Unmanned aerial vehicle conflict-free route planning method and system applied to low-altitude airspace

Abstract

The invention relates to the technical field of air traffic management, in particular to an unmanned aerial vehicle conflict-free route planning method and system applied to a low-altitude airspace, and the method comprises the steps: obtaining the starting point and terminal point information of the flight of an unmanned aerial vehicle in an airspace unit when detecting that a new unmanned aerial vehicle enters the airspace unit; acquiring information about whether the grids of the airspace units have conflicts or not; obtaining reachable grids and feasible grids of the unmanned aerial vehicle; obtaining alternative nodes based on the feasible grids of the unmanned aerial vehicle; based on the alternative nodes, a preset constraint condition and the power consumption, obtaining a directed weighted graph; a path in the directed weighted graph represents a conflict-free track; searching the directed weighted graph by adopting an A-star algorithm to obtain an optimal conflict-free track, and controlling the unmanned aerial vehicle to execute the optimal conflict-free track; according to the invention, the optimal conflict-free flight path of the unmanned aerial vehicle can be efficiently and rapidly planned.

Description

Technical Field

[0001] The present invention relates to the technical field of air traffic management, and in particular to a method and system for conflict-free trajectory planning of an unmanned aerial vehicle (UAV) applied to low-altitude airspace. Background Art

[0002] After the unmanned aircraft system traffic management service provider (UTMSP) detects a potential flight conflict, the unmanned aircraft system traffic management service provider (UTMSP) needs to quickly plan a conflict-free track for the unmanned aircraft to fly, thereby improving the operational safety and efficiency of the low-altitude airspace. Conflict-free means that in the two-dimensional space of the same flight altitude layer, the distance between any two unmanned aircraft at any time is greater than or equal to the minimum safety distance. The track is composed of a series of continuous track points, and each moment corresponds to a specific unmanned aircraft position.

[0003] Due to the high density of drone operations in low-altitude airspace and the complex airspace environment, after a conflict is detected in real-time operation, a conflict-free track needs to be generated quickly. Therefore, it has high requirements for calculation speed. In addition, due to the small size and small payload of drones, the amount of electricity they can carry is limited. Therefore, they are sensitive to power consumption, so when planning conflict-free tracks, power consumption needs to be taken into consideration to avoid generating tracks that exceed their remaining power. Existing methods are mainly divided into exact solutions and approximate solutions.

[0004] The exact solution method is to solve the problem using a planning method, such as nonlinear integer programming, nonlinear mixed integer programming, etc. Although the planning method can theoretically obtain the optimal solution, due to the high complexity of the problem, it is difficult to quickly solve it in a limited time (usually within 1 second) in real-time operation.

[0005] The approximate solution method is to use intelligent algorithms to solve, such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc. Although intelligent algorithms can obtain a feasible solution relatively quickly, they cannot guarantee its optimality. Summary of the invention

[0006] In view of the above problems, the present invention provides a method and system for unmanned aerial vehicle (UAV) conflict-free trajectory planning applied to low-altitude airspace, which solves the technical problem of how to efficiently and quickly plan the optimal conflict-free trajectory of a UAV in the prior art.

[0007] On the one hand, the present invention provides a method for conflict-free trajectory planning of a UAV applied to low-altitude airspace, comprising the following steps:

[0008] Step S1, determining a designated air area as an airspace unit; dividing the airspace unit into a plurality of grid units as grids of the airspace unit; acquiring an airspace service request when a new drone is detected to enter the airspace unit; the airspace service request includes flight information of the drone, and the flight information includes the starting point and end point information of the drone flying in the airspace unit;

[0009] Step S2, obtaining conflict-free airspace information, wherein the conflict-free airspace information includes information on whether the grid of the airspace unit has conflicts; obtaining a reachable grid of the drone based on the flight information, and obtaining a feasible grid of the drone based on the reachable grid and the conflict-free airspace information;

[0010] Step S3, obtaining candidate nodes based on the feasible grid of the UAV; obtaining a directed weighted graph based on the candidate nodes, preset constraints and power consumption; the path in the directed weighted graph represents a conflict-free track;

[0011] Step S4: Use the A-star algorithm to search the directed weighted graph to obtain the optimal conflict-free trajectory, and control the UAV to execute the optimal conflict-free trajectory.

