Method for controlling flight of aircraft

Abstract

The invention discloses a method for controlling flight of an aircraft. The method is suitable for global flight path planning of the aircraft in a complex and changeable environment. The method comprises the following steps: mapping initial positions of M aircrafts into a chaotic sequence; determining a first path according to the chaotic sequence and the flight influence information, wherein the first path is a path planned for a first aircraft; constructing a repulsive force potential field according to the distance potential field between the first aircraft and other aircrafts, the distance potential field between the first aircraft and the obstacle and the repulsive force potential field regulation factor; and adjusting the first path into a second path according to a repulsive force potential field. Through the method, iterative convergence and real-time dynamic adjustment can be enhanced, the problem that local optimum and a target point are not reachable is solved, and therefore it is guaranteed that the flight path of the aircraft is better planned.

Description

Technical Field

[0001] The present application relates to the field of aircraft technology, and in particular to a method for controlling the flight of an aircraft. Background Art

[0002] Aircraft (such as drones) are increasingly used, and planning the trajectory of aircraft is crucial. Commonly used trajectory planning methods include offline trajectory planning based on particle swarm optimization and online trajectory planning based on artificial potential field algorithm.

[0003] However, the current offline trajectory planning based on particle swarm algorithm cannot explore the entire environment space, and the accuracy of trajectory planning is low. The current online trajectory planning based on artificial potential field algorithm has the defect of being unable to plan to the target position. Summary of the invention

[0004] The present application provides a method for controlling the flight of an aircraft, which can not only improve the accuracy of trajectory planning, but also ensure that the planned trajectory can reach the target location.

[0005] In a first aspect, a method for controlling the flight of an aircraft is provided, wherein the executor of the method is an aircraft (such as a drone) or a device that can realize flight functions, such as a chip or chip system in the aircraft.

[0006] The method comprises: mapping the initial positions of M aircraft into a chaotic sequence, where M is a positive integer, wherein the chaotic sequence of the n+1th time unit has a corresponding relationship with the chaotic sequence of the nth time unit; determining a first path according to the chaotic sequence and flight impact information, wherein the flight impact information comprises at least one of the following: a meteorological parameter, a no-fly zone parameter, or an obstacle parameter; and adjusting the repulsive potential field according to a repulsive potential field adjustment factor. The distance potential field between the first aircraft and other aircraft and the distance potential field between the first aircraft and the obstacle construct a repulsive potential field; the first path is adjusted to the second path according to the repulsive potential field. Among them, the meteorological parameters include a weather threat factor, which is used to indicate the degree of influence of weather on flight. The corresponding relationship satisfies the formula:

[0007]

[0008] Among them, z n+1 is the chaotic sequence of the n+1th time unit, z n is the chaotic sequence of the nth time unit, where n is a positive integer; μ is greater than or equal to 0, and μ is less than 1.

[0009] In this method, the first path is a planned path. For example, the first path can be a path planned based on a particle swarm algorithm. By determining the first path through the above-mentioned chaotic sequence, the population diversity can be enhanced. In this way, when the first path is planned based on the particle swarm algorithm, the convergence speed of the entire group is faster, which helps to improve the accuracy of the first path planning. In addition, the first path can be adjusted to the second path based on the artificial potential field algorithm, or the first path can be corrected based on the artificial potential field algorithm. The second path can be regarded as the final path. Since the first path is a planned path, the second path is obtained based on the first path, and therefore, the path planning efficiency can be improved. In addition, when adjusting the first path based on the artificial potential field algorithm, the repulsive potential field adjustment factor is introduced to determine the repulsive potential field, which can solve the defect that the current artificial potential field algorithm cannot plan to the target position.

[0010] In one implementation, determining the first path according to the chaotic sequence and the flight impact information includes: determining a safe distance of the aircraft according to the flight impact information, determining at least one path according to the safe distance, the chaotic sequence and the collision threat fitness function, and determining the first path from the at least one path. The probability of collision of the at least one path is less than or equal to a first threshold. The collision threat fitness function is used to select a path whose probability of collision is less than or equal to the first threshold.

[0011] In this method, the impact of the environment on trajectory planning is taken into account, and the trajectory is planned based on the flight impact information. The planned trajectory has different adaptability for different environments. This helps the aircraft to operate in a wide range of complex environments.

[0012] In one implementation, determining at least one path according to the safety distance includes: determining at least one path according to the following formula:

[0013]

[0014] Among them, J T Used to indicate whether to select the corresponding path, D ij is the distance between the aircraft and the no-fly zone, d safe Indicates a safe distance.

[0015] In this method, multiple aircraft are initially mapped into chaotic sequences, which can enhance population diversity. Thus, when the first path is planned based on the particle swarm algorithm, the entire group converges faster, which helps to improve the accuracy of the first path planning.

