Method and device for planning aerial landing trajectory of son aircraft of son-mother type unmanned aerial vehicle

Abstract

The invention provides a child aircraft air landing trajectory planning method and device of a child-mother type unmanned aerial vehicle. The method comprises the steps that the position of a starting point of a child aircraft and the position of a landing point of the child aircraft on a mother aircraft are obtained; based on an adaptive particle swarm optimization algorithm, determining the positions of two optimal control points between the position of the starting point and the position of the landing point by using the cost evaluation value; the cost evaluation value is determined by a track length cost value, a track pneumatic interference cost value, a track smoothness cost value and a no-fly zone collision cost value; and based on the position of the starting point, the positions of the two optimal control points and the position of the landing point, the optimal landing track of the sub-aircraft in the air is determined. According to the method, an optimal air landing trajectory planning task of the son-mother unmanned aerial vehicle with a rapid, safe and kinematically feasible son-mother unmanned aerial vehicle under the hovering of the mother-mother unmanned aerial vehicle can be realized.

Description

Technical Field

[0001] The present application relates to the field of UAV technology, and in particular to a method and device for planning the aerial landing trajectory of a sub-unit of a mother-and-child UAV. Background Art

[0002] The key to coordinated operation of a mother-and-child drone system is landing the child drone on the hovering mother drone platform. However, rotary-wing drones experience strong downwash below their rotors and a hemispherical low-pressure zone above them. This low-pressure zone above the mother drone's propellers causes thrust loss in the child drone. The child drone's downwash also creates flow field coupling and aerodynamic interference with the mother drone's flow field, resulting in unbalanced forces and torques on both the mother drone and the child drone, causing attitude and position instability, which in turn affects the accuracy and safety of the child drone's mid-air landing. Due to the strong and complex flow field coupling and aerodynamic interference experienced by rotary-wing drones when approaching each other, mid-air landing trajectory planning for the child drone also faces the challenge of ensuring safety from aerodynamic interference.

[0003] Existing trajectory planning strategies for docking aircraft only consider the aerodynamic interference experienced by the aircraft, not the impact of the aerodynamic interference experienced by the aircraft itself. Furthermore, the differences in aerodynamic interference between the aircraft and the mother aircraft at their relative projected positions are ignored. However, during the actual docking process, the position drift and attitude oscillation of the mother aircraft caused by aerodynamic interference cannot be ignored, and the flow field coupling conditions and aerodynamic interference forces and moments experienced by the aircraft at different relative positions vary significantly.

[0004] For the above problems, no effective solutions have been proposed yet. Summary of the Invention

[0005] In view of this, the present application provides a method and device for planning the aerial landing trajectory of a sub-machine of a mother-and-child type UAV to solve the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides a method for planning the aerial landing trajectory of a sub-unit of a carrier-sub type UAV, comprising:

[0007] Obtain the position of the starting point of the slave aircraft and the position of the landing point of the slave aircraft on the mother aircraft;

[0008] Based on an adaptive particle swarm optimization algorithm, the positions of two optimal control points between the starting point and the landing point are determined using a cost evaluation value; the cost evaluation value is determined by a trajectory length cost value, a trajectory aerodynamic interference cost value, a trajectory smoothness cost value, and a no-fly zone collision cost value;

[0009] The optimal landing trajectory of the sub-machine in the air is determined based on the position of the starting point, the positions of the two optimal control points and the position of the landing point.

[0010] In one possible implementation, based on an adaptive particle swarm optimization algorithm, the cost evaluation value is used to determine the positions of two optimal control points between the starting point and the landing point; including:

[0011] Step S1: rasterizing the three-dimensional map of the trajectory planning space of the slave machine to obtain a three-dimensional raster map;

[0012] Step S2: selecting two control points between the starting point and the landing point, setting the position of a single particle to include the two control points, and the nodes of the three-dimensional grid map are the feasible domains of the particles;

[0013] Step S3: Initialize the number of particles N in the population, the total number of iterations K, and the speed and position of N particles;

[0014] Step S4: Determine the particle corresponding to the minimum cost evaluation value among the N particles in the kth iteration And the particle corresponding to the minimum cost evaluation value of all particles in the kth iteration and the first k-1 iterations The initial value of k is 1, and These are all particles corresponding to the minimum cost evaluation value among the initial N particles;

[0015] Step S5: Based on and Determine the speed and position of N particles in the k+1th iteration;

[0016] Step S6: Determine whether k is less than the total number of iterations K. If yes, then go to step S4 after k+1. Otherwise, determine The corresponding particle is the optimal particle;

[0017] Step S7: Determine the two control points in the optimal particle as the two optimal control points.

