Flight trajectory planning methods and related devices for autonomous wind threat avoidance by helicopters

Abstract

This application discloses a flight trajectory planning method and related device for autonomous wind threat avoidance by helicopters, relating to the field of path planning and decision-making algorithm technology. The method includes: constructing a terrain environment model, a wind field environment model, and a rotor model based on acquired real terrain data, environmental wind field parameters, and rotor structure; performing hydrodynamic simulation on the models to calculate first distribution characteristics and rotor aerodynamic loads; constructing a helicopter flight dynamics model based on acquired helicopter design parameters, motion state, and the first distribution characteristics, and solving it to obtain first state data; constructing a safety map based on the terrain environment model, rotor aerodynamic loads, and first state data; and performing path planning using an improved A* algorithm based on the starting position, target position, and safety map to obtain the flight trajectory, thus meeting the safe flight requirements of helicopters in complex environments.

Description

Technical Field

[0001] This application relates to the field of path planning and decision-making algorithm technology, and in particular to a flight trajectory planning method and related device for autonomous wind threat avoidance of helicopters. Background Technology

[0002] Helicopters, as aircraft capable of vertical takeoff and landing, hovering, and ultra-low-altitude flight, play an irreplaceable role in both military and civilian fields. Their unique flight capabilities enable them to perform missions in complex and constrained environments, such as urban rescue, mountain search and rescue, and maritime patrol. However, these complex environments are often accompanied by variable and complex airflow conditions (i.e., environmental wind fields), such as backdrafts and accelerated turbulence caused by urban building clusters, roller-shaped airflows and large intermontane backflows generated by mountainous terrain, and vortices detached from the island superstructure around ships. These unstable airflows directly act on the helicopter's rotor blades, generating unsteady pulsating loads that severely affect the helicopter's flight performance and handling characteristics, increasing flight risks.

[0003] Currently, path planning algorithms primarily focus on improving efficiency and intelligence, neglecting the specific characteristics of the application. Whether for intelligent robots, vehicles, or aircraft, traditional path planning algorithms often only consider obstacle avoidance or employ simple obstacle "inflation" methods to reduce collision risk. This approach ignores the environmental hazards and performance limitations faced by aircraft in actual operation. Especially for helicopters, environmental wind interference poses a significant threat to their flight safety. Therefore, it is necessary to design flight paths based on the operational characteristics of helicopters to guide the design of safe flight strategies. Summary of the Invention

[0004] The purpose of this application is to provide a flight trajectory planning method and related device for autonomous avoidance of wind threats by helicopters, which can predict and avoid flight hazard areas caused by environmental wind fields in advance, and quickly find the safest flight path in complex environments.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a flight trajectory planning method for autonomous wind threat avoidance by helicopters, including:

[0007] Based on the acquired real terrain data, environmental wind field parameters, and rotor structure, a terrain environment model, a wind field environment model, and a rotor model are constructed.

[0008] Fluid dynamics simulations were performed on the terrain environment model, wind field environment model, and rotor model to calculate the first distribution characteristics within the wind field and the rotor aerodynamic loads.

[0009] Based on the obtained helicopter design parameters and motion state, as well as the first distribution characteristics, a helicopter flight dynamics model is constructed, and the helicopter flight dynamics model is solved to obtain the first state data of the helicopter.

[0010] Based on the rotor aerodynamic load and the first state data, the hazard factors in the terrain environment model are restricted to obtain a safety map.

[0011] Based on the obtained starting and target positions and the safety map, an improved A* algorithm is used for path planning to obtain the flight trajectory; the improved A* algorithm expands the neighborhood search direction to a multi-angle range based on the traditional A* algorithm.

[0012] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the flight trajectory planning method for autonomous avoidance of wind threats by helicopters as described above.

[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the flight trajectory planning method for autonomous avoidance of wind threats by helicopters as described above.