[0016] In one implementation, the repulsive potential field in the artificial potential field algorithm satisfies the following formula:

[0017]

[0018] in, is the repulsive potential field, η is the repulsive factor, ρ0 is the influence radius of the obstacle, is the repulsive potential field adjustment factor, q obs is the distance potential field between the first aircraft and the obstacle.

[0019] The method introduces a repulsive potential field adjustment factor, which can reduce the gravitational force and repulsive force to zero at the same time when the aircraft flies to the target position, making the target position the point with the minimum potential energy value, so that the aircraft can reach the target position.

[0020] In one implementation, adjusting the first path to the second path according to the repulsive potential field includes: adjusting the first path to the second path according to the repulsive potential field and the normal force potential field; wherein the normal force based on the normal force potential field satisfies the following formula:

[0021]

[0022] Among them, k norm is the normal force coefficient, d safe is the safe distance between the aircraft and the obstacle, d obs is the distance between the aircraft and the obstacle.

[0023] This method introduces a normal force to guide the aircraft to maintain a certain distance when avoiding obstacles while flying along the boundary of the obstacle, thereby effectively avoiding the risk of directly crossing the obstacle.

[0024] In a second aspect, an aircraft is provided, for example, referred to as a first aircraft, which includes a functional module for executing the method of the first aspect. For example, the first aircraft includes a processing unit (sometimes also referred to as a processing module or processor) and / or a transceiver unit (sometimes also referred to as a transceiver module or transceiver). These units (modules) can perform the corresponding functions in the above-mentioned method example of the first aspect. For example, the processing unit can be used to map the initial positions of M aircraft into a chaotic sequence, where M is a positive integer, wherein the chaotic sequence of the n+1th time unit and the chaotic sequence of the nth time unit have a corresponding relationship; determine a first path according to the chaotic sequence and flight impact information, and the flight impact information includes at least one of the following: meteorological parameters, no-fly zone parameters, or obstacle parameters; according to the repulsive potential field adjustment factor The distance potential field between the first aircraft and other aircraft and the distance potential field between the first aircraft and the obstacle construct a repulsive potential field; the first path is adjusted to the second path according to the repulsive potential field. For details, please refer to the detailed description in the method example, which will not be repeated here. For example, the corresponding relationship satisfies the formula:

[0025]

[0026] Among them, z n+1 is the chaotic sequence of the n+1th time unit, z n is the chaotic sequence of the nth time unit, where n is a positive integer; μ is greater than or equal to 0, and μ is less than 1.

[0027] Optionally, the communication device further includes a memory, wherein the memory is used to store computer programs, instructions or data, and the processing unit is coupled with the memory and the transceiver unit, and when the processing unit reads the computer program, instructions or data, the communication device executes the method executed by the receiver in the above method embodiment.

[0028] In a third aspect, an aircraft is provided, for example, referred to as a first aircraft, the first aircraft including a functional module for executing the method of the first aspect. For example, the first aircraft includes at least one processor and a memory. At least one processor and a memory can execute the corresponding functions in the method example of the first aspect described above, for example, at least one processor can be used to map the initial positions of M aircraft into a chaotic sequence, where M is a positive integer, wherein the chaotic sequence of the n+1th time unit and the chaotic sequence of the nth time unit have a corresponding relationship; determine a first path according to the chaotic sequence and flight impact information, the flight impact information including at least one of the following: meteorological parameters, no-fly zone parameters, or obstacle parameters; according to the repulsive potential field adjustment factor The distance potential field between the first aircraft and other aircraft and the distance potential field between the first aircraft and the obstacle construct a repulsive potential field; the first path is adjusted to the second path according to the repulsive potential field. For details, please refer to the detailed description in the method example, which will not be repeated here. For example, the corresponding relationship satisfies the formula:

[0029]

[0030] Among them, z n+1 is the chaotic sequence of the n+1th time unit, z n is the chaotic sequence of the nth time unit, where n is a positive integer; μ is greater than or equal to 0, and μ is less than 1.

[0031] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, the method in the first aspect is implemented.

[0032] According to a fifth aspect, a computer program product is provided, the computer program product comprising: a computer program code, and when the computer program code is executed, the method in the first aspect is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1An architecture diagram of a communication system to which the embodiments of the present application are applicable;

[0034] Figure 2 A schematic diagram of a flow chart of a method for controlling an aircraft to perform flight provided in an embodiment of the present application;

[0035] Figure 3 A schematic diagram of the structure of an aircraft provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] Please refer to Figure 1 , is an architecture diagram of a communication system applicable to the embodiment of the present application. The communication system may include multiple aircrafts and base stations. Optionally, the base station may be a ground base station or a satellite base station. Figure 1 For example, the base station includes ground base station and satellite base station. The aircraft can be a fixed-wing relay drone ( Figure 1 For example), or other devices that may have flight and communication functions. Each aircraft can serve as a communication node in the network. Aircraft can communicate with each other, or one aircraft can communicate with other aircraft through another aircraft. In other words, aircraft in the network can act as relays. Communication between source nodes and target nodes can be achieved through one or more aircraft. For example, a ground base station or a satellite base station can send information to any aircraft in the network by connecting to and communicating with a certain aircraft, and can also receive information sent by any aircraft.