[0018] In one possible implementation, the particle with the minimum cost evaluation value among the N particles in the kth iteration is determined. And the particle corresponding to the minimum cost evaluation value of all particles in the kth iteration and the first k-1 iterations include:

[0019] Based on the position of the starting point, the positions of the two control points corresponding to the nth particle in the kth iteration, and the position of the landing point, the nth trajectory curve in the kth iteration is obtained using cubic spline interpolation. Among them, 1≤n≤N, 1≤k≤K;

[0020] Calculate the cost evaluation value of the n-th trajectory curve in the k-th iteration based on the trajectory length cost value, the trajectory aerodynamic interference cost value, the trajectory smoothness cost value, and the no-fly zone collision cost value;

[0021] The particle corresponding to the minimum cost evaluation value of the N trajectory curves in the kth iteration is determined as the particle

[0022] The particle corresponding to the minimum cost evaluation value of all trajectory curves in the kth iteration and the previous k-1 iterations is determined as the particle

[0023] In one possible implementation, a cost evaluation value of the nth trajectory curve in the kth iteration is calculated based on a trajectory length cost value, a trajectory aerodynamic interference cost value, a trajectory smoothness cost value, and a no-fly zone collision cost value; including:

[0024] The nth trajectory curve in the kth iteration Divide into M approximate straight line segments;

[0025] Calculate the nth trajectory curve in the kth iteration The estimated value of for:

[0026]

[0027] Among them, ω1 is the trajectory length cost F length The weight value of ω2 is the trajectory aerodynamic interference cost F cost The weight value, ω3 is the trajectory smoothness cost F smooth The weight value, ω4 is the no-fly zone collision cost F bump The weight value of

[0028] Trajectory length cost F length for:

[0029]

[0030] Among them, (x star ,y star ,z star ) is the three-dimensional coordinate of the starting point of the slave machine, (x end ,y end ,z end ) is the three-dimensional coordinate of the landing point of the sub-machine; d m is the length of the mth approximate straight line segment;

[0031] Trajectory aerodynamic interference cost F cost for:

[0032]

[0033] Among them, C set is the set scaling factor; C(x m,y m ,z m ) is the starting point of the mth approximate straight line segment (x m ,y m ,z m ) is obtained from the pre-established normalized aerodynamic interference cost three-dimensional map;

[0034] Trajectory smoothness cost F smooth for:

[0035] F smooth =w κ ·∫κ(t)·κ set +w τ· ∫τ(t)·τ set

[0036] Among them, w κ and w τ Represent the weight values ​​of curvature cost and torsion cost respectively; κ set is the magnification coefficient of the curvature curve κ(t), τ set is the magnification coefficient of the torsion curve τ(t); t is the independent variable;

[0037] No-fly zone collision cost F bump for:

[0038]

[0039] The no-fly zone is the area where the aerodynamic interference of the mother and child aircraft is greater than the first threshold and the impassable area. prohibit is 10 5 .

[0040] In one possible implementation, the method further includes:

[0041] With the landing point of the sub-machine on the mother machine fuselage as the origin, a three-dimensional coordinate system is established, where the XY plane is the upper plane of the mother machine fuselage;

[0042] The test points with different Z values ​​are selected for aerodynamic interference simulation test, where the projection of the test point on the XY plane is the rotor center and the fuselage center of the mother aircraft; based on the aerodynamic interference simulation results of the test point, the height interval [h min ,h max ] as the research interval, and the Z value is h min The plane is taken as the first plane, and the Z value is h max The plane of is taken as the second plane;

[0043] Select the relative projection positions of the centroids of multiple sub-machines relative to the main machine on the XY plane as simulation solution nodes;

[0044] On the first plane, a point-by-point simulation is performed to solve the physical quantity value of the evaluation disturbance influence of each simulation solution node on the first plane, and a two-dimensional distribution diagram of the physical quantity of the evaluation disturbance influence on the first plane is obtained by interpolation;

[0045] On the second plane, the physical quantity value of the evaluation disturbance influence of each simulation solution node on the second plane is solved point by point by simulation, and a two-dimensional distribution diagram of the physical quantity of the evaluation disturbance influence on the second plane is obtained by interpolation.

[0046] In one possible implementation, the method further includes:

[0047] Based on the pre-established two-dimensional distribution diagram of the evaluation disturbance influence physical quantity of the first plane, calculate the normalized mth evaluation disturbance influence physical quantity of the first plane of the i-th simulation solution node

[0048]

[0049] in, Represents the physical quantity affected by the mth evaluation disturbance on the first plane of the i-th simulation solution node; and Represent the minimum and maximum values ​​of the mth physical quantity affected by the disturbance of all simulation solution nodes in the first and second planes, respectively; 1≤m≤M, the M physical quantities affected by the disturbance include: the absolute value of the total propeller thrust loss of the daughter aircraft, the absolute value of the lift loss of the daughter aircraft, the resultant torque of the daughter aircraft, the absolute value of the total propeller thrust loss of the mother aircraft, the absolute value of the lift loss of the mother aircraft, the resultant torque of the mother aircraft, and the downforce of the mother aircraft;

[0050] Calculate the aerodynamic interference cost of the first plane of the i-th simulation solution node 1,i :

[0051]

[0052] Among them, ω m Represents the weight coefficient of the physical quantity affected by the mth evaluation disturbance;

[0053] Interpolating the aerodynamic interference cost values ​​of the first plane of all simulation solution nodes to generate a two-dimensional distribution map of the aerodynamic interference cost of the first plane;

[0054] Based on the pre-established two-dimensional distribution diagram of the evaluation disturbance influence physical quantity of the second plane, calculate the normalized mth evaluation disturbance influence physical quantity of the second plane of the i-th simulation solution node

