A method and system for conflict-free path planning of a UAV applied to low-altitude airspace

Abstract

The application relates to the technical field of air traffic management, in particular to a UAV conflict-free flight path planning method and system applied to low-altitude airspace, which comprises the following steps: when detecting that a new UAV enters an airspace unit, obtaining start point and end point information of UAV flight in the airspace unit; obtaining information whether a grid of the airspace unit has a conflict; obtaining reachable grids and feasible grids of the UAV; obtaining alternative nodes based on the feasible grids of the UAV; obtaining a directed weighted graph based on the alternative nodes, preset constraint conditions and power consumption; a path in the directed weighted graph represents a conflict-free flight path; an A-star algorithm is used to search the directed weighted graph to obtain an optimal conflict-free flight path, and the UAV is controlled to execute the optimal conflict-free flight path; the application can efficiently and quickly plan the optimal conflict-free flight path of the UAV.

Description

TECHNICAL FIELD

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

[0002] The UAV conflict-free path planning system for low-altitude airspace needs to quickly plan a conflict-free path for the UAV to fly after the UTMSP (Unmanned aircraft system traffic management service provider) detects a potential flight conflict, so as to improve the operation safety and efficiency of low-altitude airspace. Conflict-free means that the distance between any two UAVs at any time in the two-dimensional space of the same flight altitude layer is greater than or equal to the minimum safety distance. The path is composed of a series of consecutive path points, and each time point corresponds to a specific UAV position.

[0003] Due to the high operation density of UAVs in low-altitude airspace and the complex airspace environment, a conflict-free path needs to be quickly generated after a conflict is detected in real-time operation. Therefore, it has a high requirement on the calculation speed. In addition, due to the small size and small load of the UAV, the amount of electricity it can carry is limited. Therefore, it is sensitive to power consumption, so that the power consumption needs to be considered when planning a conflict-free path to avoid generating a path that exceeds the remaining power. The existing methods are mainly divided into precise solution method and approximate solution method.

[0004] The precise solution method is to use a planning method for solution, 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 within a limited time (usually within 1 second) in real-time operation.

[0005] The approximate solution method is to use an intelligent algorithm for solution, such as genetic algorithm, simulated annealing algorithm, ant colony algorithm, etc. Although the intelligent algorithm can relatively quickly obtain a feasible solution, it cannot guarantee its optimality. SUMMARY

[0006] In view of the above problems, the present application provides a UAV conflict-free path planning method and system applied to low-altitude airspace, which solves the technical problem of how to efficiently and quickly plan the optimal conflict-free path of the UAV in the prior art.

[0007] In one aspect, the present application provides a UAV conflict-free path planning method applied to low-altitude airspace, comprising the following steps:

[0008] Step S1, determining a designated airspace as an airspace unit; dividing the airspace unit into a plurality of grid units as a grid of the airspace unit; when detecting that a new unmanned aerial vehicle enters the airspace unit, obtaining an airspace service request; the airspace service request comprising flight information of the unmanned aerial vehicle, the flight information comprising start point and end point information of the unmanned aerial vehicle flying in the airspace unit;

[0009] Step S2, obtaining conflict-free airspace information, the conflict-free airspace information comprising information of whether the grid of the airspace unit has a conflict; obtaining a reachable grid of the unmanned aerial vehicle based on the flight information, and obtaining a feasible grid of the unmanned aerial vehicle based on the reachable grid and the conflict-free airspace information;

[0010] Step S3, obtaining an alternative node based on the feasible grid of the unmanned aerial vehicle; obtaining a directed weighted graph based on the alternative node, a preset constraint condition and an electric quantity consumption; a path in the directed weighted graph representing a conflict-free flight path;

[0011] Step S4, searching the directed weighted graph by using an A-star algorithm to obtain an optimal conflict-free flight path, and controlling the unmanned aerial vehicle to execute the optimal conflict-free flight path.