[0037] It should be noted that Figure 1 For illustration only, the embodiments of the present application do not limit the type and quantity of aircraft included in the trajectory planning method, and do not limit the type and quantity of equipment included in the method. For example, the aircraft trajectory planning may also include terminal equipment, etc.

[0038] Aircraft can quickly move to different locations to cover a wide range of areas, and can also fly at higher altitudes, providing a wider field of view and coverage, and have a wide range of uses. For example, aircraft can be used for disaster relief, logistics transportation, and engineering mapping. It should be understood that the aircraft flies in the environment, and obstacles in the environment (including mobile obstacles and fixed obstacles) will affect the operation of the aircraft. Therefore, it is very important to plan the trajectory for the aircraft.

[0039] Currently, trajectory planning for aircraft is mostly applicable to rotary-wing UAVs, but not to fixed-wing UAVs. Fixed-wing UAVs require higher accuracy in trajectory planning because they cannot hover, have higher flight speeds, and have larger turning radiuses.

[0040] To this end, the present application embodiment provides a method for controlling the flight of an aircraft, which is applicable to fixed-wing UAVs. Track planning includes offline track planning and online track planning. To facilitate understanding of the solution provided by the present application embodiment, some concepts involved in the present application embodiment are first introduced.

[0041] (1) Offline trajectory planning

[0042] Offline trajectory planning refers to the path pre-planned before the mission is executed, which is suitable for scenes with relatively stable environment. When the aircraft performs the mission, the pre-planned path can be directly obtained, which is more efficient.

[0043] Particle swarm optimization (PSO) is usually used to plan offline trajectories. PSO is a swarm intelligence optimization algorithm that simulates group behaviors such as bird flocks foraging or fish schools swimming in nature. Specific to this application, an aircraft can be regarded as a particle. The main principle of planning a trajectory based on PSO is: randomly generate a group of particles in the search space to solve the individual optimal solution and the global optimal solution. It should be understood that each particle has its own position, speed and fitness function for evaluating good or bad. Through multiple iterations, the current position (or updated position) is adjusted according to the historical optimal position of each particle itself, and the direction is adjusted according to the group experience to achieve offline trajectory planning.

[0044] In addition, the setting of particle parameters will also affect the individual optimal solution and the global optimal solution. For example, the individual learning factor and the group learning factor can control the strength of the particle to approach the individual optimal solution and the global optimal solution. For another example, the inertia weight coefficient can be used to balance the global search and local search capabilities of the particle. Or the inertia weight coefficient determines the update of the particle's speed. A larger inertia weight is helpful for global search, and a smaller inertia weight is helpful for local search.

[0045] Among them, the inertia weight coefficient ω satisfies formula (1-1):

[0046]

[0047] Among them, ω max is the initial value of the inertia weight, ω min is the final value of the inertia weight, iter is the current number of iterations, iter max is the maximum number of iterations.

[0048] For example, the velocity of the particle may be updated according to satisfying formula (1-2), and the position of the particle may be updated according to formula (1-3).

[0049] v i (t+1)=ω t ×v i(t)+c1×r1×(p i -X i (t))+c2×r2×( g -X i (t))

[0050] (1-2)

[0051] X i (t+1)=X i (t)+v i (t+1)

[0052] (1-3)

[0053] Among them, v i (t+1) represents the velocity of particle i at time t+1, ω t Indicates that v i (k) represents the speed of particle i at time t, c1 represents the individual learning factor, c2 represents the group learning factor, r1 and r2 represent random numbers in [0, 1], p i represents the historical optimal position of particle i, X i (t) represents the position of particle i at time t, X i (t+1) represents the position of particle i at time t+1.

[0054] c1 and c2 determine the balance between the particle's own experience and the group's experience. c1 is used for local path planning, and c2 is used for global convergence. Among them, c1 and c2 can be adjusted using nonlinear functions to adapt to the search process more flexibly. c1 and c2 satisfy formulas (1-4) and (1-5) respectively.

[0055] c1=c c1 *exp(1+k*σ 2 (iter)) (1-4)

[0056] c2=c c2 *exp(1-k*σ 2 (iter)) (1-5)

[0057] Among them, c c1 With c c2 are the initial values ​​of c1 and c2, k is the adjustment parameter, σ 2 (iter) is the variance of the current population fitness, When the fitness variance is large, it means that the solutions of the particles are widely distributed and the population diversity is high. At this time, c1 can be increased to encourage more exploration. When the fitness variance is small, it means that the solutions of the particles tend to be concentrated. At this time, c2 can be increased to promote convergence.

