CN113188547A - Unmanned aerial vehicle path planning method and device, controller and storage medium - Google Patents

Abstract

The present application relates to a UAV path planning method, device, controller and storage medium. The method includes: acquiring the signal coverage area of each base station in a task area; The man-machine two-dimensional path planning space model; according to the starting point position information, the end position information and the unmanned aerial vehicle two-dimensional path planning space model, the preliminary planning path of the unmanned aerial vehicle located in the signal coverage area of the base station is obtained; The human-machine preliminary planning path and the ant colony algorithm are used to obtain the optimal planning path of the UAV. The points on the optimal planning path are all within the signal coverage area of the base station, which ensures the stability of communication during the flight of the UAV, and ensures flight reliability and safety. The optimal planning path obtained by using the ant colony algorithm can guide the unmanned aerial vehicle. The aircraft flies safely from the starting point to the ending point at the fastest speed to complete the flight mission. It has a positive impact on the low-altitude navigation efficiency of UAVs.

Description

UAV path planning method, device, controller and storage medium

technical field

The present application relates to the technical field of UAV control, and in particular, to a UAV path planning method, device, controller and storage medium.

Background technique

With the innovation and development of UAV flight control technology, UAVs show high-quality and low-cost advantages in many commercial activities, such as cargo delivery, aerial reconnaissance and monitoring, etc. These activities mainly exist in urban environments , due to the high-rise buildings in the city, the UAV must achieve over-the-horizon work when performing the above tasks, so the cellular network UAV has become a good choice. Control its flight, but the inventor found in the implementation process that the UAV will lose contact with the ground base station and cannot distinguish the command information during the execution of the mission, and then lose control, posing a threat to the safety of residents' lives and property.

SUMMARY OF THE INVENTION

Based on this, it is necessary to provide a UAV path planning method, device, controller and storage medium that can avoid the UAV out of control due to communication problems during flight according to the planned path, so as to improve the urban environment. , especially the flight stability and safety of low-altitude UAVs.

On the one hand, the embodiments of the present application provide a method for planning a path of an unmanned aerial vehicle, and the method includes:

Obtain the signal coverage area of each base station in the task area;

According to the signal coverage area of each base station, construct a two-dimensional path planning space model of the UAV in the task area;

According to the starting point position information, the end point position information and the two-dimensional path planning space model of the UAV, the preliminary planning path of the UAV located in the signal coverage area of the base station is obtained;

According to the preliminary planning path of the UAV and the ant colony algorithm, the optimal planning path of the UAV is obtained.

The UAV path planning method provided by the embodiment of the present application fully considers the signal coverage of the base stations in the UAV flight area, and constructs a two-dimensional path plan for the UAV at the height level where the UAV is located according to the signal coverage area of each base station. In the space model, first, according to the position information of the starting point and the ending point of the UAV, it is necessary to find the shortest path of the UAV within the coverage area of the base station in the two-dimensional path planning space, and use it as the preliminary planning path of the UAV. The above points are all within the signal coverage area of the base station, which can ensure good communication conditions during the flight of the UAV under this path, and ensure flight reliability and safety. The initial planning path of the drone is optimized, and the optimal planning path of the drone is obtained, which is used as the final planning path of the drone to guide the drone to safely fly from the starting point to the end point at the fastest speed to complete the flight mission.

In one of the embodiments, the step of acquiring the signal coverage area of each base station in the task area includes:

gridding the task area;

Determine a signal transmission mode between each grid point and each of the base stations according to the positional relationship between each grid point and each of the base stations in the task area, and the signal transmission mode includes line-of-sight propagation and non-line-of-sight propagation;

According to the signal transmission mode between each grid point and each of the base stations and the calculation model of the farthest distance from the base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located, determine The signal coverage area of each base station.

