A Dynamic Planning Method for the Flight Route of an Integrated Airspace Unmanned Aerial Vehicle

Abstract

An embodiment of the present invention discloses a method for dynamically planning the flight route of an unmanned aerial vehicle in a fusion airspace, which relates to the technical field of airspace analysis. By using the airspace structure data to generate an airspace grid matrix diagram; then obtaining the pre-feasible route planning result of the unmanned aerial vehicle; and then screening out the alternative set of entry and exit points that meet the constraint conditions as decision variables, constructing a dynamic planning model for the flight route of the unmanned aerial vehicle in the fusion airspace, and automatically analyzing to obtain the airspace entry and exit points in the dynamic planning of the flight route of the unmanned aerial vehicle during fusion operation, so that on the basis of the airspace entry and exit points, it is possible to further provide real-time route planning for the unmanned aerial vehicle, ultimately improving the automation degree in the process of flight route planning of the unmanned aerial vehicle during fusion operation, reducing the time and energy occupied by manual planning, and solving the problem of low efficiency in manual planning.

Description

Technical Field

[0001] The present invention relates to the technical field of airspace analysis for manned and unmanned aircraft, and particularly to a dynamic route planning method for unmanned aerial vehicles flying in a merged airspace. Background Art

[0002] The rapid development and continuous expansion of the application fields of unmanned aerial vehicles have brought new challenges to airspace management. At present, aviation management agencies worldwide generally maintain a prudent attitude towards the operation management of unmanned aerial vehicles, restricting them to specific airspaces and operating separately from manned aircraft. However, in the face of limited airspace resources, the separate operating mode obviously cannot meet the growing application requirements of unmanned aerial vehicles. Therefore, the concept of integrated operation of manned and unmanned aerial vehicles has emerged. The International Civil Aviation Organization has also listed the technology of integrated operation of manned and unmanned aerial vehicles as a next-generation air traffic management technology with priority development.

[0003] However, there are many problems and challenges in realizing the integrated operation of manned and unmanned aerial vehicles. One of the key issues is how unmanned aerial vehicles can safely and efficiently enter the controlled airspace and conduct route flights. In the future, when unmanned aerial vehicles leave the separated airspace and enter the merged airspace, it means that the flight tasks and aircraft types in the airspace will become heavy and complex, which puts forward higher operation requirements for the safe airspace environment. Different from the separated airspace, in the merged airspace, manned and unmanned aerial vehicles are intertwined, and there are differences in the performance and speed of different types of aircraft. Facing such a complex and diverse airspace environment, for unmanned aerial vehicles performing flight tasks in the merged airspace, they will face the risk of flight conflicts with manned aircraft.

[0004] Therefore, in order to avoid flight conflicts between unmanned aerial vehicles and manned aircraft, starting from the perspective of airspace design, researching the flight route planning method for unmanned aerial vehicles in the merged airspace has become a research topic urgently needed in the industry. And there is a specific R & D requirement that needs to be solved in a timely manner, that is, how to automatically analyze and obtain the airspace entry and exit points in the dynamic route planning of unmanned aerial vehicles during integrated operation, so as to improve the automation degree in the flight route planning process of unmanned aerial vehicles during integrated operation, and reduce the time and effort 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 dynamically planning the flight route of an unmanned aerial vehicle in a fusion airspace, which can automatically analyze and obtain the airspace entry and exit points in the dynamic planning of the flight route of the unmanned aerial vehicle during fusion operation. Based on the airspace entry and exit points, it can further provide real-time route planning for the unmanned aerial vehicle, ultimately improving the automation level in the process of flight route planning of the unmanned aerial vehicle during fusion 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:

[0007] A method for dynamically planning the flight route of an unmanned aerial vehicle in a fusion airspace, comprising:

[0008] S1. Generate an airspace grid matrix map;

[0009] S2. Obtain the flight plan data of the unmanned aerial vehicle, and use the flight plan data of the unmanned aerial vehicle and the airspace grid matrix map to generate an initial path, and the initial path avoids restricted areas;

[0010] S3. Perform path reconstruction according to a preset reconstruction rule;

[0011] S4. Use the reconstructed path and sector structure data to establish an alternative set of entry and exit points for the unmanned aerial vehicle to cross the sector boundary;

[0012] S5. Screen the entry and exit points that meet the constraint conditions in the alternative set, and the constraint conditions include: the intersection angle constraint between the flight route of the unmanned aerial vehicle and the sector boundary, the position constraint between the flight route of the unmanned aerial vehicle and the restricted area, and the fusion operation risk constraint;

[0013] S6. Use the screened entry and exit points as decision variables to plan the flight route of the unmanned aerial vehicle in the airspace.

