Autonomous operation flight plan rapid generation method
By constructing a route network diagram and an adaptive optimal search algorithm, combined with dynamic altitude layer allocation, the rapid generation of autonomous flight plans is achieved, solving the problems of high computational complexity and insufficient autonomous operation capabilities in traditional methods, and improving the efficiency and safety of air traffic management.
Patent Information
- Application Number
- CN202510988121.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-17
- Publication Date
- 2025-09-26
AI Technical Summary
Traditional flight plan generation methods have high computational complexity, are difficult to meet real-time requirements, and lack autonomous operation capabilities, making it difficult to achieve efficient and automated flight plan generation in complex airspace environments.
An autonomous flight plan rapid generation method is adopted to generate real-time, safe and efficient flight plans by constructing a route network diagram, combining an adaptive optimal search algorithm and a dynamic altitude layer allocation mechanism.
It significantly improves the efficiency and autonomy of flight plan generation, optimizes path smoothness and adaptability of altitude allocation, reduces computational complexity and dependence on manual experience, ensures the feasibility and safety of flight plans, and provides intelligent and collaborative air traffic management support.
Smart Images
Figure CN120708446A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a method for quickly generating an autonomous flight plan, and belongs to the technical fields of operations planning, air traffic control, air traffic flow management, and the like. Background Art
[0002] With the continuous growth of global air traffic, airspace resources are becoming increasingly scarce, and the complexity of flight plan generation has increased significantly. Traditional flight plan generation methods have many limitations: on the one hand, the algorithm has high computational complexity and is difficult to meet the real-time generation requirements; on the other hand, the flight level allocation is highly dependent on manual experience and lacks autonomous operation capabilities. Existing technologies are difficult to achieve efficient and automated flight plan generation in complex airspace environments. Therefore, the present invention proposes a method for rapid generation of autonomous flight plans. The method aims to achieve real-time, safe, and efficient flight plan generation based on airport, waypoint, and route data, combined with an adaptive optimal search algorithm and a dynamic level allocation mechanism, to provide intelligent and collaborative technical support for air traffic management and improve operational efficiency. Summary of the Invention
[0003] The present invention provides a method for quickly generating an autonomous flight plan, which solves the problems disclosed in the background technology.
[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:
[0005] A method for rapidly generating an autonomous flight plan, the main steps of which include:
[0006] Step 1: Construction of route network diagram
[0007] Load airport, waypoint, and route data, use airports and waypoints as nodes, and use the great circle distance formula to calculate the great circle distance between adjacent nodes based on the geographic coordinates of the nodes as the edge weights to construct an undirected weighted route network graph. Traverse and remove isolated nodes to ensure the connectivity of the network graph. The formula for calculating the great circle distance is as follows:
[0008]
[0009] Among them, lat i and lon i are the latitude and longitude coordinates of node i (positive for northeast and negative for southwest); lat j and lon j It is represented by the latitude and longitude coordinates of node j connected to node i; f is the flatness of the earth; U i and Δλ are auxiliary variables, S ij is the great circle distance between adjacent nodes; u i Represented as auxiliary variable Ui The unit vector matrix of π is the ratio of a circle to its circumference.
[0010] Step 2: Flight path generation based on adaptive optimal search algorithm
[0011] The adaptive optimal search algorithm builds on the traditional Dijkstra algorithm by adding a path smoothing step to ensure the generated flight path better meets actual flight requirements. By removing redundant points and detecting heading anomalies, the path structure can be effectively simplified, eliminating unnecessary waypoints while ensuring a smooth and reasonable path, thereby improving flight safety and efficiency. The specific steps are as follows.
[0012] Step 2.1 Initial path generation:
[0013] Input the departure airport (starting point) and the destination airport (end point), and use the Dijkstra algorithm to calculate the shortest path between the two airports based on the undirected weighted route network graph to generate the initial waypoint sequence Path={Node1,Node2,…,Node n The overall process of Dijkstra's algorithm is shown in the following pseudo code.