[0014] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the flight trajectory planning method for autonomous avoidance of helicopter wind threats described above.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0016] This application provides a flight trajectory planning method and related apparatus for autonomous wind threat avoidance by helicopters. By acquiring real terrain data and environmental wind field parameters, the constructed terrain and wind field environmental models can more accurately simulate the flight behavior of helicopters under complex terrain and wind field conditions. Combined with rotor structure characteristics, hydrodynamic simulations are performed, making the flight simulation more accurate and enabling better prediction of flight status in complex wind fields. Furthermore, under environmental wind threats, helicopter hazard analysis based on terrain models, aerodynamic loads, and flight status data can identify potential danger zones during flight and construct detailed safety maps, significantly improving flight safety. In addition, by expanding the neighborhood search direction of the traditional A* algorithm to a multi-angle range, path planning becomes more flexible and efficient, better addressing the challenges of complex terrain and dynamic wind fields. This improvement effectively avoids suboptimal solutions or excessive computational loads caused by algorithm limitations, thereby improving the efficiency and accuracy of flight trajectory planning. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an application environment diagram of a flight trajectory planning method for autonomous wind threat avoidance of helicopters according to an embodiment of this application;

[0019] Figure 2 A flowchart illustrating a flight trajectory planning method for autonomous wind threat avoidance by a helicopter, provided as an embodiment of this application;

[0020] Figure 3 A detailed flowchart of a fluid dynamics solution procedure provided in an embodiment of this application;

[0021] Figure 4a A schematic diagram of rotor thrust at different windward positions above a floor in a building disturbance flow field provided in an embodiment of this application;

[0022] Figure 4b A schematic diagram of rotor torque at different windward positions above a floor in a building disturbance flow field provided in an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of the structure of a helicopter flight dynamics model provided in an embodiment of this application;

[0024] Figure 6a This is a schematic diagram of the collective pitch distribution of a helicopter rotor in a building-interference flow field according to an embodiment of this application;

[0025] Figure 6b This is a schematic diagram of the helicopter rotor pitch angle distribution in a building-disrupted flow field according to an embodiment of this application;

[0026] Figure 7a This is a schematic diagram of the structure of the original map provided in one embodiment of this application;

[0027] Figure 7b This is a schematic diagram of the structure of a security map provided in one embodiment of this application;

[0028] Figure 8a This is a schematic diagram of the pathfinding result of the conventional A* algorithm provided in an embodiment of this application;

[0029] Figure 8b A schematic diagram of the pathfinding result of the improved A* algorithm provided in an embodiment of this application;

[0030] Figure 9a A schematic diagram of flight trajectory results obtained based on the prior art, provided as an embodiment of this application;

[0031] Figure 9b A schematic diagram of flight trajectory results obtained based on the method of this application is provided as an embodiment of this application;

[0032] Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] The flight trajectory planning method for autonomous wind threat avoidance of helicopters provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send real terrain data, environmental wind field parameters, and rotor structure to server 104. After receiving the real terrain data, environmental wind field parameters, and rotor structure, server 104 constructs a terrain environment model, a wind field environment model, and a rotor model based on the real terrain data, environmental wind field parameters, and rotor structure. It then performs fluid dynamics simulation on the models to calculate the first distribution characteristics within the wind field and the rotor aerodynamic load. Based on the acquired helicopter design parameters, motion state, and the first distribution characteristics, it constructs a helicopter flight dynamics model and solves the helicopter flight dynamics model to obtain the helicopter's first state data. Based on the rotor aerodynamic load and the first state data, it constructs a safety map. Based on the starting position, target position, and safety map, it uses an improved A* algorithm for path planning to obtain the flight trajectory. Server 104 can feed back the obtained flight trajectory to terminal 102. In addition, in some embodiments, the flight trajectory planning method can also be implemented by server 104 or terminal 102 alone. For example, terminal 102 can directly process the data to be processed, or server 104 can obtain the data to be processed from the data storage system and process it.

[0036] Among them, terminal 102 can be, but is not limited to, various desktop computers, laptops and IoT devices, and server 104 can be implemented by independent server or server cluster composed of multiple servers, or it can be cloud server.

[0037] In one exemplary embodiment, such as Figure 2 As shown, a flight trajectory planning method for autonomous wind threat avoidance by helicopters is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 205. Wherein:

[0038] Step 201: Based on the acquired real terrain data, environmental wind field parameters, and rotor structure, construct the terrain environment model, wind field environment model, and rotor model.

[0039] Step 202: Perform fluid dynamics simulation on the terrain environment model, wind field environment model, and rotor model to calculate the first distribution characteristics within the wind field and the rotor aerodynamic load.

