A state grid-based conflict-free flight path planning method for civil aircraft
By combining airspace rasterization and the A* algorithm with an overload search strategy, a conflict-free flight path for civil aircraft is planned, solving the problems of fuel consumption and passenger comfort under multiple constraints and achieving the finding of the optimal path.
Patent Information
- Application Number
- CN202510197111.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-21
- Publication Date
- 2025-12-09
- Estimated Expiration
- 2045-02-21
AI Technical Summary
Existing technologies struggle to effectively plan conflict-free flight paths for commercial airliners under multi-constraint scenarios, leading to increased fuel consumption and reduced passenger comfort.
A state-grid-based trajectory planning method is adopted. Through airspace rasterization, A* algorithm and control space sampling, nine overload search strategies are formulated to find the optimal path, avoid no-fly zones and other aircraft trajectories, limit the overload range of aircraft, and ensure smooth path.
It effectively reduces fuel consumption, ensures passenger comfort, and improves the efficiency and safety of flight path planning.
Smart Images
Figure CN120141474B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of traffic management, and particularly relates to a state grid-based conflict-free flight path planning method for civil aviation passenger planes. BACKGROUND
[0002] With the continuous growth of the civil aviation transport volume in China, the use pressure of the civil aviation airspace environment is also increasing day by day. Under the contradiction between the growing traffic transport demand and the limited airspace resources, especially when affected by unexpected factors such as sudden dangerous weather and military affairs, some flights will be delayed, so that many originally planned passenger planes need to change the flight plan and fly by changing the route. The frequent flight path changes and flight delays not only affect the service quality of air transport, but also bring economic losses and safety risks to the flight.
[0003] In order to improve the service quality of air transport and reduce the economic losses and safety risks brought by the flight by changing the route as much as possible, researchers at home and abroad have proposed a series of aircraft path planning methods, taking reducing the comprehensive fuel consumption of the aircraft and improving the flight experience of passengers as the target of path planning. In order to reduce unnecessary fuel consumption and ensure passenger comfort, the constraint of the aircraft motion state in the path planning process is very important.
[0004] The basic idea of path planning is to plan an optimal or approximate optimal path from the starting point to the target point in a given environment. There are many methods for aircraft path planning, and according to different planning targets, various planning methods can provide flight path methods for air traffic controllers, airspace planners and pilots to assist them to quickly respond to changes in traffic demand. SUMMARY
[0005] In view of the problems in the prior art, the present application provides a state grid-based conflict-free flight path planning method for civil aviation passenger planes, which can provide route planning for civil aviation passenger planes in the face of multiple constraint scenarios to reduce unnecessary fuel consumption and ensure passenger comfort.
[0006] The technical scheme of the present application is implemented as follows:
[0007] A state grid-based conflict-free flight path planning method for civil aviation passenger planes comprises the following steps:
[0008] Step 1, selecting a target airspace; collecting the environmental information of the target airspace in a specified time period; the environmental information includes no-fly areas; collecting the initial state parameters of the aircraft;
[0009] Step two, discretization grid processing is carried out on the target airspace, and an airspace grid map is constructed; the airspace grid map comprises a plurality of state grids; one state grid corresponds to one cost value; the no-fly area is marked on the airspace grid map; the starting point and the ending point of the aircraft are set on the airspace grid map; the cost value of the starting point corresponding to the state grid is initialized; the state parameter is assigned to the starting point;
[0010] Step three, a search strategy is formulated: 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, the state grids in the nine state grids in front of the aircraft that do not belong to the no-fly area are searched, and are recorded as the second state grid; according to the state parameter and the cost value of the first state grid, the state parameter and the cost value of the second state grid are calculated and updated;
[0011] Step four, in the airspace grid map, for the state grids between the starting point and the ending point, traversal processing is carried out according to the search strategy combined with the A* algorithm, and the second state grid with the minimum cost value is selected constantly 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 search efficiency. During the search, the selected second state grid is used as the new first state grid, and the new second state grid is searched, and so on, to complete the traversal. The optimal path is the path that makes the cost value of the ending point the lowest.
