Civil aviation passenger plane conflict-free route planning method based on state lattice

Abstract

The invention discloses a civil aviation passenger plane conflict-free route planning method based on a state lattice, and relates to the technical field of traffic management, and the method comprises the steps: selecting a target airspace; environment information and initial state parameters of the aircraft are collected; constructing an airspace grid map based on the target airspace, marking the no-fly zone, and setting a starting point and an ending point of the aircraft; initializing the cost value of the state lattice corresponding to the starting point; assigning the state parameters to the starting point; formulating a search strategy: based on the first state lattice, according to the orientation of the aircraft in the first state lattice, searching the state lattice which does not belong to the no-fly zone in nine state lattices in front of the aircraft, and recording the state lattice as a second state lattice; calculating and updating the state parameters and the cost value of the second state lattice according to the parameters of the first state lattice; in the airspace grid map, state lattices between a starting point and an ending point are traversed according to a search strategy in combination with an A * algorithm, and a second state lattice with the minimum cost value is continuously selected; and finally, finding an optimal path between the starting point and the ending point.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and particularly to a method for conflict - free flight path planning of civil airliners based on state lattices. Background Art

[0002] With the continuous growth of China's civil aviation transportation volume, the pressure on the use of civil airspace environment is also increasing day by day. Under the contradiction between the growing transportation demand and limited airspace resources, especially in the face of uncertain factors such as sudden dangerous weather and military affairs, some flights will be delayed, causing many originally scheduled airliners to change their flight plans and divert. Frequent flight path changes and flight delays not only affect the service quality of air transportation, but also bring economic losses and safety hazards to flight.

[0003] To improve the service quality of air transportation and minimize the economic losses and safety risks brought by flight diversion, researchers at home and abroad have proposed a series of aircraft path planning methods, taking factors such as reducing the comprehensive fuel consumption of aircraft and enhancing passengers' flight experience as the goals of path planning. In order to reduce unnecessary fuel economic consumption and ensure passengers' comfort, the constraints on the aviation motion state are very important during the path planning process.

[0004] The basic idea of path planning is to plan an optimal or approximately optimal path from the starting point to the target point in a given environment. There are many methods for aircraft path planning. According to different planning goals, various planning methods can provide flight path solutions for air traffic controllers, airspace planners, and pilots, assisting them to quickly respond to changes in traffic demand. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a method for conflict - free flight path planning of civil airliners based on state lattices, which can provide route planning for civil airliners in the face of multi - constraint scenarios to reduce unnecessary fuel economic consumption and ensure passengers' comfort.

[0006] The technical solution of the present invention is realized as follows:

[0007] A method for conflict - free flight path planning of civil airliners based on state lattices includes the following steps:

[0008] Step 1: Select the target airspace; collect the environmental information of the target airspace within a specified time period; the environmental information includes no - fly zones; collect the initial state parameters of the aircraft.

[0009] Step 2: Discretize and rasterize the target airspace to construct an airspace raster map; the airspace raster map includes multiple state cells; one state cell corresponds to one cost value; mark the no-fly zone on the airspace raster map; set the starting point and the ending point of the aircraft on the airspace raster map; initialize the cost value of the state cell corresponding to the starting point; assign the state parameters to the starting point.

[0010] Step 3: Develop a search strategy: The state cell where the aircraft is currently located is denoted as the first state cell; according to the orientation of the aircraft in the first state cell, search for the state cells that do not belong to the no-fly zone among the nine state cells directly in front of the aircraft, denoted as the second state cells; calculate and update the state parameters and cost values of the second state cells according to the state parameters and cost values of the first state cell.

[0011] Step 4: In the airspace raster map, for the state cells between the starting point and the ending point, perform a traversal process according to the search strategy combined with the A* algorithm, continuously select the second state cell with the minimum cost value until the optimal path between the starting point and the ending point is found.

[0012] The A* algorithm is a heuristic search algorithm used to find the shortest path in a graph or network. It combines the breadth-first search of the algorithm and the evaluation of the heuristic function to improve the search efficiency. During the search, based on the selected second state cell as the new first state cell, search for a new second state cell, and so on, to complete the traversal. The optimal path is the path that minimizes the cost value of the ending point.

[0013] Based on the rasterization of the airspace environment, the present invention discretizes the studied airspace environment into numerous state cells. By assigning the initial state of the aircraft to the starting grid and formulating nine search strategies based on overload (nine directions), a method of controlling spatial sampling is used to continuously search for the movement paths between state cells that meet the aircraft constraint conditions (no-fly zone obstacles, costs). Finally, the conflict-free flight path of a civil airliner with the lowest cost within the studied airspace range is found.