[0040] Step 203: Based on the obtained helicopter design parameters, motion state, and first distribution characteristics, construct a helicopter flight dynamics model and solve the helicopter flight dynamics model to obtain the helicopter's first state data.

[0041] Step 204: Based on the rotor aerodynamic load and the first state data, restrict the hazard factors in the terrain environment model to obtain a safety map.

[0042] Step 205: Based on the obtained starting position, target position, and safety map, the improved A* algorithm is used for path planning to obtain the flight trajectory; the improved A* algorithm expands the neighborhood search direction to a multi-angle range based on the traditional A* algorithm.

[0043] By implementing steps 201 to 205 above, and acquiring real terrain data and environmental wind field parameters, the constructed terrain and wind field environmental models can more accurately simulate the helicopter's flight behavior under complex terrain and wind field conditions. Combined with rotor structure characteristics, hydrodynamic simulations are performed, making the flight simulation more precise and enabling better prediction of flight status in complex wind fields. Furthermore, under environmental wind threats, helicopter hazard analysis based on terrain models, aerodynamic loads, and flight status data can identify potential danger zones during flight and construct detailed safety maps, significantly improving flight safety. In addition, by expanding the neighborhood search direction of the traditional A* algorithm to a multi-angle range, path planning becomes more flexible and efficient, better addressing the challenges of complex terrain and dynamic wind fields. This improvement effectively avoids suboptimal solutions or excessive computational loads caused by algorithm limitations, thereby improving the efficiency and accuracy of flight trajectory planning.

[0044] Furthermore, in step 201, based on the acquired real terrain data, environmental wind field parameters, and rotor structure, a terrain environment model, a wind field environment model, and a rotor model are constructed, specifically including:

[0045] The first step is to construct a terrain environment model. The acquired real terrain data is appropriately simplified, including preserving the topographic relief features in the natural environment data and the overall building shape features in the urban area data. After establishing the simplified terrain geometry, the original map is obtained, and the original map is discretized into a Computational Fluid Dynamics (CFD) grid.

[0046] The second step is to construct a wind field environment model. The obtained environmental wind field parameters are calculated using the logarithmic wind profile formula to obtain the average wind speed at different heights. Based on these average wind speeds, a wind field environment model is constructed. The wind field environment only considers the average wind characteristics, describing the average wind profile according to the logarithmic law. The profile wind speed is used as the wind speed at the corresponding height, serving as the far-field inflow velocity to form the CFD inlet boundary conditions. The expression for the logarithmic wind profile, where the average wind speed varies with height (i.e., the logarithmic wind profile formula), is as follows:

[0047]

[0048] In the formula, U(z) is the horizontal wind speed at a height z above the ground; u * For friction velocity, the formula is u. * =(τ0 / ρ) 0.5 Where τ0 is the shear stress of air near the ground surface, ρ is the air density; κ is the Karman constant, approximately taken as 0.4; z is the height above the ground; z0 is the roughness length of the ground, the value of which is taken with reference to the "Code for Seismic Design of Buildings" (GB50011-2001).

[0049] The third step is to construct the rotor model. Ignoring the hub structure and manufacturing details in the rotor structure, only the blades are modeled. Based on the obtained rotor structure, the key profile airfoil of each blade is determined, and guide lines are established on the leading and trailing edges based on the key profile airfoil. The corresponding key profile airfoil is extruded along the guide lines to obtain the blade shape model of each blade. After the geometric modeling is completed, the blade mesh is drawn using ICEM or Point Wise, and the blade shape model of each blade is placed in the blade mesh according to the actual phase difference to obtain the rotor model. The rotor consists of multiple blades.

[0050] Further, in step 202, fluid dynamics simulations are performed on the terrain environment model, wind field environment model, and rotor model to calculate the first distribution characteristics within the wind field and the rotor aerodynamic loads, specifically including:

[0051] The first step is to obtain the configuration file. In the configuration file, set the parameters required for the fluid dynamics simulation, including initial flow field parameters (such as incoming flow velocity, i.e., the average wind profile mentioned above), solution control parameters (such as the spatial / temporal discretization format for each grid block, turbulence model, unsteady time step, and maximum computational iteration step), post-processing, output settings, and the file names of each grid block.

