Flight route dynamic planning method fusing airspace unmanned aircraft

Abstract

The embodiment of the invention discloses a flight route dynamic planning method for an unmanned aircraft fused with an airspace, and relates to the technical field of airspace analysis. Generating an airspace grid matrix graph by using the airspace structure data; obtaining a pre-feasible route planning result of the unmanned aircraft; then, screening out an access point alternative set meeting a constraint condition as a decision variable, constructing a flight route dynamic planning model of the unmanned aerial vehicle in the fusion airspace, and obtaining an airspace access point in flight route dynamic planning of the unmanned aerial vehicle in fusion operation through automatic analysis; therefore, real-time route planning can be further provided for the unmanned aircraft on the basis of the airspace access point, the automation degree in the flight route planning process of the unmanned aircraft during fusion operation is finally improved, the time and energy occupied by manual planning are reduced, and the problem of low efficiency during manual planning is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of manned and unmanned aerial vehicle airspace analysis, and in particular to a method for dynamically planning a flight route of an unmanned aerial vehicle with integrated airspace. Background Art

[0002] The rapid development of unmanned aerial vehicles and the continuous expansion of their application areas have brought new challenges to airspace management. At present, aviation management agencies around the world generally maintain a cautious attitude towards the operation and management of unmanned aerial vehicles, restricting them to specific airspaces and operating them separately from manned aircraft. However, in the face of limited airspace resources, the isolated operation mode obviously cannot meet the growing application needs of unmanned aerial vehicles, so the concept of integrated operation of manned and unmanned aircraft came into being. ICAO has also listed the integrated operation technology of manned and unmanned aircraft as the next generation of air traffic control technology for priority development.

[0003] However, there are many problems and challenges in realizing the integrated operation of manned and unmanned aircraft. One of the key issues is how unmanned aircraft can safely and efficiently enter the controlled airspace and conduct route flights. In the future, unmanned aircraft will leave the isolated airspace and enter the integrated airspace, which means that the flight missions and aircraft types in the airspace will become heavy and complex, which will put higher operational requirements on the safe airspace environment. Unlike isolated airspace, manned and unmanned aircraft are mixed and intertwined in the integrated airspace, and there are differences in performance and speed of different types of aircraft. Faced with such a complex and diverse airspace environment, unmanned aircraft performing flight missions in the integrated airspace will face the risk of flight conflicts with manned aircraft.

[0004] Therefore, in order to avoid flight conflicts between unmanned aircraft and manned aircraft, from the perspective of airspace design, research on flight route planning methods for unmanned aircraft in fused airspace has become a research topic that is urgently needed in the industry. One specific R&D demand is expected to be solved in a timely manner, that is, how to automatically analyze and obtain the airspace entry and exit points in the dynamic planning of unmanned aircraft flight routes during fusion operation, so as to improve the degree of automation in the process of unmanned aircraft flight route planning during fusion operation and reduce the time and energy occupied by manual planning, which has become an urgent problem to be solved. Summary of the invention

[0005] An embodiment of the present invention provides a method for dynamic planning of unmanned aerial vehicle flight routes in a fused airspace, which can automatically analyze and obtain airspace entry and exit points in the dynamic planning of unmanned aerial vehicle flight routes during fused operation, thereby further providing real-time route planning for the unmanned aerial vehicle on the basis of the airspace entry and exit points, ultimately improving the degree of automation in the unmanned aerial vehicle flight route planning process during fused operation, and reducing the time and effort occupied by manual planning.

[0006] To achieve the above object, the embodiments of the present invention adopt the following technical solutions: A method for dynamic planning of flight paths of unmanned aerial vehicles in integrated airspace, comprising: S1, generate a spatial grid matrix diagram; S2, obtaining unmanned aerial vehicle flight plan data, and using the unmanned aerial vehicle flight plan data and the airspace grid matrix diagram to generate an initial path, wherein the initial path avoids restricted areas; S3, reconstructing the path according to the preset reconstruction rules; S4. Using the reconstructed path and sector structure data, establish a candidate set of entry and exit points for the unmanned aerial vehicle to cross the sector boundary; S5. Filtering the entry and exit points that meet the constraint conditions in the candidate set, wherein the constraint conditions include: the intersection angle constraint between the route of the unmanned aerial vehicle and the sector boundary, the position constraint between the route of the unmanned aerial vehicle and the restricted area, and the fusion operation risk constraint; S6. Use the screened entry and exit points as decision variables to plan the flight routes of unmanned aerial vehicles in the airspace.

[0007] In S1, it includes: performing grid-based airspace discretization processing on the airspace structure data containing restricted area information, wherein the airspace structure data containing restricted area information includes: sector structure data and restricted area structure data; for example: the sector structure data includes: sector code, sector height upper limit, sector height lower limit, sector boundary point longitude and latitude; the restricted area structure data includes: restricted area code, restricted area height upper limit, restricted area height lower limit, restricted area boundary point longitude and latitude, and restricted time. Then, the state of the airspace grid is identified according to whether the airspace is restricted, and an airspace grid matrix diagram is generated.

[0008] The grid-based spatial domain discretization process includes: The airspace is divided into square grids with longitude as the X-axis and latitude as the Y-axis. The grid coordinates are used as coordinate identifiers to indicate the grid arrangement position. V(X,Y) The discretization of is: , the actual coordinates of the origin are , Respectively V(X,Y) The actual coordinates of the center point, lower left point, upper left point, lower right point and upper right point, L is the grid side length.