[0058] (2) Online route planning

[0059] The environment in which the aircraft performs communication tasks may include fixed obstacles as well as dynamic / moving obstacles. As the number and location of obstacles change, the path of the aircraft in the environment also needs to be adaptively changed. Accordingly, the trajectory needs to be planned in real time to avoid obstacles and guide the robot to the target location. Real-time trajectory planning is also called online trajectory planning.

[0060] The artificial potential field method (APF) is usually used to achieve online trajectory planning. The basic principle of the artificial potential field method is to regard the environment as a space composed of a gravitational field and a repulsive field. The aircraft moves in this space. By constructing a potential field function, the aircraft is always pulled by the attraction of the target position and repelled by the repulsion of obstacles, and finally reaches the target position along the gradient descent path of the potential field. For example, the aircraft, target position and obstacles can be mapped into a virtual potential field, where the target position has an attraction on the aircraft and the obstacles have a repulsive force on the aircraft. In other words, the target position is mapped to a gravitational potential field, and the obstacles are mapped to a repulsive potential field. In this way, the aircraft is simultaneously affected by the suction of the target position and the repulsion of the obstacles, and reaches the target along the gradient descent path of the potential field.

[0061] In one implementation, the gravitational potential field satisfies formula (2-1) and the repulsive potential field satisfies formula (2-2)

[0062]

[0063] Among them, ρ(q, qg) represents the Euclidean distance between the location of the drone cluster and the key points of the islands and reefs, k represents the target value function, and ρ lim represents the limit distance of the internal repulsive potential field, ρ max Represents the limit distance of the external gravitational potential field.

[0064] In particular, when the aircraft's gravitational potential field is consistent with the repulsive potential field, the aircraft reaches a state of equilibrium. At this time, the combined potential field model satisfies formula (2-3)

[0065]

[0066] In the traditional particle swarm algorithm, the diversity of particle population is poor, the iteration converges too early, and the entire environment space cannot be effectively explored, resulting in low accuracy of the planned path. The traditional artificial potential field method is prone to fall into local optimality and has the defect that the target point is unreachable. For example, if the aircraft is located in an environment with the same gravitational potential field and repulsive potential field, it cannot reach the target position.

[0067] In view of this, a solution of an embodiment of the present application is provided. In the embodiment of the present application, the initial positions of M aircraft are mapped to a chaotic sequence, where M is a positive integer, which can enhance the diversity and distribution randomness of the particle population and effectively explore the entire environmental space. The introduction of the adjustment factor and the normal potential field in the traditional artificial potential field algorithm solves the defect that the target is unreachable in the traditional artificial potential field algorithm.

[0068] The technical solution provided by the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0069] See also Figure 2 , is a flow chart of a method for controlling an aircraft to perform flight provided in an embodiment of the present application. The method can be performed by a first aircraft or a component (such as a chip) in the first aircraft. Specifically, the flow of the method for controlling an aircraft to perform flight provided in an embodiment of the present application includes the following steps. In the following introduction, planning the path of the first aircraft is taken as an example.

[0070] S201. Map the initial positions of M aircraft into a chaotic sequence, where M is a positive integer.

[0071] A spacecraft can be regarded as a particle. The initial position x of the i-th spacecraft is i (0)∈[x max , x min ],y i (0)∈[y max ,y min ],z i (0)∈[z max , z min ], that is, within the set environment, for each aircraft in the x, y, and z dimensions, a chaotic sequence is generated using Tent mapping to satisfy formula (3-1)

[0072]

[0073] Chaotic sequence z n+1 Should satisfy formula (3-2)

[0074]

[0075] Among them, μz n+1 is the chaotic sequence of the n+1th time unit, z n is the chaotic sequence of the nth time unit, where n is a positive integer; μ is greater than or equal to 0, and μ is less than 1. In other words, the chaotic sequence of the n+1th time unit and the chaotic sequence of the nth time unit satisfy formula (3-2).

[0076] By mapping multiple aircraft into chaotic sequences, the diversity of the population can be enhanced. In this way, when planning the path of the aircraft based on the particle swarm algorithm, the entire group converges faster, which helps to improve the accuracy of the first path planning.

[0077] S202: Determine a first path according to the chaotic sequence and the flight impact information, where the first path is a path that has been planned for the first aircraft.

[0078] The first path can be regarded as the optimal offline path that has been planned for the first aircraft. In this way, the online trajectory planning is performed based on the first path, which can improve the efficiency of path planning.

[0079] In the embodiment of the present application, the first path can be determined based on the particle swarm algorithm. For example, assuming that there are m users in the environment, and assuming that the coordinate position of the kth user is {(x k ,y k , z k )|k=1,2,...,m}, the position coordinates of the aircraft are expressed as {(X i , Y i , Z i )|i=1,2,...,M}, where M is the number of particles in the particle swarm, M is a positive integer, m is the number of users, m is a positive integer, and N is the number of time units (such as time slots) divided into the entire mission cycle, N is a positive integer. The speed of the aircraft at time unit t is v i (t), the position of the particle satisfies formula (3-3), and the velocity of the particle satisfies formula (3-4).

