Fixed wing flight stage trajectory planning method for mobile platform landing

Abstract

The invention discloses a fixed wing flight phase trajectory planning method for mobile platform landing, which comprises the following steps: defining an overall landing task of an unmanned aerial vehicle, and establishing an unmanned aerial vehicle motion model according to the flight speed, course angle and track angle of the unmanned aerial vehicle; and establishing a mobile platform motion model according to the platform linear velocity and the platform course of the mobile platform, and generating candidate landing guide tracks based on the unmanned aerial vehicle motion model. The method has the beneficial effects that the unmanned aerial vehicle motion model is established according to the flight parameters of the unmanned aerial vehicle, the mobile platform motion model is established according to the motion parameters of the mobile platform, and the candidate landing guide trajectory of the unmanned aerial vehicle in the transverse plane coordinate system is generated based on the unmanned aerial vehicle motion model; and further generating candidate landing guide tracks of the unmanned aerial vehicle in the three-dimensional coordinate system by combining the maximum descending angle of the longitudinal plane of the unmanned aerial vehicle, and performing track scoring on the candidate landing guide tracks in the three-dimensional coordinate system according to a track scoring function so as to screen out an optimal unmanned aerial vehicle landing guide track.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) flight trajectory technology, and particularly relates to a trajectory planning method for the fixed-wing flight phase of mobile platform landing. Background Technology

[0002] In both civilian and military fields, drones play a crucial role, with a wide range of applications covering precision mapping, emergency communications, intelligence gathering, and collaborative operations. Traditional fixed-wing drones, while possessing the advantage of long endurance, have high requirements for takeoff and landing conditions, which significantly limits their operational flexibility. In contrast, multi-rotor drones, despite their greater deployment flexibility and ability to quickly respond to various mission requirements, face the significant challenge of short range. To combine the advantages of both, vertical takeoff and landing (VTOL) fixed-wing drones have emerged, achieving both efficient cruising and vertical takeoff and landing capabilities.

[0003] However, in scenarios without fixed take-off and landing sites, such as site deployment and emergency rescue, traditional vertical take-off and landing fixed-wing UAVs usually need to rely on mobile platforms to complete take-off and landing operations. Existing technology has not yet enabled such UAVs to land autonomously and accurately on mobile platforms, making it difficult to meet the needs of efficient operations in scenarios without fixed sites.

[0004] To address the aforementioned issues, a trajectory planning method for the fixed-wing flight phase of mobile platform landing is proposed. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a trajectory planning method for the fixed-wing flight phase of landing on mobile platforms, which enables vertical take-off and landing fixed-wing UAVs to autonomously and accurately land on mobile platforms in scenarios without fixed take-off and landing sites, such as site deployment and emergency rescue.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a trajectory planning method for the fixed-wing flight phase of a mobile platform landing, comprising the following steps: S1: defining the overall landing task of the UAV, obtaining the UAV's flight speed, heading angle, track angle, minimum turning radius, and maximum descent angle in the longitudinal plane, and obtaining the platform linear velocity and platform heading of the mobile platform; S2: establishing a UAV motion model based on the UAV's flight speed, heading angle, and track angle, and establishing a mobile platform motion model based on the mobile platform's platform linear velocity and platform heading; S3: generating candidate landing guidance trajectories for the UAV in the horizontal plane coordinate system based on the UAV motion model and the UAV's minimum turning radius; S4: generating candidate landing guidance trajectories for the UAV in the three-dimensional coordinate system based on the candidate landing guidance trajectories in the horizontal plane coordinate system, combined with the UAV's maximum descent angle in the longitudinal plane; S5: scoring the candidate landing guidance trajectories in the three-dimensional coordinate system and selecting the optimal UAV landing guidance trajectory.

[0007] Optionally, in S1, defining the overall landing mission of the UAV specifically includes the following steps: S101: The initial state of the UAV is represented by a state vector, as shown in the following expression: ; in, The initial heading angle of the drone. The initial pitch angle of the drone. Let be the initial scalar velocity of the drone. The initial three-dimensional position of the UAV in the geodetic coordinate system is represented as: ; S102: Define the predicted trajectory of the mobile platform, specifically a sequence of discrete state points: ; Each state point All of them contain the position, heading, and speed information of the mobile platform at that moment.

[0008] Optionally, in S2, establishing the UAV motion model specifically includes setting the UAV as a parameter-constrained point mass, whose three-dimensional coordinates are represented as (x, y, z), and whose kinematic equations are: In the formula, V represents the flight speed, χ represents the heading angle, and γ represents the track angle. These represent the components of flight speed on the three coordinate axes.