[0014] As a further optimization of the above solution, the no-fly zone includes a controlled no-fly zone.

[0015] A controlled no-fly zone refers to the airspace above a certain territory where any aircraft without special application permission is prohibited from flying into or overflying.

[0016] As a further optimization of the above solution, in Step 1, the track information of the target airspace is also collected; the track information is the passing coordinates of all aircraft other than the aircraft in the target airspace.

[0017] The no-fly zone includes the passing coordinates.

[0018] To avoid conflicts, the aircraft should avoid the trajectories of other aircraft during flight path planning.

[0019] As a further optimization of the above solution, in step one, a safety distance is also set; the no-fly zone includes an area with the passing coordinates as the reference and the safety distance as the coverage range.

[0020] As a further optimization of the above solution, in step two, the unit size of the state cell is set according to the size of the aircraft; the target airspace is discretized into grids based on the unit size.

[0021] As a further optimization of the above solution, the state parameters include the position coordinates (x, y, z), flight speed V, deflection angle γ, pitch angle χ, and overload value (n x ,n y ,n z ); the orientation is determined according to the deflection angle and the pitch angle.

[0022] Flight yaw angle: Describes the rotation of the aircraft in the horizontal plane, that is, the horizontal angle of the aircraft's nose relative to a certain reference direction (usually the north direction). For example, a yaw angle of 0° means the aircraft's nose points due north, and a yaw angle of 90° means the aircraft's nose points due east.

[0023] Flight pitch angle: Describes the rotation of the aircraft in the vertical plane, that is, the vertical angle of the aircraft's nose relative to the horizontal plane. For example, a pitch angle of 0° means the aircraft is flying horizontally, a positive pitch angle means the aircraft is pitching up, and a negative pitch angle means the aircraft is pitching down.

[0024] The horizontal direction of the aircraft is determined by the flight yaw angle, and the vertical direction is determined by the flight pitch angle. Through the flight yaw angle and the flight pitch angle, the orientation of the aircraft in three-dimensional space can be determined.

[0025] As a further optimization of the above solution, the cost value Cost includes movement cost BCost, speed change cost VCost, and overload cost OCost;

[0026] Calculate the change in cost value TCost from the first state cell to the second state cell;

[0027] The calculation of the change in cost value TCost is as follows:

[0028]

[0029] Among them, i corresponds to the first state cell, and i + 1 corresponds to the selected second state cell; the position coordinates of the first state cell and the second state cell correspond to (x i , y i , z i ) and (x i+1 , y i+1 , z i+1 ) respectively; V max and V min are the preset maximum flight speed and minimum flight speed respectively; VC is the preset speed change cost coefficient; OC is the preset overload cost coefficient;

[0030] If Cost i+1 = 0 or Cost i+1 > Cost i + TCost, then update the cost value of the second state cell, that is, Cost i+1 = Cost i + TCost.

[0031] Only when the cost value of the second state cell is updated, update the state parameters of the second state cell.

[0032] abs() means taking the absolute value; since the second state cell is one of the 9 state cells directly in front of the first state cell, the change in position coordinates is calculated based on the changes in the three dimensions of x, y, and z, with the minimum change being 1 and the maximum being 3, that is, the corresponding sum value is one of [1, 2, 3]; the second state cell corresponding to sum = 1 is directly in front of the first state cell; the second state cells corresponding to sum = 2 are in front of the upper, lower, left, and right of the first state cell; the second state cells corresponding to sum = 3 are in the front upper left, front lower left, front upper right, and front lower right of the first state cell; the movement cost BCost correspondingly includes that is, [1, 1.4142, 1.732];

[0033] The initial value of the second state cell is 0; if the second state cell has been searched, compare it with the cost value obtained in the previous search, and select the path with the lower cost value and its state parameters.

[0034] In this solution, calculation and update are two states. The calculation result is not necessarily updated to the second state cell, but whether to update is determined based on the calculation result and preset conditions.

[0035] Preferably, V max = 27.7 m / s, V min = 22.2 m / s.

[0036] As a further optimization of the above solution, the calculation and update of some state parameters of the second state cell are as follows:

[0037]

[0038] Among them, i corresponds to the first state cell, and i + 1 corresponds to the second state cell; Δt represents the time difference from the first state cell to the second state cell; g represents the acceleration due to gravity.