[0080] v i (t+1)=ω×v i (t)+c1×r1×(p i -X i (t))+c2×r2×(gX i (t)) (3-3)

[0081] X i (t+1)=X i (t)+v i (t+1) (3-4)

[0082] Among them, v i (t+1) represents the velocity of particle i at time t+1, ω t represents the inertia weight at time t, v i (k) represents the speed of particle i at time t, c1 represents the individual learning factor, c2 represents the group learning factor, r1 and r2 represent random numbers in [0, 1], p i represents the historical optimal position of particle i, X i(t) represents the position of particle i at time t, X i (t+1) represents the position of particle i at time t+1.

[0083] Consider the impact of the environment on trajectory planning when the aircraft is performing its mission. For example, during the flight, the aircraft may encounter extreme weather such as heavy rain, strong winds, lightning, and blizzards in local areas. These extreme weather conditions often cause serious damage to the aircraft body and affect the flight mission. For another example, if the aircraft passes through a no-fly zone during the performance of its flight mission, it is prone to devastating damage. Therefore, when performing trajectory planning, the planned path cannot pass through the no-fly zone. For another example, dynamic / moving obstacles (such as birds) may be encountered during the flight. If they are not avoided in time, the aircraft will be damaged and the flight mission will be interrupted.

[0084] To this end, the embodiment of the present application also determines the first path based on the flight impact information. The planned track also has different adaptability for different environments. This helps the aircraft to operate widely in various complex environments.

[0085] Among them, the flight impact information may include one or more of the following: meteorological parameters, no-fly zone parameters, or obstacle parameters. When determining the first path, a safety distance may be determined based on the flight impact information, at least one path may be determined based on the safety distance combined with a collision threat fitness function, and the first path may be determined from the at least one path. The safety distance refers to the distance between aircraft and between aircraft and the flight impact information. At least one path refers to a path where the aircraft is away from obstacles, for example, the probability of a collision on at least one path is less than or equal to a first threshold. The first threshold may be (pre)configured or predefined.

[0086] In the specific implementation process, the equivalent method of the cylinder can be used to estimate the impact of various severe weather conditions such as hail, thunderstorms, strong winds, and blizzards on the trajectory planning. For example, the equivalent method of the cylinder can be used to establish the weather threat Z w (x,y), weather threat Z w (x, y) belongs to meteorological parameters or can be called meteorological parameters, which are used to indicate the degree of influence of weather on flight.

[0087] Similarly, the impact of a no-fly zone on flight path planning can be estimated. For example, a no-fly zone can be represented by a sphere, where the center of the sphere is the coordinates of the threat and the radius is the area affected by the threat. The coordinates of the center of the threat can be expressed as (x s ,y s ), R s is the threat radius, and the obstacle model can be established as (x s ,y s ,z s ,R s). (x s ,y s ,z s ,R s ) can also be called the no-fly zone parameter. For obstacle threats, they can also be modeled as spheres. For details, please refer to the modeling of no-fly zones, which will not be described here.

[0088] In addition, other factors are also considered during the flight of the aircraft. For example, due to the limitation of the aircraft size, the amount of fuel that can be carried is limited. The time and distance of the flight mission are related to the amount of fuel it carries. Therefore, when determining the first path, it is necessary to consider the flight distance L, L <L max , where L max It indicates the maximum track distance of the aircraft. It can also be understood that the first path is constrained by the maximum track distance.

[0089] For another example, if the horizontal steering angle of an aircraft is too large during flight, it is easy to roll over and cause the drone to crash. In order to avoid this, its steering angle needs to be constrained. In other words, the first path is constrained by the horizontal heading angle. Assuming that the steering angle of the drone is θ, and the flight time of the aircraft is divided into N time slots, then the steering angle θ of the aircraft in time slot i is i Should satisfy: θ i ∈[0,π / 2),(i=1,2,3,…,N).

[0090] For another example, a fixed-wing UAV cannot hover due to its design, and it is easy to crash if the speed is too low. Therefore, the first path is limited by speed. In this application, the speed v of the aircraft can be designed i It is 200-300km / h, where i=1,2,3,…,M.

[0091] For another example, while maintaining safe flight of the aircraft, coverage communication between the aircraft and the user must also be considered to avoid interruption of communication between the aircraft and the user.

[0092] As can be seen from the above, the determination of the first path needs to consider the following factors: flight impact information, maximum track distance constraint, horizontal heading angle constraint and speed limit. Different factors have different effects on the accuracy of the first path. Therefore, an adaptive function can be designed to estimate the first path based on the comprehensive factors.

[0093] For flight impact information, a collision threat fitness function can be designed. For example, the collision threat fitness function should satisfy formula (3-5)

[0094]

[0095] Among them, D ijIndicates the distance between the aircraft and the environmental threat area, d safe Indicates the minimum safety distance, that is, the environmental threat radius.