[0055]

[0056] in, Represents the mth evaluation disturbance impact physical quantity of the second plane of the i-th simulation solution node;

[0057] Calculate the aerodynamic interference cost of the second plane of the i-th simulation solution node 2,i :

[0058]

[0059] Interpolating the aerodynamic interference cost values ​​of the second plane of all simulation solution nodes to generate a two-dimensional distribution map of the aerodynamic interference cost of the second plane;

[0060] Performing interlayer interpolation on the two-dimensional distribution map of aerodynamic interference cost on the first plane and the two-dimensional distribution map of aerodynamic interference cost on the second plane to obtain a three-dimensional map of aerodynamic interference cost;

[0061] Rasterizing the three-dimensional map of aerodynamic interference cost to obtain multiple grid nodes;

[0062] The aerodynamic interference cost for the jth grid node is j Perform normalization to obtain the normalized aerodynamic interference cost of the jth grid node: j ′ :

[0063]

[0064] Among them, cost min and cost max Respectively represent the minimum and maximum aerodynamic interference cost values ​​of all grid nodes;

[0065] Based on the normalized aerodynamic interference cost values ​​of all grid nodes, a normalized aerodynamic interference cost three-dimensional map is obtained.

[0066] In one possible implementation, based on and Determine the velocities and positions of N particles at iteration k+1; this includes:

[0067] The velocity of the nth particle in the k+1th iteration and location

[0068]

[0069] Among them, the individual learning factor c1 and the global learning factor c2 are:

[0070]

[0071] Among them, the coefficient k1 and the coefficient k2 are:

[0072]

[0073] Δf is the change in cost evaluation value:

[0074]

[0075] ω is the inertia weight:

[0076] ω=ω max -k(ω max -ω min ) / K

[0077] Among them, ω max and ω min are the maximum and minimum values ​​of the inertia weight respectively.

[0078] In a second aspect, an embodiment of the present application provides a device for planning an aerial landing trajectory of a sub-unit of a mother-and-child type UAV, comprising:

[0079] an acquisition unit, configured to acquire the position of the starting point of the slave aircraft and the position of the landing point of the slave aircraft on the mother aircraft;

[0080] a determination unit, configured to determine positions of two optimal control points between the starting point and the landing point using a cost evaluation value based on an adaptive particle swarm optimization algorithm; the cost evaluation value being determined by a trajectory length cost value, a trajectory aerodynamic interference cost value, a trajectory smoothness cost value, and a no-fly zone collision cost value;

[0081] The trajectory planning unit is used to determine the optimal landing trajectory of the sub-machine in the air based on the position of the starting point, the positions of the two optimal control points and the position of the landing point.

[0082] In a third aspect, an embodiment of the present application provides an electronic device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of the embodiment of the present application when executing the computer program.

[0083] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method of the embodiment of the present application is implemented.

[0084] This application can achieve the optimal air landing trajectory planning task of the child drone quickly, safely and kinematically feasible when the mother drone of the mother drone is hovering in the air. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0086] Figure 1 A flowchart of a method for planning the aerial landing trajectory of a daughter aircraft of a mother-and-child type UAV provided in an embodiment of the present application;

[0087] Figure 2 This is a functional structure diagram of the aerial landing trajectory planning device for a daughter drone of a mother-and-child type UAV provided in an embodiment of the present application;

[0088] Figure 3 This is a functional structure diagram of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0089] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0090] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0091] First, a brief introduction to the design concept of the embodiments of the present application is given.

[0092] Rotary-wing drones, with their advantages of maneuverability and environmental adaptability, are playing an increasingly important role in applications such as coordinated warfare, resource exploration, and disaster relief. Small rotary-wing drones are highly maneuverable and can operate in confined environments, but they have limited endurance and poor payload capacity. Larger rotary-wing drones have long flight times and strong payload capacity, but they lack maneuverability and are unable to operate in confined environments. Using a large drone as a mother drone to transport a smaller child drone to a nearby operational area, a mother-and-child drone-based operation strategy can complement the strengths of both drones and the mother drone, offering broad application prospects.

[0093] Existing trajectory planning strategies for docking aircraft only consider the aerodynamic interference experienced by the aircraft, not the impact of the aerodynamic interference experienced by the aircraft itself. Furthermore, the differences in aerodynamic interference between the aircraft and the mother aircraft at their relative projected positions are ignored. However, during the actual docking process, the position drift and attitude oscillation of the mother aircraft caused by aerodynamic interference cannot be ignored, and the flow field coupling conditions and aerodynamic interference forces and moments experienced by the aircraft at different relative positions vary significantly.

[0094] To solve the above technical problems, it is necessary to construct an evaluation model for the aerial landing scenario of the daughter UAV, which fully considers the impact of aerodynamic interference on the daughter UAV and the mother UAV at different relative positions, and design an optimal trajectory planning strategy for the daughter UAV considering aerodynamic interference based on the evaluation model, so as to achieve a balance between the safety, rapidity and kinematic feasibility of aerodynamic interference.