[0014] In S1, it includes: performing grid-based airspace discretization processing on the airspace structure data including restricted area information, where the airspace structure data including restricted area information includes: sector structure data and restricted area structure data; for example: the sector structure data includes: sector code, upper limit of sector height, lower limit of sector height, longitude and latitude of sector boundary points; the restricted area structure data includes: restricted area code, upper limit of restricted area height, lower limit of restricted area height, longitude and latitude of restricted area boundary points, restricted time. Then, identify the status of the airspace grid according to whether the airspace is restricted to generate an airspace grid matrix map.

[0015] The grid-based airspace discretization processing includes:

[0016] Taking longitude as the X-axis and latitude as the Y-axis, the airspace is divided into square grids. Among them, the grid coordinates serve as the coordinate identifiers indicating the arrangement positions of the grids. The grid V(X,Y) is discretely represented as:

[0017] , the actual coordinates of the coordinate origin are , respectively represent V(X,Y) the actual coordinates of the center point, lower left point, upper left point, lower right point, and upper right point of L is the side length of the grid.

[0018] The state recognition of the airspace grid is performed according to whether the airspace is restricted, and an airspace grid matrix diagram is generated, including: dividing the grids 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 as restricted grids and are marked with "1" in the matrix diagram; the grids occupied by the protection area added to the outermost periphery of the restricted area are protection grids and are marked with "2" in the matrix diagram. The protection grids include: one or more layers of grids directly adjacent to the outermost restricted grids of the restricted area. Among them, when the grid size is greater than or equal to the safety interval between the aircraft and the restricted area, one layer of protection grid is set; the grids in the airspace allowing the aircraft to pass through are available grids and are marked with "0" in the matrix diagram.

[0019] The unmanned aircraft flight plan data includes: the center point of the grid where the planned entry airspace point of the unmanned aircraft is located and the center point

[0020] of the grid where the planned exit airspace point is located; , where N represents the number of hop points from to , represents the actual distance cost of the unmanned aircraft from to the current position , represents the estimated cost of the remaining path of the unmanned aircraft from to , x i represents from v I to v C the abscissa of the center point of the hop points passed through, y i represents from v I to v CThe ordinate of the center point passing through the jump point x C represents v C the abscissa of y C represents v C the ordinate of x G represents v G the abscissa of y G represents v G the ordinate of represents the set of unavailable grid center points.

[0021] The reconstruction rules include: not intersecting with unavailable grids, the distance cost decreasing or remaining unchanged, and the number of turns decreasing or remaining unchanged. In S3, it includes:

[0022] For the initial path set, starting from the first point traverse and sequentially detect whether it satisfies the reconstruction rules until it does not satisfy the reconstruction rules, then the points are all removed as redundant points, where j is a positive integer; then starting from traverse until reaching the penultimate point in the initial path set and terminate the traversal, and use the current path set as the reconstructed path set.

[0023] In S5, the intersection angle constraint between the route of the unmanned aerial vehicle and the sector boundary includes: , where θ is the intersection angle between the route and the sector boundary, and the intersection vector angle when the α-th flight segment intersects the β-th sector boundary is θ αβ , θ min is the minimum constraint value of the intersection angle; the position constraint between the route of the unmanned aerial vehicle and the restricted area includes: , where the ε th boundary is , and are the vertex sequences of two consecutive restricted areas numbered in counterclockwise order, represents the intersection situation between route b and the ε th boundary of the restricted area, represents intersection or tangency, represents non-intersection, G bindicates the number of intersection points between airway b and the boundary of the restricted area, and ρ represents the total number of vertices of the restricted area;

[0024] The fusion operation risk constraint includes: , where is the comprehensive horizontal conflict risk of sector η , is the threshold of the comprehensive horizontal conflict risk of the sector, is the comprehensive vertical conflict risk of the sector, is the threshold of the comprehensive vertical conflict risk of the sector, is the complexity of the intersection point η in sector n , is the horizontal conflict risk existing at the intersection point η of the altitude layer h m in sector n . The constraint condition also includes: the interval constraint of the sector entry and exit points is , η where i represents the minimum interval of the sector entry and exit points, is the interval between the entry and exit point σ and the entry and exit point η numbered i on the airway in sector h m when the aircraft crosses the altitude layer . represents the minimum interval of the sector entry and exit points, represents the interval between the entry and exit point σ and the entry and exit point .