[0009] Optionally, in S2, establishing the motion model of the mobile platform specifically includes defining the mobile platform as a rigid body moving in a two-dimensional plane, whose two-dimensional coordinates are represented as follows: The equation of motion is: In the formula, Indicates the platform line speed. For the platform's angular velocity, The trajectory of the mobile platform is composed of straight line segments and circular arc segments, and the speed within each segment remains constant.

[0010] Optionally, in S3, generating the candidate landing guidance trajectory of the UAV in the horizontal plane coordinate system specifically includes, given the initial pose of the UAV as... The target pose is The minimum turning radius of the drone is The path lengths of various CLC paths, including RSR paths, LSL paths, and RSL paths, are solved using Dubins path theory.

[0011] Optionally, the steps for calculating the RSR path length are as follows: The initial center of the circle is calculated using the following formula: ; The formula for calculating the center of the target circle is as follows: ; The formulas for calculating the distance between the centers and the length of the external common tangent are as follows: ; The straight segment of the RSR path The length is the distance between the centers, D. The length of the straight segment and the turning angle of the straight segment are calculated as follows: ; The initial arc length is: ; The arc length at the end is: ; The total path length is: ; Use a similar method to calculate the path lengths of the LSL path, RSL path, and LSR path in sequence.

[0012] Optionally, the end arc length J of various CLC paths can be calculated separately, using the following formula: In the formula, R is the turning radius of the drone.

[0013] Optionally, in S4, generating the candidate landing guidance trajectory of the UAV in the three-dimensional coordinate system specifically includes calculating the track angle: In the formula, Let L be the height difference and L be the lateral path length. The following conditions must be met: Combining the two-dimensional Dubins algorithm, we can obtain the shortest horizontal path and its corresponding time: ; like If the target point is not feasible, then the time margin is not calculated. with path increment ; The method for calculating the number of hovering circles of a drone while it is waiting is as follows: ; Early arrival time is: ; like If the landing is successful, the landing requirements are met; otherwise, the disk rotation angle needs to be adjusted and the trajectory replanned. Finally, a reasonable starting point is selected in the horizontal plane trajectory so that the vertical plane descent angle is close to the maximum value, thereby shortening the low-altitude flight time.

[0014] Optionally, in S5, the trajectory scoring of candidate landing guidance trajectories in the three-dimensional coordinate system specifically includes calculating the UAV's go-around capability score J. refly The calculation formula is: In the formula, This represents the number of remaining landable points in the trajectory. Total number of landing points The length of the straight segment at the end. To find the shortest path length back to the platform, The total distance traveled by the mobile platform. The corresponding weighting coefficients (0.3, 0.5, and 0.2 respectively) are used to select the trajectory with the highest go-around capability score as the final execution path through the scoring calculation function.

[0015] Optionally, selecting the optimal UAV landing guidance trajectory refers to generating an optimal trajectory for the UAV so that it will land at a certain future moment. Accurately reach the target state The target state is the same as the state of the mobile platform at the same time. Matching, that is: The matching criteria are that the final position, heading, and speed of the drone are completely consistent with the platform.

[0016] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: A UAV motion model is established based on the UAV's flight parameters; a mobile platform motion model is established based on the mobile platform's motion parameters; candidate landing guidance trajectories for the UAV in the horizontal plane coordinate system are generated based on the UAV motion model; further candidate landing guidance trajectories for the UAV in the three-dimensional coordinate system are generated by combining the UAV's maximum descent angle in the vertical plane; and the candidate landing guidance trajectories in the three-dimensional coordinate system are scored according to the trajectory scoring function, thereby selecting the optimal UAV landing guidance trajectory. This optimal UAV landing guidance trajectory can stably plan landing trajectories that meet spatiotemporal coordination constraints for different initial situations within a horizontal distance of 1.5km, and can adapt to different time margins by flexibly adjusting the hovering waiting time, thus improving the universality and robustness in multiple scenarios. Attached Figure Description

[0017] Figure 1 The diagram shown is a flowchart of the present invention. Figure 2 The diagram shown illustrates the calculation of the tangent point and path length for the RSR of this invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0019] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances. Example