[0096] For the maximum track distance, a path length fitness function can be designed. For example, the path length fitness function satisfies formula (3-6)

[0097]

[0098] Among them, L i represents the flight path length of aircraft i in time slot interval; J L Represents the aircraft path length fitness function

[0099] For coverage, a coverage fitness function can be designed. For example, the coverage fitness function satisfies formula (3-7)

[0100]

[0101] Among them, R cover (t) represents the number of users covered at time t, total(t) represents the total number of users, and α represents the reward function value.

[0102] The collision threat fitness function, the flight path length fitness function and the coverage fitness function are used together as the evaluation index for determining the first path. For example, the evaluation index for the first path can be determined according to formula (3-8):

[0103] fit = ω1 × J T +ω2×J L +ω3×J R

[0104] (3-8)

[0105] Among them, ω1, ω2, and ω3 represent weight coefficients and ω1+ω2+ω3=1. Specifically, the weight coefficient can be set according to one's own needs. By adjusting the weight coefficient, the degree of demand for different evaluation indicators can be set.

[0106] At least one path can be determined according to the evaluation index function fit, and the at least one path can be considered as a better path. For example, the probability of collision of at least one path is less than or equal to a first threshold. For another example, the coverage rate of at least one path is greater than or equal to a second threshold.

[0107] Further, the first path may be determined from at least one path. For example, at least one path may be determined according to a safety distance. For example, at least one path may be determined according to the following formula:

[0108]

[0109] Among them, J T Used to indicate whether to select the corresponding path, D ij is the distance between the aircraft and the no-fly zone, d safe Indicates a safe distance.

[0110] S203, adjusting the potential field according to the distance between the first aircraft and other aircraft, the distance between the first aircraft and the obstacle, and the repulsive potential field. Construct a repulsive potential field.

[0111] In an embodiment of the present application, the aircraft determines that the first path can store the first path. The first path can be regarded as a path for implementing the plan. Since the actual flight environment may be different from the flight environment referenced for planning the first path, in order to ensure the smooth completion of the flight mission, the first path needs to be adjusted. For example, the first path is adjusted to the second path, and the second path is suitable for the current flight environment. It can be considered that the second path is planned in real time according to the current flight environment. Adjusting the first path to obtain the second path can save path planning time and improve path planning efficiency compared to directly planning the path.

[0112] The process of adjusting the first path to the second path is online track planning using an artificial potential field algorithm. The artificial potential field algorithm provided in the embodiment of the present application includes constructing a gravitational potential field, constructing a repulsive potential field, and constructing a normal force potential field.

[0113] The repulsive potential field is composed of the distance potential field between the aircraft and the distance potential field between the aircraft and the obstacle. In the embodiment of the present application, the distance potential field between the first aircraft and the other aircraft, the distance potential field between the first aircraft and the obstacle and the repulsive potential field adjustment factor Construct a repulsive potential field.

[0114] For example, the repulsive potential field satisfies formula (4-1)

[0115]

[0116] in, represents the repulsive potential field; η represents the repulsive factor; ρ0 represents the influence radius of the obstacle; represents the repulsive potential field adjustment factor, q obs is the distance potential field between the first aircraft and the obstacle.

[0117] In the embodiment of the present application, the repulsive potential field adjustment factor is introduced, so that when the aircraft flies to the target position, the attraction and repulsion are simultaneously reduced to zero, and the target position becomes the point with the minimum potential energy value, so that the aircraft can reach the target position.

[0118] The normal force potential field can be regarded as a force orthogonal to the current moving direction used to guide the aircraft to avoid obstacles. The magnitude of the normal force satisfies formula (4-2), and the direction of the normal force satisfies formula (4-3)

[0119]

[0120] Among them, k norm represents the normal force coefficient, d safe Indicates the safe distance between the aircraft and obstacles, d obs Indicates the distance between the aircraft and the obstacle, satisfying formula (4-4)

[0121]

[0122] d obs =||X UAV -X obs || (4-4)

[0123] in, Represents the obstacle normal vector, satisfying formula (4-5)

[0124]

[0125] In an embodiment of the present application, taking into account the real-time changes in the position of obstacles in actual online trajectory planning, a dynamic normal force potential field is introduced to adjust the size and direction of the original normal force in real time. In this way, while guiding the aircraft to avoid obstacles and maintain a certain distance, the aircraft can be ensured to fly along the boundary of the obstacle, thereby effectively avoiding the risk of directly crossing the obstacle.

[0126] S204: Adjust the first path to a second path according to the repulsive potential field.

[0127] It should be understood that the online flight planning using the artificial potential field algorithm provided in the embodiment of the present application introduces a repulsive potential field adjustment factor into the repulsive potential field to increase the normal force, that is, the aircraft environment is guided by the repulsive force, the gravitational force and the normal force, and the direction of the aircraft movement is the direction of the resultant force, satisfying formula (4-6)

[0128]

[0129] S205: Execute the flight mission according to the second path.