[0025] The method for dynamically planning the flight route of an unmanned aerial vehicle (UAV) in a fusion airspace provided by an embodiment of the present invention 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 map; uses the UAV flight plan data and the airspace grid matrix map to obtain the pre-feasible route planning result of the UAV, where the pre-feasible route planning result of the UAV includes: a reconstructed path that satisfies the restricted area constraint and removes unnecessary turning points; uses the airspace structure data, the pre-feasible route planning result of the UAV during the target airspace research period, and the manned aircraft flight plan data to obtain an alternative set of entry and exit points for the UAV to cross the sector that satisfies the constraint conditions, and uses it as a decision variable. With the goal of minimizing the trajectory complexity and flight time of all UAVs in the target airspace, a dynamic planning model for the flight route of the UAV in the fusion airspace is constructed. By automatically analyzing, the entry and exit points of the airspace in the dynamic planning of the UAV flight route during the fusion operation are obtained, so that on the basis of the entry and exit points of the airspace, real-time route planning can be further provided for the UAV, ultimately improving the automation degree in the process of planning the flight route of the UAV during the fusion operation, reducing the time and energy occupied by manual planning, and solving the problem of low efficiency in manual planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0027] Figure 1 It is a schematic flowchart of the method provided by an embodiment of the present invention;

[0028] Figure 2 It is a diagram of the main position points of the grid provided by an embodiment of the present invention;

[0029] Figure 3 It is an airspace grid map provided by an embodiment of the present invention;

[0030] Figure 4 It is an airspace grid matrix map provided by an embodiment of the present invention;

[0031] Figure 5 It is a path reconstruction demonstration diagram provided by an embodiment of the present invention;

[0032] Figure 6 It is an aircraft search space map provided by an embodiment of the present invention;

[0033] Figure 7Schematic diagram of the system architecture provided by the embodiments of the present invention. Detailed implementation manners

[0034] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners. The embodiments of the present invention will be described in detail hereinafter. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described by referring to the accompanying drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention. Those skilled in the art of the present technology can understand that unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described 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 herein may include wireless connection or coupling. The phrase "and / or" used herein includes any unit and all combinations of one or more of the associated listed items. Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless defined as herein.

[0035] An embodiment of the present invention provides a method and device for dynamically planning the flight route of an unmanned aerial vehicle (UAV) applied to the integrated airspace, providing theoretical and technical support for the integration of UAVs into China's airspace system. To achieve the above design objective, the embodiment of the present invention adopts the following design idea: obtaining airspace structure data and aircraft operation data, where the airspace structure data includes: sector structure data, restricted area structure data, and flight route data of manned aircraft, and the aircraft operation data includes: flight plan data of manned aircraft and flight plan data of UAVs; using the airspace structure data, performing grid-based airspace discretization processing, identifying the status of airspace grids according to whether the airspace is restricted, and generating an airspace grid matrix diagram; using the flight plan data of UAVs and the airspace grid matrix diagram, obtaining the preliminary feasible route planning result of UAVs, where the preliminary feasible route planning result of UAVs includes: a reconstructed path that satisfies the restricted area constraint and removes unnecessary turning points; using the airspace structure data, the preliminary feasible route planning result of UAVs within the target airspace research period, and the flight plan data of manned aircraft, obtaining an alternative set of entry and exit points for UAVs to cross sectors that meet the constraint conditions, where the constraint conditions include: sector entry and exit point interval constraint, intersection angle constraint between the route and the sector boundary, position constraint between the route and the restricted area, and integrated operation risk constraint; using the alternative set of entry and exit points that meet the constraint conditions as decision variables, aiming at the lowest trajectory complexity and shortest flight time of all UAVs in the target airspace, constructing a dynamic planning model for the flight route of UAVs in the integrated airspace, and using a multi-objective optimization algorithm to solve it to obtain a dynamic planning scheme for the flight route of UAVs during integrated operation.

[0036] Specifically, the embodiment of the present invention provides a method for dynamically planning the flight route of UAVs in the integrated airspace, as Figure 1 shown, which is mainly carried out in two parts: I. The preliminary feasible route planning part of UAVs, which mainly faces the "individual" of UAVs. Since each UAV has its own independent flight task, first, a global initial feasible route planning needs to be carried out. Starting from the perspectives of safety, efficiency, and economy, at this time, the flight conflict problem of integrated operation is not considered, and mainly the conflict problem between UAVs and the static airspace environment (such as restricted areas) is considered, and obstacle avoidance planning is carried out for UAVs to generate the preliminary feasible route of UAVs; II. The dynamic planning part of the flight route of UAVs in the integrated airspace, which, aiming at the pre-generated feasible route of UAVs, starts from the orderliness and operation safety of the integrated airspace traffic flow, and further carries out the dynamic planning of the flight route of UAVs in the integrated airspace according to the time-varying traffic demand.