[0013] The present application is based on airspace environment rasterization, and the researched airspace environment is discretized into numerous state grids. 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 path between the state grids that meet the aircraft constraint conditions (no-fly zone obstacles, cost). Finally, the lowest-cost civil aviation passenger aircraft conflict-free path in the researched airspace range is found.
[0014] As a further optimization of the above scheme, the no-fly area comprises a controlled no-fly area.
[0015] The controlled no-fly area refers to an airspace over a certain territory that prohibits any aircraft without special application permission from flying into or flying over.
[0016] As a further optimization of the above scheme, in step one, the trajectory information of the target airspace is also collected; the trajectory information is the passing coordinates of all aircrafts in the target airspace except the aircraft;
[0017] The no-fly area comprises the passing coordinates.
[0018] To avoid conflict, aircrafts plan their flight path to avoid other aircrafts' trajectories.
[0019] As a further optimization of the above solution, in step one, a safety distance is set; the no-fly zone includes an area with the safety distance as the coverage range based on the passing coordinates.
[0020] As a further optimization of the above solution, in step two, the unit size of the state grid is set according to the size of the aircraft; the target airspace is discretely grid processed based on the unit size.
[0021] As a further optimization of the above solution, the state parameters include the position coordinates (x, y, z) of the aircraft, the flight speed V, the yaw angle γ, the pitch angle χ, and the overload value (n x , n y , n z ); the orientation is determined according to the yaw angle and the pitch angle.
[0022] Yaw Angle: describes the rotation of the aircraft in the horizontal plane, i.e. the horizontal angle of the aircraft's nose relative to a certain reference direction (usually north). For example, a yaw angle of 0° indicates that the aircraft's nose points north, and a yaw angle of 90° indicates that the aircraft's nose points east.
[0023] Pitch Angle: describes the rotation of the aircraft in the vertical plane, i.e. the vertical angle of the aircraft's nose relative to the horizontal plane. For example, a pitch angle of 0° indicates that the aircraft is flying horizontally, a positive pitch angle indicates that the aircraft is pitching up, and a negative pitch angle indicates that the aircraft is pitching down.
[0024] The horizontal direction of the aircraft is determined by the yaw angle, and the vertical direction is determined by the pitch angle. Through the yaw angle and the 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 the movement cost BCost, the speed change cost VCost, and the overload cost OCost.
[0026] Calculate the cost value change amount TCost from the first state grid to the second state grid.
[0027] The calculation of the cost value change amount TCost is:
[0028]
[0029] 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 ) respectively; V max and V min are preset maximum flight speed and minimum flight speed respectively; VC is preset speed change cost coefficient; OC is preset overload cost coefficient;
[0030] If Cost i+1 =0 or Cost i+1 >Cost i +TCost, then the cost value of the second state grid is updated, i.e. Cost i+1 =Cost i +TCost.
[0031] The state parameters of the second state grid are updated only when the cost value of the second state grid is updated.
[0032] abs() means taking absolute value; since the second state grid is one of the nine state grids in front of the first state grid, the change amount of the position coordinates is calculated based on the changes in x, y and z dimensions, the minimum change is 1 and the maximum change is 3, i.e. the corresponding sum value is one of [1, 2, 3]; sum=1 corresponds to the second state grid being located in front of the first state grid; sum=2 corresponds to the second state grid being located in front upper, front lower, front left and front right of the first state grid; sum=3 corresponds to the second state grid being located in front left upper, front left lower, front right upper and front right lower of the first state grid; the movement cost BCost corresponds to including i.e. [1, 1.4142, 1.732];
[0033] The initial value of the second state grid is 0; if the second state grid has been searched, then the cost value obtained by the last search is compared, and the path and the state parameters with lower cost value are reserved.