[0014]
[0015] Step 2.2 Path smoothing optimization:
[0016] Smoothing is performed on the basis of the initial waypoint sequence Path to improve the rationality of the path and flight efficiency, including: Redundant point removal: traverse the waypoint sequence, calculate the geographic great circle distance between adjacent waypoints, and if the distance between consecutive waypoints is less than the distance threshold Dis th , then remove redundant points to simplify the path; Heading anomaly detection: calculate the heading angle of adjacent segments, if the heading angle of the adjacent segment exceeds the angle threshold θ th , then remove the non-compliant waypoints.
[0017] Step 2.3 Path reconstruction:
[0018] After removing redundant or non-compliant points, the path is checked for connectivity. If the path is broken, the previous and next waypoints are connected and the distance threshold and angle threshold checks are performed again until the path meets all constraints.
[0019] Step 3: Calculate estimated time of flight and dynamically allocate flight levels
[0020] The main steps of autonomous flight plan generation include:
[0021] Step 3.1: Initialize the flight distance S = 0 and the takeoff time t = t departureAnd the flight speed.
[0022] Step 3.2: Traverse the flight path generated in step 2, for adjacent nodes (airports and waypoints) i to Node i+1 , calculate the distance Δd between the two nodes, and update the cumulative distance d = d + △d, then calculate the cumulative time increment At the same time, the estimated passing time of each waypoint is generated, and its update formula is t=t+Δt, thereby generating a preliminary flight plan including waypoint information and estimated passing time.
[0023] Step 3.3: Dynamically allocate flight levels. The dynamic allocation of flight levels is mainly based on the total range, the number of waypoints, and the heading angle. The flight process is divided into three phases: climb, cruise, and descent. For short-range flights (short total range), the flight process is divided into climb and descent phases. The number of available altitude levels in the climb phase is determined by |H climb | indicates the number of waypoints N during the climb phase climb The ratio of the total distance D to the segment distance threshold δ and |H climb The number of waypoints N in the descent phase is determined by the smaller of | descend The number of waypoints N and N in the flight path climb The difference between the two is calculated, and then the altitude layer is indexed from the climb altitude layer and the descent altitude layer in turn to assign the flight altitude layer to each waypoint. For long-distance flights (long total distance), the flight process is divided into the climb phase, the cruise phase and the descent phase. The number of waypoints in the climb phase and the descent phase is determined by the number of available altitude layers, that is, N climb =|H climb |, N descend =|H descend |, the number of waypoints in the cruise phase is determined by the number of waypoints in the flight path N and N climb and N descend The difference is calculated. During the climb and descent phases, the flight levels in the climb and descent levels are indexed sequentially. During the cruise phase, cruising levels are assigned based on the heading angle. According to the International Civil Aviation Organization (ICAO) RVSM airspace rules, if the true heading angle is between 0° and 179° (eastbound flight), odd-numbered levels (such as FL330 and FL350) are assigned, and the cruising level is selected from the odd cruising levels based on aircraft performance. If the true heading angle is between 180° and 359° (westbound flight), even-numbered levels (such as FL320 and FL340) are assigned, and the cruising level is selected from the even cruising levels based on aircraft performance. This dynamic allocation ensures that the flight plan complies with air traffic management regulations and meets aircraft performance requirements.
[0024] Step 4: Output the autonomous flight plan
[0025] Integrate waypoint information (including node ID, geographic coordinates), estimated time to flight (ETA), and flight level to generate a structured flight plan. Path connectivity and time consistency verification ensure plan feasibility. The final output is in a unified format for easy system integration and data exchange.
[0026] The beneficial effects of the present invention are:
[0027] This method significantly improves the efficiency and autonomy of flight plan generation. By integrating a path search algorithm and a dynamic optimization strategy, it optimizes the smoothness of flight paths and the adaptability of altitude allocation, reducing computational complexity and reliance on manual experience, ensuring the feasibility and safety of flight plans. This method provides key technical support for the intelligent, collaborative, and efficient operation of air traffic management systems, effectively addressing the challenges of airspace resource constraints and traffic growth, and providing reliable support for air traffic management in complex airspace environments, demonstrating broad application prospects and significant practical value. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 A technical roadmap for a method to rapidly generate autonomous flight plans is presented, outlining the complete process from building a route network diagram to outputting a flight plan.