[0012] Preferably, step S2 specifically comprises: step S2-1, obtaining conflict-free airspace information, determining a set of grids with a conflict as a conflict grid and a set of grids without a conflict as a conflict-free grid based on the conflict-free airspace information; step S2-2, obtaining a reachable range of the unmanned aerial vehicle based on positions and times of the start point and the end point of the unmanned aerial vehicle flying in the airspace unit and a maximum flight speed of the unmanned aerial vehicle, and determining a set of grids with a center point in the reachable range as a reachable grid; step S2-3, determining a set of grids that are both the reachable grid and the conflict-free grid as a feasible grid, and determining a set of grids that are both the reachable grid and the conflict grid as an infeasible grid.

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

[0014] The reachable grid of the unmanned aerial vehicle i at the time step τ is determined in the following manner

[0015] wherein g(m, n, τ) represents a center point of a grid with an index of (m, n) at a time step τ, represents a distance between two points, c(m, n, τ) represents a center point of a grid with an index of (m, n) at a time step τ, and respectively represent positions of the start point and the end point of the unmanned aerial vehicle i, G τ represents a set of grids of the airspace unit at the time step τ; wherein tτ is the time of the time step t, is the time when the unmanned aerial vehicle i enters the airspace unit, V i max is the maximum flight speed of the unmanned aerial vehicle i, is the time when the unmanned aerial vehicle i leaves the airspace unit.

[0016] Preferably, step S2-3 specifically comprises: determining the feasible grid and the infeasible grid in accordance with the following manner:

[0017]

[0018] wherein, and are the feasible grid, the reachable grid, the conflict-free grid, the infeasible grid, the reachable grid and the conflict grid of the unmanned aerial vehicle i at the time step t, and is the intersection.

[0019] Preferably, step S3 specifically comprises: step S3-1, taking the center points of all grids in the feasible grid as the candidate nodes; step S3-2, determining preset constraints, the preset constraints comprising connection constraints, speed constraints, reachability constraints and direction constraints; step S3-3, generating a set of directed edges between adjacent time steps based on the candidate nodes and based on the connection constraints, the speed constraints, the reachability constraints and the direction constraints; and step S3-4, weighting each directed edge in the set of directed edges according to the power consumption amount of the flight path corresponding to the directed edge, and determining the set of weighted directed edges as a directed weighted graph.

[0020] Preferably, step S3-2 specifically comprises:

[0021] The connection constraint is expressed as:

[0022]

[0023] wherein, represents the grid where the unmanned aerial vehicle i starts, represents "existence", represents the directed edge connected between the grid (m", n") in the time step t-2 and the grid (m', n') in the time step t-1, represents the set of directed edges generated between adjacent time steps (t-2) and (t-1);

[0024] The speed constraint is expressed as:

[0025]

[0026] wherein, V i min is the minimum speed of the unmanned aerial vehicle i;

[0027] The reachability constraint is represented as:

[0028]

[0029] wherein, represents the grid where the end point of the UAV i is located, is the time step where the end point of the UAV i is located;

[0030] The direction constraint is specifically represented as:

[0031]

[0032] wherein, and represent the grids where the start point and the end point of the UAV i are located, respectively.

[0033] Preferably, the step S3-3 specifically comprises:

[0034] Based on the connection constraint, the speed constraint, the reachability constraint and the direction constraint, the set of directed edges generated between the adjacent time steps (τ-1) and τ can be represented as:

[0035]

[0036] wherein, is the time step where the start point of the UAV i is located.

[0037] Preferably, the step S4 specifically comprises:

[0038] (1) An evaluation function of the A-star algorithm is obtained:

[0039]

[0040] wherein, represents the directed weighted graph the power consumption from the node to the node (m, n, τ), P i Min,Require is the minimum flight power of the UAV i;

[0041] (2) Based on the evaluation function, the directed weighted graph is searched by the A-star algorithm to obtain an optimal conflict-free flight path with the minimum power consumption of the UAV i, and the UAV i is controlled to execute the optimal conflict-free flight path.

[0042] 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 flight path 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 flight path.