[0009] The state of the airspace grid is identified according to whether the airspace is restricted, and an airspace grid matrix diagram is generated, including: dividing the grid into two state types according to whether the airspace is restricted, including available grids and unavailable grids; the grids occupied by the restricted area are determined to be restricted grids, and are marked by "1" in the matrix diagram; the grids occupied by the protection area added at the outermost edge of the restricted area are protection grids, and are marked by "2" in the matrix diagram, and the protection grids include: one or more layers of grids directly adjacent to the outermost restriction grid of the restricted area, wherein when the grid size is greater than or equal to the safety interval between the aircraft and the restricted area, a layer of protection grid is set; the grids in the airspace that allow aircraft to pass are available grids, and are marked by "0" in the matrix diagram.

[0010] The unmanned aerial vehicle flight plan data includes: the center point of the grid where the unmanned aerial vehicle plans to enter the airspace And the center point of the grid where the airspace point is planned ; In S2, an initial path is generated by a path planning model, wherein the path planning model includes: , where N represents the arrive The number of jump points, Indicates that unmanned aerial vehicles To current location The actual distance cost, Indicates that unmanned aerial vehicles arrive The estimated cost of the remaining path, x i Indicates from v I arrive v C The horizontal coordinate of the center point of the jump point, y i Indicates from v I arrive v C The vertical coordinate of the center point of the jump point, x C express v C The horizontal axis of y C express v C The vertical coordinate of xG express v G The horizontal axis of y G express v G The vertical coordinate of Represents a collection of unavailable grid center points.

[0011] The reconstruction rules include: no intersection with unavailable grids, reduced or unchanged distance cost, and reduced or unchanged number of turns. In S3, it includes: For the initial path set, from the first point Start traversing and detect one by one Whether the reconstruction rules are met until If the reconstruction rule is not satisfied, then are removed as redundant points, j is a positive integer; then Start traversal until the third to last point in the initial path set is reached, then terminate the traversal and use the current path set as the reconstructed path set.

[0012] In S5, the intersection angle constraint between the route of the unmanned aerial vehicle and the sector boundary includes: ,in, i is the intersection angle between the route and the sector boundary. The intersection vector angle when the αth segment intersects with the βth sector boundary is i αβ , i min is the minimum constraint value of the intersection angle; the position constraint of the route of the unmanned aerial vehicle and the restricted area includes: , among which, e The border is , and The vertices of two consecutive restricted areas are numbered in counterclockwise order. Indicates route b and restricted area e The intersection of the boundaries, Indicates intersection or tangency, Indicates no intersection, G b represents the number of intersections between route b and the boundary of the restricted area, and ρ represents the total number of vertices in the restricted area; The fusion operation risk constraints include: ,in, For sectors or The sector-wide comprehensive level conflict risk, is the sector comprehensive level conflict risk threshold, The vertical conflict risk of the sector is summarized as follows: is the sector comprehensive vertical conflict risk threshold, For sectors or Middle intersection n The complexity of For sectors or Mid-level h m The intersection n The risk of horizontal conflict exists at For sectors or Zhonghang Road i The complexity of For sectors or The number is i Aircraft on route pass through altitude levels h m The constraint conditions also include: the sector entry and exit point interval constraint is , Indicates the minimum interval between sector entry and exit points. Indicates the entry and exit points σ and the entry and exit points interval.

[0013] The embodiment of the present invention provides a method for dynamic planning of flight routes of unmanned aerial vehicles in integrated airspace, which obtains airspace structure data and aircraft operation data; uses the airspace structure data to perform grid-based airspace discretization processing to generate an airspace grid matrix diagram; uses the flight plan data of the unmanned aerial vehicle and the airspace grid matrix diagram to obtain the pre-feasible route planning results of the unmanned aerial vehicle, and the pre-feasible route planning results of the unmanned aerial vehicle include: a reconstructed path that meets the constraints of the restricted area and removes unnecessary turning points; uses the airspace structure data, the pre-feasible route planning results of the unmanned aerial vehicle in the target airspace research period, and the flight plan data of the manned aircraft to obtain the path that meets the constraints of the restricted area. The alternative set of entry and exit points for unmanned aerial vehicles crossing the sector with constraints is used as the decision variable. With the goal of minimizing the trajectory complexity and the shortest flight time of all unmanned aerial vehicles in the target airspace, a dynamic planning model for the flight route of unmanned aerial vehicles in the fused airspace is constructed. The airspace entry and exit points in the dynamic planning of the flight route of unmanned aerial vehicles during fusion operation are obtained through automatic analysis. Therefore, on the basis of the airspace entry and exit points, real-time route planning can be further provided for the unmanned aerial vehicles, which ultimately improves the degree of automation in the flight route planning process of unmanned aerial vehicles during fusion operation, reduces the time and energy occupied by manual planning, and solves the problem of low efficiency in manual planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention; Figure 2 A grid main position point map provided by an embodiment of the present invention; Figure 3 A spatial grid map provided by an embodiment of the present invention; Figure 4 A spatial grid matrix diagram provided by an embodiment of the present invention; Figure 5 A demonstration diagram of path reconstruction provided by an embodiment of the present invention; Figure 6 An aircraft search space diagram provided by an embodiment of the present invention; Figure 7 A schematic diagram of a system architecture provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention will be described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention. It can be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "one", "said" and "the" used herein may also include plural forms. It should be further understood that the term "including" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used here may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items. It can be understood by those skilled in the art that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0017] The embodiments of the present invention provide a method and device for dynamic planning of unmanned aerial vehicle flight routes for integrated airspace, providing theoretical and technical support for the integration of unmanned aerial vehicles into my country's airspace system. In order to achieve the above-mentioned design objectives, the embodiments of the present invention adopt the following design ideas: obtaining airspace structure data and aircraft operation data, wherein the airspace structure data includes: sector structure data, restricted area structure data, manned aircraft route data, and the aircraft operation data includes: manned aircraft flight plan data and unmanned aircraft flight plan data; using the airspace structure data, grid-based airspace discretization processing is performed, and the state of the airspace grid is identified according to whether the airspace is restricted, and an airspace grid matrix diagram is generated; using the unmanned aircraft flight plan data and the airspace grid matrix diagram, the unmanned aircraft pre-feasible route planning results are obtained, and the unmanned aircraft pre-feasible route planning results include: satisfying restricted area constraints and removing unnecessary turns. The reconstructed path of the point; using the airspace structure data, the pre-feasible route planning results of the unmanned aerial vehicle in the target airspace research period and the flight plan data of the manned aircraft, obtain the entry and exit point candidate set of the unmanned aerial vehicle crossing the sector that meets the constraint conditions, wherein the constraint conditions include: sector entry and exit point interval constraint, route and sector boundary intersection angle constraint, route and restricted area position constraint and fusion operation risk constraint; using the entry and exit point candidate set that meets the constraint conditions as the decision variable, with the goal of minimizing the trajectory complexity and the shortest flight time of all unmanned aerial vehicles in the target airspace, construct a dynamic planning model for the flight route of the unmanned aerial vehicle in the fusion airspace, and use a multi-objective optimization algorithm to solve it, and obtain the dynamic planning scheme of the flight route of the unmanned aerial vehicle during the fusion operation.