[0095] This application proposes a trajectory planning method that takes aerodynamic interference into account for the task of landing a sub-unit of a rotary-wing mother-and-child drone onto a hovering mother unit. By constructing an aerodynamic interference assessment model for the sub-unit based on aerodynamic simulation results, the aerodynamic interference cost of the sub-unit trajectory is quantified. By mathematically modeling the constraints on the safety, dynamics, and rapidity of the trajectory, a multi-objective optimization cost evaluation function for trajectory planning is constructed. Using a general global optimization algorithm (such as the particle swarm optimization algorithm), trajectory planning that takes into account aerodynamic interference safety, rapidity, and kinematic feasibility can be achieved.

[0096] After introducing the application scenarios and design concepts of the embodiments of the present application, the technical solutions provided by the embodiments of the present application are described below.

[0097] like Figure 1 As shown, the embodiment of the present application provides a method for planning the aerial landing trajectory of a sub-unit of a mother-and-child type UAV, comprising:

[0098] Step 101: Obtain the starting point of the slave unit and the landing point of the slave unit on the master unit;

[0099] Step 102: Based on an adaptive particle swarm optimization algorithm, the positions of two optimal control points between the starting point and the landing point are determined using a cost evaluation value; the cost evaluation value is determined by a trajectory length cost value, a trajectory aerodynamic interference cost value, a trajectory smoothness cost value, and a no-fly zone collision cost value;

[0100] Step 103: Determine the optimal landing trajectory of the sub-machine in the air based on the position of the starting point, the positions of the two optimal control points, and the position of the landing point.

[0101] In some embodiments, based on an adaptive particle swarm optimization algorithm, determining the positions of two optimal control points between the starting point and the landing point using a cost evaluation value includes:

[0102] Step S1: rasterizing the three-dimensional map of the trajectory planning space of the slave machine to obtain a three-dimensional raster map;

[0103] Step S2: selecting two control points between the starting point and the landing point, setting the position of a single particle to include the two control points, and the nodes of the three-dimensional grid map are the feasible domains of the particles;

[0104] Step S3: Initialize the number of particles N in the population, the total number of iterations K, and the speed and position of N particles;

[0105] Step S4: Determine the particle corresponding to the minimum cost evaluation value among the N particles in the kth iteration And the particle corresponding to the minimum cost evaluation value of all particles in the kth iteration and the first k-1 iterations The initial value of k is 1, and These are all particles corresponding to the minimum cost evaluation value among the initial N particles;

[0106] Step S5: Based on and Determine the speed and position of N particles in the k+1th iteration;

[0107] Step S6: Determine whether k is less than the total number of iterations K. If yes, then go to step S4 after k+1. Otherwise, determine The corresponding particle is the optimal particle;

[0108] Step S7: Determine the two control points in the optimal particle as the two optimal control points.

[0109] In some embodiments, the particle corresponding to the minimum cost evaluation value among the N particles in the kth iteration is determined. And the particle corresponding to the minimum cost evaluation value of all particles in the kth iteration and the first k-1 iterations include:

[0110] Based on the position of the starting point, the positions of the two control points corresponding to the nth particle in the kth iteration, and the position of the landing point, the nth trajectory curve in the kth iteration is obtained using cubic spline interpolation. Among them, 1≤n≤N, 1≤k≤K;

[0111] Calculate the cost evaluation value of the n-th trajectory curve in the k-th iteration based on the trajectory length cost value, the trajectory aerodynamic interference cost value, the trajectory smoothness cost value, and the no-fly zone collision cost value;

[0112] The particle corresponding to the minimum cost evaluation value of the N trajectory curves in the kth iteration is determined as the particle

[0113] The particle corresponding to the minimum cost evaluation value of all trajectory curves in the kth iteration and the previous k-1 iterations is determined as the particle

[0114] In some embodiments, calculating a cost evaluation value of the nth trajectory curve in the kth iteration based on the trajectory length cost value, the trajectory aerodynamic interference cost value, the trajectory smoothness cost value, and the no-fly zone collision cost value includes:

[0115] The nth trajectory curve in the kth iteration Divide into M approximate straight line segments; calculate the nth trajectory curve in the kth iteration The estimated value of for:

[0116]

[0117] Among them, ω1 is the trajectory length cost F length The weight value of ω2 is the trajectory aerodynamic interference cost F cost The weight value, ω3 is the trajectory smoothness cost F smooth The weight value, ω4 is the no-fly zone collision cost F bump The weight value of

[0118] The cost estimate is determined by four constraints:

[0119] The first constraint is that the trajectory length should be as short as possible, and the trajectory length cost value F length for:

[0120]

[0121] Among them, (x star ,y star ,z star ) is the three-dimensional coordinate of the starting point of the slave machine,

[0122] (x end ,y end ,z end ) is the three-dimensional coordinate of the landing point of the sub-machine; d m is the length of the mth approximate straight line segment;

[0123] The second constraint is that the aerodynamic disturbance of the trajectory should be as small as possible. The trajectory aerodynamic disturbance cost F cost for:

[0124]

[0125] Among them, C set is the set scaling factor; C(x m ,y m ,z m ) is the starting point of the mth approximate straight line segment (x m ,y m ,z m ) is obtained from the pre-established normalized aerodynamic interference cost three-dimensional map;

