A flight path planning method applied to low altitude airspace

Abstract

The application relates to the technical field of air traffic management, in particular to a flight path planning method applied to low-altitude airspace, which comprises the following steps: obtaining airspace data and flight plan data representing an air region and time and position of flight; dividing the air region into a plurality of grids, setting an entry point and an exit point on each edge of each grid, the entry point being an entry of a UAV into a current grid, and the exit point being an exit of the UAV from the current grid; determining all the entry points and the exit points as network nodes of the air region; 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, as a finally planned flight path; the flight path planned by the application can reduce complex flight, conflict, improve flight safety and improve flight path planning efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air traffic management, in particular to a flight path planning method applied to low-altitude airspace. BACKGROUND

[0002] Due to the complex operation scene and high operation density of low-altitude unmanned aerial vehicles, a digital unmanned aerial system traffic management (UTM) system is urgently needed to apply advanced artificial intelligence technology. A digital low-altitude airspace model is a prerequisite for applying advanced artificial intelligence technology in UTM. The flight path planning method based thereon affects the operation efficiency of the airspace.

[0003] The current widely used low-altitude airspace model is a grid airspace model, that is, the airspace plane is divided into square grids arranged in a matrix. The current airspace operation mode is to make the unmanned aerial vehicle take the center points of adjacent grids as waypoints to form a flight path. In one operation mode, the eight grids around the grid, i.e., the upper, lower, left, right, lower left, lower right, upper left, and upper right grids, are determined as adjacent grids, and the waypoints connecting the adjacent grids form the planned flight path. In another mode, the four grids around the grid, i.e., the upper, lower, left, and right grids, are determined as adjacent grids, and the waypoints connecting the adjacent grids form the planned flight path.

[0004] The current flight path planning method usually takes the center points of the grids as the waypoints, which usually leads to flight paths with serious head-on conflicts. In addition, the exit points of the grids are also the entrance points of the grids, which leads to complex flight conflicts at the entrance and exit points of the grids. When the center points of two diagonal grids are taken as adjacent flight path points, since the flight path passes through the vertex shared by the two grids, if the positioning deviates slightly in actual flight, the unmanned aerial vehicle will fly into a third grid adjacent to the two grids. Therefore, the current flight path planning method of the low-altitude airspace model affects the operation efficiency of the airspace. SUMMARY

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

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

[0007] Step S1, acquiring airspace data and flight plan data, the airspace data being information of an unmanned aerial vehicle flyable airspace region; the flight plan data being time and position information of a planned flight of the unmanned aerial vehicle;

[0008] Step S2, based on the airspace data, divide the airspace into a plurality of grids, set an entry point and an exit point on each edge of each grid, the entry point is the entry of the UAV into the current grid, and the exit point is the exit of the UAV from the current grid; all entry points and exit points are determined as network nodes of the airspace;

[0009] Step S3, according to the network nodes and the flight plan data, generate a directed graph representing the flight path of the UAV; search the directed graph according to the flight plan data to obtain a flight path satisfying the flight plan as the final planned track.

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

[0011] Preferably, step S2 specifically includes:

[0012] Step S2-1, divide the airspace plane into a matrix of square grids, and the grid coordinates are The grid represents the mth row and nth column in the matrix of square grids, and an entry point and an exit point are set on each edge of each grid to serve as a node for the UAV to fly through, and the node set expression is:

[0013]

[0014] Wherein, is the set of nodes on the grid , represents the node position coordinates marked on the grid , when the values of are , , and respectively, it means that the node is on the left, right, bottom and top edge of the current grid respectively, when takes 0, the node is an entry, and when it takes 1, the node is an exit.

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

[0016] Step S2-3, determine the set of all nodes of all grids in the airspace as the network nodes of the airspace.

[0017] Preferably, the node The position coordinates of the nodes are represented as:

[0018]

[0019] wherein, denotes the side length of the grid, and are the ratios of the distance from the node to the top vertex of the side of the grid to the side length of the grid, and have and ;

[0020] The corresponding relationship of the coincident entry point and exit point on the common side of the two adjacent grids is:

[0021]

[0022] wherein, denotes the node marked as on the grid with coordinates .

[0023] Preferably, step S3 specifically comprises:

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

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

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

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

[0028] Step S3-3, returning to step S3-1 until the planned track is determined, and if the departure time is postponed to the extent that the arrival time constraint cannot be met, determining that the track meeting the demand cannot be provided.