[0052] The second step is flow field solution. Impenetrable surfaces such as terrain / obstacles in the terrain environment model are designated as walls, while other boundaries allowing free fluid flow are designated as free-flow conditions. Based on the wall distribution and free-flow condition distribution in the terrain environment model, the boundary conditions for the fluid dynamics simulation are obtained. Based on the configuration file and boundary conditions, a fluid dynamics simulation is performed on the wind field environment model; that is, the velocity input uses the synthesized average wind. After the calculation converges, the first distribution characteristics within the wind field are obtained. These first distribution characteristics include velocity distribution, pressure distribution, and density distribution. The pressure distribution on the rotor surface is integrated along a direction perpendicular to the rotor surface in the rotor model to obtain the rotor aerodynamic load.

[0053] like Figure 3 The diagram illustrates the established fluid dynamics solution process: 1) Reading the configuration, which describes the initial flow field parameters and solution control parameters; 2) Reading the mesh file, discretizing the terrain environment model into a fluid dynamics-compatible mesh format; 3) Flow field initialization, using a logarithmic mean wind profile to directly assign values ​​to the global flow field, reducing subsequent convergence time; 4) Flow field solution, selecting the spatial / temporal discretization format and turbulence model according to the solution configuration of the corresponding mesh blocks, and performing iterative calculations; 5) Output and post-processing, outputting wind speed and pressure at each mesh point within the computational domain, using isolated environmental wind speed distribution for flight dynamics interpolation, integrating the rotor surface pressure to obtain rotor aerodynamic loads, and simulating rotor aerodynamic loads under environmental wind interference as shown in Figure 4. Specifically, Figure 4a Given the rotor thrust at different windward positions above the floor in the building's disturbed flow field, the product of drag and lever arm over the spanwise airfoil profile of the blade is obtained. Figure 4b The value represents the rotor torque at different windward positions above the building in the building's disturbance flow field. The distance between analysis points is approximately the length of a fuselage. When any load among the three forces and moments between adjacent analysis points changes by more than 10%, it can be identified as a hazard.

[0054] After completing the CFD simulation, a helicopter flight dynamics model is created to analyze the helicopter's balance characteristics during flight in complex environments. First, the coordinate transformation relationships between the components need to be established. Then, aerodynamic models are created for each component. An interface for coupling environmental wind speed is reserved in the resultant velocity solution module for each component, allowing the influence of environmental wind speed to be incorporated into the calculation.

[0055] Further, in step 203, based on the acquired helicopter design parameters, motion state, and first distribution characteristics, a helicopter flight dynamics model is constructed, and the helicopter flight dynamics model is solved to obtain the helicopter's first state data, specifically including:

[0056] The first step is to establish the transformation relationships between coordinate systems. An inertial coordinate system is established and fixed to the ground, using the Earth's surface as a reference. The body coordinate system is obtained based on the attitude angles, and the wind axis system is determined according to the body's angle of attack and sideslip angle. Rotor calculations need to consider the motion state of individual blades; therefore, a hub coordinate system is obtained based on the hub's longitudinal and lateral installation angles. An azimuth angle is then introduced to establish the rotor rotation coordinate system, and finally, the blade coordinate system is obtained based on the blade flapping motion. If the tail rotor shaft is not perpendicular to the body's longitudinal plane, a transformation relationship between the tail rotor coordinate system and the body coordinate system needs to be established using the tail rotor tilt angle. These transformation relationships are then formed into a matrix, and the transformation of forces and moments of each component between any coordinate system can be achieved by multiplying vectors by the matrix.

[0057] The second step is to establish aerodynamic models of the components. Based on the obtained helicopter design parameters (such as size, mass, and shape) and motion states (including velocity, angular velocity, acceleration, and angular acceleration), as well as the first distribution characteristics, considering the mutual interference between components, and combining the lift and drag coefficients of each component in the wind tunnel test, the triaxial forces and moments of each component at its aerodynamic center are interpolated to obtain the aerodynamic models of each helicopter component. Next, by interpolating the wind field data using distance derivative weighting, the wind speeds at the aerodynamic centers of the components are linearly superimposed on the component motion velocities, thus establishing an aerodynamic model coupled with the environmental wind field.