[0018] Specifically, the embodiment of the present invention provides a method for dynamically planning a flight path of an unmanned aerial vehicle in a fusion airspace, such as Figure 1 As shown, it is mainly divided into two parts: First, the part of pre-feasible route planning of unmanned aerial vehicles, which is mainly for the "individual" unmanned aerial vehicles. Since each unmanned aerial vehicle has its own independent flight mission, it is necessary to first carry out global initial feasible route planning, mainly from the perspective of safety, efficiency and economy. At this time, the flight conflict problem of integrated operation is not considered. The main consideration is the conflict between the unmanned aerial vehicle and the static environment of the airspace (such as restricted areas). Obstacle avoidance planning is carried out for the unmanned aerial vehicle to generate a pre-feasible route for the unmanned aerial vehicle; Second, the part of dynamic planning of flight routes of unmanned aerial vehicles in integrated airspace. This part is aimed at the pre-generated feasible routes of unmanned aerial vehicles. From the perspective of the orderliness and operational safety of the integrated airspace traffic flow, according to the time-varying traffic demand, the dynamic planning of the flight routes of unmanned aerial vehicles in the integrated airspace is further carried out.

[0019] For ease of understanding, the method flow can be divided into S1-S7: S1. Using the airspace structure data containing restricted area information, grid-based airspace discretization processing is performed. According to whether the airspace is restricted, the state of the airspace grid is identified to generate an airspace grid matrix diagram.

[0020] Among them, the airspace structure data containing restricted area information includes: sector structure data and restricted area structure data; for example: the sector structure data includes: sector code, sector height upper limit, sector height lower limit, sector boundary point longitude and latitude; the restricted area structure data includes: restricted area code, restricted area height upper limit, restricted area height lower limit, restricted area boundary point longitude and latitude, and restriction time.

[0021] S2. Based on the airspace grid matrix diagram and the flight plan data of the unmanned aerial vehicle, the initial path to avoid the restricted area is obtained through the Jump Point Search (JPS) algorithm with the shortest path as the goal.

[0022] Among them, the unmanned aerial vehicle flight plan data includes: flight number, registration number, aircraft type, planned take-off airport, planned landing airport, planned entry time into the airspace, planned entry point into the airspace (longitude, latitude, altitude), planned entry speed and planned exit point from the airspace (longitude, latitude, altitude).

[0023] S3. In order to solve the problem of extra unnecessary turning points in the initial path obtained, the path is reconstructed by removing unnecessary turning points to obtain a feasible route for the unmanned aerial vehicle with fewer turns and a shorter path.

[0024] S4. Based on the pre-feasible route planning results of the unmanned aerial vehicle during the target airspace study period, retain its starting position, target position, turning point and flight time of each segment.

[0025] S5. Utilize the feasible route planning results of the unmanned aerial vehicle in advance during the target airspace research period and the sector structure data, calculate and obtain the sector boundary traversed by the unmanned aerial vehicle based on geometric principles, and discretize the boundary with the minimum interval value of the sector entry and exit points as the interval, and obtain the entry and exit point candidate set that meets the sector entry and exit point interval constraint.

[0026] S6. The starting position, target position, turning point and alternative entry and exit points on the crossed sector boundary that meet the sector entry and exit point interval constraint are connected in sequence (i.e., forming a route), and combined with the airspace structure data containing restricted area information, manned aircraft flight plan data, and manned aircraft route data, the entry and exit points that meet the intersection angle constraint between the route and the sector boundary, the position constraint between the route and the restricted area, and the fusion operation risk constraint are calculated and screened out, and a candidate set of entry and exit points that meet the constraints is obtained as a decision variable.