[0039] As a further optimization of the above solution, Δt is a preset simulation time step, that is, a fixed duration value obtained according to the actual flight time between two state cells, which can be adjusted according to the cruising speed of different civil airliners.

[0040] As a further optimization of the above solution, the calculation of the overload value of the second state cell is as follows:

[0041]

[0042] Among them, i corresponds to the first state cell, and i + 1 corresponds to the selected second state cell; random(·) represents selecting a random value within the target range.

[0043] n x The value range of is [-1, 2.5]; n y The value range of is [-1, 2]; n z The value range of is

[0044] [-1, 1.5]. By restricting the overload range of the aircraft, it is possible to avoid an increase in fuel consumption caused by excessive changes in the motion state of the aircraft in a short period of time, and at the same time, it can ensure the smoothness of the flight path, thus ensuring the comfort of passengers.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] (1) Based on the grid-based airspace environment, the studied airspace environment is discretized into numerous state cells. By assigning the initial state of the aircraft to the starting grid and formulating nine search strategies based on overload (nine directions), the method of controlling space sampling is used to continuously search for the motion paths that meet the aircraft constraint conditions between state cells, and finally find the conflict-free flight path of the civil airliner with the lowest cost within the studied airspace range.

[0047] (2) By restricting the overload range of the aircraft, it is possible to avoid an increase in fuel consumption caused by excessive changes in the motion state of the aircraft in a short period of time, and at the same time, it can ensure the smoothness of the flight path, thus ensuring the comfort of passengers. Brief Description of the Drawings

[0048] Figure 1It is a schematic flow chart of a conflict-free flight path planning method for civil airliners based on state lattices provided by an embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of the state lattice search effect provided by an embodiment of the present invention;

[0050] Figure 3 is Figure 2 a schematic diagram from another perspective. Detailed implementation manners

[0051] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0052] As Figures 1 to 3 shown, this embodiment provides a conflict-free flight path planning method for civil airliners based on state lattices, including the following steps:

[0053] Step 1: Select the target airspace; specifically, select the area between the specified adjacent waypoints of the target aircraft. Adjacent waypoints refer to two specific locations that an aircraft passes through in sequence during flight. The distance between adjacent waypoints is affected by various factors such as route planning, flight conditions, and navigation technology. Generally, the distance between adjacent waypoints may vary from dozens of kilometers to hundreds of kilometers. In this study, an adjacent waypoint within approximately 200 kilometers is selected, and the target airspace is constructed in sequence.

[0054] Set the safety distance; collect the environmental information of the target airspace within the specified time period; the environmental information includes no-fly zones; the environmental information includes the flight path information of the target airspace; the flight path information is the passing coordinates of all aircraft other than the target aircraft within the target airspace.

[0055] The no-fly zones include controlled no-fly zones, passing coordinates, and areas with the passing coordinates as the benchmark and the safety distance as the coverage range. A controlled no-fly zone refers to an airspace where the airspace above a certain territory prohibits any aircraft from flying into or over without special application permission. To avoid conflicts, an aircraft should avoid the trajectories of other aircraft during flight path planning.

[0056] Collect the initial state parameters of the aircraft; in this embodiment, the state parameters include the position coordinates (x, y, z) of the aircraft, flight speed V, deflection angle γ, pitch angle χ, and overload value (n x , n y , n z); Determine the orientation based on the deflection angle and pitch angle.

[0057] Flight yaw angle: Describes the rotation of the aircraft in the horizontal plane, that is, the horizontal angle of the aircraft's nose relative to a certain reference direction (usually the north direction). For example, a yaw angle of 0° means the aircraft's nose points due north, and a yaw angle of 90° means the aircraft's nose points due east.

[0058] Flight pitch angle: Describes the rotation of the aircraft in the vertical plane, that is, the vertical angle of the aircraft's nose relative to the horizontal plane. For example, a pitch angle of 0° means the aircraft is flying horizontally, a positive pitch angle means the aircraft is pitching up, and a negative pitch angle means the aircraft is pitching down.

[0059] The horizontal direction of the aircraft is determined by the flight yaw angle, and the vertical direction is determined by the flight pitch angle. Through the flight yaw angle and flight pitch angle, the orientation of the aircraft in three-dimensional space can be determined.

[0060] Step 2: Set the unit size of the status cell based on the size of the aircraft, such as 100 m × 100 m × 100 m; perform a discretized grid processing on the target airspace based on the unit size to construct an airspace grid map; the airspace grid map includes multiple status cells; one status cell corresponds to a cost value; mark the no-fly zone on the airspace grid map; set the starting point and ending point of the aircraft on the airspace grid map; initialize the cost value of the status cell corresponding to the starting point; assign the status parameters to the starting point.