[0043] In one aspect, the application provides a UAV conflict-free flight path planning system applied to low-altitude airspace, the system comprising:

[0044] a flight information acquisition module, configured to determine a specified airspace as an airspace unit, divide the airspace unit into a plurality of grid units as a grid of the airspace unit, and acquire an airspace service request when detecting that a new UAV enters the airspace unit, wherein the airspace service request comprises flight information of the UAV, and the flight information comprises start point and end point information of the UAV flying in the airspace unit;

[0045] a grid acquisition module, configured to acquire conflict-free airspace information comprising information about whether the grid of the airspace unit has a conflict, acquire reachable grids of the UAV based on the flight information, and acquire feasible grids of the UAV based on the reachable grids and the conflict-free airspace information;

[0046] a graph construction module, configured to acquire candidate nodes based on the feasible grids of the UAV, acquire a directed weighted graph based on the candidate nodes, a preset constraint condition and an electric quantity consumption, and wherein a path in the directed weighted graph represents a conflict-free flight path;

[0047] a search module, configured to search the directed weighted graph by using an A-star algorithm to acquire an optimal conflict-free flight path, and control the UAV to execute the optimal conflict-free flight path.

[0048] Compared with the prior art, the application has at least the following beneficial effects:

[0049] (1) The application converts a complex conflict-free flight path planning problem into a graph-based path search problem, greatly simplifies the calculation process by using a directed weighted graph, effectively reduces the calculation amount, and improves the speed of flight path planning.

[0050] (2) The application realizes minimization of electric quantity consumption when generating a conflict-free flight path by taking the electric quantity consumption as a weight of an edge in the directed weighted graph, thereby improving the endurance and operation efficiency of the UAV.

[0051] (3) The application uses a reachable grid technology to effectively reduce the number of nodes in the generated directed weighted graph, thereby further reducing the calculation amount and improving the efficiency and scalability of the algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0052] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not considered to be a limitation of the application.

[0053] Figure 1The flow chart of the conflict-free path planning method of the unmanned aerial vehicle applied to the low-altitude airspace is provided for the application;

[0054] Figure 2 The schematic diagram of the feasible grid acquisition mode is provided for the application;

[0055] Figure 3 The schematic diagram of the directed graph generation method is provided for the application;

[0056] Figure 4 The schematic diagram of the rapid conflict-free path planning system is provided for the application;

[0057] Figure 5 The schematic diagram of the conflict-free path planning system of the unmanned aerial vehicle applied to the low-altitude airspace is provided for the application.

[0058] The figure legend: 101-flight information acquisition module, 102-grid acquisition module, 103-graph construction module, 104-search module. DETAILED DESCRIPTION

[0059] In order to more clearly understand the above-mentioned purposes, features and advantages of the application, the 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 application and the features in the embodiments can be combined with each other without conflict. In addition, the application can also be implemented in other ways different from those described herein, therefore, the protection scope of the application is not limited by the specific embodiments disclosed below.

[0060] On the two-dimensional space of the same flight altitude layer, conflict-free means that the distance between any two unmanned aerial vehicles (unmanned aerial vehicle i, unmanned aerial vehicle j) at any time is greater than or equal to the minimum safety distance S. The mathematical description is as follows:

[0061]

[0062] Among them, "all" is represented by T i and T j respectively represent the flight time of unmanned aerial vehicle i and unmanned aerial vehicle j in the airspace, x i (t) and y i (t) are respectively the horizontal and vertical coordinates of the position of unmanned aerial vehicle i at time t, x j (t) and y j (t) are respectively the horizontal and vertical coordinates of the position of unmanned aerial vehicle i at time t. The path is composed of a series of continuous path points, and each time corresponds to a specific unmanned aerial vehicle position, and the mathematical description is as follows:

[0063] p i (t) = [[x i (t),yi (t)]

[0064] wherein, p i (t) represents the position of the unmanned aerial vehicle i at time t. The application provides a conflict-free path planning method and system for the unmanned aerial vehicle.

[0065] In order to illustrate the effectiveness of the method of the application, the above technical solutions of the application are described in detail through a specific embodiment. As shown in the figure, Figure 1 A conflict-free path planning method for unmanned aerial vehicles applied to low-altitude airspace is disclosed, which includes the following steps:

[0066] Step S1, determining a designated air region as an airspace unit; dividing the airspace unit into a plurality of grid units as a grid of the airspace unit; when detecting that a new unmanned aerial vehicle enters the airspace unit, obtaining an airspace service request; the airspace service request includes flight information of the unmanned aerial vehicle, and the flight information includes start point and end point information of the unmanned aerial vehicle flying in the airspace unit.