[0126] The third constraint is that the trajectory should meet the requirements of dynamic feasibility and be as smooth as possible to reduce the difficulty of flight. The trajectory smoothness cost F smooth for:

[0127] F smooth =w κ· ∫κ(t)·κ set +w τ ·∫τ(t)·τ set

[0128] Among them, w κ and w τ Represent the weight values ​​of curvature cost and torsion cost respectively; κ set is the magnification coefficient of the curvature curve κ(t), τ set is the magnification coefficient of the torsion curve τ(t); t is the independent variable;

[0129] When the trajectory is expressed in parametric equation form:

[0130] r(t)=(x(t),y(t),z(t))

[0131] The curvature curve is expressed as:

[0132]

[0133] The torsion curve is expressed as:

[0134]

[0135] The fourth constraint is that the trajectory of the child aircraft must not pass through the no-fly zone. The area with large aerodynamic interference between the parent and child aircraft and the impassable area are set as the no-fly zone (such as the inner area of ​​the outer envelope of the optional mother aircraft downwash airflow velocity of 8% and dynamic pressure of 2 Pa). The collision cost value of the no-fly zone is F bump for:

[0136]

[0137] The no-fly zone is the area where the aerodynamic interference of the mother and child aircraft is greater than the first threshold and the impassable area.prohibit is 10 5 , a larger value.

[0138] In some embodiments, the method further comprises:

[0139] With the landing point of the sub-machine on the mother machine fuselage as the origin, a three-dimensional coordinate system is established, where the XY plane is the upper plane of the mother machine fuselage;

[0140] The test points with different Z values ​​are selected for aerodynamic interference simulation test, where the projection of the test point on the XY plane is the rotor center and the fuselage center of the mother aircraft; based on the aerodynamic interference simulation results of the test point, the height interval [h min ,h max ] as the research interval, and the Z value is h min The plane is taken as the first plane, and the Z value is h max As the second plane, since the speed of the sub-machine is not large, the steady-state results of the sub-machine flow field simulation when the sub-machine is hovering at the test point can be used to approximate the instantaneous flow field condition when the sub-machine flies to this position.

[0141] Select the relative projection positions of the centroids of multiple sub-machines relative to the main machine on the XY plane as simulation solution nodes;

[0142] On the first plane, a point-by-point simulation is performed to solve the physical quantity value of the evaluation disturbance influence of each simulation solution node on the first plane, and a two-dimensional distribution diagram of the physical quantity of the evaluation disturbance influence on the first plane is obtained by interpolation;

[0143] On the second plane, the physical quantity value of the evaluation disturbance influence of each simulation solution node on the second plane is solved point by point by simulation, and a two-dimensional distribution diagram of the physical quantity of the evaluation disturbance influence on the second plane is obtained by interpolation.

[0144] The CFD simulation model for the carrier-type UAV uses the SST k-ω turbulence model. A sliding mesh approach is used to simulate the propeller rotation of the carrier-type UAV. The flow field is divided into three regions: the propeller sliding mesh region, the fuselage region, and the background flow field region. The fluid type is set to air, and a relative rotation boundary condition is applied at the interface between the sliding mesh region and the background mesh. A tetrahedral mesh type is selected, and a patch conformal algorithm is used. The propeller sliding mesh region is locally refined to improve simulation accuracy.

[0145] In some embodiments, the method further comprises:

[0146] Based on the pre-established two-dimensional distribution diagram of the evaluation disturbance influence physical quantity of the first plane, calculate the normalized mth evaluation disturbance influence physical quantity of the first plane of the i-th simulation solution node

[0147]

[0148] in, Represents the physical quantity affected by the mth evaluation disturbance on the first plane of the i-th simulation solution node; and Represent the minimum and maximum values ​​of the mth physical quantity affected by the disturbance of all simulation solution nodes in the first and second planes, respectively; 1≤m≤M, the M physical quantities affected by the disturbance include: the absolute value of the total propeller thrust loss of the daughter aircraft, the absolute value of the lift loss of the daughter aircraft, the resultant torque of the daughter aircraft, the absolute value of the total propeller thrust loss of the mother aircraft, the absolute value of the lift loss of the mother aircraft, the resultant torque of the mother aircraft, and the downforce of the mother aircraft;

[0149] Calculate the aerodynamic interference cost of the first plane of the i-th simulation solution node 1,i :

[0150]

[0151] Among them, ω m Represents the weight coefficient of the physical quantity affected by the mth evaluation disturbance;

[0152] Interpolating the aerodynamic interference cost values ​​of the first plane of all simulation solution nodes to generate a two-dimensional distribution map of the aerodynamic interference cost of the first plane;

[0153] Based on the pre-established two-dimensional distribution diagram of the evaluation disturbance influence physical quantity of the second plane, calculate the normalized mth evaluation disturbance influence physical quantity of the second plane of the i-th simulation solution node

[0154]

[0155] in, Represents the mth evaluation disturbance impact physical quantity of the second plane of the i-th simulation solution node;

[0156] Calculate the aerodynamic interference cost of the second plane of the i-th simulation solution node 2,i :

[0157]

[0158] Interpolating the aerodynamic interference cost values ​​of the second plane of all simulation solution nodes to generate a two-dimensional distribution map of the aerodynamic interference cost of the second plane;