[0061] Step 3: Develop a search strategy: The status cell where the aircraft is currently located is denoted as the first status cell; according to the orientation of the aircraft in the first status cell, search for the status cells that are not in the no-fly zone among the nine status cells directly in front of the aircraft, denoted as the second status cells; calculate and update the status parameters and cost values of the second status cells according to the status parameters and cost values of the first status cell.

[0062] In this embodiment, the calculation and update of some status parameters of the second status cell are as follows:

[0063]

[0064] Among them, i corresponds to the first status cell, and i + 1 corresponds to the second status cell; Δt represents the time difference from the first status cell to the second status cell; g represents the acceleration due to gravity.

[0065] In this embodiment, Δt is 0.1 s.

[0066] In this embodiment, the calculation of the overload value of the second status cell is as follows:

[0067]

[0068] Among them, i corresponds to the first state cell, and i + 1 corresponds to the selected second state cell; random(·) represents selecting a random value within the target range.

[0069] n x The value range of n is [-1, 2.5]; n y The value range of n is [-1, 2]; n z The value range of n is [-1, 1.5]. By restricting the overload range of the aircraft, it is possible to avoid an increase in fuel consumption caused by excessive changes in the motion state of the aircraft in a short period of time, and at the same time, the flight path can be ensured to be smooth, thereby ensuring the comfort of passengers.

[0070] In this embodiment, the cost value Cost includes the movement cost BCost, the speed change cost VCost, and the overload cost OCost;

[0071] Calculate the change amount TCost of the cost value from the first state cell to the second state cell;

[0072] The calculation of the change amount TCost of the cost value is as follows:

[0073]

[0074] Among them, i corresponds to the first state cell, and i + 1 corresponds to the selected second state cell; the position coordinates of the first state cell and the second state cell correspond to (x i , y i , z i ) and (x i+1 , y i+1 , z i+1 ); V max and V min are respectively the preset maximum flight speed and the minimum flight speed;

[0075] In this embodiment, V max = 27.7 m / s, V min = 22.2 m / s.

[0076] VC is the preset speed change cost coefficient; OC is the preset overload cost coefficient;

[0077] If Cost i+1 = 0 or Cost i+1 > Cost i + TCost, then update the cost value of the second state cell, that is, Cost i+1 = Cost i + TCost.

[0078] Only when the cost value of the second state cell is updated, update the state parameters of the second state cell.

[0079] abs() means taking the absolute value; since the second state cell is one of the nine state cells directly in front of the first state cell, the change in position coordinates is calculated based on the changes in the three dimensions of x, y, and z, with the minimum change being 1 and the maximum being 3, that is, the corresponding sum value is one of [1, 2, 3]; the second state cell corresponding to sum = 1 is located directly in front of the first state cell; the second state cells corresponding to sum = 2 are located above, below, to the left, and to the right in front of the first state cell; the second state cells corresponding to sum = 3 are located in the upper left front, lower left front, upper right front, and lower right front of the first state cell; the movement cost BCost correspondingly includes that is, [1, 1.4142, 1.732];

[0080] The initial value of the second state cell is 0; if the second state cell has been searched, it is compared with the cost value obtained in the previous search, and the path and its state parameters with the lower cost value are selected for retention.

[0081] In this solution, calculation and update are two states. The calculation result is not necessarily updated to the second state cell, but whether to update is determined based on the calculation result and preset conditions.

[0082] Step 4: In the airspace grid map, for the state cells between the starting point and the ending point, traverse them according to the search strategy combined with the A* algorithm, continuously select the second state cell with the minimum cost value until the optimal path between the starting point and the ending point is found.

[0083] The A* algorithm is a heuristic search algorithm used to find the shortest path in a graph or network. It combines the breadth-first search of the algorithm and the evaluation of the heuristic function to improve the search efficiency. During the search, based on the selected second state cell as the new first state cell, a new second state cell is searched, and so on, to complete the traversal. The optimal path is the path with the lowest cost value at the ending point.

[0084] Based on the gridification of the airspace environment, the present invention discretizes the studied airspace environment into numerous state cells. By assigning the initial state of the aircraft to the starting grid and formulating nine search strategies based on overload (nine directions), the method of controlling space sampling is used to continuously search for the movement paths that satisfy the aircraft constraint conditions (no-fly zones, costs) between the state cells. Finally, the conflict-free flight path of the civil airliner with the lowest cost within the studied airspace range is found.