[0027] Among them, the flight plan data of manned aircraft include: flight number, registration number, aircraft type, planned take-off airport, planned landing airport, planned sector entry code, planned sector entry time, planned sector entry point (longitude, latitude, altitude) and planned entry speed; the route data of manned aircraft include: route name, route type, longitude and latitude of waypoints.

[0028] S7. Taking the alternative set of entry and exit points that meet the constraints as decision variables and aiming at minimizing the trajectory complexity and the shortest flight time of all unmanned aerial vehicles in the target airspace, a dynamic planning model for the flight routes of unmanned aerial vehicles in the fused airspace is constructed, and a multi-objective optimization algorithm, such as the non-dominated sorting genetic algorithm (NSGA II) with an elite retention strategy, is used to solve the problem and obtain the flight route planning scheme for unmanned aerial vehicles during fusion runtime.

[0029] Specifically, the unmanned aerial vehicle's feasible route planning in advance includes: 1.1. Grid-based spatial discretization: With longitude as the X-axis and latitude as the Y-axis, the spatial domain is divided into several square grids. Each grid has its own coordinate identifier, where the coordinate identifier used to indicate the grid arrangement position is defined as "grid coordinate", such as V(X,Y) Represents the grid coordinates as (X,Y) The grid is a grid; the coordinates used to indicate the actual position of the location point are defined as "actual coordinates", such as express V(X,Y) Each grid has five main points, namely the center point, lower left point, upper left point, lower right point and upper right point. Figure 2 Assume that the grid side length is L , the actual coordinates of the origin are , then the grid V(X,Y) The actual coordinates of the five main location points are: .

[0030] 1.2. Identification of airspace grid status: Depending on whether the airspace is restricted, two types of grids are defined, namely, available grids and unavailable grids. Among them, the unavailable grid specifically includes restriction grids and protection grids. The restriction grid refers to the grid occupied by the restricted area. In order to ensure the safe operation of aircraft, as long as there is restricted airspace in the grid, it is determined to be a restriction grid, and it is marked with "1" in the matrix diagram. The protection grid refers to the grid occupied by the protection area added at the outermost edge of the restricted area. It is generally one or more layers of grids directly adjacent to the outermost restriction grid. When the grid size is greater than or equal to the safety interval between the aircraft and the restricted area, a layer of protection grid is set. Otherwise, multiple layers of grids need to be set. The number of layers is the ratio of the safety interval to the grid size rounded up, and it is marked with "2" in the matrix diagram. The available grid refers to the grid in the airspace where aircraft are allowed to pass, that is, the grids in the airspace other than the restriction grid and the protection grid are determined to be available grids, and they are marked with "0" in the matrix diagram. The spatial domain grid obtained by discretization is as follows Figure 3 As shown in the figure, the corresponding spatial grid matrix obtained by state recognition is as follows Figure 4 shown.

[0031] 1.3. Initial path planning to avoid restricted areas: Based on the airspace grid matrix diagram, the JPS algorithm is used to obtain the shortest initial path to avoid restricted areas. and The center point of the grid where the unmanned aerial vehicle plans to enter the airspace (referred to as the initial position) and the center point of the grid where the unmanned aerial vehicle plans to exit the airspace (referred to as the target position), respectively, the model is as follows: , N is the algorithm from the initial position Start counting to the current position The number of jump points; For the unmanned aerial vehicle from the initial position To current location Actual distance cost; For unmanned aerial vehicles from the current position To the target location The estimated cost of the remaining path; is the set of unavailable grid center points; Formula (5) indicates that the jump point position should not be in the restricted area and the protected area.

[0032] 1.4. Path reconstruction: Assume that the initial path to avoid the restricted area obtained by the JPS algorithm is Since the search direction of the JPS algorithm is either along a straight line or along a diagonal line, this will result in some unnecessary turning points, limiting the generation of a more cost-effective path. Therefore, in order to shorten the path length and reduce the number of turns in the path, this paper reconstructs the path by removing some unnecessary grid center points in the shortest path Ω obtained by the JPS algorithm to obtain a pre-feasible route for the unmanned aerial vehicle.

[0033] The basic principles of path reconstruction are: no intersection with unavailable grids, reduced or unchanged cost, and reduced or unchanged number of turns. Therefore, the reconstruction method of the initial path Ω is: for the initial path set, from the first point Start traversing and check Whether the three basic principles are met, among which non-intersection with unavailable grids is the primary condition, the cost can be evaluated according to formula (2) until If the basic principles are not met, then are all redundant points and can be directly removed; then Start traversing until you reach the third-to-last point in the path set, terminate the traversal, and output the reconstructed path, where the initial position and target location Must be retained.

[0034] For example: Figure 5 As shown in the figure, it is a demonstration diagram of path reconstruction, where the initial position of the aircraft is S , the target location is E , the initial path planned by the JPS algorithm is { S,A,B,C,D,E}, from the initial position S Start traversing and check SA、SB、SC、SD、SE Whether the three basic principles are met, SA、SB Satisfied, and SC If not satisfied, A is a redundant point and can be directly removed; then B Click to start traversal and check BC、BD、BE ,in BC satisfy, BD If it is not satisfied, there is no redundant point and no processing is done; finally, C Click to start traversal and check CD , CE ,in CD satisfy, CE If it is not satisfied, there is no redundant point and no processing is done. The output reconstruction path is { S,B,C,D,E}.