[0159] Performing interlayer interpolation on the two-dimensional distribution map of aerodynamic interference cost on the first plane and the two-dimensional distribution map of aerodynamic interference cost on the second plane to obtain a three-dimensional map of aerodynamic interference cost;

[0160] Rasterizing the three-dimensional map of aerodynamic interference cost to obtain multiple grid nodes;

[0161] The aerodynamic interference cost for the jth grid node is j Perform normalization to obtain the normalized aerodynamic interference cost of the jth grid node: j ′ :

[0162]

[0163] Among them, cost min and cost max Respectively represent the minimum and maximum aerodynamic interference cost values ​​of all grid nodes;

[0164] Based on the normalized aerodynamic interference cost values ​​of all grid nodes, a normalized aerodynamic interference cost three-dimensional map is obtained.

[0165] In some embodiments, based on and Determine the velocities and positions of N particles at iteration k+1; this includes:

[0166] The velocity of the nth particle in the k+1th iteration and location

[0167]

[0168] Among them, the individual learning factor c1 and the global learning factor c2 are:

[0169]

[0170] Among them, the coefficient k1 and the coefficient k2 are:

[0171]

[0172] Δf is the change in cost evaluation value:

[0173]

[0174] ω is the inertia weight:

[0175] ω=ω max -k(ω max -ω min ) / K

[0176] Among them, ω max and ω min are the maximum and minimum values ​​of the inertia weight respectively.

[0177] Based on the above embodiments, the present application provides a device for planning the landing trajectory of a sub-machine of a mother-and-child type UAV. Figure 2 As shown, the aerial landing trajectory planning device 200 of the carrier-sub UAV provided in the embodiment of the present application includes at least:

[0178] An acquisition unit 201 is configured to acquire the position of the starting point of the slave aircraft and the position of the landing point of the slave aircraft on the base aircraft;

[0179] A determination unit 202 is configured to determine positions of two optimal control points between the starting point and the landing point using a cost evaluation value based on an adaptive particle swarm optimization algorithm; the cost evaluation value is determined by a trajectory length cost value, a trajectory aerodynamic interference cost value, a trajectory smoothness cost value, and a no-fly zone collision cost value;

[0180] The trajectory planning unit 203 is configured to determine the optimal landing trajectory of the sub-machine in the air based on the position of the starting point, the positions of the two optimal control points, and the position of the landing point.

[0181] It should be noted that the principle of solving the technical problem of the sub-machine air landing trajectory planning device 200 of the mother-and-child type UAV provided in the embodiment of the present application is similar to the method provided in the embodiment of the present application. Therefore, the implementation of the sub-machine air landing trajectory planning device 200 of the mother-and-child type UAV provided in the embodiment of the present application can refer to the implementation of the method provided in the embodiment of the present application, and the repeated parts will not be repeated.

[0182] Based on the above embodiments, the present application also provides an electronic device, referring to Figure 3 As shown, the electronic device 300 provided in the embodiment of the present application includes at least: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the method for planning the aerial landing trajectory of the sub-machine of the mother-sub type UAV provided in the embodiment of the present application is implemented.

[0183] The electronic device 300 provided in the embodiment of the present application may further include a bus 303 connecting different components (including the processor 301 and the memory 302). The bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and the like.

[0184] The memory 302 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 3021 and / or a cache memory 3022 , and may further include a read-only memory (ROM) 3023 .

[0185] The memory 302 may also include a program tool 3025 having a set (at least one) of program modules 3024, including but not limited to: an operating subsystem, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0186] The electronic device 300 may also communicate with one or more external devices 304 (e.g., keyboards, remote controls, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 300 (e.g., mobile phones, computers, etc.), and / or any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 305. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 306. Figure 3 As shown, the network adapter 306 communicates with other modules of the electronic device 300 via the bus 303. Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, and data backup storage subsystems.

[0187] It should be noted that Figure 3 The electronic device 300 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0188] The present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method for planning the aerial landing trajectory of a child drone of a carrier-and-child type drone provided in the present invention. Specifically, the executable program can be built into or installed in the electronic device 300. Thus, the electronic device 300 can implement the method for planning the aerial landing trajectory of a child drone of a carrier-and-child type drone provided in the present invention by executing the built-in or installed executable program.

[0189] The method for planning the aerial landing trajectory of a sub-machine of a mother-and-child type UAV provided in an embodiment of the present application can also be implemented as a program product, which includes a program code. When the program product can be run on an electronic device 300, the program code is used to enable the electronic device 300 to execute the method for planning the aerial landing trajectory of a sub-machine of a mother-and-child type UAV provided in an embodiment of the present application.

[0190] The program product provided in the embodiments of the present application may adopt any combination of one or more readable media, wherein the readable medium may be a readable signal medium or a readable storage medium, and the readable storage medium may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, RAM, ROM, Erasable Programmable Read Only Memory (EPROM), optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0191] The program product provided in the embodiments of the present application may be a CD-ROM and include program code, and may also be run on a computing device. However, the program product provided in the embodiments of the present application is not limited thereto. In the embodiments of the present application, the readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0192] It should be noted that although several units or subunits of the device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, depending on the embodiment of the application, the features and functions of two or more units described above can be embodied in a single unit. Conversely, the features and functions of a single unit described above can be further divided and embodied by multiple units.