[0012] Preferably, step S2 specifically includes: step S2-1, obtaining conflict-free airspace information, and determining a set of conflicting grids as conflicting grids, and a set of conflict-free grids as conflict-free grids based on the conflict-free airspace information; step S2-2, obtaining the reachable range of the UAV based on the position and time of the starting point and end point of the UAV's flight in the airspace unit and the maximum flight speed of the UAV, and determining a set of grids whose center points are within the reachable range as reachable grids; step S2-3, determining a set of grids that are both reachable grids and conflict-free grids as feasible grids; determining a set of grids that are both reachable grids and conflict grids as infeasible grids.

[0013] Preferably, step S2-2 specifically includes:

[0014] The reachable grid of UAV i at time step τ is determined as follows:

[0015]

[0016]

[0017]

[0018] Where g(m,n,τ) represents the grid with index (m,n) at time step τ, represents the distance between two points, c(m,n,τ) represents the center point of the grid indexed (m,n) at time step τ, and Respectively represent the starting and ending positions of UAV i, G τ represents the set of grids in the spatial unit at time step τ; where t τ is the time of time step τ, is the time when UAV i enters the airspace cell, V i max is the maximum flight speed of drone i, is the time when UAV i leaves the airspace cell.

[0019] Preferably, step S2-3 specifically includes: determining a feasible grid and an infeasible grid according to the following method:

[0020]

[0021] in, and They are respectively the feasible grid, reachable grid, conflict-free grid, infeasible grid, reachable grid and conflict grid of UAV i at time step τ, and ∩ is the intersection.

[0022] Preferably, step S3 specifically includes: step S3-1, taking the center point of all grids in the feasible grid as the candidate node; step S3-2, determining preset constraints, the preset constraints including connection constraints, speed constraints, reachability constraints and direction constraints; step S3-3, based on the candidate nodes, and based on the connection constraints, speed constraints, reachability constraints and direction constraints, generating a set of directed edges between adjacent time steps; step S3-4, assigning weights to each directed edge according to the power consumption of the track corresponding to each directed edge in the directed edge set, and determining the set of weighted directed edges as a directed weighted graph.

[0023] Preferably, step S3-2 specifically includes:

[0024] The connection constraints are expressed as:

[0025]

[0026] in, Indicates the grid where the starting point of drone i is located, It means "existence". represents the directed edge connecting the grid at (m″,n″) in time step τ-2 and the grid at (m′,n′) in time step τ-1, represents the set of directed edges generated between adjacent time steps (τ-2) and (τ-1);

[0027] The speed constraint is expressed as:

[0028]

[0029] Among them, V i min is the minimum speed of UAV i;

[0030] The reachability constraint is expressed as:

[0031]

[0032] in, Indicates the grid where the end point of drone i is located, is the time step where UAV i’s endpoint is located;

[0033] The direction constraint is specifically expressed as:

[0034]

[0035] in, and They represent the grids where the starting point and the end point of UAV i are located respectively.

[0036] Preferably, step S3-3 specifically includes:

[0037] Based on the connection constraints, speed constraints, reachability constraints and direction constraints, the set of directed edges generated between adjacent time steps (τ-1) and τ can be expressed as:

[0038]

[0039] in, is the time step where UAV i starts.

[0040] Preferably, step S4 specifically includes:

[0041] (1) Obtain the evaluation function of the A-star algorithm:

[0042]

[0043] in, Represents a directed weighted graph Up from the node The power consumption to node (m,n,τ), P i Min,Require is the minimum flight power of UAV i;

[0044] (2) Based on the evaluation function, the directed weighted graph is searched by the A-star algorithm to obtain the optimal conflict-free trajectory of UAV i that minimizes power consumption, and UAV i is controlled to execute the optimal conflict-free trajectory.

[0045] Preferably, the airspace service request is sent by a ground control station; the conflict-free airspace information is provided by a public information service provider; the optimal conflict-free trajectory is sent to the public information service provider and the ground control station, and the ground control station controls the UAV to execute the optimal conflict-free trajectory.