[0037] For ease of understanding, the method process can be divided into steps S1 - S7:

[0038] S1. Using airspace structure data containing restricted area information, perform grid - based airspace discretization processing. According to whether the airspace is restricted, identify the status of airspace grids and generate an airspace grid matrix diagram.

[0039] 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, upper limit of sector altitude, lower limit of sector altitude, longitude and latitude of sector boundary points; the restricted area structure data includes: restricted area code, upper limit of restricted area altitude, lower limit of restricted area altitude, longitude and latitude of restricted area boundary points, restricted time.

[0040] S2. Based on the airspace grid matrix diagram and the flight plan data of the unmanned aerial vehicle, aiming at the shortest path, obtain an initial path to avoid the restricted area through the Jump Point Search (JPS) algorithm.

[0041] Among them, the flight plan data of the unmanned aerial vehicle includes: flight number, registration number, aircraft type, planned departure airport, planned landing airport, planned airspace entry time, planned airspace entry point (longitude, latitude, altitude), planned entry speed, and planned airspace exit point (longitude, latitude, altitude).

[0042] S3. For the problem of additional unnecessary turning points in the obtained initial path, reconstruct the path by removing unnecessary turning points to obtain a pre - feasible route for the unmanned aerial vehicle with fewer turning times and a shorter path.

[0043] S4. Based on the pre - feasible route planning result of the unmanned aerial vehicle during the research period of the target airspace, retain its starting position, target position, turning points, and the flight time of each flight segment.

[0044] S5. Using the pre - feasible route planning result of the unmanned aerial vehicle during the research period of the target airspace and the sector structure data, calculate and obtain the sector boundaries crossed by the unmanned aerial vehicle according to geometric principles, and discretize the boundaries at intervals of the minimum interval value of sector entry and exit points to obtain an alternative set of entry and exit points that meet the sector entry and exit point interval constraints.

[0045] S6. Connect the starting position, target position, turning points, and alternative entry and exit points on the sector boundary traversed that meet the sector entry and exit point interval constraint in sequence (i.e., form an airway). Combining the airspace structure data containing restricted area information, manned aircraft flight plan data, and manned aircraft airway and route data, calculate and screen out the entry and exit points that meet the crossing angle constraint between the airway and the sector boundary, the position constraint between the airway and the restricted area, and the fusion operation risk constraint, and obtain the alternative set of entry and exit points that meet the above constraint conditions as the decision variable.

[0046] Among them, the manned aircraft flight plan data includes: flight number, registration number, aircraft type, planned departure airport, planned landing airport, planned sector entry code, planned sector entry time, planned sector entry point (longitude, latitude, altitude), and planned entry speed; the manned aircraft airway and route data includes: airway name, airway type, longitude and latitude of airway points.

[0047] S7. Taking the alternative set of entry and exit points that meet the constraint conditions as the decision variable, aiming at the lowest trajectory complexity and shortest flight time of all unmanned aircraft in the target airspace, construct a dynamic programming model for the flight route of unmanned aircraft in the integrated airspace, and use a multi-objective optimization algorithm, such as the Non-dominated Sorting Genetic Algorithm II (NSGA II) with elitist retention strategy, to solve it and obtain the flight route planning scheme of unmanned aircraft during the fusion operation.

[0048] Specifically, the pre-feasible route planning of the unmanned aircraft includes:

[0049] 1.1. Grid-based airspace discretization: Taking longitude as the X-axis and latitude as the Y-axis, divide the airspace into several square grids. Each grid has its own coordinate identifier. Among them, the coordinate identifier used to indicate the grid arrangement position is defined as "grid coordinate", such as V(X,Y) indicating the grid with grid coordinate (X,Y) ; the coordinate used to indicate the actual position of the position point is defined as "actual coordinate", such as indicating the actual coordinate of the center point of V(X,Y) . Each grid has five main position points, namely the center point, lower left point, upper left point, lower right point, and upper right point, as shown in Figure 2 . Assuming the side length of the grid is L , the actual coordinate of the coordinate origin is , then the actual coordinates of the five main position points of the grid V(X,Y) are:

[0050] .

[0051] 1.2. Identification of airspace grid status: According to whether the airspace is restricted, two types of grids are defined, namely available grids and unavailable grids. Among them, the unavailable grids specifically include restricted grids and protected grids. The restricted grid refers to the grid occupied by the restricted area. To ensure the safe operation of the aircraft, as long as there is a restricted airspace within the grid, it is determined as a restricted grid, and is marked with "1" in the matrix diagram. The protected grid refers to the grid occupied by the protection area added to the outermost periphery of the restricted area, generally one or more layers of grids directly adjacent to the outermost restricted grid. When the grid size is greater than or equal to the safety interval between the aircraft and the restricted area, one layer of protected grid is set; otherwise, multiple layers of grids need to be set, and the number of layers is the ceiling of the ratio of the safety interval to the grid size, and is marked with "2" in the matrix diagram. The available grid refers to the grid in the airspace that allows the aircraft to pass through, that is, the grid in the airspace other than the restricted grid and the protected grid is determined as an available grid, and is marked with "0" in the matrix diagram. The airspace grid map obtained through discretization is as Figure 3 shown, and the corresponding airspace grid matrix map obtained through status identification is as Figure 4 shown.