[0193] Furthermore, although the operations of the method of the present application are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0194] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of this application and are not intended to limit the scope of the present invention. Although this application has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application and should be encompassed by the claims of this application.

Claims

1. A method for planning the aerial landing trajectory of a daughter drone of a mother-and-child type, characterized in that: include: Obtain the position of the starting point of the slave aircraft and the position of the landing point of the slave aircraft on the mother aircraft; Based on an adaptive particle swarm optimization algorithm, the positions of two optimal control points between the starting point and the landing point are determined using a cost evaluation value; the cost evaluation value is determined by a trajectory length cost value, a trajectory aerodynamic interference cost value, a trajectory smoothness cost value, and a no-fly zone collision cost value; The optimal landing trajectory of the sub-machine in the air is determined based on the position of the starting point, the positions of the two optimal control points and the position of the landing point.

2. The method for planning the aerial landing trajectory of a daughter aircraft of a mother-and-child type UAV according to claim 1, characterized in that: Based on the adaptive particle swarm optimization algorithm, the cost evaluation value is used to determine the positions of the two optimal control points between the starting point and the landing point; including: Step S1: rasterizing the three-dimensional map of the trajectory planning space of the slave machine to obtain a three-dimensional raster map; Step S2: selecting two control points between the starting point and the landing point, setting the position of a single particle to include the two control points, and the nodes of the three-dimensional grid map are the feasible domains of the particles; Step S3: Initialize the number of particles N in the population, the total number of iterations K, and the speed and position of N particles; Step S4: Determine the particle corresponding to the minimum cost evaluation value among the N particles in the kth iteration And the particle corresponding to the minimum cost evaluation value of all particles in the kth iteration and the first k-1 iterations The initial value of k is 1, and These are all particles corresponding to the minimum cost evaluation value among the initial N particles; Step S5: Based on and Determine the speed and position of N particles in the k+1th iteration; Step S6: Determine whether k is less than the total number of iterations K. If yes, then go to step S4 after k+1. Otherwise, determine The corresponding particle is the optimal particle; Step S7: Determine the two control points in the optimal particle as the two optimal control points.

3. The method for planning the aerial landing trajectory of a daughter aircraft of a mother-and-child UAV according to claim 2, characterized in that: Determine the particle corresponding to the minimum cost evaluation value among the N particles in the kth iteration And the particle corresponding to the minimum cost evaluation value of all particles in the kth iteration and the first k-1 iterations include: Based on the position of the starting point, the positions of the two control points corresponding to the nth particle in the kth iteration, and the position of the landing point, the nth trajectory curve in the kth iteration is obtained using cubic spline interpolation. Among them, 1≤n≤N, 1≤k≤K; Calculate the cost evaluation value of the n-th trajectory curve in the k-th iteration based on the trajectory length cost value, the trajectory aerodynamic interference cost value, the trajectory smoothness cost value, and the no-fly zone collision cost value; The particle corresponding to the minimum cost evaluation value of the N trajectory curves in the kth iteration is determined as the particle The particle corresponding to the minimum cost evaluation value of all trajectory curves in the kth iteration and the previous k-1 iterations is determined as the particle 4. The method for planning the aerial landing trajectory of a daughter aircraft of a mother-and-child type UAV according to claim 3, characterized in that: Based on the trajectory length cost, trajectory aerodynamic interference cost, trajectory smoothness cost, and no-fly zone collision cost, the cost evaluation value of the n-th trajectory curve in the k-th iteration is calculated; including: The nth trajectory curve in the kth iteration Divide into M approximate straight line segments; Calculate the nth trajectory curve in the kth iteration The estimated value of for: Among them, ω1 is the trajectory length cost value F length The weight value of ω2 is the trajectory aerodynamic interference cost F cost The weight value, ω3 is the trajectory smoothness cost F smooth The weight value, ω4 is the no-fly zone collision cost F bump The weight value of Trajectory length cost F length for: Among them, (x star ,y star ,z star ) is the three-dimensional coordinate of the starting point of the slave machine, (x end ,y end ,z end ) is the three-dimensional coordinate of the landing point of the sub-machine; d m is the length of the mth approximate straight line segment; Trajectory aerodynamic interference cost F cost for: Among them, C set is the set scaling factor; C(x m ,y m ,z m ) is the starting point of the mth approximate straight line segment (x m ,y m ,z m ) is obtained from the pre-established normalized aerodynamic interference cost three-dimensional map; Trajectory smoothness cost F smooth for: F smooth =w κ ·∫κ(t)·κ set +w τ ·∫τ(t)·τ set Among them, w κ and w τ Represent the weight values ​​of curvature cost and torsion cost respectively; κ set is the magnification factor of the curvature curve k(t), τ set is the magnification coefficient of the torsion curve τ(t); t is the independent variable; No-fly zone collision cost F bump for: The no-fly zone is the area where the aerodynamic interference of the mother and child aircraft is greater than the first threshold and the impassable area. prohibit is 10 5 .