In one embodiment, the process of constructing a calculation model of the farthest distance from the base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located includes:

Using the signal power transmitted by each base station and the current position information of the UAV to obtain the signal-to-noise ratio model of the signal received by the current UAV and transmitted from each base station by the following formula:

Among them, ρ _k (ν(t)) represents the signal-to-noise ratio of the signal transmitted from the k-th base station received by the current drone, P represents the signal power transmitted by the base station, and ν(t) represents the location where the drone is located. Two-dimensional coordinate information in a two-dimensional plane, γ _k,s (t) represents the channel gain from the k-th base station to the UAV channel, s∈{LoS,NLoS}, LoS represents line-of-sight propagation, and NLoS represents non-line-of-sight propagation Propagation, σ ² represents the noise power of the UAV;

其中，d_k(t)为无人机到第k个基站的距离，α_s和β_s为取决于与各所述基站之间的信号传输方式的两个常数参数；Among them, the channel gain calculation model of the kth base station to the UAV channel is:

Wherein, d _k (t) is the distance from the drone to the k-th base station, and α _s and β _s are two constant parameters depending on the signal transmission mode with each of the base stations;

并联合所述信号信噪比模型和所述信道增益计算模型，得到无人机所在二维平面内的各基站覆盖点到基站在无人机所在二维平面内的投影的最远距离计算模型：Let the signal-to-noise ratio of the signals transmitted from each base station received by the drone be equal to the minimum received signal-to-noise ratio of the drone

And combine the signal-to-noise ratio model and the channel gain calculation model to obtain the calculation model of the farthest distance from each base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located :

Among them, d _s is the farthest distance from each base station coverage point in the two-dimensional plane where the drone is located to the projection of the base station in the two-dimensional plane where the drone is located, h represents the height of the drone, and h _g represents the base station the height of.

In one embodiment, according to the signal transmission mode between each grid point and each of the base stations and the coverage point of the base station in the two-dimensional plane where the UAV is located to the base station in the two-dimensional plane where the UAV is located. The farthest distance calculation model of the projection, the steps of determining the signal coverage area of each base station include:

According to the following formula and the calculation model of the farthest distance from the base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located, determine the grid points located in the signal coverage area of the base station Coordinates (x,y,h):

(xx _k ) ² +(yy _k ) ² ≤d _s ²

Wherein, (x _k , y _k ) represents the projected coordinates of the base station on the two-dimensional plane at the height of the UAV.

In one of the embodiments, the step of constructing a two-dimensional path planning space model of the UAV in the mission area according to the signal coverage area of each base station includes:

Based on the signal coverage area of each base station and the MAKLINK graph theory method, a plurality of MAKLINK connection lines are generated in the mission area, and a two-dimensional path planning space model of the UAV in the mission area is established; the MAKLINK connection line refers to the A vertex connection line between two areas not covered by base station signals that does not intersect with the area not covered by base station signals and a connection line between the vertex of the area not covered by base station signals and the boundary of the task area.

In one embodiment, the step of obtaining the preliminary planned path of the UAV located in the signal coverage area of the base station according to the starting point position information, the end position information and the two-dimensional path planning space model of the UAV includes:

Use the Dijkstra algorithm and the starting point position information and the ending point position information to solve the two-dimensional path planning space model of the UAV, and obtain the shortest path from the starting point position of the UAV to the midpoint of each MAKLINK connecting line and the end position of the UAV. The preliminary planned path of the UAV is the shortest path from the starting point of the UAV to the midpoint of each MAKLINK connection line and the end position of the UAV.

In one embodiment, the step of obtaining the optimal planned path of the UAV according to the preliminary planned path of the UAV and the ant colony algorithm includes:

Initialize the number of ants m, the maximum number of iterations, the pheromone of each path, the parameter α that reflects the pheromone trajectory of the ants in the process of activity, the parameter β that reflects the relative importance of visibility in the ants’ path selection, and the attenuation coefficient of the pheromone trajectory ρ;

At the starting point, each ant selects the node j on the next connecting line L _i+1 successively according to the following formula, until it reaches the end position of the drone:

Wherein, the preliminary planned path of the UAV passes through nodes S, P ₁ , P ₂ ,...P _d , T; S represents the node at the starting point of the UAV in the two-dimensional path planning space model of the UAV, and T represents the unmanned aerial vehicle. P ₁ , P ₂ ,...P _d represents the midpoint of each MAKLINK connection line that the UAV preliminary planned path passes through; I represents the next connection line L The set of all points on _i+1 , τ _ik represents the pheromone intensity on the path (i, k), η _ik =1/d _ik represents the visibility on the path (i, k), and d _ik represents the path (i, k) ), q is a random number between [0, 1], q ₀ is an adjustable parameter between [0, 1]; J represents the last connection line Li ( _i =1,2,…, d) is the _probability of selecting the node _j of the next connecting line when the node _i of the d _ij represents the length of the path (i, j), τ _is represents the pheromone intensity on each node path from node i to the next connecting line _Li+1 , η _is =1/d _is represents the node i to the next connecting line The visibility on each node path of Li ₊₁ , d _is the length of each node path from node i to the next connecting line _Li+1 ;

Each ant updates the pheromone of each path traversed by the ant according to the following formula:

τ _ij =(1-ρ)τ _ij +Δτ _ij

表示第k只蚂蚁在本次循环中留在路径(i,j)上的信息素量，Δτ_ij表示本次循环中路径(i,j)的信息素量的增量，L_k为第k只蚂蚁在本次循环中所走的路径长度，Q为设定的常数；in,

represents the amount of pheromone left by the kth ant on the path (i, j) in this cycle, Δτ _ij represents the increment of the pheromone amount on the path (i, j) in this cycle, and L _k is the kth pheromone The length of the path taken by the ants in this cycle, Q is the set constant;

Record and update the shortest path traversed by all ants in this iteration as the global optimal path;

If the number of iterations plus 1 is not greater than the maximum number of iterations, each ant on the current connection line Li selects node _j on the next connection line _Li+1 at node i according to the following formula , until the steps to reach the end position of the drone;

If the number of iterations plus 1 is greater than the maximum number of iterations, the updated global optimal path is output as the optimal planned path of the UAV.

On the other hand, the embodiment of the present application also provides a UAV path planning device, the device includes:

The base station coverage area acquisition module is used to obtain the signal coverage area of each base station in the task area;

A two-dimensional path planning space building module, used for constructing a two-dimensional path planning space model of the UAV in the task area according to the signal coverage area of each base station;

The preliminary path planning module is used to obtain the preliminary planned path of the UAV located in the signal coverage area of the base station according to the starting point position information, the end point position information and the two-dimensional path planning space model of the UAV;

The optimal path planning module is used for obtaining the optimal planning path of the UAV according to the preliminary planning path of the UAV and the ant colony algorithm.

A controller includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when the processor executes the computer program.

A computer-readable storage medium having a computer program stored thereon, the computer program implementing the steps of the above method when executed by a processor.

Description of drawings

1 is a schematic flowchart of a UAV path planning method in one embodiment;

2 is a schematic flowchart of a UAV path planning method in another embodiment;

3 is a schematic diagram of an environment within a task area in one embodiment;

4 is a schematic flowchart of a step of acquiring the signal coverage area of each base station in a task area in one embodiment;

5 is a schematic diagram of signal coverage of a base station in a task area in an embodiment;

6 is a schematic diagram of task area division in one embodiment;

7 is a schematic diagram of the two-dimensional path planning space model of the UAV in the mission area and the MAKLINK line in the mission area in one embodiment;

8 is an undirected network diagram of each path planning from the starting point through the midpoint of the MAKLINK line to the end point in the task area in one embodiment;

Fig. 9 is the solution of the two-dimensional path planning space model of the UAV by using Dijkstra algorithm and starting point position information and ending point position information in one embodiment, and obtaining the starting point position of the UAV to the midpoint of each MAKLINK connecting line and the UAV end point Schematic diagram of the steps of the shortest path to the location;

10 is a schematic diagram of a preliminary planned path of the UAV in the mission area shown in FIG. 3 in one embodiment;