[0067] In some embodiments, the application provides an application scenario of the conflict-free path planning method for unmanned aerial vehicles, which includes a common information service provider (CISP), an unmanned aerial system traffic management service provider (UTMSP), a ground control station (GCS) and an unmanned aerial vehicle. The conflict-free path planning method is executed by the UTMSP.

[0068] The unmanned aerial vehicle sends its position information to its GCS in real time. When the GCS identifies that the unmanned aerial vehicle is about to enter a new airspace unit, it sends an airspace service request for the unmanned aerial vehicle to the UTMSP, which includes the current flight information of the unmanned aerial vehicle, including flight plan, aircraft type, remaining power; the flight plan includes the start point and end point and position and time information of the flight.

[0069] Step S2, obtaining conflict-free airspace information, which includes information whether the grid of the airspace unit has a conflict; obtaining the reachable grid of the unmanned aerial vehicle based on the flight information, and obtaining the feasible grid of the unmanned aerial vehicle based on the reachable grid and the conflict-free airspace information.

[0070] The airspace time-space grid is a method for describing the conflict relationship of the UAV. The time-space grid is combined by the space dimension and the time dimension. In the space dimension, the airspace uses the way of running in the height layer. In each height layer of 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 time dimension, the time is divided into discrete time steps with a certain time interval ΔT, and the time step is represented by τ. The grid (m, n) in the time step τ is recorded as the time-space grid (m, n, τ).

[0071] The airspace unit is an airspace range composed of a plurality of airspace grids.

[0072] The conflict-free airspace information is a 0-1 matrix, denoted as M A , with a dimension of m x n x τ. M A (m, n, τ) = 1 indicates that the UAV is conflict-free in the grid (m, n) at the time step τ. M A (m, n, τ) = 0 indicates that the UAV is in conflict in the grid (m, n) at the time step τ.

[0073] As shown in Figure 2 , the low-altitude airspace UAV conflict-free path planning method provided by the application defines five kinds of grids, which are: reachable grid, conflict grid, conflict-free grid, feasible grid, and infeasible grid.

[0074] The reachable grid refers to the grid with the center point within the reachable range.

[0075] The conflict grid refers to the grid with the element at the corresponding position of the matrix M A being 0.

[0076] The conflict-free grid refers to the grid with the element at the corresponding position of the matrix M A being 1.

[0077] The feasible grid refers to the grid that is both the reachable grid and the conflict-free grid.

[0078] The infeasible grid refers to the grid that is both the reachable grid and the conflict grid.

[0079] The reachable range is obtained by the time-space 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 UAV 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 UAV as the center and r2 as the radius. The calculation method of r1 and r2 is as follows:

[0080]

[0081] Where t τ is the time of the time step τ, is the time when the UAV i enters the airspace cell, V i max is the maximum flight speed of the UAV i, is the time when the UAV i leaves the airspace cell. The intersection of the two circles is the reachable range at time step tau.

[0082] For the UAV i, the set of reachable grids at time step tau is denoted as:

[0083]

[0084] where g(m, n, tau) represents the grid (m, n, tau), denotes the distance between two points, and c(m, n, tau) represents the center point of the grid (m, n, tau), and respectively represent the starting point and the ending point of the UAV i, G τ denotes the set of grids at time step tau.

[0085] Based on the conflict-free matrix M A , the set of grids with a value of 1 in M A is determined as the conflict grid set The set of grids with a value of 1 in M A is determined as the conflict-free grid set

[0086] Then the feasible grid set of the UAV i at time step tau and the infeasible grid set

[0087]

[0088] Step S3, obtaining an alternative node based on the feasible grid of the UAV; obtaining a directed weighted graph based on the alternative node, a preset constraint condition and an electric quantity consumption amount; and a path in the directed weighted graph represents a conflict-free flight path.