[0034] In the present scheme, calculation and update are two states, the result of calculation is not necessarily updated to the second state grid, 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 scheme, the calculation and update of the partial state parameters of the second state grid are:
[0037]
[0038] Wherein, 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 acceleration of gravity.
[0039] As a further optimization of the above scheme, Δt is a preset simulation time step, that is, a fixed time length value obtained according to the flight time between the actual two state grids, which can be adjusted according to the cruising speed of different civil aviation passenger planes.
[0040] As a further optimization of the above scheme, the calculation of the overload value of the second state grid is:
[0041]
[0042] Wherein, i corresponds to the first state grid, i+1 corresponds to the selected second state grid; random(·) represents a random value selected within a target range.
[0043] n x The value range of n y The value range of n z The value range of n
[0044] [-1, 1.5]. By limiting the overload range of the aircraft, the increase of fuel consumption caused by the excessive change of the motion state of the aircraft in a short time is avoided, and at the same time the smoothness of the flight path is ensured to ensure the comfort of passengers.
[0045] Compared with the prior art, the present application has the following beneficial effects:
[0046] (1) The present application is based on airspace environment gridding, and the airspace environment studied is discretized into a large number of state grids. By assigning the initial state of the aircraft to the starting grid and formulating nine search strategies based on overload (nine directions), the control space sampling method is used to continuously search for the motion path between the state grids that meet the aircraft constraint conditions, and finally the lowest-cost civil aviation passenger plane conflict-free flight path in the studied airspace range is found.
[0047] (2) By limiting the overload range of the aircraft, the increase of fuel consumption caused by the excessive change of the motion state of the aircraft in a short time is avoided, and at the same time the smoothness of the flight path is ensured to ensure the comfort of passengers. BRIEF DESCRIPTION OF DRAWINGS
[0048] Figure 1is a flowchart of a state grid-based civil aviation passenger plane conflict-free path planning method provided by the embodiment of the present application;
[0049] Figure 2 is a state grid search effect diagram provided by the embodiment of the present application;
[0050] Figure 3 is Figure 2 is a diagram in another angle. DETAILED DESCRIPTION
[0051] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.
[0052] As Figures 1 to 3 shown, the embodiment provides a state grid-based civil aviation passenger plane conflict-free path planning method, which comprises the following steps:
[0053] Step one, selecting a target airspace; specifically, selecting the region between the adjacent waypoints of the target aircraft. Adjacent waypoints refer to two specific locations that the aircraft passes through in turn during flight. The distance between adjacent waypoints is affected by many factors such as route planning, flight conditions, navigation technology, etc. Generally speaking, the distance between adjacent waypoints may vary from tens of kilometers to hundreds of kilometers. In this study, a certain adjacent waypoint within about 200 kilometers is selected to construct the target airspace in turn.
[0054] Setting a safety distance; collecting environmental information of the target airspace within a specified time period; the environmental information includes no-fly zones; the environmental information includes flight path information of the target airspace; the flight path information is the coordinates of all aircrafts passing through the target airspace except the aircraft.
[0055] The no-fly zone includes the controlled no-fly zone, the passing coordinates, and the region with the passing coordinates as the reference and the safety distance as the coverage range. The controlled no-fly zone refers to the airspace above a certain territory that prohibits any aircraft without special application permission from flying into or flying over. In order to avoid conflicts, the aircraft should avoid the trajectories of other aircraft when planning the flight path.
[0056] Collecting initial state parameters of the aircraft; in this embodiment, 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);determine the orientation according to the yaw angle and the pitch angle.
[0057] Yaw Angle: describes the rotation of the aircraft in the horizontal plane, i.e. 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° indicates that the aircraft's nose points directly north, and a yaw angle of 90° indicates that the aircraft's nose points directly east.