[0035] Specifically, the fused airspace unmanned aerial vehicle flight path dynamic planning includes: 2.1. Establish an airspace trajectory complexity model based on a linear dynamic system: Considering that the flight route planning of unmanned aerial vehicles needs to reduce the complexity of airspace operations, this paper uses airspace trajectory complexity Φ to evaluate the fused airspace complexity based on linear dynamic system modeling to measure the orderliness of traffic flow under the coordinated planning of multi-sector entry and exit points.

[0036] In order to evaluate the complexity of a set of four-dimensional trajectories in the airspace, this paper uses a linear dynamic system model to model the four-dimensional trajectory traffic structure at each time point. The general equation of the linear dynamic system is as follows: .

[0037] in, is the state vector of the system; Represents the static state of the system; matrix A represents the state vector and velocity vector The eigenvalue of matrix A reflects the evolution of the system, so this eigenvalue is used as an indicator to measure the complexity of traffic conditions in the airspace.

[0038] For aircraft i At the point in time k The linear dynamic system of the surrounding traffic structure is modeled. Figure 6 As shown, the aircraft i The sampling point is P i,k , then set P i,k A cylinder with a center, a radius of 25 nautical miles, and an altitude of 2000 feet is used as the search space to obtain aircraft i Observation vectors of neighboring aircraft. i Adjacent aircraft j The observation vector includes the sampling points P j,k The position vector and velocity vector of . In the three-dimensional space, these two observation vectors are expressed as: ,in, x j 、y j 、z j Aircraft j exist x、y、z Coordinates in the axis direction; vx j ,vy j ,vz j is the velocity vector in x、y、z Component in the direction of the axis.

[0039] In order to determine the linear dynamic system model that best fits the observation vectors of the reference aircraft and the adjacent aircraft, it is necessary to find the model parameters that minimize the error between the dynamic system and the observations. This minimization problem can be expressed as Equation (9). By using the least mean square minimization method, the model parameters A and .

[0040] ,in, M is the number of aircraft that can be identified in the search space.

[0041] After obtaining the linear dynamic system model, the complex eigenvalues ​​of the matrix A can be calculated. Sampling point P i,k The complex eigenvalue set of is as follows (10): , sampling point P i,k Complexity index It is related to the negative real part of the complex eigenvalue, and the expression is shown in formula (11).

[0042] , calculate the complexity index of all sampling points along the entire track and sum them up to get the aircraft i The complexity of the track , as shown in formula (12): ,in, N i It is an aircraft i The number of sampling points.

[0043] The spatial trajectory complexity Φ is given by The spatial trajectory complexity expression is: ,in, I is the number of aircraft in the airspace during the study period.

[0044] 2.2. Constructing a dynamic planning model for unmanned aerial vehicle flight routes in fused airspace: 1) Decision variables: Let the set of unmanned aerial vehicle flights entering the target airspace during the study period be , R is the total number of unmanned aerial vehicles, of which each flight u ( r )Entry and exit points across sectors is the decision variable.

[0045] 2) Objective function: In order to ensure the orderliness of the integrated airspace traffic flow and the safety and efficiency of operation, the dynamic route planning in this paper takes the trajectory complexity and flight time of unmanned aerial vehicles in the airspace into consideration. That is, when planning the flight route of unmanned aerial vehicles, the objective function is to minimize the trajectory complexity and the shortest flight time of all unmanned aerial vehicles in the airspace, which is recorded as: ,in, L r For unmanned aerial vehicles u ( r ) The flight distance calculated by connecting the entry and exit points of the sector traversed; V r Unmanned aerial vehicles u ( r )’s planned entry speed.

[0046] 3) Constraints: The dynamic planning of unmanned aerial vehicle flight routes in integrated airspace needs to consider the influence of factors such as route structure, restricted areas, and the risk of conflict between manned and unmanned aerial vehicles to ensure the flight safety of the aircraft. In this paper, route planning must meet the following constraints: (1) The intersection angle constraint between the route and the sector boundary: When the aircraft flies along the route, it may deviate to the left or right. If the angle between the route and the sector boundary is too small, it will increase the risk of the aircraft flying out of the boundary and lead to unclear allocation of control responsibilities at the sector boundary. Let the intersection vector angle when the αth segment intersects with the βth sector boundary be i αβ , i min is the minimum constraint value of the intersection angle, then the intersection angle between the route and the sector boundary is i Need to meet: , (2) Position constraints of the route and restricted area: Assume that the vertex sequence of the restricted area is arranged in counterclockwise order as ,in , No. e The border is , route b Restricted Area e The intersection of the boundaries is express, Indicates intersection or tangency, Indicates disjointness.

[0047] definition For route b The number of intersections with the restricted area boundary is .

[0048] (3) Sector entry and exit point spacing constraints: When the spacing between entry and exit points on the same sector boundary is too small, it will not only cause inconvenience in control work, but also the possibility of potential conflicts between aircraft. Therefore, it is necessary to set sector entry and exit point spacing constraints. This paper sets the minimum spacing of sector entry and exit points to , then the entry and exit points With entry and exit points Interval The following formula should be satisfied: .