[0021] Reference Figure 1 This embodiment provides a trajectory planning method for the fixed-wing flight phase of a mobile platform landing, specifically including the following steps: S1: Define the overall landing task of the UAV, obtain the UAV's flight speed, heading angle, track angle, minimum turning radius, and maximum descent angle in the longitudinal plane, and obtain the platform linear velocity and platform heading of the mobile platform; S2: Establish a UAV motion model based on the UAV's flight speed, heading angle, and track angle, and establish a mobile platform motion model based on the mobile platform's platform linear velocity and platform heading; S3: Based on the UAV motion model and the UAV's minimum turning radius, generate candidate landing guidance trajectories for the UAV in the horizontal plane coordinate system; S4: Based on the candidate landing guidance trajectories in the horizontal plane coordinate system, combine the UAV's maximum descent angle in the longitudinal plane to generate candidate landing guidance trajectories for the UAV in the three-dimensional coordinate system; S5: Perform trajectory scoring on the candidate landing guidance trajectories in the three-dimensional coordinate system, and select the optimal UAV landing guidance trajectory.

[0022] In S1, the overall landing mission for the UAV is defined by the following steps: S101: The initial state of the UAV is represented by a state vector, as shown in the following expression: ; in, The initial heading angle of the drone. The initial pitch angle of the drone. Let be the initial scalar velocity of the drone. The initial three-dimensional position of the UAV in the geodetic coordinate system is represented as: ; S102: Define the predicted trajectory of the mobile platform, specifically a sequence of discrete state points: ; Each state point All of them contain the position, heading, and speed information of the mobile platform at that moment.

[0023] In S2, establishing the UAV motion model specifically includes defining the UAV as a point mass subject to parameter constraints, with its three-dimensional coordinates represented as (x, y, z), and the kinematic equations as follows: In the formula, V represents the flight speed, χ represents the heading angle, and γ represents the track angle. These represent the components of flight speed on the three coordinate axes.

[0024] The above parameters are strictly constrained by the physical performance of the UAV, including the minimum turning radius in the horizontal plane, the vertical track angle limit, and altitude constraints.

[0025] In S2, establishing the motion model of the mobile platform specifically includes defining the mobile platform as a rigid body moving in a two-dimensional plane, whose two-dimensional coordinates are represented as follows: The equation of motion is: In the formula, Indicates the platform line speed. For the platform's angular velocity, The trajectory of the mobile platform is composed of straight line segments and circular arc segments, and the speed within each segment remains constant.

[0026] In S3, generating the candidate landing guidance trajectory of the UAV in the horizontal plane coordinate system specifically includes, given the initial pose of the UAV as... The target pose is The minimum turning radius of the drone is The path lengths of various CLC paths, including RSR paths, LSL paths, and RSL paths, are solved using Dubins path theory.

[0027] The steps to calculate the RSR path length are as follows: The initial center of the circle is calculated using the following formula: ; The formula for calculating the center of the target circle is as follows: ; The formulas for calculating the distance between the centers and the length of the external common tangent are as follows: ; The straight segment of the RSR path The length is the distance between the centers, D. The length of the straight segment and the turning angle of the straight segment are calculated as follows: ; The initial arc length is: ; The arc length at the end is: ; The total path length is: ; Use a similar method to calculate the path lengths of the LSL path, RSL path, and LSR path in sequence.

[0028] Calculate the end arc length J of various CLC paths using the following formulas: In the formula, R is the turning radius of the drone.

[0029] The Dubins path is the shortest of the four CLC paths. However, the risk of low-altitude circling during descent is relatively high, so the Dubins path is not necessarily the optimal choice. Therefore, this application incorporates terminal arc length considerations in the path selection to reduce the risks associated with low-altitude circling.

[0030] The Dubins path is a classic model in motion planning, used to find the shortest path connecting two attitudes (position and orientation) in a two-dimensional plane while satisfying curvature constraints (i.e. minimum turning radius limit). The path is composed of a straight line segment (S) and a circular arc segment with a fixed radius (L represents a left turn and R represents a right turn).

[0031] The CLC path consists of three segments: a circular arc segment, a straight line segment, and another circular arc segment. It is a common path form used by vehicles such as drones that have minimum turning radius constraints.

[0032] The first segment of the RSR path is a right-turning arc, the second segment is a straight line, and the third segment is a right-turning arc.

[0033] The first segment of the LSL path is a left-turning arc, the second segment is a straight line, and the third segment is a left-turning arc.

[0034] The first segment of the RSL path is a right-turning arc, the second segment is a straight line, and the third segment is a left-turning arc.