[0058] The third step is to establish a full-fledged flight dynamics model. Based on the transformation relationships between coordinate systems, the triaxial forces and moments of all helicopter components are integrated at the helicopter's center of gravity, resulting in the target dynamic equations: the dynamic equations for the movement of the helicopter's center of gravity and the dynamic equations for the helicopter's rotation around the center of gravity. Finally, the helicopter's flight dynamics model is obtained using the six-degree-of-freedom rigid body motion equations. The helicopter flight dynamics model is as follows: Figure 5 As shown.

[0059] The dynamic equations for the movement of the helicopter's center of gravity and its rotation around the center of gravity are as follows:

[0060]

[0061] Where u, v, and w are the three-dimensional velocities; p, q, and r are the derivatives of the three-dimensional velocities; p, q, and r are the three-dimensional angular velocities. F is the derivative of the triaxial angular velocity; m is the mass; F XYZ It is a triaxial force; I and ω * Let represent the cross product of the inertia matrix and the angular velocity matrix, respectively.

[0062]

[0063] By combining the target dynamic equations (i.e., those calculated using aerodynamic models of the helicopter's various components) with the helicopter's rigid body dynamic equations, a helicopter flight dynamic model can be obtained, which can be simplified as follows:

[0064]

[0065] Where t is time; U is the control variable, including collective pitch, lateral and longitudinal cyclic pitch, and tail rotor collective pitch; X is the state variable, including velocity, angular velocity, and attitude angle; W wind The ambient wind speed was used. The Newton-Raphson method was used to solve the helicopter flight dynamics model, obtaining the helicopter's first-state data. This first-state data represents the control parameters, attitude, and engine power in equilibrium under complex wind conditions. The results of the overall aircraft balance characteristics analysis in the ambient wind field are shown in Figure 6. Specifically, Figure 6a The graph shows the collective pitch and strut distribution of a helicopter rotor in a building-affected flow field. The legend represents the percentage of strut. Near the windward roof corner, the collective pitch gradually decreases, indicating a gradual reduction in the lift provided by the rotor. Figure 6b The diagram shows the helicopter rotor pitch angle distribution within the building's disturbance flow field. The rotor pitch reaches 5° above the roof, indicating a potential danger of further pitching up as wind speeds increase. By displaying the overall aircraft balance characteristics in the spatial domain as a plan view, systemic risks can be predicted more intuitively.

[0066] Furthermore, in step 204, based on the rotor aerodynamic load and the first state data, the hazard factors in the terrain environment model are restricted to obtain a safety map, specifically including:

[0067] The first step is to delineate the aerodynamic load threat zone. Based on the rotor aerodynamic load and the first judgment condition, the aerodynamic load threat zone is delineated. Specifically: First, the velocity distribution in the environmental wind field is analyzed. The area where the standard deviation of the environmental vertical velocity exceeds 1.75 m / s is the critical wind influence zone. Taking into account obstacles and helicopter size, discrete analysis points are set in the critical wind influence zone, and environmental-rotor coupled flow field analysis is carried out at the discrete analysis points to obtain the rotor aerodynamic load. When the change in rotor aerodynamic load between adjacent discrete points exceeds 10%, it is judged as an aerodynamic load threat zone.

[0068] The second step is to delineate the aircraft's response threat zone. Based on the first state data and the second judgment condition, the aircraft's response threat zone is delineated. Specifically, using the helicopter flight dynamics model, the helicopter's center of gravity is placed at the discrete analysis point selected in the first step. Considering the influence of ambient wind (by opening the wind speed interpolation interface), the overall aircraft balance characteristics are calculated. The control stick position, attitude angle, and engine power in the helicopter's balanced state are calculated (i.e., the first state data). It is specified that a control stick position margin of more than 10% is safe for each channel, roll angle -8° < Φ < 8°, and pitch angle -4° < Θ < 7° are safe. Combining the helicopter engine model and power, a 10% engine margin is required to be maintained for safety. All locations where limits are exceeded are marked as the aircraft's response threat zone.