[0052] 1.3. Initial path planning for avoiding restricted areas: Based on the airspace grid matrix map, the JPS algorithm is used to obtain the shortest initial path for avoiding restricted areas. Let and be the center points of the grids where the planned entry airspace point of the unmanned aircraft is located (referred to as the initial position) and the center point of the grid where the planned exit airspace point is located (referred to as the target position) respectively, then the model is as follows: , N is the number of jump points calculated by the algorithm from the initial position to the current position ; is the actual distance cost of the unmanned aircraft from the initial position to the current position ; is the estimated cost of the remaining path of the unmanned aircraft from the current position to the target position ; is the set of center points of unavailable grids; Equation (5) indicates that the jump point position should not be within the restricted area and the protected area.

[0053] 1.4. Path reconstruction: Let the initial path for avoiding restricted areas obtained by the JPS algorithm be 。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, restricting 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.

[0054] The basic principles of path reconstruction are: not intersecting with unavailable grids, cost reduction or unchanged, and the number of turns reduced or unchanged. Therefore, the reconstruction method of the initial path Ω is: for the initial path set, starting from the first point to traverse, check whether it meets the above three basic principles, where not intersecting with unavailable grids is the primary condition, and the cost can be evaluated according to Equation (2), until it does not meet the basic principles, then the points are all redundant points and can be directly removed; then starting from to traverse until the third-to-last point in the path set is traversed, terminate the traversal, and output the reconstructed path, where the initial position and the target position must be retained.

[0055] Specifically, for example: as Figure 5 shown, it is a path reconstruction demonstration diagram, where the initial position of the aircraft is S and the target position is E , and the initial path planned by the JPS algorithm is { S, A, B, C, D, E}, starting from the initial position S to traverse and check SA, SB, SC, SD, SE whether it meets the above three basic principles, where SA, SB meets, while SC does not meet, then A is a redundant point and can be directly removed; then starting from the B point to traverse and check BC, BD, BE , where BC meets, BD does not meet, then there are no redundant points and no processing is done; finally starting from the C point to traverse and check CD , CE , where CD meets, CE does not meet, then there are no redundant points and no processing is done, and the output reconstructed path is { S, B, C, D, E}.

[0056] Specifically, the dynamic route planning of the unmanned aerial vehicle in the integrated airspace includes:

[0057] 2.1 Establish an airspace trajectory complexity model based on a linear dynamic system: Considering that the flight route planning of unmanned aircraft needs to reduce the complexity of airspace operations, this paper models based on a linear dynamic system and uses the airspace trajectory complexity Φ to evaluate the integrated airspace complexity to measure the orderliness of traffic flow under the collaborative planning of multi-sector entry and exit points.

[0058] To evaluate the complexity of a set of four-dimensional flight tracks in the airspace, this paper uses a linear dynamic system model to model the four-dimensional flight track traffic structure at each time point. The general equation of the linear dynamic system is as shown in Equation (6):

[0059] 。

[0060] Among them, is the state vector of the system; represents the static state of the system; the matrix A represents the linear relationship between the state vector and the velocity vector , and the eigenvalues of the matrix A reflect the evolution process of the system. Therefore, these eigenvalues are used as indicators to measure the complexity of traffic conditions in the airspace.

[0061] For the aircraft i at the time point k surrounding traffic structure, a linear dynamic system is modeled. As Figure 6 shown, let the sampling point of the aircraft i be P i,k , then a cylinder centered at P i,k with a radius of 25 nautical miles and a height of 2,000 feet is set as the search space to obtain the observation vectors of the adjacent aircraft of the aircraft i . The observation vectors of the adjacent aircraft i of the aircraft j include the position vector and velocity vector of the sampling point P j,k . In the three-dimensional airspace, these two observation vectors are expressed as: , Among them, x j 、y j 、z j are the coordinates of the aircraft j in the x, y, z axis directions respectively; vx j , vy j , vz j is the component of the velocity vector in the x, y, z axis direction.

[0062] To determine the linear dynamic system model that best fits the observed vectors of the reference aircraft and adjacent aircraft, it is necessary to find the model parameters that minimize the error between the dynamic system and the observed values. This minimization problem can be expressed as Equation (9), and the model parameters A and .