[0046] On the one hand, the present invention provides a conflict-free trajectory planning system for unmanned aerial vehicles applied to low-altitude airspace, the system comprising:

[0047] A flight information acquisition module is used to determine a designated air area as an airspace unit; divide the airspace unit into a plurality of grid units as grids of the airspace unit; when a new drone is detected to enter the airspace unit, obtain an airspace service request; the airspace service request includes the flight information of the drone, and the flight information includes the starting point and end point information of the drone flying in the airspace unit;

[0048] A grid acquisition module is used to acquire non-conflict airspace information, wherein the non-conflict airspace information includes information on whether the grids of the airspace unit are in conflict; acquire a reachable grid of the drone based on the flight information, and acquire a feasible grid of the drone based on the reachable grid and the non-conflict airspace information;

[0049] A graph construction module is used to obtain candidate nodes based on the feasible grid of the UAV; based on the candidate nodes, preset constraints and power consumption, obtain a directed weighted graph; the path in the directed weighted graph represents a conflict-free track;

[0050] The search module is used to use the A-star algorithm to search the directed weighted graph, obtain the optimal conflict-free track, and control the UAV to execute the optimal conflict-free track.

[0051] Compared with the prior art, the present invention has at least the following beneficial effects:

[0052] (1) The present invention converts the complex conflict-free trajectory planning problem into a graph-based path search problem, and greatly simplifies the calculation process by using a directed weighted graph, effectively reducing the amount of calculation and improving the speed of trajectory planning.

[0053] (2) The present invention minimizes power consumption when generating conflict-free tracks by using power consumption as the weight of the edge in the directed weighted graph, thereby improving the endurance and operating efficiency of the UAV.

[0054] (3) The present invention uses the reachable grid technology to effectively reduce the number of nodes in the generated directed weighted graph, thereby further reducing the amount of calculation and improving the efficiency and scalability of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0056] Figure 1 A flow chart of a method for conflict-free trajectory planning of a UAV applied in low-altitude airspace provided by the present invention;

[0057] Figure 2 A schematic diagram of a feasible grid acquisition method provided by the present invention;

[0058] Figure 3 A schematic diagram of a directed graph generation method provided by the present invention;

[0059] Figure 4 A schematic diagram of a fast conflict-free trajectory planning system provided by the present invention;

[0060] Figure 5 A schematic diagram of a conflict-free trajectory planning system for a UAV applied in low-altitude airspace provided by the present invention.

[0061] Reference numerals: 101 - flight information acquisition module, 102 - grid acquisition module, 103 - graph construction module, 104 - search module. DETAILED DESCRIPTION

[0062] 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.

[0063] In the two-dimensional space at the same flight altitude, conflict-free means that the distance between any two drones (drone i, drone j) at any time is greater than or equal to the minimum safety distance S. Its mathematical description is:

[0064]

[0065] in, means "all", T i and T j Respectively represent the flight time of UAV i and UAV j in the airspace, x i (t) and y i(t) are the horizontal and vertical coordinates of the position of UAV i at time t, x j (t) and y j (t) are the horizontal and vertical coordinates of the position of drone i at time t. The track is composed of a series of continuous track points, each moment corresponds to a specific drone position, and its mathematical description is:

[0066] p i (t) = [x i (t),y i (t)]

[0067] Among them, p i (t) represents the position of UAV i at time t. The present invention provides a method and system for performing conflict-free trajectory planning for the above-mentioned UAV.

[0068] 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 method for conflict-free trajectory planning of a UAV applied in low-altitude airspace is disclosed, comprising the following steps:

[0069] Step S1, determine the designated air area as an airspace unit; divide the airspace unit into multiple grid units as the grid of the airspace unit; when a new drone is detected to enter the airspace unit, obtain an airspace service request; the airspace service request includes the flight information of the drone, and the flight information includes the starting point and end point information of the drone's flight in the airspace unit.

[0070] In some embodiments, the application scenario of the conflict-free trajectory planning method for unmanned aerial vehicles provided by the present invention includes a common information service provider (CISP), an unmanned aircraft system traffic management service provider (UTMSP), a ground control station (GCS) and a drone. The conflict-free trajectory planning method is executed by the UTMSP.

[0071] The drone sends its location information to its GCS in real time. When the GCS recognizes that the drone is about to enter a new airspace unit, it sends an airspace service request for the drone to the UTMSP, which includes the drone's current flight information. The flight information includes the flight plan, aircraft type, and remaining power; the flight plan contains the start and end point of the flight and the location and time information.