[0069] The third step is to construct a safety map. Obstacles in the original map are described using the eight vertices of a cube (the solid yellow areas in the diagram). The aerodynamic loads, control stick measurements, attitude angles, and power threat zones obtained above are uniformly designated as wind field limitations. The terrain in the original map (i.e., the terrain environment model) is designated as obstacle avoidance limitations. The longitudinal distance reserved during flight to reduce collision risk is designated as altitude limitations. The eight vertices of the obstacles in the "expanded" original map incorporate these three limitations (the transparent blue areas in the diagram) to obtain the safety map. The wind field limitations, obstacle avoidance limitations, and altitude limitations are input into the terrain environment model to obtain the safety map; the wind field limitations include the aerodynamic load threat zone and the airframe response threat zone; the obstacle avoidance limitations and altitude limitations are determined based on the terrain environment model. The original map (i.e., the terrain environment model) and the safety map are shown below. Figure 7a and Figure 7b As shown.

[0070] Further, in step 205, based on the acquired starting and target positions and the safety map, an improved A* algorithm is used for path planning. For the designed path, parameterized descriptions of four basic maneuvers are established, ultimately yielding a flight trajectory containing acceleration and velocity information, specifically including:

[0071] The first step is to expand the neighborhood search using the A* algorithm. Child nodes are placed on grid points. Traditional A* algorithms only search adjacent grid areas; that is, they search 8 neighborhoods in 2D space and 24 neighborhoods in 3D space. This limits the designed path to a 45° deflection angle. Therefore, a new neighborhood search direction is added. While retaining the original search grid for each node, the search range is expanded outwards, increasing the deflection angle range to 30°, 35°, 45°, 55°, and 63°.

[0072] The second step is the path design of the neighborhood expansion A* algorithm. In environments where obstacle information is known, the A* algorithm can perform a simple and direct global search. This algorithm combines the advantages of Dijkstra's algorithm and breadth-first search, using an evaluation function as its core, ensuring search efficiency while finding the shortest path. The A* algorithm uses two lists during the search calculation process: a Close list to record nodes that have been calculated, and an Open list to store all visited but uncalculated nodes. The search process can be summarized as follows: starting from the starting point, expand the visited nodes, storing visited but uncalculated nodes in the Open list and calculated nodes in the Close list. Then, select the node with the minimum total path cost in the Open list as the next search node, continuing until the target point is found. Finally, backtrack from the target point to the starting point to obtain the final path. The pathfinding results based on the traditional A* algorithm and the improved A* algorithm of this application are shown below. Figure 8a and Figure 8b As shown.

[0073] The evaluation function of the A* algorithm is expressed as:

[0074] f(n) = g(n) + h(n);

[0075] In the formula, f(n) represents the cost estimate of reaching the target state from the initial state via state node n in the grid map, g(n) is the actual cost from the initial state to node n in the state space, and h(n) is the estimated cost of the optimal path from state node n to the target state.

[0076] The third step is the parameterized description of the trajectory. Any flight trajectory can be decomposed into four basic maneuvers: uniform speed (uniform acceleration) flight, stable descent, heading and attitude adjustment, and climb. For each basic maneuver, the flight process can be divided into three phases: initiation, stabilization, and termination. Assume the start times of these three phases are t0, t1, and t2, and the end times are t1, t2, and t3, respectively. The time intervals for each phase are Δt1, Δt2, and Δt3, and the total duration is Δt. A constant c is defined. t Given a time factor, it is required that Δt1 = Δt3 = c t Δt. In the description, a represents acceleration, v represents velocity, Ψ represents yaw angle, the subscript x represents longitudinal, y represents lateral, z represents vertical, 0 represents the initial value, and D represents the final value. The units are: time (s), acceleration (m / s²), velocity (m / s), and distance (m).

[0077] 1)a. Stable level flight

[0078] Uniform flight is characterized by accelerations of 0 m / s². 2The speed is constant, and the distance changes linearly with time:

[0079] a x (t) = 0;

[0080] a y (t) = 0;

[0081] a z (t) = 0.

[0082] b. Uniform acceleration

[0083] During uniform acceleration / deceleration flight, the acceleration is constant, the velocity change is the time integral of the acceleration, and the distance is the time integral of the actual velocity. Different accelerations can be used for different flight segments.