[0089] In order to adapt to the space-time grid model, the application provides a conflict-free flight path planning mode based on a directed weighted graph. When a directed graph is generated, the starting point and the ending point of the UAV in the airspace cell are taken as the starting point and the ending point of the directed graph. At each time step between the starting point and the ending point, the center point of the conflict-free grid can be selected as a node of the graph, and the nodes are connected according to specific constraint conditions, so as to form a directed graph.

[0090] In the present application, in order to reduce the nodes of the directed graph, thereby reducing 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.

[0091] As shown in Figure 3 , the edges of the directed graph are generated in time step order, first generating the edges from time step 1 to time step 2, then generating the edges from time step 2 to time step 3, and so on until the time step of the end point of the UAV i.

[0092] Whether an edge is generated between two candidate nodes depends on whether it meets the connection constraint, the speed constraint, the reachability constraint and the direction constraint at the same time.

[0093] 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 UAV. The candidate directed edge set between adjacent time steps (τ-1) and τ based on the connection constraint is denoted as:

[0094]

[0095] wherein, denotes the grid where the starting point of the UAV i is located, denotes "existence", denotes the directed edge connecting the grid (m", n") in time step τ-2 and the grid (m', n) in time step τ-1, denotes the set of directed edges generated between adjacent time steps (τ-2) and (τ-1).

[0096] The speed constraint means that the flight (i.e. distance and time) represented by the edge is within the speed performance range of the UAV. The speed constraint is specifically denoted as:

[0097]

[0098] wherein, V i min is the minimum speed of the UAV i.

[0099] The reachability constraint means that the time step length from the end point of the current edge to the end point of the entire graph meets the controlled time of arrival (CTA) requirement of the UAV to reach the end point. The reachability constraint is specifically denoted as:

[0100]

[0101] wherein, denotes the grid where the end point of the UAV i is located, ​is the time step where UAV i ends.

[0102] The direction constraint means that the flight indicated by the generated directed edge is closer to the drone's destination from the current position rather than away from the drone's destination. The direction constraint is specifically expressed as:

[0103]

[0104] in, and They represent the grids where the starting point and the ending point of UAV i are located.

[0105] 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:

[0106]

[0107] in, is the time step where UAV i starts.

[0108] 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.

[0109] 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.

[0110] The conflict-free trajectory method provided by the present invention uses the A-star algorithm to search for the optimal conflict-free trajectory, that is, the conflict-free trajectory with the least power consumption, in a generated directed weighted graph.

[0111] The evaluation function of the A-star algorithm consists of actual value and heuristic value, and its mathematical expression is:

[0112]

[0113] 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.

[0114] 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:

[0115]

[0116] representing a directed weighted graph upstream node the power consumption of the node (m, n, t).

[0117] The A-star algorithm guarantees to obtain the optimal solution under the condition that the heuristic cost is always less than or equal to the actual cost from the node (m, n, t) to the destination. Since the flight time between two adjacent time steps is constant, the actual power consumption must be greater than or equal to the power consumption when the UAV always flies at the minimum power. Therefore, the mathematical expression of the heuristic value that meets the condition of obtaining the optimal solution is:

[0118]

[0119] wherein, is the heuristic value of the UAV i at the node (m, n, t) in the directed weighted graph , P i Min,Require is the minimum flight power of the UAV i.

[0120] The final mathematical expression of the evaluation function of the A-star algorithm is:

[0121]

[0122] Based on the evaluation function, the A-star algorithm generates the optimal conflict-free flight path of the UAV i with the minimum power consumption, thereby completing the conflict-free flight path planning of the UAV of the present application.

[0123] In some embodiments, as Figure 4 shown, the application provides an application scenario of the conflict-free flight path planning method of the UAV, which includes a common information service provider (CISP), an unmanned aircraft system traffic management service provider (UTMSP), a ground control station (GCS), and a UAV. The conflict-free flight path planning method is executed by the UTMSP.

[0124] The UAV sends its position information to its GCS in real time. When the GCS identifies that the UAV is about to enter a new airspace unit, it sends an airspace service request for the UAV to the UTMSP, which includes the current flight information of the UAV, including the flight plan, the aircraft type, and the remaining power.