[0072] Step S2, obtaining conflict-free airspace information, wherein the conflict-free airspace information includes information on whether the grid of the airspace unit has a conflict; obtaining a reachable grid of the drone based on the flight information, and obtaining a feasible grid of the drone based on the reachable grid and the conflict-free airspace information.

[0073] The airspace space-time grid is a method to describe the conflict relationship between drones. The space-time grid is composed of the spatial dimension and the temporal dimension. In the spatial dimension, the airspace uses the method of operating in different altitude layers. At each altitude layer in the airspace, the two-dimensional space plane is rasterized to obtain the airspace grid, and the position of the airspace grid is represented by (m,n). In the temporal dimension, time is divided into discrete time steps at a specific time interval ΔT, and the time step is represented by τ. The grid (m,n) at the time step τ is recorded as the space-time grid (m,n,τ).

[0074] An airspace unit is an airspace range composed of several airspace grids.

[0075] The conflict-free airspace information is a 0-1 matrix, denoted by M A , the dimension is m×n×τ. A (m,n,τ)=1 means that the UAV is conflict-free in the grid (m,n) at time step τ. A (m,n,τ)=0 means that the UAV is in conflict within the grid (m,n) at time step τ.

[0076] like Figure 2 As shown, the conflict-free trajectory planning method for UAVs in low-altitude airspace provided by the present invention defines five grids, namely: a reachable grid, a conflict grid, a conflict-free grid, a feasible grid, and an infeasible grid.

[0077] A reachable grid refers to a grid whose center point is within the reachable range.

[0078] The conflict grid refers to the matrix M A The grid element at the corresponding position is 0.

[0079] The conflict-free grid is the matrix M A The grid element at the corresponding position is 1.

[0080] A feasible grid is a grid that is both reachable and conflict-free.

[0081] An infeasible grid is a grid that is both a reachable grid and a conflict grid.

[0082] The reachable range is obtained by the space-time pyramid theory. For the time step τ, two circular ranges are generated in the two-dimensional plane of the airspace unit. One circle is a circle with the starting point of the drone entering the airspace unit as the center and r1 as the radius, and the other circle is a circle with the end point of the drone as the center and r2 as the radius. The calculation method of r1 and r2 is:

[0083]

[0084] Among them, t τ is the time of time step τ, is the time when UAV i enters the airspace cell, V i max is the maximum flight speed of drone i, is the time when UAV i leaves the airspace unit. The intersection of the two circles is the reachable range at time step τ.

[0085] For UAV i, the set of reachable grids at time step τ is It is expressed as:

[0086]

[0087] Among them, g(m,n,τ) represents the grid (m,n,τ), represents the distance between two points, c(m,n,τ) represents the center point of the grid (m,n,τ), and Respectively represent the starting and ending positions of UAV i, G τ represents a collection of grid cells at time step τ.

[0088] Based on the conflict-free matrix M A , M A The set of grids with a median value of 1 is determined as the conflict grid set. M A The set of grids with a median value of 1 is determined as a set of grids without conflict.

[0089] Then the feasible grid set of UAV i at time step τ is obtained in the following way and infeasible grid sets

[0090]

[0091] Step S3, obtaining candidate nodes based on the feasible grid of the UAV; obtaining a directed weighted graph based on the candidate nodes, preset constraints and power consumption; the path in the directed weighted graph represents a conflict-free track.

[0092] In order to adapt to the space-time grid model, the present invention provides a conflict-free trajectory planning method based on a directed weighted graph. When generating a directed graph, the starting point and end point of the drone in the airspace unit are used as the starting point and end point of the directed graph. At each time step between the starting point and the end point, the center point of the conflict-free grid can be selected as the node of the graph, and connected according to specific constraints to form a directed graph.

[0093] In the present invention, in order to reduce the nodes of the directed graph and thus reduce the amount of calculation when generating the optimal path, the selected nodes are not all conflict-free grids, but the center points of all feasible grids are selected as candidate nodes for generating the directed weighted graph.

[0094] like Figure 3 As shown, the edges of the directed graph are generated in the order of time steps. The edge from time step 1 to time step 2 is generated first, and then the edge from time step 2 to time step 3 is generated, until the time step of the end point of drone i.

[0095] Whether an edge is generated between two candidate nodes depends on whether they simultaneously satisfy the connection constraint, speed constraint, reachability constraint, and direction constraint.

