectory planning method based on a directed graph. Due to the inlet and outlet separation design of the grid, the trajectory planning method based on the directed graph can avoid serious head-on conflicts and reduce complex flight conflicts at the entry and exit points.

[0049] (2) In the trajectory planning method provided by the present invention, since all trajectories will not pass through the vertices of the grid, the risk of the drone entering a grid that it should not enter due to positioning deviation can be reduced, thereby improving flight safety.

[0050] (3) The grid model of low-altitude airspace provided by the present invention facilitates the calculation of the minimum and maximum path distances between two nodes, thereby facilitating the prediction of the flight time and required energy of a flight mission during operation, thereby improving the efficiency of trajectory planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0052] Figure 1 A schematic diagram of the steps of the trajectory planning method for low-altitude airspace provided by the present invention.

[0053] Figure 2 A schematic diagram of spatial domain model parameter encoding provided by the present invention.

[0054] Figure 3 This is a comparison diagram of the differences between the traditional airspace grid model and the airspace grid model provided by the present invention when the tracks intersect.

[0055] Figure 4 This is a schematic diagram of the shortest path in the airspace model provided by the present invention.

[0056] Figure 5 This is a schematic diagram of the longest path in the airspace model provided by the present invention.

[0057] Figure 6 A flow chart of the low-altitude airspace trajectory planning method provided by the present invention. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0059] In order to illustrate the effectiveness of the method proposed by the present invention, the above technical solution of the present invention is described in detail below through a specific embodiment. Figure 1 As shown, a trajectory planning method for low-altitude airspace is disclosed, and the specific implementation steps are as follows:

[0060] Step S1, obtaining airspace data and flight plan data, wherein the airspace data is information about the air area where the drone can fly; and the flight plan data is information about the time and location of the drone's planned flight.

[0061] For low-altitude UAV operation scenarios, the airspace is the designated aerial area when the UAV flies, and the low-altitude airspace model is used to describe the spatial position and driving path of the UAV flight in the aerial area.

[0062] In some embodiments, airspace data can be received through a pre-set related database or real-time monitoring system to ensure that the airspace data accurately reflects the geographical location and spatial range of the air area where the current drone can fly. At the same time, flight plan data can be received from the drone control system or dispatch center, and the flight plan data records the flight speed, starting position, departure time, end position and expected arrival time of the drone, providing necessary information support for subsequent airspace modeling and trajectory planning.

[0063] In some embodiments, the airspace data includes the location and size of the airspace area, and the flight plan data includes flight speed, starting location, departure time, end location and arrival time.

[0064] Step S2: Based on the airspace data, the aerial area is divided into multiple grids, and an entry point and an exit point are set on each edge of each grid, where the entry point is the entrance for the drone to enter the current grid, and the exit point is the exit for the drone to leave the current grid; all entry points and exit points are determined as network nodes of the aerial area.

[0065] The low-altitude airspace model used in the present invention is a gridded airspace model, such as Figure 2 , Figure 3As shown, the airspace plane is divided into square grids arranged in a matrix. The grid with grid coordinates (m,n) represents the grid of the mth row and nth column in the square grid arranged in a matrix. An entry point and an exit point are set on each edge of each grid to serve as the path points for the drone to fly through. Its mathematical description is:

[0066]

[0067] in, is a set of nodes on the grid (m,n), containing 8 nodes in total. Represents one of the eight nodes, which can be marked by its subscripts φ1, φ2, and φ3. The values ​​of φ1 and φ2 jointly determine which edge of the grid the node is located on. When the values ​​of (φ1, φ2) are (0, 0), (0, 1), (1, 0), and (1, 1), they indicate that the node is on the left, right, bottom, and top, respectively. The value of φ3 is used to mark whether the node is the entry or exit point of the grid (m, n), that is, φ3 takes 0 as the entry and 1 as the exit. Finally, the values ​​of φ1, φ2, and φ3 can uniquely determine the node on the grid.

[0068] like Figure 3 As shown, for the entry point and exit point determined by the present invention, the entry point and exit point on the common edge of two adjacent grids coincide, that is, the exit point of one grid is exactly the entry point of another grid, and the entry point of one grid is exactly the exit point of another grid.

[0069] A set of all nodes of all grids in an aerial region is determined as a network node of the aerial region.

[0070] The gridded airspace model determined by the present invention has the following characteristics:

[0071] (1) The position coordinates of the nodes on the grid (m,n) can be consistently expressed as:

[0072]

[0073] Where L Unit Represents the side length of the grid, α and β are the ratios of the distance from the node to the vertex of the grid edge to the length of the grid edge, and α+β=1 and α<β. Through this expression, the node coordinates can be obtained from the values ​​of φ1, φ2 and φ3 of the node, which are used to represent the geometric position of the node and facilitate calculations in path planning.