[0029] Figure 2 The structure of the airway route network graph is shown, which is an undirected weighted graph with airports and waypoints as nodes and great circle distance as edge weights;
[0030] Figure 3 A schematic diagram of the flight path generated by the adaptive optimal search algorithm is shown, showing the sequence of waypoints after the path is smoothly optimized;
[0031] Figure 4 A schematic diagram of the dynamic allocation of flight altitude layers is shown, comparing the altitude layer allocation methods for the climb, cruise and descent phases of short-range and long-range flights respectively. DETAILED DESCRIPTION
[0032] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.
[0033] like Figure 1 As shown, the present invention provides a method for quickly generating an autonomous flight plan, which includes the following main steps:
[0034] Step 1: Construction of route network diagram; Step 2: Flight path generation based on adaptive optimal search algorithm; Step 3: ETA calculation and dynamic allocation of flight altitude layers; Step 4: Output of flight plan for autonomous aircraft.
[0035] Step 1: Construction of route network diagram:
[0036] Step 1.1 Data preprocessing
[0037] First, we retrieve airport, waypoint, and route data from a predefined database. Airport data includes an identifier and geographic coordinates (latitude and longitude), waypoint data includes the waypoint ID and geographic coordinates, and route data refers to the waypoint information included in the route.
[0038] Step 1.2 Build an undirected weighted graph
[0039] (1) Node
[0040] Airports and waypoints are used as nodes, and their identifiers, IDs, and geographic coordinates are stored.
[0041] (2) Edge
[0042] The routes connecting the nodes are represented as edges, and the great circle distance between the nodes is calculated as the edge weight. The calculation formula is as follows:
[0043]
[0044] Among them, lat i and lon i are the latitude and longitude coordinates of node i (positive for northeast and negative for southwest); f is the flattening of the earth, which is based on the 2000 Chinese Geodetic Coordinate System (CGCS2000); U i and Δλ are auxiliary variables, S ij is the great circle distance between adjacent nodes; u i Represented as auxiliary variable U i The unit vector matrix of π is the ratio of a circle to its circumference.
[0045] (3) Remove isolated nodes
[0046] The isolated node removal step ensures the connectivity of the route network graph, avoiding unreachable nodes and ensuring the feasibility of subsequent flight path generation. During the traversal process, each node is checked for degree. If its degree is 0, it is marked as an isolated node and removed. After removing isolated nodes, the graph connectivity is rechecked to ensure that all remaining nodes have reachable paths.
[0047] Step 2: Flight path generation based on adaptive optimal search algorithm
[0048] Step 2.1 Input and Initialization
[0049] Input the departure airport Start, the arrival airport End, the distance threshold dis th and the heading change threshold θ th , the airway route network graph G(V, E, ω); for v ∈ V, initialize dis[v] = ∞, and set the predecessor node array
[0050] Step 2.2 Generate the Initial Path
[0051] Use the Dijkstra algorithm to calculate the shortest path from Start to End: Select the node μ with the minimum distance from the unvisited nodes, and traverse the neighbor nodes v of μ.
[0052] If the condition dis[Start] + ω(μ, ν) < dis[ν] holds, update the dis list and the prev list, and then repeat the above steps until all nodes are visited. Finally, reconstruct the shortest path according to prev to generate the initial path sequence Path.
[0053] Step 2.3 Path Smoothing
[0054] First, traverse the nodes in Path, check the relationship of the distances between adjacent waypoints. If the distance is less than Dis th , remove the redundant waypoints to simplify the path structure. Then, check the heading angle change of adjacent flight segments. If the angle between adjacent flight segments is greater than θ th then regard this waypoint as a non - compliant waypoint and remove it to ensure the path is smooth. <http: / / www.