[0096] The connection constraint means that the starting point of the candidate edge must be the end point of the edge generated in the previous time step, unless it is the starting point of the drone. The candidate directed edge set between the adjacent time steps (τ-1) and τ based on the connection constraint is It is expressed as:

[0097]

[0098] in, represents the grid where the starting point of drone i is located, It means "existence". represents the directed edge connecting the grid located at (m￠￠,n￠￠) in time step τ-2 and the grid located at (m￠,n￠) in time step τ-1, represents the set of directed edges generated between adjacent time steps (τ-2) and (τ-1).

[0099] The speed constraint means that the flight (i.e., distance and time) represented by the edge is within the speed performance range of the drone. The speed constraint is specifically expressed as:

[0100]

[0101] Among them, V i min is the minimum speed of UAV i.

[0102] The reachability constraint means that the time step from the end point of the current edge to the end point of the entire graph meets the controlled time of arrival (CTA) requirement for the drone to reach the end point. The reachability constraint is specifically expressed as:

[0103]

[0104] in, Indicates the grid where the end point of drone i is located, is the time step where UAV i ends.

[0105] The direction constraint means that the flight indicated by the generated directed edge is closer to the end point of the drone from the current position rather than away from the end point of the drone. The direction constraint is specifically expressed as:

[0106]

[0107] in, and They represent the grids where the starting point and the end point of UAV i are located respectively.

[0108] Based on the connection constraints, speed constraints, reachability constraints and direction constraints, the set of directed edges generated between adjacent time steps (τ-1) and τ can be expressed as:

[0109]

[0110] in, is the time step where UAV i starts.

[0111] After generating the directed graph, each edge is weighted according to the power consumption of the track represented by each edge, thus obtaining a directed weighted graph Any path connecting the starting point to the end point on a directed weighted graph is a feasible conflict-free trajectory.

[0112] Step S4: Use the A-star algorithm to search the directed weighted graph to obtain the optimal conflict-free trajectory, and control the UAV to execute the optimal conflict-free trajectory.

[0113] The conflict-free track method provided by the present invention uses the A-star algorithm to search for the optimal conflict-free track in the generated directed weighted graph, that is, the conflict-free track with the least power consumption.

[0114] The evaluation function of the A-star algorithm consists of actual value and heuristic value, and its mathematical expression is:

[0115]

[0116] in, and Represent the current weighted directed graph of UAV i The evaluation value of the node (m,n,τ), the actual value from the starting point to the node (m,n,τ), and the heuristic value from the node (m,n,τ) to the end point.

[0117] The actual value is the optimal path with the minimum energy consumption from the starting point to the node (m,n,τ), and its mathematical expression is:

[0118]

[0119] Represents a directed weighted graph Up from the node The power consumption to the node (m,n,τ).

[0120] The A-star algorithm ensures that the optimal solution is obtained under the condition that the heuristic cost is always less than or equal to the actual cost from the node (m,n,τ) to the destination. Since the flight time between two adjacent time steps is constant, the actual energy consumption must be greater than or equal to the energy consumption when the drone always flies at the minimum power. Therefore, the mathematical expression of the heuristic value that satisfies the optimal solution condition is:

[0121]

[0122] in, is the number of drone i in the directed weighted graph Ω i WDG In the example, the heuristic value of node (m,n,τ), P i Min,Require is the minimum flight power of UAV i.

[0123] The final mathematical expression of the evaluation function of the A-star algorithm is:

[0124]

[0125] Based on the evaluation function, the optimal conflict-free trajectory of UAV i that minimizes power consumption is generated by the A-star algorithm, thereby completing the conflict-free trajectory planning of the UAV of the present invention.

[0126] In some embodiments, Figure 4As shown, the application scenario of the conflict-free trajectory planning method for unmanned aerial vehicles provided by the present invention includes a common information service provider (CISP), an unmanned aircraft system traffic management service provider (UTMSP), a ground control station (GCS) and a unmanned aerial vehicle. The conflict-free trajectory planning method is executed by the UTMSP.