[0130] The second path of the first aircraft is determined, and the first aircraft can perform the flight mission according to the second path. Since the second path is a track planned in real time, collisions can be avoided as much as possible to ensure smooth execution of the flight mission.

[0131] The following describes a communication device (eg, aircraft) used to implement the above method in an embodiment of the present application in conjunction with the accompanying drawings. Therefore, the above contents can be used in subsequent embodiments, and repeated contents will not be repeated.

[0132] See also Figure 3 The embodiment of the present application also provides an aircraft for implementing the functions of the above-mentioned various method embodiments, for example, a first aircraft. The first aircraft at least includes a processing module 320 and a transceiver module 310. The processing module 320 may be a chip in the first aircraft, and the transceiver module 310 may be an antenna or an interface circuit of the first aircraft. Optionally, a storage module is also included.

[0133] The processing module 320 is used to map the initial positions of the M aircraft into a chaotic sequence, where M is a positive integer; determine the first path according to the chaotic sequence and the flight impact information, the distance potential field between the first aircraft and the other aircraft, the distance potential field between the first aircraft and the obstacle, and the repulsive potential field adjustment factor Construct a repulsive potential field; adjust the first path to the second path according to the repulsive potential field. Among them, the meteorological parameters include weather threat factors, which are used to indicate the degree of weather impact on flight. The chaotic sequence satisfies the formula:

[0134]

[0135] Among them, z n+1 is the chaotic sequence of the n+1th time unit, z n is the chaotic sequence of the nth time unit, where n is a positive integer; μ is greater than or equal to 0, and μ is less than 1.

[0136] Optionally, the processing module 320 is specifically used to: determine the safe distance of the aircraft according to the flight impact information, determine at least one path according to the safe distance, the chaotic sequence and the collision threat fitness function, and determine the first path from the at least one path. The collision threat fitness function is used to select a path whose probability of collision is less than or equal to a first threshold. The probability of collision of at least one path is less than or equal to the first threshold.

[0137] Optionally, the processing module 320 is specifically configured to determine at least one path according to the following formula:

[0138]

[0139] J T Used to indicate whether to select the corresponding path, D ij is the distance between the aircraft and the no-fly zone, d safe Indicates the safety distance.

[0140] Optionally, the repulsive potential field satisfies:

[0141]

[0142] in, is the repulsive potential field, η is the repulsive factor, and ρ0 is the influence radius of the obstacle.

[0143] Optionally, adjusting the first path to the second path according to the repulsive potential field includes: adjusting the first path to the second path according to the repulsive potential field and the normal force potential field; wherein the normal force based on the normal force potential field satisfies the following formula:

[0144]

[0145] Among them, k norm is the normal force coefficient, d safe is the safe distance between the aircraft and obstacles, d obs is the distance between the aircraft and the obstacle.

[0146] The present application also provides a communication device that implements the functions of the above-mentioned method embodiments. The communication device can be an aircraft or a chip (system) in an aircraft. In the present application, the chip system can be composed of a chip, or can include a chip and other discrete devices. For specific functions, please refer to the description in the above-mentioned method embodiments. Among them, the communication device may include one or more processors for implementing the functions provided in the present application. Figure 2 The functions of the methods shown are described in detail in the method examples, which will not be described here.

[0147] The processor may be a general-purpose processor or a special-purpose processor, etc. For example, it includes: a baseband processor, a central processing unit, an application processor, a modem processor, a graphics processor, an image signal processor, a digital signal processor, a video codec processor, a controller, a memory, and / or a neural network processor, etc. The baseband processor can be used to process communication protocols and communication data. The central processing unit can be used to control a communication device (such as a terminal device or a network device), execute software programs and / or process data. Different processors can be independent devices or integrated in one or more processors, for example, integrated in one or more dedicated integrated circuits.

[0148] In one design, the processor may include a program (sometimes also referred to as code or instruction), which may be executed on the processor to cause the communication device to perform the method described in the following embodiments.

[0149] In one design, the communication device may include one or more memories on which programs (sometimes also referred to as codes or instructions) are stored. The programs can be executed on the processor so that the communication device performs the methods described in the above method embodiments.

[0150] In a possible design, data may also be stored in the processor and / or memory. The processor and memory may be provided separately or integrated together.

[0151] In an embodiment of the present application, a computer-readable storage medium is also provided, including instructions, which, when executed on a computer, enable the computer to execute the method in the above method example. For details, please refer to the detailed description in the method example, which will not be repeated here.

[0152] In order to realize the functions of the method provided in the above embodiment of the present application, the electronic device for processing space radio signals may include a hardware structure and / or a software module, and the above functions are realized in the form of a hardware structure, a software module, or a hardware structure plus a software module. Whether one of the above functions is executed in the form of a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application and design constraints of the technical solution.