[0035] The first segment of the LSR path is a left-turning arc, the second segment is a straight line, and the third segment is a right-turning arc.

[0036] In S4, generating a candidate landing guidance trajectory for the UAV in a three-dimensional coordinate system specifically includes calculating the track angle: In the formula, Let L be the height difference and L be the lateral path length. The following conditions must be met: Combining the two-dimensional Dubins algorithm, we can obtain the shortest horizontal path and its corresponding time: ; like If the target point is not feasible, then the time margin is not calculated. with path increment ; The method for calculating the number of hovering circles of a drone while it is waiting is as follows: ; Early arrival time is: ; like If the landing is successful, the landing requirements are met; otherwise, the disk rotation angle needs to be adjusted and the trajectory replanned. Finally, a reasonable starting point is selected in the horizontal plane trajectory so that the vertical plane descent angle is close to the maximum value, thereby shortening the low-altitude flight time.

[0037] In S5, trajectory scoring for candidate landing guidance trajectories in the three-dimensional coordinate system specifically includes calculating the UAV's go-around capability score J. refly The calculation formula is: In the formula, This represents the number of remaining landable points in the trajectory. Total number of landing points The length of the straight segment at the end. To find the shortest path length back to the platform, The total distance traveled by the mobile platform. The corresponding weighting coefficients (0.3, 0.5, and 0.2 respectively) are used to select the trajectory with the highest go-around capability score as the final execution path through the scoring calculation function.

[0038] The go-around capability scoring method is designed based on three factors: the number of subsequent landing points in the trajectory, the length of the remaining straight segment at the end of the current trajectory, and the shortest path length from the end of the current trajectory back to the target platform.

[0039] This scoring method selects the trajectory with the highest go-around capability score as the final execution path. This method effectively balances go-around capability and mission efficiency, providing safety assurance for mobile platform tracking tasks.

[0040] Numerical simulation results based on real-world scenarios demonstrate that the trajectory planning method for fixed-wing aircraft landing on mobile platforms described in this invention can stably plan landing trajectories that satisfy spatiotemporal constraints for different initial states within a lateral distance of 1.5 km. Furthermore, it can adapt to different time margins by flexibly adjusting the hovering waiting time. This invention's trajectory planning method exhibits universality and robustness across multiple scenarios.

[0041] Selecting the optimal drone landing guidance trajectory refers to generating an optimal trajectory for the drone so that it lands at a certain future moment. Accurately reach the target state The target state is the same as the state of the mobile platform at the same time. Matching, that is: The matching criteria are that the final position, heading, and speed of the drone are completely consistent with the platform.

[0042] This scheme first establishes a dynamic model of a vertical take-off and landing fixed-wing UAV and a model of a mobile platform to be landed; using an improved three-dimensional Dubins path theory, multiple candidate trajectories are generated that connect the current state of the UAV with the predicted future pose of the mobile platform; a trajectory scoring mechanism based on "go-around capability" is designed, and this mechanism is used to select the optimal candidate trajectory; through numerical simulations in various typical cooperative scenarios, the effectiveness and robustness of the proposed algorithm are verified.

[0043] By combining an improved Dubins path generation and go-around capability scoring function, mission efficiency and flight safety are effectively balanced. This invention can stably plan landing trajectories that meet the dynamic constraints and spatiotemporal coordination requirements of the UAV under various initial relative situations. Through a comprehensive evaluation of future landing opportunities, terminal approach stability, and potential return-to-home costs, the selected trajectory possesses higher fault tolerance and safety, providing important safety redundancy for dealing with unexpected situations during landing (such as sensor loss of lock-on or external gust interference).

[0044] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for fixed-wing flight phase trajectory planning for a mobile platform landing, characterized in that, The method comprises the following steps: S1: defining the overall landing task of the UAV, obtaining the flight speed, heading angle, track angle, minimum turning radius and maximum longitudinal plane descent angle of the UAV, and obtaining the platform linear speed and platform heading of the mobile platform; S2: establishing a UAV motion model according to the flight speed, heading angle and track angle of the UAV, and establishing a mobile platform motion model according to the platform linear speed and platform heading of the mobile platform; S3: generating a candidate landing guide trajectory of the UAV in the horizontal plane coordinate system based on the UAV motion model and the minimum turning radius of the UAV; S4: generating a candidate landing guide trajectory of the UAV in the three-dimensional coordinate system based on the candidate landing guide trajectory in the horizontal plane coordinate system and in combination with the maximum longitudinal plane descent angle of the UAV; S5: performing trajectory scoring on the candidate landing guide trajectory in the three-dimensional coordinate system, and screening out the optimal UAV landing guide trajectory.