[0049] (4) Fusion operation risk constraints: The premise of the fusion operation of manned and unmanned aircraft is that unmanned aircraft should achieve the same safety level as manned aircraft, that is, the operation safety level should not be reduced when unmanned aircraft join the controlled airspace, which means that the operation risk during fusion operation cannot exceed the historical risk threshold. Therefore, in addition to the structural constraints such as the intersection angle constraint between the route and the sector boundary, the position constraint between the route and the restricted area, and the sector entry and exit point interval constraint mentioned above, the dynamic planning of the flight route of unmanned aircraft in fusion airspace also needs to consider the operation risk constraints of manned and unmanned aircraft, that is, when the unmanned aircraft joins the controlled airspace according to the planned route, the operation risk of each sector it crosses cannot exceed its corresponding historical risk threshold. The sector operation risk in this paper includes two types, namely, the sector comprehensive horizontal conflict risk and the sector comprehensive vertical conflict risk. The sector comprehensive horizontal conflict risk is the sum of the product of the complexity of all intersections in the target sector during the statistical period and the horizontal conflict risk; the sector comprehensive vertical conflict risk is the sum of the product of the complexity of all routes in the target sector during the statistical period and the vertical conflict risk.

[0050] Set the sector to be crossed or The comprehensive horizontal conflict risk of the sector is , the sector comprehensive level conflict risk threshold is , the sector's comprehensive vertical conflict risk is , the sector comprehensive vertical conflict risk threshold is , then it should satisfy: ,in, For sectors or Middle intersection n complexity; For sectors or Mid-level h m The intersection n The risk of horizontal conflict exists at the location; For sectors or Zhonghang Road i complexity; For sectors or Zhonghang Roadi Aircraft above the altitude level h m The risk of vertical conflict caused by

[0051] 4) Solution process: According to the set constraints, we can get the set of optional entry and exit points for the unmanned aerial vehicle flight route. The specific steps are as follows: (1) Based on the pre-planned feasible route of the unmanned aerial vehicle during the target airspace study period, retain its starting position, target position, turning point and flight time of each segment; (2) Using the planning results of the unmanned aerial vehicle's feasible route in advance within the specified time period of the target airspace and the sector structure data, the sector boundary that the unmanned aerial vehicle's feasible route passes through is calculated based on geometric principles to obtain the target airspace's feasible route. Obtaining a set of candidate entry and exit points that satisfy the entry and exit point interval constraints of the sector for the discrete sector boundaries; (3) The starting position, the target position, the turning point, and the candidate entry and exit points on the sector boundary that meet the sector entry and exit point interval constraint are sequentially connected, and the airspace structure data containing the restricted area information, the manned aircraft flight plan data, and the manned aircraft route data are combined to calculate and screen the entry and exit points that meet the intersection angle constraint between the route and the sector boundary, the position constraint between the route and the restricted area, and the fusion operation risk constraint, so as to obtain a candidate set of entry and exit points that meet the constraints.

[0052] The dynamic planning problem of unmanned aerial vehicle flight routes in fused airspace is a multi-objective optimization problem. After obtaining the candidate set of entry and exit points that the unmanned aerial vehicle can select within the specified time period of the target airspace, it is used as a decision variable and a multi-objective optimization algorithm (such as the NSGA II algorithm) is used to solve the problem to obtain the dynamic planning solution of the unmanned aerial vehicle flight route during fused runtime.

[0053] In summary, the design idea of ​​this embodiment is mainly to perform grid-based airspace discretization processing by using airspace structure data containing restricted area information, and to identify the state of airspace grid according to whether the airspace is restricted, and generate an airspace grid matrix diagram, wherein the airspace structure data containing restricted area information includes: sector structure data and restricted area structure data; specifically, using the airspace grid matrix diagram and unmanned aerial vehicle flight plan data to obtain the unmanned aerial vehicle pre-feasible route planning result, and the unmanned aerial vehicle pre-feasible route planning result includes: a reconstructed path that meets the restricted area constraints and removes unnecessary turning points; wherein, using the airspace grid matrix diagram and unmanned aerial vehicle flight plan data to obtain the unmanned aerial vehicle pre-feasible route planning result includes: according to the airspace grid matrix diagram and the unmanned aerial vehicle flight plan data, with the shortest path as the goal, obtaining the initial path to avoid the restricted area through the Jump Point Search (JPS) algorithm; the initial path to avoid the restricted area is reconstructed by removing unnecessary turning points to obtain the unmanned aerial vehicle pre-feasible route planning result.

[0054] Then, the unmanned aerial vehicle pre-feasible route planning results, sector structure data, restricted area structure data, manned aircraft flight plan data and manned aircraft route data within the target airspace study period are used to obtain a candidate set of entry and exit points for the unmanned aerial vehicle to cross the sector that meets the constraint conditions; wherein, the method of using the unmanned aerial vehicle pre-feasible route planning results, sector structure data, restricted area structure data, manned aircraft flight plan data and manned aircraft route data within the target airspace study period to obtain a candidate set of entry and exit points for the unmanned aerial vehicle to cross the sector that meets the constraint conditions includes: based on the unmanned aerial vehicle pre-feasible route planning results within the target airspace study period, retaining its starting position, target position, turning point and flight time of each segment; Afterwards, the sector boundary traversed by the unmanned aerial vehicle is calculated and obtained according to the geometric principle using the pre-feasible route planning results of the unmanned aerial vehicle within the target airspace research period and the sector structure data, and the boundary is discretized with the minimum interval value of the sector entry and exit points as the interval to obtain an alternative set of entry and exit points that meet the sector entry and exit point interval constraints; the starting position, target position, turning point and alternative entry and exit points on the traversed sector boundary that meet the sector entry and exit point interval constraints are connected in sequence, and combined with the sector structure data, restricted area structure data, manned aircraft flight plan data, and manned aircraft route data, the entry and exit points that meet the intersection angle constraints between the route and the sector boundary, the position constraints between the route and the restricted area, and the fusion operation risk constraints are calculated and screened to obtain an alternative set of entry and exit points that meet the constraints.