[0063] , where, M is the number of aircraft that can be identified in the search space.

[0064] After obtaining the linear dynamic system model, the complex eigenvalues of matrix A can be calculated. The set of complex eigenvalues of the sampling points P i,k is as shown in Equation (10): , and the complexity index P i,k of the sampling points is related to the negative real part of the complex eigenvalues, and the expression is as shown in Equation (11).

[0065] , calculate the complexity index of all sampling points of the entire track and sum them to obtain the track complexity i of the aircraft , and the expression is as shown in Equation (12): , where, N i is the number of sampling points of the aircraft i .

[0066] The airspace track complexity Φ is composed of superimposed, then the expression of the airspace track complexity is: , where, I is the number of aircraft in the airspace during the research period.

[0067] 2.2. Construct a dynamic programming model for the flight route of unmanned aircraft in the integrated airspace:

[0068] 1) Decision variables: Let the set of unmanned aircraft flights entering the target airspace during the research period be , R be the total number of unmanned aircraft, and for each flight u ( r ) the entry and exit points of passing through the sector are decision variables.

[0069] 2) Objective function: To ensure the orderliness, safety, and high efficiency of the air traffic flow in the integrated airspace, the dynamic route planning in this paper takes into account the trajectory complexity and flight time of unmanned aerial vehicles (UAVs) in the airspace. That is, when planning the flight routes of UAVs, the objective function is to minimize the trajectory complexity and flight time of all UAVs in the airspace, denoted as: , where L r is the flight distance calculated based on the connecting line of the entry and exit points of the sectors passed through by the UAV u ( r ). V r The planned entry speed of the UAV u ( r ).

[0070] 3) Constraint conditions: The dynamic route planning of UAVs in the integrated airspace needs to consider the influence of factors such as route structure, restricted areas, and the risk of conflicts between manned and unmanned aircraft operations to ensure the flight safety of aircraft. In this paper, the route planning needs to meet the following constraints: (1) Cross-angle constraint between the route and the sector boundary: When the aircraft flies along the route, it may deviate 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 cross-vector angle when the α-th flight segment intersects the β-th sector boundary be θ αβ , θ min is the minimum constraint value of the cross angle, then the cross angle θ between the route and the sector boundary needs to satisfy: , (2) Location constraint between the route and the restricted area: Let the vertex sequence of the restricted area be arranged in counterclockwise order as , where , the ε -th boundary is , and the intersection situation between the route b and the ε -th boundary of the restricted area is represented by , represents intersection or tangency, represents non-intersection.

[0071] Define as the number of intersection points between the route b and the boundary of the restricted area, then .

[0072] (3) Sector entry and exit point interval constraint: When the distance between the entry and exit points on the boundary of the same sector is too small, it will not only cause inconvenience to air traffic control work, but there is also a potential possibility of conflicts between aircraft. Therefore, it is necessary to set the sector entry and exit point interval constraint. In this paper, the minimum interval of the sector entry and exit points is set to , then the interval between the entry and exit points and the entry and exit point should satisfy the following formula: . .

[0073] (4) Converged operation risk constraint: The premise of the integrated operation of manned and unmanned aircraft is that the unmanned aircraft should reach the same safety level as the manned aircraft, that is, the unmanned aircraft should not reduce the operation safety level when it joins the controlled airspace, which means that the operation risk during the integrated operation cannot exceed the historical risk threshold. Therefore, in addition to the aforementioned 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, the dynamic route planning of unmanned aircraft in the integrated 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 passes through 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 products of the complexity of all intersection points in the target sector and the horizontal conflict risk during the statistical period; the sector comprehensive vertical conflict risk is the sum of the products of the complexity of all routes in the target sector and the vertical conflict risk during the statistical period.

[0074] Let the sector comprehensive horizontal conflict risk of the sector η passed through be , the sector comprehensive horizontal conflict risk threshold be , the sector comprehensive vertical conflict risk be , the sector comprehensive vertical conflict risk threshold be , then it should satisfy: , where is the complexity of the intersection point η in the sector n ; is the horizontal conflict risk existing at the intersection point η of the altitude layer h m in the sector n ; is the complexity of the route η in the sector i ; is the complexity of the route η in the sector i where the aircraft in the sector passes through the altitude layerh m The risk of vertical conflict caused when

[0075] 4) Solution process: According to the set constraint conditions, an alternative set of access points available for the flight route of the unmanned aerial vehicle can be obtained. The specific steps are as follows:

[0076] (1) Based on the pre-feasible route planning results of the unmanned aerial vehicle during the research period of the target airspace, retain its starting position, target position, turning points, and the flight time of each flight segment;