11 is a schematic diagram of dividing the connecting lines found by using the Dijkstra algorithm in one embodiment;

12 is a schematic flowchart of steps of obtaining the optimal planned path of the UAV according to the preliminary planning path of the UAV and the ant colony algorithm in one embodiment;

Fig. 13 is a schematic diagram of the optimal planning path of the UAV in the mission area shown in Fig. 3;

14 is a schematic structural diagram of a UAV path planning device in one embodiment;

FIG. 15 is a diagram of the internal structure of the controller in one embodiment.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

As described in the background art, the realization of UAV flight control in a low-altitude urban agglomeration often requires over-the-horizon work through networking with a cellular network. When the drone is connected to the Internet to control its flight, the drone is required to maintain contact with the ground base station at all times during the process of performing the task, that is, the signal sent by a ground base station received by the drone The signal-to-noise ratio needs to be greater than its resolution, otherwise the drone may be out of control because it cannot distinguish the command information, thereby threatening the safety of residents' lives and property. At the same time, in order to carry out the task with the lowest cost, after knowing the starting point and ending point of the task, we need to plan a shortest path for the UAV that meets the above conditions, so that the task can be completed safely and efficiently.

Based on this, an embodiment of the present application provides, on the one hand, a UAV path planning method. As shown in FIG. 1 , the method includes:

S200: Acquire the signal coverage area of each base station in the task area.

Among them, the mission area refers to the area where the UAV performs the flight mission. For example, it can be a rectangular area formed by the starting point and the end point of the UAV performing the flight mission. This area can include multiple base stations and other objects, such as buildings. Wait. In the signal coverage area of the base station, the signal-to-noise ratio of the signal transmitted by the base station received by the UAV is greater than its resolution, that is, the area refers to the area that can ensure the normal communication of the UAV.

S400: Build a two-dimensional path planning space model of the UAV in the task area according to the signal coverage area of each base station. The two-dimensional path planning space model of the UAV refers to the space model that can represent the signal coverage of the base station in the horizontal plane at the height of the UAV.

S600: Obtain a preliminary planned path of the UAV located in the signal coverage area of the base station according to the starting point position information, the end point position information and the two-dimensional path planning space model of the UAV.

The starting point position information refers to the position information of the starting point given when the UAV performs the task. For example, the position information may be the coordinate data of the starting point in the world coordinate system. Similarly, the end point position information refers to the position information of the given flight end point when the UAV performs the task, and the position information may be the coordinate data of the end point in the world coordinate system. The preliminary planning path of the UAV refers to the shortest flight path obtained by calculation in the two-dimensional path planning space of the UAV.

S800: According to the preliminary planning path of the UAV and the ant colony algorithm, obtain the optimal planning path of the UAV. The optimal planning path refers to finding the points in the base station coverage area around the point where the UAV's preliminary planning path passes on the basis of the preliminary planning path of the UAV, and using the ant colony algorithm to find the shortest flight path from these points. , that is, the optimization of the preliminary planning path of the UAV, so that the UAV flies according to the optimal planning path, the flight time is the shortest, and the communication during the flight is stable and safe.

The UAV path planning method provided by the embodiment of the present application fully considers the signal coverage of the base stations in the UAV flight area, and constructs a two-dimensional path plan for the UAV at the height level where the UAV is located according to the signal coverage area of each base station. In the space model, first, according to the position information of the starting point and the ending point of the UAV, it is necessary to find the shortest path of the UAV within the coverage area of the base station in the two-dimensional path planning space, and use it as the preliminary planning path of the UAV. The above points are all within the signal coverage area of the base station, which can ensure good communication conditions during the flight of the UAV under this path, and ensure flight reliability and safety. The initial planning path of the drone is optimized, and the optimal planning path of the drone is obtained, which is used as the final planning path of the drone to guide the drone to safely fly from the starting point to the end point at the fastest speed to complete the flight mission.