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

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

[0031] Step S3-1-2, determining the energy constraint and the 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, express The drone currently plans to pass through the node Enter the grid moment, express Drone slave node Fly to the grid Time in grid The flight time, express Drone from grid Fly to the grid The minimum time required to reach the final node; Indicates drone The moment of entering the target airspace, Indicates drone The flight time that the energy capacity can sustain in the target airspace;

[0036] The arrival time constraint is expressed as:

[0037]

[0038] in, Indicates drone The latest acceptable arrival time, Determined by the arrival time in the flight plan data.

[0039] Preferably, The calculation method is:

[0040]

[0041]

[0042] in, For drones The flight speed in the flight plan data, For drones In the grid Flight distance within , , , They are The unmanned aerial vehicle flies from the grid to the grid at the entry and exit nodes of the grid , the length of the edge of the grid is represented;

[0043] The calculation method is as follows:

[0044]

[0045]

[0046] wherein, is the end node reached by the unmanned aerial vehicle, the end node being determined by the end position in the flight plan data; and respectively and the distance in the lateral direction and the longitudinal direction of the airspace plane.

[0047] Preferably, step S3-1-3 specifically comprises:

[0048] A breadth-first strategy is adopted to start from the starting node in the graph, and all network nodes are traversed according to the energy constraint and the arrival time constraint, so as to generate a directed graph for representing all possible flight paths of the unmanned aerial vehicle under the energy constraint and the arrival time constraint.

[0049] Compared with the prior art, the present application has at least the following beneficial effects:

[0050] (1) The grid model of the low-altitude airspace is optimized by separating the entry and exit points, and on this basis, a flight path planning method based on a directed graph is provided. Due to the separation design of the entry and exit points of the grid, the flight path planning method based on the directed graph can avoid serious head-on conflicts and reduce complex flight conflicts at the entry and exit points.

[0051] (2) In the flight path planning method provided by the present application, all flight paths will not pass through the vertices of the grid, so that the risk of the unmanned aerial vehicle entering a grid that should not be entered due to positioning deviation can be reduced, and the flight safety can be improved.

[0052] (3) The grid model of the low-altitude airspace provided by the present application is convenient for calculating the minimum and maximum path distances between two nodes, so as to facilitate the prediction of the flight time and the required energy of the flight task in operation, and the efficiency of the flight path planning is improved. BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not considered to be a limitation of the present application.

[0054] ​Figure 1 A schematic diagram of the steps of the low-altitude airspace path planning method provided by the present application.

[0055] Figure 2 A schematic diagram of airspace model parameter coding provided by the present application.

[0056] Figure 3 A difference comparison diagram of the airspace grid model provided by the present application and the traditional airspace grid model when the paths intersect.

[0057] Figure 4 A schematic diagram of the shortest path in the airspace model provided by the present application.

[0058] Figure 5 A schematic diagram of the longest path in the airspace model provided by the present application.

[0059] Figure 6 A flowchart of the low-altitude airspace path planning method provided by the present application. DETAILED DESCRIPTION

[0060] In order to enable a person skilled in the art to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict. In addition, the present application can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below.

[0061] In order to illustrate the effectiveness of the method proposed by the present application, the above-mentioned technical solutions of the present application will be described in detail below through a specific embodiment. As shown in Figure 1 A low-altitude airspace path planning method is disclosed, and the specific implementation steps are as follows:

[0062] Step S1, obtain airspace data and flight plan data, the airspace data is information of an unmanned aerial vehicle flyable airspace region; the flight plan data is time and position information of an unmanned aerial vehicle planned flight.

[0063] For a low-altitude unmanned aerial vehicle operation scenario, the airspace is a specified airspace region for unmanned aerial vehicle flight, and a low-altitude airspace model is used to describe the spatial position and travel path of unmanned aerial vehicle flight in the airspace region.

[0064] In some embodiments, airspace data can be received from a pre-defined database or real-time monitoring system to ensure that it accurately reflects the geographic location and spatial extent of the airspace within which the drone can currently fly. Simultaneously, flight plan data can be received from the drone control system or dispatch center. This flight plan data records the drone's flight speed, starting position, departure time, end position, and estimated arrival time, providing essential information support for subsequent airspace modeling and trajectory planning.

[0065] 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, ending location, and arrival time.

[0066] 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. 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.