[0125] UTMSP receives the airspace service request of GCS, extracts the latest real-time airspace information obtained from CISP, i.e. the current conflict-free matrix M of the airspace unit A UTMSP performs conflict detection based on the current conflict-free matrix M of the airspace unit A and the current flight information of the UAV. After detecting the conflict, UTMSP performs conflict-free path planning to obtain the optimal conflict-free path of the UAV. UTMSP sends the current optimal conflict-free path of the UAV to CISP and the corresponding GCS, and the GCS controls the UAV to execute the updated conflict-free path.

[0126] As shown in Figure 5 , the application provides a UAV conflict-free path planning system applied to low-altitude airspace, which comprises:

[0127] A flight information acquisition module 101 is configured to determine a designated air region as an airspace unit, divide the airspace unit into a plurality of grid units as a grid of the airspace unit, acquire an airspace service request when detecting that a new UAV enters the airspace unit, and acquire flight information of the UAV, wherein the flight information comprises start point and end point information of the UAV flying in the airspace unit.

[0128] A grid acquisition module 102 is configured to acquire conflict-free airspace information, which comprises information about whether the grid of the airspace unit has a conflict, acquire reachable grids of the UAV based on the flight information, and acquire feasible grids of the UAV based on the reachable grids and the conflict-free airspace information.

[0129] A graph construction module 103 is configured to acquire candidate nodes based on the feasible grids of the UAV, acquire a directed weighted graph based on the candidate nodes, a preset constraint condition and an electric quantity consumption, and acquire a path in the directed weighted graph representing a conflict-free path.

[0130] A search module 104 is configured to acquire an optimal conflict-free path by searching the directed weighted graph using an A-star algorithm, and control the UAV to execute the optimal conflict-free path.

[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.

[0132] 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 conflict-free trajectory planning method for UAVs in low-altitude airspace, characterized in that: The steps include: Step S1: Determine a designated airspace area as an airspace unit; divide the airspace unit into a plurality of grid units as grids of the airspace unit; upon detecting a new drone entering the airspace unit, obtain an airspace service request; the airspace service request includes flight information of the drone, and the flight information includes the starting and ending points of the drone's flight in the airspace unit; Step S2: Acquire conflict-free airspace information, where the conflict-free airspace information includes information about whether the grids of the airspace unit have conflicts; Obtain a reachable grid of the drone based on the flight information, and obtain 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 paths in the directed weighted graph represent conflict-free trajectories; Step S4: using the A-star algorithm to search the directed weighted graph to obtain an optimal conflict-free trajectory, and controlling the UAV to execute the optimal conflict-free trajectory; 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 non-conflicting grids as non-conflicting grids based on the conflict-free airspace information; Step S2-2: Based on the positions and times of the start and end points of the UAV's flight 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 the set of grids that are both reachable grids and conflict-free grids as feasible grids; determine the set of grids that are both reachable grids and conflict-free grids as infeasible grids; Step S2-2 specifically includes: Determine the drone in the following ways At time step The reachable grid on : in, Represents the time step The upper index is The grid, Represents the distance between two points, Represents the time step The upper index is The center point of the grid, and Respectively represent drones The starting and ending locations, Represents the time step A set of grids in the spatial unit; wherein, is the time step time, It's a drone Time of entry into the airspace unit, It's a drone The maximum flight speed, It's a drone Time of departure from the airspace cell; Step S2-3 specifically includes: determining feasible grids and infeasible grids according to the following method: in, , , , and UAVs At time step The feasible grid, reachable grid, conflict-free grid, infeasible grid, reachable grid and conflict grid on To take the intersection.

2. The conflict-free trajectory planning method for a UAV in low-altitude airspace according to claim 1 is 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, where the preset constraints include connection constraints, speed constraints, reachability constraints, and direction constraints; Step S3-3: generating a directed edge set between adjacent time steps based on the candidate nodes and based on the connection constraints, speed constraints, reachability constraints, and direction constraints; Step S3-4: assigning a weight 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.