[0058] Pitch Angle: describes the rotation of the aircraft in the vertical plane, i.e. the vertical angle of the aircraft's nose relative to the horizontal plane. For example, a pitch angle of 0° indicates that the aircraft is flying horizontally, a positive pitch angle indicates that the aircraft is pitching up, and a negative pitch angle indicates that the aircraft is pitching down.
[0059] The horizontal direction of the aircraft is determined by the yaw angle, and the vertical direction is determined by the pitch angle. Through the yaw angle and the pitch angle, the orientation of the aircraft in three-dimensional space can be determined.
[0060] Step two, set the unit size of the state grid based on the size of the aircraft, such as 100 meters x 100 meters x 100 meters; discretize the target airspace into a grid based on the unit size, and construct an airspace grid map; the airspace grid map includes multiple state grids; one state grid corresponds to one cost value; mark the no-fly area 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; assign state parameters to the starting point;
[0061] Step three, develop 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 in the nine state grids in front of the aircraft that does not belong to the no-fly area, and record it as the second state grid; according to the state parameters and cost value of the first state grid, calculate and update the state parameters and cost value of the second state grid;
[0062] In this embodiment, the calculation and update of part of the state parameters of the second state grid are as follows:
[0063]
[0064] Where 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 acceleration of gravity.
[0065] In this embodiment, Δt is 0.1s.
[0066] In this embodiment, the calculation of the overload value of the second state grid is as follows:
[0067]
[0068] Wherein, i corresponds to the first state grid, i+1 corresponds to the selected second state grid; random(·) represents selecting a random value in 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 limiting the overload range of the aircraft, the increase of fuel consumption caused by the large motion state change of the aircraft in a short time is avoided, and at the same time the smoothness of the flight path is ensured to ensure the comfort of passengers.
[0070] In this embodiment, the cost value Cost includes a movement cost BCost, a speed change cost VCost and an overload cost OCost.
[0071] The cost value change amount TCost from the first state grid to the second state grid is calculated.
[0072] The calculation of the cost value change amount TCost is as follows:
[0073]
[0074] 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 ) respectively; V max and V min are the preset maximum flight speed and minimum flight speed respectively.
[0075] In this embodiment, V max = 27.7 m / s, V min = 22.2 m / s.
[0076] VC is a preset speed change cost coefficient; OC is a preset overload cost coefficient.
[0077] If Cost i+1 = 0 or Cost i+1 > Cost i + TCost, the cost value of the second state grid is updated, that is, Cost i+1 = Cost i + TCost.
[0078] Only when the cost value of the second state grid is updated, the state parameters of the second state grid are updated.
[0079] abs() means taking absolute value; since the second state grid is one of the nine state grids directly in front of the first state grid, the change in position coordinates is calculated based on changes in three dimensions of x, y and z, the minimum change is 1 and the maximum change is 3, that is, the corresponding sum value is one of [1, 2, 3]; sum = 1 corresponds to the second state grid being directly in front of the first state grid; sum = 2 corresponds to the second state grid being in front of the first state grid, front upper, front lower, front left and front right; sum = 3 corresponds to the second state grid being in front left upper, front left lower, front right upper and front right lower of the first state grid; the movement cost BCost corresponds to including That is, [1, 1.4142, 1.732];
[0080] The initial value of the second state grid is 0; if the second state grid has been searched, the cost value obtained by the last search is compared, and the path and state parameters with a lower cost value are selected and reserved.
[0081] In the present scheme, calculation and update are two states, and the result of calculation is not necessarily updated to the second state grid, but whether to update is determined based on the calculation result and a preset condition.
[0082] Step four, in the airspace grid map, for the state grids between the starting point and the ending point, according to the search strategy combined with the A* algorithm, the second state grid with the minimum cost value is selected for traversal processing 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, the selected second state grid is used as the new first state grid, and the new second state grid is searched, and so on, to complete the traversal. The optimal path is the path with the lowest cost value of the ending point.