[0074] (2) The entry and exit points on the common edges of two adjacent grids coincide, so the following corresponding relationship can be determined:

[0075]

[0076] Through this expression, a grid with coordinates (m,n) and its adjacent grid with coordinates (m,n) are established. The correspondence between the node coordinates in the grid.

[0077] Figure 3 The figure shows the difference between the traditional airspace grid model and the airspace grid model provided by the present invention in the track intersection situation. It can be found from the figure that in the traditional airspace grid model, all tracks have direct conflicts, and such conflicts persist. In the airspace grid model described in the present invention, the track intersection area is distributed in a point-like manner, which is conducive to alleviating the severity of conflicts in the airspace.

[0078] (3) When a drone enters a grid through an entrance and wants to leave the current grid, there are three possible trajectories. After entering the node, you can fly to and Nodes correspond to the flying grids (m-1,n), (m,n-1) and (m+1,n) respectively. In general, the starting node of the device edge selection is Then the expression of the end node set is:

[0079]

[0080] in, Indicates that drone i is in the grid (m,n) starting at node The set of end nodes that are candidate edges.

[0081] According to the property that the entry points and exit points on the common edges of two adjacent grids coincide, the end node set can also be expressed as:

[0082]

[0083] Through this expression, the relationship between the node currently entered by the drone and the next node is established.

[0084] (4) When the UAV enters a grid through an entrance and is about to leave the current grid, the expression of the set of grids where the next track node is located is:

[0085]

[0086] in, Indicates that drone i is in the grid (m,n) When is the starting node, the set of grids where the next track node is located. Through this expression, the relationship between the grid currently entered by the drone and the next grid is established.

[0087] (5) Node and The shortest path length between It can be expressed as:

[0088]

[0089] where Δx and Δy are and Distances in the horizontal and vertical directions of the spatial plane, which are non-negative.

[0090] By summarizing the calculation method of the shortest path length into the above expression, the shortest path length between two nodes can be directly obtained from the coordinates of the two nodes, which can be used to calculate the flight time and required energy of the flight mission, and can improve the efficiency of subsequent trajectory planning.

[0091] (6) If the track point is always getting closer to the end point, then and The longest path length between It can be expressed as:

[0092]

[0093] Among them, f S (a,b) is a preset custom function. Indicates the power of the custom function value, f S (a,b) is defined as:

[0094]

[0095] Here, mod is the modulus operation, which means finding the remainder after dividing two values.

[0096] Figure 4 and Figure 5 Schematic diagrams of the shortest and longest paths in the airspace grid model provided by the present invention. In the figure, according to the value and size relationship of Δm=|mm′| and Δn=|nn′|, there are 15 possibilities, which illustrate the shortest and longest paths of all possible scene types. Here, the starting point and the end point of the track must be on two different sides of the rectangular airspace, and the nodes on the track are always getting closer and closer to the end point. The trajectory that reaches the minimum or maximum path length may not be unique, Figure 4 and Figure 5 The examples are provided for reference only.

[0097] Step S3: Generate a directed graph representing the flight path of the UAV based on the network nodes and the flight plan data; search the directed graph based on the flight plan data to obtain a flight path that satisfies the flight plan as the final planned track.

[0098] Step S3-1: Generate a directed graph according to the network nodes based on energy constraints and arrival time constraints.

[0099] Step S3-1-1, determining a starting node according to a starting position in the flight plan data;

[0100] Step S3-1-2, determining an energy constraint and an arrival time constraint from the flight plan data, wherein the energy constraint is used to ensure that the UAV has sufficient energy to complete the flight path, and the arrival time constraint is used to ensure that the UAV arrives before the arrival time;

[0101] Energy constraints and arrival time constraints should be considered when generating the directed graph. In the present trajectory planning method, the speed of the UAV is extracted from the flight plan data, and the speed of the UAV is a constant value.

[0102] Energy constraints have two purposes: first, to ensure the feasibility of the generated edges from the perspective of energy consumption, i.e., the drone has enough energy to complete the generated flight path; second, to narrow the search space by checking the feasibility of the drone reaching the destination. Since the speed of the drone is set to be constant, the power required is fixed. and end node (The grid corresponding to the end node The energy constraint can be expressed as:

[0103]

[0104] in, Indicates the node that the drone currently plans to pass through The moment of entering the grid (m,n), Indicates i drone slave node The flight time in grid (m,n) when flying to grid (m′,n′), It represents the minimum time required for UAV i to reach the end point after flying from grid (m,n) to grid (m′,n′), where the end point is determined by the end position in the flight plan data; Indicates the moment when UAV i enters the target airspace, It indicates the flight time that the energy capacity of UAV i can maintain in the target airspace.