[0084]

[0085] 2) Steady decline

[0086] a. Flight path in the horizontal plane

[0087] The acceleration segmentation along the horizontal plane is as follows:

[0088]

[0089] The initial distance ΔX0 from the target point to the starting position, and the velocity difference ΔV between the target velocity and the initial velocity are related to time as follows:

[0090]

[0091] acceleration during uniform flight The relationship between the velocity difference ΔV between the target velocity and the initial velocity and time is as follows:

[0092]

[0093] b. Vertical Plane Flight Path

[0094] The acceleration along the vertical descent is segmented as follows:

[0095]

[0096] The flight time Δt can be obtained from the flight process in the horizontal plane, and the vertical distance ΔH from the target point to the starting position is given. The constant descent speed is calculated using the following formula:

[0097]

[0098] At this point, the accelerations during the start and end phases are as follows:

[0099]

[0100] 3) Heading and attitude adjustment

[0101] The mathematical description of acceleration during the heading and attitude adjustment process is as follows:

[0102]

[0103] Assume the time of the three flight phases has the following relationship:

[0104]

[0105] 4) Climbing flight

[0106] a. Horizontal flight path (XY direction)

[0107] The acceleration segmentation along the horizontal plane is as follows:

[0108]

[0109] acceleration during uniform flight The relationship between the velocity difference ΔV between the target velocity and the initial velocity and time is as follows:

[0110]

[0111] b. Vertical flight path (Z-axis)

[0112] The acceleration along the vertical climb is segmented as follows:

[0113]

[0114] The flight time Δt can be obtained from the flight process in the horizontal plane, and the vertical distance ΔH from the target point to the starting position is given. The constant descent speed is calculated using the following formula:

[0115]

[0116] At this point, the accelerations during the start and end phases are as follows:

[0117]

[0118] The fourth step is flight trajectory generation. By superimposing the segments of the basic flight path according to their time characteristics, a flight trajectory description function containing acceleration, velocity, and position information can be obtained. Finally, the flight trajectories generated based on existing methods and those generated based on the method of this application are shown below. Figure 9a and Figure 9b As shown.

[0119] This application discloses a flight trajectory planning method for autonomous wind threat avoidance of helicopters. Environmental wind threat identification is based on aerodynamics and flight mechanics analysis methods. A smart path planning algorithm is used to design flight paths, guiding helicopters in selecting flight trajectories when performing missions in complex environments (mountains, cities, ships, etc.). This method performs terrain modeling and accurate wind field numerical simulation of complex environments. Then, analysis points are selected in key wind field areas to conduct in-depth coupled flow field simulation and overall aircraft balance characteristic calculations. After incorporating load abrupt changes and over-limit areas of overall aircraft response into new obstacles, a safety map is formed. Finally, the smart path planning algorithm is used to obtain the helicopter's flight trajectory in complex environments, achieving early prediction and autonomous avoidance of hazards in actual flight environments. This method has good scalability and environmental adaptability; it relates to the fields of numerical simulation of rotorcraft, computer science, and automatic navigation technology.

[0120] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 10 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores and processes data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a flight trajectory planning method for autonomous wind threat avoidance by helicopters.