[0084] The present application is based on airspace environment rasterization, and discretizes the studied airspace environment into numerous state grids. 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 path between the state grids that meet the aircraft constraint conditions (forbidden zone obstacles, cost). Finally, the lowest-cost civil aviation passenger aircraft conflict-free track path in the studied airspace range is found.
[0085] Those skilled in the art can make various modifications and changes to the above embodiments according to the disclosure and teachings herein. Therefore, the application is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the application shall fall within the protection scope of the claims of the application. In addition, although some specific terms are used in the specification, these terms are only for convenience of description and do not constitute any limitation on the application.
Claims
1. A state lattice based conflict-free trajectory planning method for civil aircraft, characterized in that, The method comprises the following steps: Step one, selecting a target airspace; collecting environmental information of the target airspace in a specified time period; the environmental information includes no-fly areas; collecting initial state parameters of an aircraft; the state parameters include position coordinates of the aircraft , flight speed V, deflection angle γ, pitch angle χ, and overload value ; Step two, discretizing and gridding the target airspace to construct an airspace grid map; the airspace grid map comprises a plurality of state grids; one state grid corresponds to one cost value; marking the no-fly area on the airspace grid map; setting the starting point and the ending point of the aircraft on the airspace grid map; initializing the cost value of the state grid corresponding to the starting point; Assigning the state parameters to the starting point; Step three, formulating 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, searching for the state grids not belonging to the no-fly area in the nine state grids in front of the aircraft, and recording the state grids as the second state grids; according to the state parameters and the cost value of the first state grid, calculating and updating the state parameters and the cost value of the second state grids; Wherein, the orientation is determined according to the deflection angle and the pitch angle; the cost value Cost comprises a moving cost BCost, a speed change cost VCost and an overload cost OCost; Calculating the cost value change amount TCost from the first state grid to the second state grid; The calculation of the cost value change amount TCost is as follows: ； 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 and ; and are respectively a preset maximum flight speed and a minimum flight speed; VC is a preset speed change cost coefficient; OC is a preset overload cost coefficient; If , then update the cost value of the second state lattice, i.e. ; Only when the cost value of the second state grid is updated, the state parameters of the second state grid are updated; Step four, in the airspace grid map, for the state grids between the starting point and the ending point, according to the search strategy combined with the A* algorithm, the second state grid with the minimum cost value is selected constantly until the optimal path between the starting point and the ending point is found.
2. The state grid based conflict-free path planning method for civil aviation passenger aircraft according to claim 1, characterized in that, The no-fly area comprises a controlled no-fly zone.
3. The state grid based conflict-free path planning method for civil aviation passenger aircraft according to claim 1, characterized in that, In step one, the track information of the target airspace is also collected; the track information is the passing coordinates of all aircrafts in the target airspace except the aircraft; The no-fly area comprises the passing coordinates.
4. The state grid based conflict-free path planning method for civil aviation passenger aircraft according to claim 3, characterized in that, In step one, a safety distance is also set; the no-fly area comprises an area with the passing coordinates as the reference and the safety distance as the coverage range.
5. The state grid based conflict-free path planning method for civil aviation passenger aircraft according to claim 1, characterized in that, In step two, the unit size of the state grid is set according to the size of the aircraft; the target airspace is discretized and gridded based on the unit size.
6. The state grid based conflict-free path planning method for civil aviation passenger aircraft according to claim 1, characterized in that, The calculation and update of part of the state parameters of the second state grid are as follows: ； Wherein, 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 acceleration of gravity.
7. The state grid based conflict-free path planning method for civil aviation passenger aircraft according to claim 6, characterized in that, Δt is a preset simulation time step.
8. The state grid based conflict-free path planning method for civil aviation passenger aircraft according to claim 1, characterized in that, The calculation of the overload value of the second state grid is as follows: ； Wherein, i corresponds to the first state grid, i+1 corresponds to the selected second state grid; random(·) represents selecting a random value in the target range.