[0077] (2) Using the pre-feasible route planning results of the unmanned aerial vehicle during the specified time period of the target airspace and the sector structure data, calculate and obtain the sector boundaries crossed by the pre-feasible route of the unmanned aerial vehicle according to geometric principles, and obtain an alternative set of access points that meet the access point interval constraints of the sector by discretizing the sector boundaries at intervals;

[0078] (3) Connect the starting position, target position, turning points, and alternative access points that meet the access point interval constraints on the crossed sector boundaries in sequence. Combining the airspace structure data containing restricted area information, manned aircraft flight plan data, and manned aircraft route data, calculate and screen out the access 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, and obtain an alternative set of access points that meet the above constraint conditions.

[0079] The dynamic route planning problem of unmanned aerial vehicles in the fusion airspace is a multi-objective optimization problem. After obtaining the alternative set of access points available for the unmanned aerial vehicle during the specified time period of the target airspace, use it as a decision variable and use a multi-objective optimization algorithm (such as the NSGA II algorithm) to solve the problem and obtain the dynamic route planning scheme of the unmanned aerial vehicle during the fusion operation.

[0080] In summary, the design concept of this embodiment mainly lies in performing grid-based airspace discretization processing by using airspace structure data containing restricted area information, identifying the status of airspace grids according to whether the airspace is restricted, and generating an airspace grid matrix map. Among them, the airspace structure data containing restricted area information includes: sector structure data and restricted area structure data; specifically, by using the airspace grid matrix map and the unmanned aircraft flight plan data, the pre-feasible route planning result of the unmanned aircraft is obtained. The pre-feasible route planning result of the unmanned aircraft includes: a reconstructed path that satisfies the restricted area constraint and removes unnecessary turning points; among them, the process of using the airspace grid matrix map and the unmanned aircraft flight plan data to obtain the pre-feasible route planning result of the unmanned aircraft includes: according to the airspace grid matrix map and the unmanned aircraft flight plan data, aiming at the shortest path, obtaining an initial path to avoid the restricted area through the JumpPoint Search (JPS); reconstructing the initial path to avoid the restricted area by removing unnecessary turning points to obtain the pre-feasible route planning result of the unmanned aircraft.

[0081] Then, by using the pre-feasible route planning result of the unmanned aircraft during the research period of the target airspace, the sector structure data, the restricted area structure data, the manned aircraft flight plan data, and the manned aircraft route data, an alternative set of entry and exit points for the unmanned aircraft to cross the sector that meets the constraint conditions is obtained; among them, the process of using the pre-feasible route planning result of the unmanned aircraft during the research period of the target airspace, the sector structure data, the restricted area structure data, the manned aircraft flight plan data, and the manned aircraft route data to obtain an alternative set of entry and exit points for the unmanned aircraft to cross the sector that meets the constraint conditions includes: based on the pre-feasible route planning result of the unmanned aircraft during the research period of the target airspace, retaining its starting position, target position, turning points, and the flight time of each flight segment.

[0082] After that, by using the pre-feasible route planning result of the unmanned aircraft during the research period of the target airspace and the sector structure data, the sector boundary crossed by the unmanned aircraft is calculated according to geometric principles, and the boundary is discretized at intervals of the minimum interval value of the sector entry and exit points to obtain an alternative set of entry and exit points that meets the sector entry and exit point interval constraint; connecting the starting position, target position, turning points, and the alternative entry and exit points that meet the sector entry and exit point interval constraint on the crossed sector boundary in sequence, and combining the sector structure data, the restricted area structure data, the manned aircraft flight plan data, and the manned aircraft route data, calculate and screen out 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 to obtain an alternative set of entry and exit points that meets the constraint conditions.

[0083] Finally, using the alternative set of entry and exit points that meet the constraint conditions as decision variables, a dynamic programming model for the flight route of unmanned aerial vehicles (UAVs) in the integrated airspace is constructed to obtain the flight route planning scheme of UAVs during integrated operations. Among them, the construction of the dynamic programming model for the flight route of UAVs in the integrated airspace includes: using the entry and exit points of UAVs passing through sectors that meet the sector entry and exit point interval constraints, the intersection angle constraints between the flight route and the sector boundary, the position constraints between the flight route and the restricted area, and the integrated operation risk constraints as decision variables, and taking the lowest trajectory complexity and the shortest flight time of all UAVs in the target airspace as the objectives, constructing a dynamic programming model for the flight route of UAVs in the integrated airspace.