In addition, traditional path planning algorithms include global planning and local planning. Global planning algorithms such as vertex image method, grid division method, and local planning algorithms are mainly artificial potential field methods. When the propagation mode of electromagnetic waves between the base station and the base station is uniformly simplified as line-of-sight propagation, the above method can obtain a good solution. However, the actual situation in the flying area of drones is often far from the case. Due to the high-rise buildings in the urban environment, there is a shadow effect on the propagation of electromagnetic waves. Therefore, it is unscientific and meaningless to directly simplify the propagation of the electromagnetic wave to line-of-sight propagation. After the electromagnetic wave propagation mode, the path planning of the UAV becomes very complicated, and it is difficult for the traditional algorithm to obtain a good result. However, with the proposal and improvement of bionic algorithms, the idea of using the wisdom of natural organisms to solve optimization problems has entered the public's field of vision, and the ant colony algorithm is one of the representatives. Considering that the ant colony algorithm has a good ability of global optimization and solving complex problems, the UAV path planning method provided by the embodiment of the present application uses the ant colony algorithm to optimize the path to obtain the optimal planned path, which can better to solve the practical problem of UAV path planning in urban environment.

In one embodiment, as shown in FIG. 2 , the step of acquiring the signal coverage area S200 of each base station in the task area includes:

S220: Gridize the task area.

S240: Determine a signal transmission mode between each grid point and each of the base stations according to the positional relationship between each grid point and each of the base stations in the task area, where the signal transmission mode includes line-of-sight propagation and non-line-of-sight propagation spread. In the urban environment, there are many high-rise buildings in the urban environment, and there is a shadow effect in the propagation of electromagnetic waves. Therefore, it is unscientific to directly simplify it to line-of-sight propagation. , and determine whether the signal transmission mode between each grid point and the base station is the transmission mode or the non-line-of-sight propagation. Among them, line-of-sight propagation refers to the propagation of electromagnetic rays along a straight line. The non-line-of-sight propagation method refers to the indirect point-to-point communication between the UAV and the base station.

S260: Calculate the model for the farthest distance based on the signal transmission mode between each grid point and each of the base stations and the projection of the base station from the base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located , to determine the signal coverage area of each base station. The calculation model of the farthest distance from the base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located is about when the UAV communicates with the base station at each grid point. A model for the minimum resolution (ie, the minimum signal-to-noise ratio allowed) of the human-machine.

Specifically, through gridding, multiple grid points in the task area are obtained. According to the positional relationship between the grid points and each base station, the signal transmission method between each UAV and the base station at each grid point can be determined. Whether it is line-of-sight propagation or non-line-of-sight propagation, we can obtain the signal strength of the UAV when it communicates with the base station at each grid point under different propagation methods, and then use the base station in the two-dimensional plane where the UAV is located to cover the point to the base station. The maximum distance of the projection in the two-dimensional plane where the UAV is located is determined by the model. When the UAV communicates with the base station at each grid point, it can meet the minimum resolution of the UAV (ie, the minimum allowable signal-to-noise ratio). For the far grid point, the area formed by the grid points closer to the base station than the farthest grid point is the signal coverage area of the base station. The signal coverage area obtained by this method fully considers the situation that the electromagnetic wave propagation between the two is not line-of-sight when communicating with the base station due to the high buildings in the urban environment, so that the final determined base station signal coverage In the area, the communication between the drone and the base station is more stable, which further improves the safety of the drone during flight.

In one of the embodiments, to determine the signal transmission mode of the drone between each grid point and each base station, the following method can be used to determine: when the connection between the drone's location and the base station is higher than the two The height of any building in between is regarded as line-of-sight transmission, otherwise it is non-line-of-sight transmission.