[0127] The drone sends its location information to its GCS in real time. When the GCS recognizes that the drone is about to enter a new airspace unit, it sends an airspace service request for the drone to the UTMSP, which includes the drone's current flight information, including flight plan, aircraft type, and remaining battery power;

[0128] After receiving the airspace service request from GCS, UTMSP extracts the latest real-time airspace information obtained from CISP, that is, the current conflict-free matrix M of the airspace unit A , UTMSP is based on the current conflict-free matrix M of the spatial unit A Conflict detection is performed with the current flight information of the drone. After detecting a conflict, UTMSP performs conflict-free trajectory planning to obtain the optimal conflict-free trajectory of the drone. UTMSP sends the optimal conflict-free trajectory of the current drone to CISP and the corresponding GCS, and GCS controls the drone to execute the updated conflict-free trajectory.

[0129] like Figure 5 As shown, the present invention provides a conflict-free trajectory planning system for unmanned aerial vehicles applied to low-altitude airspace, the system comprising:

[0130] The flight information acquisition module 101 is used to determine the designated air area as an airspace unit; divide the airspace unit into a plurality of grid units as grids of the airspace unit; when a new drone is detected to enter the airspace unit, acquire an airspace service request; the airspace service request includes the flight information of the drone, and the flight information includes the starting point and end point information of the drone flying in the airspace unit;

[0131] The grid acquisition module 102 is used to acquire non-conflict airspace information, wherein the non-conflict airspace information includes information on whether the grids of the airspace unit are in conflict; acquire a reachable grid of the drone based on the flight information, and acquire a feasible grid of the drone based on the reachable grid and the non-conflict airspace information;

[0132] A graph construction module 103 is used to obtain candidate nodes based on the feasible grid of the drone; obtain a directed weighted graph based on the candidate nodes, preset constraints and power consumption; the paths in the directed weighted graph represent conflict-free tracks;

[0133] The search module 104 is used to use the A-star algorithm to search the directed weighted graph, obtain the optimal conflict-free track, and control the UAV to execute the optimal conflict-free track.

[0134] 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.

[0135] 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 conflict-free trajectory planning method for unmanned aerial vehicles applied in low-altitude airspace, characterized in that: The steps include: Step S1, determining a designated air area as an airspace unit; dividing the airspace unit into a plurality of grid units as grids of the airspace unit; acquiring an airspace service request when a new drone is detected to enter the airspace unit; the airspace service request includes flight information of the drone, and the flight information includes the starting point and end point information of the drone flying in the airspace unit; Step S2, obtaining non-conflict airspace information, wherein the non-conflict airspace information includes information on whether the grids of the airspace unit have conflicts; Acquire a reachable grid of the drone based on the flight information, and acquire a feasible grid of the drone based on the reachable grid and the conflict-free airspace information; Step S3, obtaining candidate nodes based on the feasible grid of the UAV; obtaining a directed weighted graph based on the candidate nodes, preset constraints and power consumption; the path in the directed weighted graph represents a conflict-free track; Step S4: Use the A-star algorithm to search the directed weighted graph to obtain the optimal conflict-free trajectory, and control the UAV to execute the optimal conflict-free trajectory.

2. The conflict-free trajectory planning method for unmanned aerial vehicles applied to low-altitude airspace according to claim 1 is characterized in that: Step S2 specifically includes: Step S2-1, obtaining non-conflict airspace information, and determining a set of conflicting grids as conflicting grids and a set of non-conflicting grids as non-conflict grids based on the non-conflict airspace information; Step S2-2, based on the position and time of the starting point and the end point of the flight of the UAV in the airspace unit and the maximum flight speed of the UAV, the reachable range of the UAV is obtained, and a set of grids whose center points are within the reachable range is determined as a reachable grid; Step S2-3: determine a set of grids that are both reachable grids and conflict-free grids as feasible grids; and determine a set of grids that are both reachable grids and conflict-free grids as infeasible grids.

3. The conflict-free trajectory planning method for unmanned aerial vehicles applied to low-altitude airspace according to claim 2 is characterized in that: Step S2-2 specifically includes: The reachable grid of UAV i at time step t is determined as follows: Among them, g(m,n,t) represents the grid with index (m,n) at time step t, represents the distance between two points, c(m,n,t) represents the center point of the grid indexed (m,n) at time step t, and Respectively represent the starting and ending positions of UAV i, G t represents the set of grids in the spatial unit at time step t; where t t is the time of time step t, is the time when UAV i enters the airspace cell, V i max is the maximum flight speed of drone i, is the time when UAV i leaves the airspace cell.