[0055] Step 2.4 Path Reconstruction
[0056] For the path missing caused by removing redundant waypoints and non - compliant waypoints, connect the front and rear waypoints to reconstruct the path, and repeat Step 2.3 until the obtained flight path meets the threshold constraints. Finally, output the smoothed flight path flightpath. <00..00186>The overall process of the adaptive optimal search algorithm is shown in the following pseudocode.
[0058]
[0059]
[0060] Step Three: Calculation of Predicted Passing Time and Dynamic Allocation of Altitude Levels
[0061] After obtaining the flightpath, it is necessary to calculate the ETA and assign a flight level for each waypoint. This step aims to accurately calculate the ETA for each waypoint based on aircraft performance parameters and flight path length, and dynamically assign a flight level based on the flight phase (climb, cruise, descent) to ensure the safety, efficiency, and compatibility of the flight plan with air traffic management regulations. The specific steps are as follows:
[0062] Step 3.1: Initialize the cumulative distance S = 0 and the take-off time t departure And the aircraft's flight speed.
[0063] Step 3.2: Calculate the estimated time to pass. For the flightpath generated in step 2, analyze the nodes segment by segment. i With Node i+1 , calculate the distance △d between the two nodes, and update the cumulative distance d = d + Δd, and then calculate the cumulative time increment At the same time, the ETA of each waypoint is generated, and its update formula is t=t+Δt.
[0064] Step 3.3: Dynamically assign altitude levels. Determine the flight phase based on the total distance, number of waypoints, and heading angle, and assign a flight level to each waypoint. During the climb and descent phases, index the flight levels in the climb and descent levels in sequence. During the cruise phase, select the appropriate flight level from the odd or even cruise levels based on the heading angle. This ensures that the flight plan complies with air traffic management regulations while meeting aircraft performance requirements. The main logic is as follows:
[0065] (4) Input parameters
[0066] The number of waypoints in the flight path, N; the total distance, D; the cumulative distance from the departure airport to each waypoint, d i ; Heading angle θ; Each predetermined altitude layer: climbing altitude layer Descending level Odd-numbered cruising altitudes Even-numbered cruising altitudes Waypoint threshold Δ=|H climb |+|H descend |; Segment distance threshold δ.
[0067] (5) Short range (N≤Δ)
[0068] For short-range flight paths, the flight process is divided into climb and descent phases. The number of waypoints in the climb and descent phases is calculated as follows:
[0069]
[0070] N descend =NN climb (5)
[0071] Then, in order from H climb With H descend The medium index altitude level assigns a flight altitude level to each waypoint.
[0072] (6) Long range (N>Δ)
[0073] For long-distance flight paths, the flight process is divided into the climb phase, cruise phase, and descent phase. The number of waypoints in each phase is calculated as follows:
[0074] N climb =|H climb | (6)
[0075] N descend =|H descend | (7)
[0076] N cruise =NN climb -N descend (8)
[0077] During the climbing and descending phases, index H climb With H descend In the cruise phase, according to the size of the heading angle θ, if 0≤θ≤179°, the flight altitude layer is odd Select the cruising altitude layer according to the aircraft performance. If 180°≤θ≤359°, select the cruising altitude layer from H even The cruising altitude layer is selected according to the aircraft performance. The pseudo code of the dynamic allocation algorithm of flight altitude layer is as follows:
[0078]
[0079] Step 4: Autonomous flight plan generation
[0080] The node identifier, geographic coordinates (latitude and longitude), estimated transit time, and flight altitude of each waypoint are recorded as structured data, and a unified data structure is used to store the information of each waypoint. Next, the feasibility and rationality of the flight plan are verified, including: (1) path connectivity verification to ensure that there are valid connections between each waypoint; (2) time consistency verification to check whether the estimated transit time of adjacent waypoints increases in chronological order and whether the flight time meets the aircraft performance constraints. If the verification passes, the final flight plan is generated and output in a standard format to support system integration and data exchange.
[0081] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.
Claims
1. A method for rapidly generating an autonomous flight plan, characterized in that: The following steps are involved: Step 1: Construction of air route network diagram; Step 2: Flight path generation based on adaptive optimal search algorithm; Step 3: Calculate the estimated time of passing the point and dynamically allocate the flight altitude layer; Step 4: Output the autonomous flight plan.