[0121] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0122] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0123] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0124] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0125] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0126] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0127] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A flight trajectory planning method for autonomous wind threat avoidance by helicopters, characterized in that, The flight trajectory planning method for autonomous wind threat avoidance of helicopters includes: Based on the acquired real terrain data, environmental wind field parameters, and rotor structure, a terrain environment model, a wind field environment model, and a rotor model are constructed. Fluid dynamics simulations are performed on a terrain environment model, a wind field environment model, and a rotor model to calculate the first distribution characteristics within the wind field and the rotor aerodynamic loads. Specifically, this includes: acquiring a configuration file containing the parameters required for the fluid dynamics simulation; the configuration file includes initial flow field parameters, solution control parameters, post-processing, output settings, each grid block, and the filename of each grid block; obtaining the boundary conditions for the fluid dynamics simulation based on the wall distribution and free flow condition distribution in the terrain environment model; the wall is an impenetrable surface; the free flow condition is a boundary that allows fluid to freely enter and exit; performing fluid dynamics simulations on the wind field environment model based on the configuration file and the boundary conditions to obtain the first distribution characteristics within the wind field; the first distribution characteristics include velocity distribution, pressure distribution, and density distribution; and integrating the pressure distribution on the rotor surface in a direction perpendicular to the rotor surface in the rotor model to obtain the rotor aerodynamic loads. Based on the acquired helicopter design parameters, motion state, and the first distribution characteristics, a helicopter flight dynamics model is constructed, and the helicopter flight dynamics model is solved to obtain the helicopter's first state data. Specifically, this includes: establishing transformation relationships between coordinate systems; the coordinate systems include an inertial coordinate system, a body coordinate system, and a rotor rotation coordinate system; constructing aerodynamic models for each component of the helicopter based on the acquired helicopter design parameters, motion state, and the first distribution characteristics; the aerodynamic models represent the triaxial forces and moments at the aerodynamic centers of each component; integrating the triaxial forces and moments of all helicopter components at the helicopter's center of gravity based on the transformation relationships between coordinate systems to obtain the target dynamic equations; and using the six-degree-of-freedom rigid body motion equations and the target dynamic equations, obtaining the helicopter flight dynamics model; the target dynamic equations represent the dynamic equations for the movement of the helicopter's center of gravity and the rotation of the helicopter around its center of gravity; and solving the helicopter flight dynamics model to obtain the helicopter's first state data; the first state data includes the control parameters, attitude, and engine power in equilibrium under complex wind conditions. Based on the rotor aerodynamic load and the first state data, a safety map is obtained by limiting the hazardous factors in the terrain environment model. Specifically, this includes: delineating the aerodynamic load threat zone based on the rotor aerodynamic load and a first judgment condition; delineating the airframe response threat zone based on the first state data and a second judgment condition; and inputting wind field restrictions, obstacle avoidance restrictions, and height restrictions into the terrain environment model to obtain the safety map. The wind field restrictions include the aerodynamic load threat zone and the airframe response threat zone. The obstacle avoidance restrictions and height restrictions are determined based on the terrain environment model. Based on the obtained starting and target positions and the safety map, an improved A* algorithm is used for path planning to obtain the flight trajectory; the improved A* algorithm expands the neighborhood search direction to a multi-angle range based on the traditional A* algorithm.

2. The flight trajectory planning method for autonomous wind threat avoidance of helicopters according to claim 1, characterized in that, Based on the acquired real terrain data, environmental wind field parameters, and rotor structure, a terrain environment model, a wind field environment model, and a rotor model are constructed, specifically including: The acquired real terrain data is simplified to obtain an original map, and the original map is discretized into a grid to obtain a terrain environment model; the real terrain data includes natural environment data and urban area data; the simplification operation is to preserve the topographic relief features in the natural environment data and the overall building shape features in the urban area data. The obtained environmental wind field parameters are calculated using the logarithmic wind profile formula to obtain the average wind speed at different heights, and a wind field environment model is constructed based on the average wind speed at different heights. Based on the obtained rotor structure, the key profile airfoil and guide line of each blade are determined, and the corresponding key profile airfoil is stretched along the guide line to obtain the blade shape model of each blade. According to the actual phase difference, the blade shape model of each blade is placed in the blade grid to obtain the rotor model; the rotor is composed of multiple blades.

3. The flight trajectory planning method for autonomous wind threat avoidance of helicopters according to claim 2, characterized in that, The formula for the logarithmic wind profile is: ； In the formula, Height above ground Horizontal wind speed at the location; The friction speed; Karman's constant; Height above the ground; The length of the ground roughness.

4. The flight trajectory planning method for autonomous wind threat avoidance of helicopters according to claim 1, characterized in that, Solving the helicopter flight dynamics model yields the helicopter's first state data, specifically including: The helicopter flight dynamics model was solved using the Newton-Raphson method to obtain the helicopter's first state data.

5. The flight trajectory planning method for autonomous wind threat avoidance of helicopters according to claim 1, characterized in that, The first judgment condition is to classify the region where the change in rotor aerodynamic load between adjacent discrete points exceeds 10% as the aerodynamic load threat zone; The second judgment condition is to classify the area exceeding the safe condition as the aircraft response threat zone; the safe condition is that the control margin in the first state data is greater than 10%, and the roll angle in the attitude angle is... The range is greater than -8° and less than 8°, and the pitch angle is among the attitude angles. The range is greater than -4° and less than 7°, and the engine power retains a 10% margin.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the flight trajectory planning method for autonomous wind threat avoidance of helicopters according to any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the flight trajectory planning method for autonomous wind threat avoidance of helicopters as described in any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the flight trajectory planning method for autonomous wind threat avoidance of helicopters as described in any one of claims 1-5.

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