4. The conflict-free trajectory planning method for unmanned aerial vehicles applied to low-altitude airspace according to claim 3 is characterized in that: Step S2-3 specifically includes: determining feasible grids and infeasible grids according to the following method: in, and are respectively the feasible grid, reachable grid, conflict-free grid, infeasible grid, reachable grid and conflict grid of UAV i at time step t, and ∩ is the intersection.

5. The conflict-free trajectory planning method for a UAV applied to low-altitude airspace according to any one of claims 2-4, characterized in that: Step S3 specifically includes: Step S3-1, taking the center points of all grids in the feasible grid as the candidate nodes; Step S3-2, determining preset constraints, wherein the preset constraints include connection constraints, speed constraints, reachability constraints and direction constraints; Step S3-3, based on the candidate nodes and based on the connection constraints, speed constraints, reachability constraints and direction constraints, generating a directed edge set between adjacent time steps; Step S3-4: assigning weights to each directed edge in the directed edge set according to the power consumption of the track corresponding to each directed edge, and determining the set of weighted directed edges as a directed weighted graph.

6. The conflict-free trajectory planning method for unmanned aerial vehicles applied to low-altitude airspace according to claim 5 is characterized in that: Step S3-2 specifically includes: The connection constraints are expressed as: in, Indicates the grid where the starting point of drone i is located, $ indicates "existence", represents the directed edge connecting the grid located at (m￠￠,n￠￠) in time step t-2 and the grid located at (m￠,n￠) in time step t-1, represents the set of directed edges generated between adjacent time steps (t-2) and (t-1); The speed constraint is expressed as: Among them, V i min is the minimum speed of UAV i; The reachability constraint is expressed as: in, Indicates the grid where the end point of drone i is located, is the time step where UAV i’s endpoint is located; The direction constraint is specifically expressed as: in, and They represent the grids where the starting point and the end point of UAV i are located respectively.

7. The conflict-free trajectory planning method for unmanned aerial vehicles applied to low-altitude airspace according to claim 6 is characterized in that: Step S3-3 specifically includes: Based on the connection constraints, speed constraints, reachability constraints and direction constraints, the set of directed edges generated between adjacent time steps (t-1) and t can be expressed as: in, is the time step where UAV i starts.

8. The conflict-free trajectory planning method for unmanned aerial vehicles applied to low-altitude airspace according to claim 7 is characterized in that: Step S4 specifically includes: (1) Obtain the evaluation function of the A-star algorithm: in, Represents a directed weighted graph Up from the node Power consumption to node (m,n,t), P i Min,Require is the minimum flight power of UAV i; (2) Based on the evaluation function, the directed weighted graph is searched by the A-star algorithm to obtain the optimal conflict-free trajectory of UAV i that minimizes power consumption, and UAV i is controlled to execute the optimal conflict-free trajectory.

9. The conflict-free trajectory planning method for unmanned aerial vehicles applied to low-altitude airspace according to claim 1 is characterized in that: The airspace service request is sent by a ground control station; the conflict-free airspace information is provided by a public information service provider; the optimal conflict-free trajectory is sent to the public information service provider and the ground control station, and the ground control station controls the UAV to execute the optimal conflict-free trajectory.

10. A conflict-free trajectory planning system for unmanned aerial vehicles applied in low-altitude airspace, characterized in that: The system comprises: A flight information acquisition module is used to determine a designated air area as an airspace unit; divide the airspace unit into a plurality of grid units as grids of the airspace unit; when a new drone is detected to enter the airspace unit, obtain an airspace service request; the airspace service request includes the flight information of the drone, and the flight information includes the starting point and end point information of the drone flying in the airspace unit; A grid acquisition module is used to acquire non-conflict airspace information, wherein the non-conflict airspace information includes information on whether the grids of the airspace unit are in conflict; acquire a reachable grid of the drone based on the flight information, and acquire a feasible grid of the drone based on the reachable grid and the non-conflict airspace information; A graph construction module is used to obtain candidate nodes based on the feasible grid of the UAV; based on the candidate nodes, preset constraints and power consumption, obtain a directed weighted graph; the path in the directed weighted graph represents a conflict-free track; The search module is used to use the A-star algorithm to search the directed weighted graph, obtain the optimal conflict-free track, and control the UAV to execute the optimal conflict-free track.

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