2. The method for rapidly generating an autonomous flight plan according to claim 1, wherein: Step 1 includes: constructing an undirected weighted route network graph based on airport, waypoint and route data, where the nodes are airports and waypoints, and the weight of the edge is the geographical great circle distance between two adjacent nodes; and checking the graph nodes to ensure the connectivity of the network graph by removing isolated nodes without connections.
3. The method for rapidly generating an autonomous flight plan according to claim 2, wherein: The edge weight is calculated as follows: Among them, lat i and lon i are the latitude and longitude coordinates of node i respectively; lat j and lon j It is represented by the latitude and longitude coordinates of node j connected to node i; f is the flatness of the earth; U i and Δλ are auxiliary variables, S ij is the great circle distance between adjacent nodes; u i Represented as auxiliary variable U i The unit vector matrix of π is the ratio of a circle to its circumference.
4. The method for rapidly generating an autonomous flight plan according to claim 1, wherein: Step 2 includes: first, using the Dijkstra algorithm, with the departure airport and the destination airport as the starting and end points, the shortest path is calculated based on the undirected weighted route network diagram to generate an initial waypoint sequence; second, the initial waypoint sequence is subjected to path smoothing optimization: the waypoint sequence is traversed, and the geographic great circle distance between adjacent waypoints is calculated. If the distance is less than a preset distance threshold, redundant waypoints are removed to simplify the path; the heading angles of adjacent segments are calculated. If the heading angle exceeds a preset heading change threshold, the waypoints that cause heading anomalies are eliminated; finally, the optimized waypoint sequence is subjected to a connectivity check. If the path is broken, the previous and next waypoints are connected to ensure the continuity and rationality of the path.
5. The method for rapidly generating an autonomous flight plan according to claim 1, wherein: Step three includes: calculating the estimated transit time for each waypoint based on the takeoff time, the waypoint distance, and the flight speed, and generating a preliminary flight plan including the waypoint information and the estimated transit time.
6. The method for rapidly generating an autonomous flight plan according to claim 1, wherein: Step three also includes: determining the flight phases, including climb, cruise and descent, based on the total length of the route and the number of waypoints; allocating odd-numbered altitude levels to aircraft flying eastward and even-numbered altitude levels to aircraft flying westward according to the size of their true heading angles in accordance with the ICAO RVSM airspace rules.
7. The method for rapidly generating an autonomous flight plan according to claim 6, characterized in that: The specific logic is: (1) Input parameters The number of waypoints in the flight path, N; the total distance, D; the cumulative distance from the departure airport to each waypoint, d i ; Heading angle θ; Each predetermined altitude layer: climbing altitude layer Descending level Odd-numbered cruising altitudes Even-numbered cruising altitudes Waypoint threshold Δ=|H climb |+|H descend |; Segment distance threshold δ; (2) Short range, N≤Δ; For short-range flight paths, the flight process is divided into climb and descent phases. The number of waypoints in the climb and descent phases is calculated as follows: N descend =N-N climb Then start from H climb With H descend The medium index altitude layer assigns a flight altitude layer to each waypoint; (3) Long range, N>Δ; For long-distance flight paths, the flight process is divided into the climb phase, cruise phase, and descent phase. The number of waypoints in each phase is calculated as follows: N climb =|H climb | N descend =|H descend | N cruise =N-N climb -N descend During the climbing and descending phases, index H climb With H descend In the cruising phase, according to the size of the heading angle θ, if 0≤θ≤179°, the flight altitude layer is H odd Select the cruising altitude layer according to the aircraft performance. If 180°≤θ≤359°, select the cruising altitude layer from H even The cruising altitude layer is selected based on the aircraft performance.
8. The method for rapidly generating an autonomous flight plan according to claim 1, wherein: Step 4 includes: integrating waypoint information, estimated transit time, flight altitude layer and other information to generate the final flight plan, and ensuring the feasibility and accuracy of the plan by verifying path connectivity and time consistency.