3. The conflict-free trajectory planning method for a UAV in low-altitude airspace according to claim 2 is characterized in that: Step S3-2 specifically includes: The connection constraint is expressed as: in, Indicates drone The grid where the starting point is located, means "existence", Represents the time step Located in the middle Grid and time step Located in the middle The directed edges connecting the grids, Indicates that in adjacent time steps and The set of directed edges generated between them; Represents adjacent time steps and The set of candidate directed edges between ; Represents the time step Located in the middle Grid and time step Located in the middle The directed edges connecting the grids; Represents the time step The upper index is Grid; Represents the time step The upper index is Grid; Indicates that UAV i is at time step The feasible grid on ; The speed constraint is expressed as: in, For drones The minimum speed, Indicates the time interval, is the maximum speed of UAV i, Represents the time step The upper index is The center point of the grid; The reachability constraint is expressed as: in, Indicates drone The grid where the end point is located, For drones The time step where the end point is located; The direction constraint is specifically expressed as: in, and Respectively represent drones The grid where the start and end points are located.

4. The conflict-free trajectory planning method for a UAV in low-altitude airspace according to claim 3 is characterized in that: Step S3-3 specifically includes: Based on connection constraints, speed constraints, reachability constraints and direction constraints, adjacent time steps and The set of directed edges generated between can be expressed as: in, For drones The time step at the starting point, Represents the time step Located in the middle Grid and time step Located in the middle The directed edges connecting the grids, Indicates drone The time spent in the airspace.

5. The conflict-free trajectory planning method for a UAV in low-altitude airspace according to claim 4 is characterized in that: Step S4 specifically includes: (1) Obtain the evaluation function of the A-star algorithm: in, Represents a directed weighted graph Upload from node To Node Power consumption, It's a drone Minimum flight power; (2) Based on the evaluation function, the directed weighted graph is searched by the A-star algorithm to obtain the drone The optimal non-conflict trajectory with minimal power consumption is used to control the drone. The optimal conflict-free trajectory is executed.

6. The conflict-free trajectory planning method for a UAV in low-altitude airspace according to claim 1 is characterized in that: The airspace service request is sent by the 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.

7. A conflict-free trajectory planning system for UAVs in low-altitude airspace, characterized in that: The system comprises: A flight information acquisition module is configured to determine a designated airspace area as an airspace unit; divide the airspace unit into a plurality of grid cells, which serve as grids of the airspace unit; and acquire an airspace service request upon detecting the entry of a new drone into the airspace unit; the airspace service request includes the flight information of the drone, and the flight information includes the starting and ending points of the drone's flight within the airspace unit; a grid acquisition module, configured to acquire conflict-free airspace information, the conflict-free airspace information including information on whether the grids of the airspace unit 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 conflict-free airspace information; A graph construction module is configured to obtain candidate nodes based on a feasible grid of the UAV; obtain a directed weighted graph based on the candidate nodes, preset constraints, and power consumption; wherein the paths in the directed weighted graph represent conflict-free trajectories; A search module is used to search the directed weighted graph using the A-star algorithm to obtain an optimal conflict-free trajectory and control the UAV to execute the optimal conflict-free trajectory; The grid acquisition module is used for: Acquire non-conflict airspace information, and determine 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; Obtaining a reachable range of the UAV based on the positions and times of the start and end points 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; The set of grids that are both reachable grids and conflict-free grids is determined as a feasible grid; the set of grids that are both reachable grids and conflict grids is determined as an infeasible grid; Determine the drone in the following ways At time step The reachable grid on : in, Represents the time step The upper index is The grid, Represents the distance between two points, Represents the time step The upper index is The center point of the grid, and Respectively represent drones The starting and ending locations, Represents the time step A set of grids in the spatial unit; wherein, is the time step time, It's a drone Time of entry into the airspace unit, It's a drone The maximum flight speed, It's a drone Time of departure from the airspace cell; The feasible and infeasible grids are determined as follows: in, , , , and UAVs At time step The feasible grid, reachable grid, conflict-free grid, infeasible grid, reachable grid and conflict grid on To take the intersection.

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