[0153] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0154] In the embodiments of the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in the embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: Universal Serial Bus flash disk (USB), mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0158] The various illustrative logic units and circuits described in the embodiments of the present application can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or the design of any combination of the above functions. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any traditional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration to implement.

[0159] The steps of the method or algorithm described in the embodiments of the present application can be directly embedded in the software unit executed by the hardware, the processor, or a combination of the two. The software unit can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM or other storage media of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Alternatively, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0161] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for controlling the flight of an aircraft, characterized in that: include: Mapping the initial positions of M aircraft to chaotic sequences, where M is a positive integer, wherein when the value of the chaotic sequence of the nth time unit is at (0, u), the value of the chaotic sequence of the n+1th time unit is obtained by scaling the value of the chaotic sequence of the nth time unit; when the value of the chaotic sequence of the nth time unit is at (u, 1), the value of the chaotic sequence of the n+1th time unit is obtained by calculating the symmetric inverse relationship of the chaotic sequence of the nth time unit; Determining a first path according to the chaotic sequence and the flight impact information, wherein the first path is a path that has been planned for the first aircraft, and the flight impact information includes at least one of the following: a meteorological parameter, a no-fly zone parameter, or an obstacle parameter, wherein the meteorological parameter includes a weather threat factor, which is used to indicate the degree of influence of weather on the flight; According to the repulsive potential field adjustment factor The distance potential field between the first aircraft and other aircraft and the distance potential field between the first aircraft and an obstacle determine a repulsive potential field, and adjust the first path to a second path according to the repulsive potential field; The first aircraft is controlled to perform a flight mission according to the second path.

2. The method according to claim 1, characterized in that Determining a first path according to the chaotic sequence and the flight impact information includes: Determining a safe distance of the aircraft according to the flight impact information; Determining at least one path according to the safety distance, the chaotic sequence and a collision threat fitness function, wherein the probability of collision of the at least one path is less than or equal to a first threshold, and the collision threat fitness function is used to select a path whose probability of collision is less than or equal to the first threshold; The first path is determined from the at least one path.

3. The method according to claim 2, characterized in that Determining at least one path according to the safety distance includes: determining at least one path according to the following formula: J T Used to indicate whether to select the corresponding path, D ij is the distance between the aircraft and the no-fly zone, d safe Indicates the safety distance.

4. The method according to claim 2 or 3, characterized in that The repulsive potential field satisfies: in, is the repulsive potential field, η is the repulsive factor, ρ0 is the influence radius of the obstacle, is the repulsive potential field adjustment factor, q obs is the distance potential field between the first aircraft and the obstacle.

5. The method according to claim 2, characterized in that Adjusting the first path to a second path according to the repulsive potential field comprises: The first path is adjusted to the second path according to the repulsive potential field and the normal force potential field; wherein the normal force based on the normal force potential field satisfies the following formula: Among them, k norm is the normal force coefficient, d safe is the safe distance between the aircraft and the obstacle, d obs is the distance between the aircraft and the obstacle.

6. The method according to any one of claims 1 to 5, characterized in that The chaotic sequence of the n+1th time unit and the chaotic sequence of the nth time unit satisfy the formula: Among them, z n+1 is the chaotic sequence of the n+1th time unit, z n is the chaotic sequence of the nth time unit, where n is a positive integer; u is greater than or equal to 0, and u is less than 1.

7. A first aircraft, characterized in that: include: At least one processor is used to: map the initial positions of M aircraft into a chaotic sequence, where M is a positive integer, Wherein, when the value of the chaotic sequence of the nth time unit is at (0,u), the value of the chaotic sequence of the n+1th time unit is obtained by scaling the value of the chaotic sequence of the nth time unit; when the value of the chaotic sequence of the nth time unit is at (u,1), the value of the chaotic sequence of the n+1th time unit is obtained by calculating the symmetric inverse relationship of the chaotic sequence of the nth time unit; according to the chaotic sequence and the flight impact information, a first path is determined to be obtained, the first path is a path planned for the first aircraft, and the flight impact information includes at least one of the following: meteorological parameters, no-fly zone parameters, or obstacle parameters, wherein the meteorological parameters include weather threat factors, which are used to indicate the degree of influence of weather on flight; according to the repulsive potential field adjustment factor The distance potential field between the first aircraft and other aircraft and the distance potential field between the first aircraft and an obstacle construct a repulsive potential field, and the first path is adjusted to a second path according to the repulsive potential field; and the first aircraft is controlled to perform a flight mission according to the second path; A memory is used to store the second path.

8. The aircraft according to claim 6, characterized in that The at least one processor is specifically configured to: Determining a safe distance of the aircraft according to the flight impact information; Determine at least one path according to the safety distance and the chaotic sequence, wherein a probability of collision of the at least one path is less than or equal to a first threshold; The first path is determined from the at least one path.

9. A computer storage medium, characterized in that The computer-readable storage medium is used to store a computer program, and when the computer program is run on a computer, the method according to any one of claims 1 to 6 is executed.

Images