[0067] The low-altitude airspace model used in the present invention is a gridded airspace model, such as Figure 2 、 Figure 3 As shown, the spatial plane is divided into square grids arranged in a matrix, and the grid coordinates are The grid represents a grid with m rows and n columns in a square grid arranged in a matrix. An entry point and an exit point are set on each side of each grid to serve as the path points for the drone to fly through. Its mathematical description is:

[0068]

[0069] in, Is a grid The set of nodes on contains 8 nodes in total. Indicates one of the eight nodes, which can be identified by its subscript 、 and Marking, and The values ​​of jointly determine which edge of the grid the node is located on. The values ​​of 、 、 and , indicating that the nodes are on the left, right, bottom and top respectively. The value is used to mark whether the node is used as a grid The entry or exit point of Take 0 as the entrance and 1 as the exit. Finally, 、 and The value of can uniquely identify a node on the grid.

[0070] 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.

[0071] A set of all nodes of all grids in the aerial region is determined as the network node of the aerial region.

[0072] The gridded airspace model determined by the present invention has the following characteristics:

[0073] (1) Grid The position coordinates of the nodes on can be consistently expressed as:

[0074]

[0075] in represents the side length of the grid, and are the ratios of the distance from the node to the vertex of the grid edge to the length of the grid edge, and and Through this expression, the node 、 and The node coordinates are obtained by using the value of , which is used to represent the geometric position of the node and facilitate calculations in path planning.

[0076] (2) The entry and exit points on the common edge of two adjacent grids coincide, so the following correspondence can be determined:

[0077]

[0078] Through this expression, the coordinates are established. The grid and its adjacent coordinates are The correspondence between the node coordinates in the grid.

[0079] Figure 3 This figure compares the differences in track intersections between a traditional airspace grid model and the airspace grid model provided by the present invention. As can be seen from the figure, in the traditional airspace grid model, all tracks experience direct and persistent conflicts. In contrast, in the airspace grid model provided by the present invention, track intersections are distributed in a dispersed, point-like pattern, which helps mitigate the severity of conflicts in the airspace.

[0080] (3) When a UAV enters a grid through an entrance and wants to leave the current grid, there are three possible tracks. of After entering the node, you can fly to 、 and Nodes, corresponding to the flying grid 、 and In general, the starting node of the device edge selection is , then the expression of the end node set is:

[0081]

[0082] in, Indicates drone In the grid Start node The set of end nodes that serve as candidate edges.

[0083] According to the property that the entry points and exit points on the common edge of two adjacent grids coincide, the end node set can also be expressed as:

[0084]

[0085] Through this expression, the relationship between the node currently entered by the drone and the next node is established.

[0086] (4) When a 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:

[0087]

[0088] in, Indicates drone In the grid China-Israel When is the starting node, the set of grids where the next track node is located. Through this expression, the relationship between the current grid entered by the drone and the next grid is established.

[0089] (5) Node and The shortest path length between It can be expressed as:

[0090]

[0091] in and They are and The distances in the horizontal and vertical directions of the airspace plane are non-negative.

[0092] By summarizing the calculation method of the shortest path length into the above expression, the shortest path length of two nodes can be directly obtained from the coordinates of the two nodes, which can be used for the calculation of the flight time and required energy of the flight task, and the efficiency of subsequent trajectory planning can be improved.

[0093] (6) If the trajectory points are always closer to the end point, then The longest path length between can be expressed as:

[0094]

[0095] wherein, is a preset custom function, represents the power of the custom function value, is defined as:

[0096]

[0097] wherein mod is a modulus operation, and represents the remainder after two numerical values are divided.

[0098] Figure 4 and Figure 5 are respectively the shortest path and the longest path in the airspace grid model provided by the present application. In the figure, according to the value and size relationship of and , there are 15 possibilities, which illustrate the shortest and longest paths of all possible scenario types. Here, the starting point and the ending point of the trajectory must be on two different edges of the rectangular airspace, and the nodes on the trajectory are always closer to the ending point. The trajectory reaching the minimum or maximum path length can not be unique, Figure 4 and Figure 5 only provide examples for reference.

[0099] Step S3, generating a directed graph representing the flight path of the unmanned aerial vehicle according to the network node and the flight plan data; searching the directed graph according to the flight plan data to obtain a flight path meeting the flight plan as a final planned trajectory.

[0100] Step S3-1, generating a directed graph based on energy constraints and arrival time constraints according to the network node.