[0105] The calculation method is:

[0106]

[0107] Among them, V i Pre is the flight speed in the flight plan data of UAV i, is the flight distance of UAV i in grid (m,n), which can be consistently expressed as:

[0108]

[0109] Among them, φ 1′ ,φ 2′ is the exit node mark of the grid (m,n) when the i-th UAV flies from the grid (m,n) to the grid (m′,n′).

[0110] The calculation method is:

[0111]

[0112] in, is the terminal node reached by UAV i, which is determined by the end position in the flight plan data, Δx and Δy are and The lateral and longitudinal distances in the airspace plane.

[0113] The arrival time constraint can be expressed as:

[0114]

[0115] in, represents the latest acceptable arrival time of drone i, Determined by the arrival time in the flight plan data.

[0116] The energy constraint and arrival time constraint can be combined as:

[0117]

[0118] Step S3-1-3: According to the network node, based on the energy constraint and the arrival time constraint, generate a directed graph with the starting node.

[0119] A breadth-first strategy is adopted starting from the starting node in the graph. Starting from the starting node, all its neighbor nodes are visited in turn according to the energy constraint and arrival time constraint according to the hierarchy, and then the neighbor nodes of these neighbor nodes are visited, and so on to traverse all the network nodes to generate a directed graph for representing all possible flight paths of the UAV under the energy constraint and arrival time constraint.

[0120] Step S3-2, determining the destination node of the drone according to the flight plan data, and searching the directed graph;

[0121] If the destination node exists in the directed graph, the flight path between the start node and the destination node is determined as the planned track;

[0122] If the destination node does not exist in the directed graph, the midway departure time of the flight plan data is postponed by a preset time interval.

[0123] In this step, the path search method is used to search the directed graph, and at the same time, it is determined whether the destination node exists in the directed graph. As long as the destination node of drone i exists in the generated directed graph, it is considered that there is a feasible track. In the absence of a feasible track, an adaptive delay strategy is used to explore a new solution space: only when the arrival time constraint is met, it is delayed for a period of time Δt. Pos , and try to regenerate the graph; when it cannot be satisfied, the generation of hotspot-free trajectories is marked as failed, and the flight plan can be rescheduled or canceled.

[0124] In some embodiments, the path search method may adopt a path search method compatible with directed graphs, such as A-star, Dijkstra, etc. The present invention does not limit the specific path search method.

[0125] Step S3-3, return to step S3-1 until the planned track is determined. If the departure time is postponed to the point where the arrival time constraint cannot be met, it is determined that a track that meets the requirements cannot be provided.

[0126] Through the above method, the directed graph trajectory planning of the airspace grid model provided by the present invention can be completed, and the trajectory formed by the path points will not have serious head-on conflicts, thereby improving flight safety. Based on the calculation of the minimum and maximum path distances between two nodes, it is convenient to obtain the flight time and required energy, thereby improving the efficiency of trajectory planning.

[0127] Although the specific embodiments of the present invention depict various actions or steps in a specific order, this should be understood as requiring such actions or steps to be performed in the specific order shown or in a sequential order, or requiring all illustrated actions or steps to be performed to obtain the desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

[0128] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A trajectory planning method applied to low-altitude airspace, characterized in that: The following steps are involved: Step S1, obtaining airspace data and flight plan data, wherein the airspace data is information about the air area in which the drone can fly; the flight plan data is information about the time and location of the drone's planned flight; Step S2: based on the airspace data, the aerial area is divided into a plurality of grids, and an entry point and an exit point are set on each edge of each grid, wherein the entry point is the entrance for the drone to enter the current grid, and the exit point is the exit for the drone to leave the current grid, and the positions of the entry point and the exit point do not overlap; all the entry points and the exit points are determined as network nodes of the aerial area; Step S3: Generate a directed graph representing the flight path of the UAV based on the network nodes and the flight plan data; search the directed graph based on the flight plan data to obtain a flight path that satisfies the flight plan as the final planned track.

2. The method for trajectory planning applied to low-altitude airspace according to claim 1, characterized in that: In step S1, the airspace data includes the position and size of the airspace; the flight plan data includes the flight speed, starting position, departure time, end position and arrival time.