[0055] Finally, the entry and exit point candidate set that meets the constraint conditions is used as a decision variable to construct a dynamic planning model for the flight path of unmanned aerial vehicles in the fusion airspace, and obtain the flight path planning scheme of unmanned aerial vehicles during fusion operation. Among them, the construction of the dynamic planning model for the flight path of unmanned aerial vehicles in the fusion airspace includes: the entry and exit points of unmanned aerial vehicles crossing the sector that meet the constraints of the interval of the entry and exit points of the sector, the intersection angle constraints between the route and the sector boundary, the position constraints between the route and the restricted area, and the fusion operation risk constraints are used as decision variables, and the trajectory complexity of all unmanned aerial vehicles in the target airspace is minimized and the flight time is minimized. The dynamic planning model for the flight path of unmanned aerial vehicles in the fusion airspace is constructed.

[0056] Based on the above design ideas, this embodiment can implement the above ideas and method processes in practical applications with the help of currently available computer and server hardware devices. Therefore, this embodiment also provides a device for implementing the above-mentioned integrated airspace unmanned aerial vehicle flight path dynamic planning method, including: The unmanned aerial vehicle pre-feasible route planning module is used to use the airspace structure data to perform grid-based airspace discretization processing to generate an airspace grid matrix diagram; and obtain the unmanned aerial vehicle pre-feasible route planning results based on the airspace grid matrix diagram and the unmanned aerial vehicle flight plan data. The unmanned aerial vehicle pre-feasible route planning results include: a reconstructed path that meets the restricted area constraints and removes unnecessary turning points.

[0057] The fused airspace unmanned aerial vehicle flight route dynamic planning module is used to utilize the airspace structure data, the pre-feasible route planning results of the unmanned aerial vehicle in the target airspace research period and the flight plan data of the manned aircraft to obtain the entry and exit point alternative set for the unmanned aerial vehicle to cross the sector that meets the constraints; and use the entry and exit point alternative set as decision variables, with the goal of minimizing the trajectory complexity and the shortest flight time of all unmanned aerial vehicles in the target airspace, to construct a fused airspace unmanned aerial vehicle flight route dynamic planning model, and use a multi-objective optimization algorithm to solve it, so as to obtain the unmanned aerial vehicle flight route dynamic planning scheme during fusion runtime.

[0058] Specifically, the unmanned aerial vehicle pre-feasible route planning module includes: The feasible route planning submodule is used to utilize the airspace structure data containing restricted area information to perform grid-based discretization processing on the airspace, identify the state of the airspace grid according to whether the airspace is restricted, and generate an airspace grid matrix diagram; based on the airspace grid matrix diagram and the unmanned aerial vehicle flight plan data, with the shortest path as the goal, the JPS algorithm is used to obtain the initial feasible route to avoid the restricted area.

[0059] The feasible route reconstruction submodule is used to reconstruct the initial feasible route that avoids the restricted area by removing unnecessary turning points, and obtain the preliminary feasible route planning result of the unmanned aerial vehicle. The preliminary feasible route planning result of the unmanned aerial vehicle includes: a reconstructed path that meets the restricted area constraints and removes unnecessary turning points.

[0060] Specifically, the fused airspace unmanned aerial vehicle flight route dynamic planning module includes: The entry and exit point candidate set acquisition submodule is used to use the airspace structure data, the pre-feasible route planning results of the unmanned aerial vehicle during the target airspace research period and the manned aircraft flight plan data to obtain the entry and exit point candidate set for the unmanned aerial vehicle to cross the sector that meets the constraints.

[0061] The route dynamic planning submodule is used to take the alternative set of entry and exit points of the unmanned aerial vehicle crossing the sector that meets the constraints as decision variables, and to build a dynamic planning model for the flight routes of unmanned aerial vehicles in the fused airspace with the goal of minimizing the trajectory complexity and the shortest flight time of all unmanned aerial vehicles in the target airspace. It is also used to solve the problem using a multi-objective optimization algorithm to obtain a dynamic planning solution for the flight routes of unmanned aerial vehicles during fusion runtime.

[0062] The device described in this embodiment can be specifically implemented in the current air traffic control system, and realizes corresponding functions through various subsystems, such as Figure 7 As shown, including: The air traffic control surveillance subsystem is used to monitor and obtain airspace structure data, manned aircraft flight plan data and unmanned aircraft flight plan data in real time.

[0063] The feasible route pre-planning subsystem is used to obtain airspace structure data and unmanned aircraft flight plan data from the air traffic control monitoring subsystem, and then generate an airspace grid matrix diagram, and obtain the unmanned aircraft's feasible route pre-planning results through the JPS algorithm and path reconstruction.

[0064] The fused airspace flight route dynamic planning subsystem is used to obtain airspace structure data and manned aircraft flight plan data from the air traffic control monitoring subsystem, obtain the pre-feasible route planning results of unmanned aircraft within the target airspace research period from the feasible route pre-planning subsystem, calculate and screen out the alternative set of entry and exit points for unmanned aircraft to cross the sector that meets the constraints, and use this as the decision variable to perform fused airspace unmanned aircraft flight route dynamic planning based on a multi-objective optimization algorithm.