2. The method of claim 1, wherein, In S1, the definition of the overall landing task of the UAV specifically comprises the following steps: S101: using a state vector to represent the initial state of the UAV, and the expression is as follows: ； wherein, is an initial yaw angle of the UAV, is an initial pitch angle of the UAV, is an initial scalar velocity of the UAV, is an initial three-dimensional position of the UAV in the ECEF coordinate system, denoted as: ； S102: defining the predicted trajectory of the mobile platform, specifically a sequence composed of discrete state points: ； wherein each state point contains the position, heading and velocity information of the mobile platform at that instant.

3. The method of claim 2, wherein, In S2, the establishment of the UAV motion model specifically comprises setting the UAV as a parameter-constrained particle, and the three-dimensional coordinates of the UAV are represented as (x, y, z), and the kinematics equation is: where V represents the flight velocity, χ represents the heading angle, γ represents the track angle, respectively represent the components of the flight velocity on the three coordinate axes.

4. The method of claim 3, wherein, In S2, the establishing the mobile platform motion model specifically includes setting the mobile platform as a rigid body moving in a two-dimensional plane, and a two-dimensional coordinate of the mobile platform is represented as , and a motion equation is wherein, denotes the platform linear velocity, is the platform angular velocity, denotes the platform heading, the motion trajectory of the moving platform is spliced by straight line segments and circular arc segments, and the speed in each segment remains constant.

5. The method of claim 1, wherein, In S3, the generating candidate landing guide trajectory of the UAV in the horizontal plane coordinate system specifically comprises that, given an initial pose of the UAV as , a target pose as , a minimum turning radius of the UAV as , a path length of a plurality of CLC paths is respectively solved by using Dubins path theory, the CLC paths including an RSR path, an LSL path, an RSL path and an LSR path.

6. The method of fixed-wing flight phase trajectory planning for a mobile platform landing according to claim 5, wherein, The solving steps of the RSR path length are as follows: Calculate the initial center, and the formula is as follows: ； The center of the target circle is calculated by the following formula: ; The circle center distance and the outer tangent length are calculated, and the formula is as follows: ; wherein the straight segments of the RSR path The length is the center distance D; Calculate the segment length, and the linear segment turning angle is: ； The initial arc length is: ； The end arc length is: ； The total path length is: ； The path lengths of the LSL path, the RSL path and the LSR path are calculated in a similar manner.

7. The method of fixed-wing flight phase trajectory planning for a mobile platform landing according to claim 6, wherein, The end arc length J of a plurality of CLC paths is calculated, and the calculation formula is as follows: where R is the turning radius of the drone.

8. The method of claim 6, wherein, In S4, the generation of the candidate landing guide trajectory of the UAV in the three-dimensional coordinate system specifically comprises calculating the track angle: wherein is the height difference, L is the lateral path length, and needs to satisfy: Combining the two-dimensional Dubins algorithm, the shortest path in the lateral direction and the corresponding time can be obtained: ; If , the target point is infeasible, otherwise continue to calculate the time margin and path increment ; The number of circling when the UAV waits is calculated as follows: ； The advance arrival time is: ； If The landing requirement is met, otherwise the disc rotation angle needs to be adjusted and re-planned, and finally a reasonable starting point is selected in the horizontal plane trajectory to make the longitudinal plane descent angle close to the maximum value to shorten the low-altitude flight time.

9. The method of claim 8, wherein, In S5, the trajectory scoring of the candidate landing guide trajectory in the three-dimensional coordinate system specifically comprises calculating the reflight capability score J of the unmanned aerial vehicle refly , and the calculation formula is: In the formula, is the number of remaining landing points in the trajectory, is the total number of landing points, is the length of the terminal straight line segment, is the length of the shortest path back to the platform, is the total movement distance of the moving platform, is the corresponding weight coefficient (0.3, 0.5, 0.2 respectively), and through the scoring calculation function, the trajectory with the highest reflight capability score is selected as the final execution path.

10. The method of claim 2, wherein, The selection of the optimal UAV landing guidance trajectory refers to generating an optimal trajectory for the UAV so that it will land at a certain future moment. Accurately reach the target state The target state is the same as the state of the mobile platform at the same time. Matching, that is: The matching criteria are that the final position, heading, and speed of the drone are completely consistent with the platform.

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