[0084] Based on the above design idea, in practical applications, this embodiment can implement the above ideas and method processes by means of existing computer and server hardware devices. Therefore, this embodiment also provides a device for implementing the above method for dynamic programming of the flight route of UAVs in the integrated airspace, including:

[0085] The pre-feasible route planning module for UAVs is used to perform grid-based discretization processing of the airspace using the airspace structure data to generate an airspace grid matrix diagram; and according to the airspace grid matrix diagram and the UAV flight plan data, obtain the pre-feasible route planning result of the UAV, and the pre-feasible route planning result of the UAV includes: a reconstructed path that meets the restricted area constraints and removes unnecessary turning points.

[0086] The dynamic programming module for the flight route of UAVs in the integrated airspace is used to use the airspace structure data, the pre-feasible route planning result of UAVs during the research period of the target airspace, and the manned aircraft flight plan data to obtain an alternative set of entry and exit points for UAVs passing through sectors that meet the constraint conditions; and using the alternative set of entry and exit points as decision variables, taking the lowest trajectory complexity and the shortest flight time of all UAVs in the target airspace as the objectives, constructing a dynamic programming model for the flight route of UAVs in the integrated airspace, and using a multi-objective optimization algorithm to solve it to obtain the dynamic programming scheme of the flight route of UAVs during integrated operations.

[0087] Specifically, the pre-feasible route planning module for UAVs includes:

[0088] The feasible route planning sub-module is used to perform grid-based discretization processing of the airspace using the airspace structure data containing restricted area information, identify the status of airspace grids according to whether the airspace is restricted, and generate an airspace grid matrix diagram; according to the airspace grid matrix diagram and the UAV flight plan data, with the shortest path as the objective, obtain the initial feasible route for avoiding the restricted area through the JPS algorithm.

[0089] A feasible route reconstruction sub-module, which is used to reconstruct the path of the initial feasible route for avoiding restricted areas 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 satisfies the restricted area constraints and removes unnecessary turning points.

[0090] Specifically, the flight route dynamic planning module for unmanned aerial vehicles in the integrated airspace includes:

[0091] An access point alternative set acquisition sub-module, which is used to utilize the airspace structure data, the preliminary feasible route planning result of the unmanned aerial vehicle during the research period of the target airspace, and the flight plan data of manned aircraft to obtain an alternative set of access points for the unmanned aerial vehicle to cross the sector that meets the constraint conditions.

[0092] A route dynamic planning sub-module, which uses the alternative set of access points for the unmanned aerial vehicle to cross the sector that meets the constraint conditions as decision variables, constructs a flight route dynamic planning model for unmanned aerial vehicles in the integrated airspace with the goal of minimizing the trajectory complexity and flight time of all unmanned aerial vehicles in the target airspace, and uses a multi-objective optimization algorithm to solve it to obtain a flight route dynamic planning scheme for unmanned aerial vehicles during integrated operation.

[0093] The device described in this embodiment can be specifically implemented in the current air traffic control system, and the corresponding functions are realized through each subsystem, such as Figure 7 shown, including:

[0094] An air traffic control surveillance subsystem, which is used to monitor in real time and obtain airspace structure data, flight plan data of manned aircraft, and flight plan data of unmanned aerial vehicles.

[0095] A preliminary feasible route planning subsystem for unmanned aerial vehicles, which is used to obtain airspace structure data and flight plan data of unmanned aerial vehicles from the air traffic control surveillance subsystem, and then generate an airspace grid matrix map, and obtain the preliminary feasible route planning result of the unmanned aerial vehicle through the JPS algorithm and path reconstruction.

[0096] A flight route dynamic planning subsystem for the integrated airspace, which is used to obtain airspace structure data and flight plan data of manned aircraft from the air traffic control surveillance subsystem, obtain the preliminary feasible route planning result of the unmanned aerial vehicle during the research period of the target airspace from the preliminary feasible route planning subsystem for unmanned aerial vehicles, calculate and screen out an alternative set of access points for the unmanned aerial vehicle to cross the sector that meets the constraint conditions, and use this as a decision variable to perform flight route dynamic planning for unmanned aerial vehicles in the integrated airspace based on a multi-objective optimization algorithm.

[0097] A display subsystem for displaying sector structures, restricted area structures, routes of manned aircraft, planned routes of unmanned aircraft, and real-time operation tracks of manned and unmanned aircraft on a two-dimensional plane.

[0098] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, they are described relatively simply. For the relevant parts, reference can be made to the partial description of the method embodiments. As mentioned above, the above are only specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to 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. Plan the flight path of the unmanned aerial vehicle in the airspace by using the entry and exit points obtained by screening as decision variables; 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 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 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 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 According to whether the airspace is restricted, the state of the airspace grid is identified, and the airspace grid matrix diagram is generated, including: 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 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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