[0065] The display subsystem is used to display the sector structure, restricted area structure, manned aircraft route, unmanned aircraft planned route, and real-time operation tracks of manned and unmanned aircraft on a two-dimensional plane.

[0066] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The above is only a specific implementation method 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. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for dynamic planning of flight paths of unmanned aerial vehicles in fusion airspace, characterized in that: include: S1, generate a spatial grid matrix diagram; S2, obtaining unmanned aerial vehicle flight plan data, and using the unmanned aerial vehicle flight plan data and the airspace grid matrix diagram to generate an initial path, wherein the initial path avoids restricted areas; S3, reconstructing the path according to the preset reconstruction rules; S4. Using the reconstructed path and sector structure data, establish a candidate set of entry and exit points for the unmanned aerial vehicle to cross the sector boundary; S5. Filtering the entry and exit points that meet the constraint conditions in the candidate set, wherein the constraint conditions include: the intersection angle constraint between the route of the unmanned aerial vehicle and the sector boundary, the position constraint between the route of the unmanned aerial vehicle and the restricted area, and the fusion operation risk constraint; S6. Use the screened entry and exit points as decision variables to plan the flight routes of unmanned aerial vehicles in the airspace.

2. The method according to claim 1, characterized in that In S1, it includes: Performing grid-based spatial discretization processing on the spatial structure data containing the restricted area information, wherein the spatial structure data containing the restricted area information includes: sector structure data and restricted area structure data; Then, the state of the airspace grid is identified according to whether the airspace is restricted, and a spatial grid matrix diagram is generated.

3. The method according to claim 2, characterized in that The grid-based spatial domain discretization process includes: The airspace is divided into square grids with longitude as the X-axis and latitude as the Y-axis. The grid coordinates are used as coordinate identifiers to indicate the grid arrangement position. V(X,Y) The discretization of is: , the actual coordinates of the origin are , Respectively V(X,Y) The actual coordinates of the center point, lower left point, upper left point, lower right point and upper right point, L is the grid side length.

4. The method according to claim 1, characterized in that: The state identification of the airspace grid according to whether the airspace is restricted and the generation of the airspace grid matrix diagram include: The grids are divided into two status types according to whether the airspace is restricted or not, including available grids and unavailable grids; The grids occupied by the restricted area are determined as restricted grids and are marked with "1" in the matrix diagram; The grids occupied by the protection area added at the outermost edge of the restricted area are protection grids, which are marked by "2" in the matrix diagram. The protection grids include: one or more layers of grids directly adjacent to the outermost restriction grid of the restricted area. When the grid size is greater than or equal to the safety interval between the aircraft and the restricted area, a layer of protection grid is set; The grids within the airspace where aircraft are allowed to pass are available grids and are marked with "0" in the matrix diagram.

5. The method according to claim 1, characterized in that The unmanned aerial vehicle flight plan data includes: the center point of the grid where the unmanned aerial vehicle plans to enter the airspace And the center point of the grid where the airspace point is planned ; In S2, an initial path is generated by a path planning model, wherein the path planning model includes: , where N represents the arrive The number of jump points, Indicates that unmanned aerial vehicles To current location The actual distance cost, Indicates that unmanned aerial vehicles arrive The estimated cost of the remaining path, x i Indicates from v I arrive v C The horizontal coordinate of the center point of the jump point, y i Indicates from v I arrive v C The vertical coordinate of the center point of the jump point, x C express v C The horizontal axis of y C express v C The vertical coordinate of x G express v G The horizontal axis of y G express v G The vertical coordinate of Represents a collection of unavailable grid center points.

6. The method according to claim 1, characterized in that The reconstruction rules include: no intersection with unavailable grids, reduced or unchanged distance cost, and reduced or unchanged number of turns.

7. The method according to claim 6, characterized in that In S3, this includes: For the initial path set, from the first point Start traversing and detect one by one Whether the reconstruction rules are met until If the reconstruction rule is not satisfied, then are removed as redundant points, j is a positive integer; then Start traversal until the third to last point in the initial path set is reached, then terminate the traversal and use the current path set as the reconstructed path set.

8. The method according to claim 1, characterized in that In S5, the intersection angle constraint between the route of the unmanned aerial vehicle and the sector boundary includes: ,in, θ is the intersection angle between the route and the sector boundary. The intersection vector angle when the αth segment intersects with the βth sector boundary is θ αβ , θ min is the minimum constraint value of the intersection angle; the position constraint of the route of the unmanned aerial vehicle and the restricted area includes: , among which, ε The border is , and The vertices of two consecutive restricted areas are numbered in counterclockwise order. Indicates route b and restricted area ε The intersection of the boundaries, Indicates intersection or tangency, Indicates no intersection, G b represents the number of intersections between route b and the boundary of the restricted area, and ρ represents the total number of vertices in the restricted area; The fusion operation risk constraints include: ,in, For sectors η The sector-wide comprehensive level conflict risk, is the sector comprehensive level conflict risk threshold, The vertical conflict risk of the sector is summarized as follows: is the sector comprehensive vertical conflict risk threshold, For sectors η Middle intersection n The complexity of For sectors η Mid-level h m The intersection of n The risk of horizontal conflict exists at For sectors η Zhonghang Road i The complexity of For sectors η The number is i Aircraft on route pass through altitude levels h m The risk of vertical conflict caused by 9. The method according to claim 8, characterized in that The constraint conditions also include: the sector entry and exit point interval constraint is , Indicates the minimum interval between sector entry and exit points. Indicates the entry and exit points σ and the entry and exit points interval.

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