5. The method for planning the aerial landing trajectory of a daughter aircraft of a mother-and-child UAV according to claim 4, characterized in that: The method further comprises: With the landing point of the sub-machine on the mother machine fuselage as the origin, a three-dimensional coordinate system is established, where the XY plane is the upper plane of the mother machine fuselage; The test points with different Z values ​​are selected for aerodynamic interference simulation test, where the projection of the test point on the XY plane is the rotor center and the fuselage center of the mother aircraft; based on the aerodynamic interference simulation results of the test point, the height interval [h min ,h max ] as the research interval, and the Z value is h min The plane is taken as the first plane, and the Z value is h max The plane of is taken as the second plane; Select the relative projection positions of the centroids of multiple sub-machines relative to the main machine on the XY plane as simulation solution nodes; On the first plane, a point-by-point simulation is performed to solve the physical quantity value of the evaluation disturbance influence of each simulation solution node on the first plane, and a two-dimensional distribution diagram of the physical quantity of the evaluation disturbance influence on the first plane is obtained by interpolation; On the second plane, the physical quantity value of the evaluation disturbance influence of each simulation solution node on the second plane is solved point by point by simulation, and a two-dimensional distribution diagram of the physical quantity of the evaluation disturbance influence on the second plane is obtained by interpolation.

6. The method for planning the aerial landing trajectory of a daughter aircraft of a mother-and-child type UAV according to claim 5, characterized in that: The method further comprises: Based on the pre-established two-dimensional distribution diagram of the evaluation disturbance influence physical quantity of the first plane, calculate the normalized mth evaluation disturbance influence physical quantity of the first plane of the i-th simulation solution node in, Represents the physical quantity affected by the mth evaluation disturbance on the first plane of the i-th simulation solution node; and Represent the minimum and maximum values ​​of the mth physical quantity affected by the disturbance of all simulation solution nodes in the first and second planes, respectively; 1≤m≤M, the M physical quantities affected by the disturbance include: the absolute value of the total propeller thrust loss of the daughter aircraft, the absolute value of the lift loss of the daughter aircraft, the resultant torque of the daughter aircraft, the absolute value of the total propeller thrust loss of the mother aircraft, the absolute value of the lift loss of the mother aircraft, the resultant torque of the mother aircraft, and the downforce of the mother aircraft's fuselage; Calculate the aerodynamic interference cost of the first plane of the i-th simulation solution node 1,i : Among them, ω m Represents the weight coefficient of the physical quantity affected by the mth evaluation disturbance; Interpolating the aerodynamic interference cost values ​​of the first plane of all simulation solution nodes to generate a two-dimensional distribution map of the aerodynamic interference cost of the first plane; Based on the pre-established two-dimensional distribution diagram of the evaluation disturbance influence physical quantity of the second plane, calculate the normalized mth evaluation disturbance influence physical quantity of the second plane of the i-th simulation solution node in, Represents the mth evaluation disturbance impact physical quantity of the second plane of the i-th simulation solution node; Calculate the aerodynamic interference cost of the second plane of the i-th simulation solution node 2,i : Interpolating the aerodynamic interference cost values ​​of the second plane of all simulation solution nodes to generate a two-dimensional distribution map of the aerodynamic interference cost of the second plane; Performing interlayer interpolation on the two-dimensional distribution map of aerodynamic interference cost on the first plane and the two-dimensional distribution map of aerodynamic interference cost on the second plane to obtain a three-dimensional map of aerodynamic interference cost; Rasterizing the three-dimensional map of aerodynamic interference cost to obtain multiple grid nodes; The aerodynamic interference cost for the jth grid node is j Perform normalization to obtain the normalized aerodynamic interference cost of the jth grid node: j ′ : Among them, cost min and cost max Respectively represent the minimum and maximum aerodynamic interference cost values ​​of all grid nodes; Based on the normalized aerodynamic interference cost values ​​of all grid nodes, a normalized aerodynamic interference cost three-dimensional map is obtained.

7. The method for planning the aerial landing trajectory of a daughter aircraft of a mother-and-child UAV according to claim 4, characterized in that: based on and Determine the velocities and positions of N particles at iteration k+1; this includes: The velocity of the nth particle in the k+1th iteration and location Among them, the individual learning factor c1 and the global learning factor c2 are: Among them, the coefficient k1 and the coefficient k2 are: Δf is the change in cost evaluation value: ω is the inertia weight: oh = oh max -k(ω max -oh min ) / K Among them, ω max and ω min are the maximum and minimum values ​​of the inertia weight respectively.

8. A device for planning the landing trajectory of a daughter aircraft of a mother-and-child type UAV, characterized in that: include: an acquisition unit, configured to acquire the position of the starting point of the slave aircraft and the position of the landing point of the slave aircraft on the mother aircraft; a determination unit, configured to determine positions of two optimal control points between the starting point and the landing point using a cost evaluation value based on an adaptive particle swarm optimization algorithm; the cost evaluation value being determined by a trajectory length cost value, a trajectory aerodynamic interference cost value, a trajectory smoothness cost value, and a no-fly zone collision cost value; The trajectory planning unit is used to determine the optimal landing trajectory of the sub-machine in the air based on the position of the starting point, the positions of the two optimal control points and the position of the landing point.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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