[0101] Step S3-1-1, determining a starting node from a starting position in the flight plan data;

[0102] ​Step S3-1-2, determining energy constraint and arrival time constraint from the flight plan data, the energy constraint is used to ensure that the UAV has enough energy to complete the flight path, and the arrival time constraint is used to ensure that the UAV arrives before the arrival time;

[0103] The energy constraint and the arrival time constraint are considered when generating the directed graph. In the present trajectory planning method, the UAV speed is extracted from the flight plan data, and the UAV speed is a constant value.

[0104] The energy constraint has two purposes, one is to ensure the feasibility of the generated edges from the energy consumption perspective, that is, the UAV has enough energy to complete the generated flight path, and the other is to narrow the search space by checking the feasibility of the UAV reaching the destination. Since the speed of the UAV is set to be constant, the required power is fixed. For the starting node and the ending node of the alternative edge (where the ending node corresponds to the grid ), the energy constraint can be expressed as:

[0105]

[0106] wherein, represents the time at which the UAV currently plans to enter the grid through the node , represents the flight time of the UAV in the grid from the node to the grid , represents the minimum time required for the UAV to reach the end point after flying from the grid to the grid , the end point being determined by the ending position in the flight plan data; represents the time at which the UAV enters the target airspace, represents the flight time that can be maintained by the energy capacity of the UAV in the target airspace.

[0107] The calculation method of is as follows:

[0108]

[0109] wherein, is the flight speed in the flight plan data of the UAV , is the flight time of the UAV in the grid The flight distance from the inner node can be consistently represented as:

[0110]

[0111] wherein, , is the exit node of the grid that the UAV flies to, the entry node of the grid that the UAV flies from.

[0112] The calculation method is as follows:

[0113]

[0114]

[0115] wherein, is the end node reached by the UAV, which is determined by the end position in the flight plan data, and are and the lateral and longitudinal distances in the airspace plane.

[0116] The arrival time constraint can be represented as:

[0117]

[0118] wherein, represents the latest acceptable arrival time of the UAV , which is determined by the arrival time in the flight plan data. The energy constraint and the arrival time constraint can be combined as:

[0119]

[0120]

[0121] Step S3-1-3, generating a directed graph from the starting node based on the energy constraint and the arrival time constraint according to the network node.

[0122] A breadth-first strategy is adopted to start from the starting node in the graph, and according to the energy constraint and the arrival time constraint, all neighbor nodes of the starting node are visited in turn, and then the neighbor nodes of these neighbor nodes are visited, and so on to traverse all the network nodes, thereby generating a directed graph for representing all possible flight paths of the UAV under the energy constraint and the arrival time constraint.

[0123] ​Step S3-2: determining the destination node of the UAV according to the flight plan data, and searching the directed graph;

[0124] If the destination node exists in the directed graph, determining the flight path between the start node and the destination node as the planned trajectory;

[0125] If the destination node does not exist in the directed graph, the departure time in the flight plan data is postponed by a preset time interval.

[0126] In this step, the path search method is used to search the directed graph and determine whether the destination node exists in the directed graph. As long as there is a drone in the generated directed graph, If the destination node is not reached, 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, the route is delayed for a period of time. , and try to regenerate the graph; when it cannot be satisfied, the generation of hotspot-free trajectory is marked as failed, and the flight plan can be rearranged or canceled.

[0127] 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.

[0128] Step S3-3: Return to step S3-1 until the planned trajectory is determined. If the departure time is postponed to the point where the arrival time constraint cannot be met, it is determined that a trajectory that meets the requirements cannot be provided.

[0129] This approach enables directed graph trajectory planning for the airspace grid model provided by the present invention. The trajectory formed by the path points avoids serious head-on collisions, improving flight safety. By calculating the minimum and maximum path distances between two nodes, flight time and required energy can be easily determined, improving trajectory planning efficiency.

[0130] The detailed description of the application describes and illustrates with reference to certain specific embodiments. However, the description and illustrations are intended to be merely illustrative and not restrictive of the application. While the application has been described and illustrated with reference to specific embodiments, it will be recognized that variations and modifications can be made by persons skilled in the art depending upon the overall teachings of the present application. In particular, those skilled in the art will recognize that elements of the present application can readily be combined to provide further embodiments of the present application. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments described herein. It is, therefore, to be understood that the application is not to be limited to the specific embodiments disclosed and that modifications and / or substitutions are intended to be included within the scope of the present application. Such equivalents are considered within the scope of the present application.