3. The method for trajectory planning applied to low-altitude airspace according to claim 2 is characterized in that: Step S2 specifically includes: Step S2-1, divide the airspace plane into square grids arranged in a matrix. The grid with grid coordinates (m, n) represents the grid of the mth row and nth column in the square grid arranged in a matrix. An entry point and an exit point are set on each edge of each grid as nodes that the drone flies through. The node set expression is: in, is the set of nodes on the grid (m,n), Indicates the position coordinates of the nodes marked as φ1, φ2, and φ3 on the grid (m, n). When the values ​​of (φ1, φ2) are (0, 0), (0, 1), (1, 0), and (1, 1), they indicate that the nodes are on the left, right, bottom, and top of the current grid, respectively. When φ3 is 0, the node is the entrance, and when it is 1, the node is the exit. Step S2-2, setting the entry point and exit point on the common edge of two adjacent grids to coincide, that is, the exit point of one grid is exactly the entry point of another grid, and the entry point of one grid is exactly the exit point of another grid; Step S2-3: Determine a set of all nodes of all grids in the aerial region as the network nodes of the aerial region.

4. The method for trajectory planning applied to low-altitude airspace according to claim 3 is characterized in that: node The position coordinates are expressed as: Among them, L Unit Represents the side length of the grid, α and β are the ratios of the distance from the node to the vertex of the grid edge to the length of the grid edge, and α+β=1 and α<β; The corresponding relationship between the entry points and exit points that coincide on the common edge of two adjacent grids is: in, The coordinates are The nodes on the grid are labeled φ1, (1-φ2), (1-φ3).

5. The method for trajectory planning applied to low-altitude airspace according to any one of claims 1 to 4, characterized in that: Step S3 specifically includes: Step S3-1, generating a directed graph according to the network nodes based on energy constraints and arrival time constraints; Step S3-2, determining the destination node of the drone according to the flight plan data, and searching the directed graph; If the destination node exists in the directed graph, the flight path between the start node and the destination node is determined as the planned track; If the destination node does not exist in the directed graph, the midway departure time of the flight plan data is postponed by a preset time interval; Step S3-3, return to step S3-1 until the planned track is determined. If the departure time is postponed to the point where the arrival time constraint cannot be met, it is determined that a track that meets the requirements cannot be provided.

6. The method for trajectory planning applied to low-altitude airspace according to claim 5 is characterized in that: Step S3-1 specifically includes: Step S3-1-1, determining a starting node according to a starting position in the flight plan data; Step S3-1-2, determining an energy constraint and an arrival time constraint from the flight plan data, wherein the energy constraint is used to ensure that the UAV has sufficient energy to complete the flight path, and the arrival time constraint is used to ensure that the UAV arrives before the arrival time; Step S3-1-3: According to the network node, based on the energy constraint and the arrival time constraint, generate a directed graph with the starting node.

7. The method for trajectory planning applied to low-altitude airspace according to claim 6 is characterized in that: In step S3-1-2, the energy constraint is expressed as: in, Indicates the node that the drone currently plans to pass through The moment of entering the grid (m,n), Indicates i drone slave node The flight time in grid (m,n) when flying to grid (m′,n′), It represents the minimum time required for UAV i to reach the destination node after flying from grid (m,n) to grid (m′,n′); Indicates the moment when UAV i enters the target airspace, It represents the flight time that the energy capacity of UAV i can sustain in the target airspace; The arrival time constraint is expressed as: Among them, t i LAA represents the latest acceptable arrival time of drone i, Determined by the arrival time in the flight plan data.

8. The method for trajectory planning applied to low-altitude airspace according to claim 7, characterized in that: The calculation method is: Among them, V i Pre is the flight speed in the flight plan data of UAV i, is the flight distance of UAV i in the grid (m,n), φ1, φ2, φ 1′ ,φ 2′ are the entry and exit node marks of the grid (m, n) when the i-th UAV flies from the grid (m, n) to the grid (m′, n′), L Unit Indicates the side length of the grid; The calculation method is: in, is the terminal node reached by UAV i, which is determined by the end position in the flight plan data; Δx and Δy are and The lateral and longitudinal distances in the airspace plane.

9. The method for trajectory planning applied to low-altitude airspace according to claim 8, characterized in that: Step S3-1-3 specifically includes: A breadth-first strategy is adopted starting from the starting node in the graph, and all the network nodes are traversed according to energy constraints and arrival time constraints to generate a directed graph representing all possible flight paths of the UAV under energy constraints and arrival time constraints.

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