[0085] Based on the disclosure and teachings of the above specification, those skilled in the art to which the present invention pertains can also make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should also fall within the scope of protection of the claims of the present invention. In addition, although some specific terms are used in this specification, these terms are only for convenience of description and do not constitute any limitation to the present invention.

Claims

1. A conflict-free trajectory planning method for civil aircraft based on state lattice, characterized in that: The following steps are involved: Step 1: Select a target airspace; collect environmental information of the target airspace within a specified time period; the environmental information includes a no-fly zone; collect initial state parameters of the aircraft; Step 2: discretize the target airspace to construct an airspace grid map; the airspace grid map includes a plurality of state grids; one state grid corresponds to one cost value; mark the no-fly zone on the airspace grid map; set the starting point and the end point of the aircraft on the airspace grid map; initialize the cost value of the state grid corresponding to the starting point; Assigning the state parameter to the starting point; Step 3, formulate a search strategy: the state grid where the aircraft is currently located is recorded as the first state grid; according to the orientation of the aircraft in the first state grid, search for the state grid that does not belong to the no-fly zone in the nine state grids directly in front of the aircraft, which are recorded as the second state grid; according to the state parameters and cost values ​​of the first state grid, calculate and update the state parameters and cost values ​​of the second state grid; Step 4: In the airspace grid map, for the state grids between the starting point and the end point, traverse the state grids according to the search strategy combined with the A* algorithm, and continuously select the second state grid with the smallest cost value until the optimal path between the starting point and the end point is found.

2. A method for conflict-free trajectory planning for civil aircraft based on state lattice according to claim 1, characterized in that: The no-fly zone includes a controlled no-fly zone.

3. The conflict-free trajectory planning method for civil aircraft based on state grid according to claim 1, characterized in that: In the step 1, the track information of the target airspace is also collected; the track information is the coordinates of all aircraft except the aircraft passing through the target airspace; The no-fly zone includes the passing coordinates.

4. The method for conflict-free trajectory planning of a civil aircraft based on a state grid according to claim 3, characterized in that: Step one also includes setting a safety distance; the no-fly zone includes an area based on the passing coordinates and with the safety distance as the coverage area.

5. The method for conflict-free trajectory planning of a civil aircraft based on a state grid according to claim 1, characterized in that: In the step 2, the unit size of the state grid is set according to the size of the aircraft; and the target airspace is discretized into a grid based on the unit size.

6. The method for conflict-free trajectory planning of civil aircraft based on state grid according to claim 1, characterized in that: The state parameters include the position coordinates (x, y, z) of the aircraft, the flight speed V, the deflection angle γ, the pitch angle χ and the overload value (n x ,n y ,n z ); determining the orientation according to the deflection angle and the pitch angle.

7. The method for conflict-free trajectory planning of a civil aircraft based on a state grid according to claim 6, characterized in that: The cost value Cost includes movement cost BCost, speed change cost VCost and overload cost OCost; Calculate the cost value change TCost from the first state grid to the second state grid; The cost value change TCost is calculated as: Wherein, i corresponds to the first state grid, i+1 corresponds to the selected second state grid; the position coordinates of the first state grid and the second state grid correspond to (x i ,y i ,z i ) and (x i+1 ,y i+1 ,z i+1 );V max and V min are the preset maximum flight speed and minimum flight speed respectively; VC is the preset speed change cost coefficient; OC is the preset overload cost coefficient; If Cost i+1 =0 or Cost i+1 >Cost i +TCost, then update the cost value of the second state grid, that is, Cost i+1 =Cost i +TCost; The state parameter of the second state grid is updated only when the cost value of the second state grid is updated.

8. The method for conflict-free trajectory planning of a civil aircraft based on a state grid according to claim 6, characterized in that: The calculation and update of some state parameters of the second state grid are as follows: Among them, i corresponds to the first state grid, i+1 corresponds to the second state grid; Δt represents the time difference from the first state grid to the second state grid; g represents the gravitational acceleration.

9. The method for conflict-free trajectory planning of a civil aircraft based on a state grid according to claim 8, characterized in that: Δt is the preset simulation time step.

10. The method for conflict-free trajectory planning of civil aircraft based on state grid according to claim 1, characterized in that: The calculation of the overload value of the second state grid is: Among them, i corresponds to the first state grid, i+1 corresponds to the selected second state grid; random(·) means selecting a random value within the target range.

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