[0131] The detailed description of the application describes and illustrates with reference to certain specific embodiments. However, the description and illustrations are intended to be merely illustrative and not restrictive of the application. While the application has been described and illustrated with reference to specific embodiments, it will be recognized that variations and modifications can be made by persons skilled in the art depending upon the overall teachings of the present application. In particular, those skilled in the art will recognize that elements of the present application can readily be combined to provide further embodiments of the present application. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments described herein. It is, therefore, to be understood that the application is not to be limited to the specific embodiments disclosed and that modifications and / or substitutions are intended to be included within the scope of the present application. Such equivalents are considered within the scope of the present application.

Claims

1. A trajectory planning method applied to low-altitude airspace, characterized in that: The following steps are involved: Step S1: Acquire airspace data and flight plan data, wherein the airspace data is information about the airspace area in which the UAV can fly; and the flight plan data is information about the time and location of the UAV's planned flight. Step S2: Based on the airspace data, the aerial area is divided into multiple grids. An entry point and an exit point are set on each edge of each grid. 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. The positions of the entry point and exit point do not overlap. All entry points and exit points are determined as network nodes of the aerial area. Step S2 specifically include: Step S2-1: Divide the airspace plane into square grids arranged in a matrix. The grid coordinates are The grid represents a grid with the mth row and nth column in a 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 nodes that the drone flies through. The node set expression is: in, Is a grid The set of nodes on Represents a grid Marked as The node position coordinates, when The values ​​of 、 、 and When , it means the nodes are on the left, right, bottom and top of the current grid respectively. When 0 is taken, the node is the entrance, and when 1 is taken, the node is the exit; Step S2-2: Set 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 the other grid, and the entry point of one grid is exactly the exit point of the other grid; Step S2-3, determining a set of all nodes of all grids in the aerial area as the network nodes of the aerial area; Step S3: generating a directed graph representing the flight path of the UAV based on the network nodes and the flight plan data; searching the directed graph based on the flight plan data to obtain a flight path that satisfies the flight plan as the final planned trajectory; Step S3 specifically includes: Step S3-1: Generate a directed graph based on the network nodes, energy constraints and arrival time constraints; Step S3-2: determining the destination node of the UAV 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 trajectory; If the destination node does not exist in the directed graph, delaying the departure time in the flight plan data by a preset time interval; Step S3-3: Return to step S3-1 until the planned trajectory is determined. If the departure time is postponed to the point where the arrival time constraint cannot be met, it is determined that a trajectory that meets the requirements cannot be provided.

2. The trajectory planning method for low-altitude airspace according to claim 1, characterized in that: 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.

3. The trajectory planning method applied to low-altitude airspace according to claim 2, characterized in that: node The position coordinates are expressed as: in, represents the side length of the grid, and are the ratios of the distance from the node to the vertex of the grid edge to the length of the grid edge, and and ; 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 grid is marked as Node.

4. The method for trajectory planning applied to low-altitude airspace according to claim 3, 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: Determine an energy constraint and an arrival time constraint based on 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.

5. The method for trajectory planning applied to low-altitude airspace according to claim 4, characterized in that: In step S3-1-2, the energy constraint is expressed as: in, express The drone currently plans to pass through the node Enter the grid moment, express Drone slave node Fly to the grid Time in grid The flight time, express Drone from grid Fly to the grid The minimum time required to reach the final node; Indicates drone The moment of entering the target airspace, Indicates drone The flight time that the energy capacity can sustain in the target airspace; The arrival time constraint is expressed as: in, Indicates drone The latest acceptable arrival time, Determined by the arrival time in the flight plan data.

6. The trajectory planning method for low-altitude airspace according to claim 5, characterized in that: The calculation method is: in, For drones The flight speed in the flight plan data, For drones In the grid Flight distance within , , , They are Drone from the grid Fly to the grid Time in grid The entry and exit node marks, Indicates the side length of the grid; The calculation method is: in, for The destination node reached by the UAV, where the destination node is determined by the end position in the flight plan data; and They are and The horizontal and vertical distances in the airspace plane.

7. The trajectory planning method for low-altitude airspace according to claim 6, characterized in that: Step S3-1-3 specifically includes: A breadth-first strategy is adopted starting from the starting node in the graph, traversing all the network nodes according to the energy constraint and the arrival time constraint, and generating a directed graph for representing all possible flight paths of the UAV under the energy constraint and the arrival time constraint.

Images