In one embodiment, the process of constructing a calculation model of the farthest distance from the base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located includes:

Using the signal power transmitted by each base station and the current position information of the UAV to obtain the signal-to-noise ratio model of the signal received by the current UAV and transmitted from each base station by the following formula:

Among them, ρ _k (ν(t)) represents the signal-to-noise ratio of the signal transmitted from the k-th base station received by the current drone, P represents the signal power transmitted by the base station, and ν(t) represents the location where the drone is located. Two-dimensional coordinate information in a two-dimensional plane, γ _k,s (t) represents the channel gain from the k-th base station to the UAV channel, s∈{LoS,NLoS}, LoS represents line-of-sight propagation, and NLoS represents non-line-of-sight propagation Propagation, σ ² represents the noise power of the UAV;

其中，d_k(t)为无人机到第k个基站的距离，α_s和β_s为取决于与各所述基站之间的信号传输方式的两个常数参数；Among them, the channel gain calculation model of the kth base station to the UAV channel is:

Wherein, d _k (t) is the distance from the drone to the k-th base station, and α _s and β _s are two constant parameters depending on the signal transmission mode with each of the base stations;

并联合所述信号信噪比模型和所述信道增益计算模型，得到无人机所在二维平面内的各基站覆盖点到基站在无人机所在二维平面内的投影的最远距离计算模型：Let the signal-to-noise ratio of the signals transmitted from each base station received by the drone be equal to the minimum received signal-to-noise ratio of the drone

And combine the signal-to-noise ratio model and the channel gain calculation model to obtain the calculation model of the farthest distance from each base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located :

Among them, d _s is the farthest distance from each base station coverage point in the two-dimensional plane where the drone is located to the projection of the base station in the two-dimensional plane where the drone is located, h represents the height of the drone, and h _g represents the base station the height of.

In order to better illustrate the implementation process of the UAV path planning method provided by the embodiments of the present application, in a mission area of (m×n) km ² , the starting point of the UAV is a vertex of the mission area, and there is no unmanned aerial vehicle. The machine task end point is another vertex of the diagonal as an example to illustrate. In the mission area, there are randomly distributed several base stations with a certain transmission power and a certain height, and buildings whose positions are randomly distributed and whose heights obey the Rayleigh distribution within a certain range. Now it is necessary to plan a shortest path for the UAV to complete the task. Ensure that the drone keeps in touch with a base station on the ground at all times. The schematic diagram of the environment in the task area is shown in Figure 3. The inner rectangular box in the figure represents the building, different gray scales represent different heights, the five-pointed star represents the starting point and the end point, and the four-pointed star represents the base station.

In the above task area, the total time spent by the UAV to perform a certain task is T, then for time t∈[0,T], use v(t)=[x(t),y(t),h] ^T to represent The position of the drone, h represents the flying height of the drone, which is a constant value. In order to avoid collision, its size can be determined by the height of the tallest building in the city. At the same time, the drone can be equipped with GPS and other positioning functions. The device can obtain the current position v(t) of the drone.

k∈[1,K]代表基站在无人机所在高度的二维水平面里的投影位置。If the position of the drone at time 0 is v _I and the end position is v _F , it flies at a constant speed. During the whole process of the UAV's mission, it must maintain contact with one of the K randomly distributed base stations on the ground. Denote the position of the kth base station as _uk =[x _k ,y _k ,h _g ] ^T ,k∈[1,K], h _g represents the height of the base station and assumes that all base stations have the same height. At the same time, set

k∈[1,K] represents the projected position of the base station in the two-dimensional horizontal plane at the height of the drone.

所以建立单目标优化模型如下：Since the purpose of path planning is to find the shortest path of the UAV from the starting point v _I to the end point v _F , since the speed of the UAV is constant, it can be converted into the shortest time for the UAV to perform the task, and at the same time, the whole path is The signal-to-noise ratio SNR(ρ _k (v(t))) of the UAV received signal needs to be not less than the minimum received signal-to-noise ratio of the UAV

Therefore, the single-objective optimization model is established as follows:

Since this optimization problem is difficult to solve directly, and we note that satisfying the constraints in the optimization model means that the flight path of the UAV is within the coverage area of the base station, the problem can be transformed into finding out within the coverage area of the base station. A shortest path from the start point to the end point.

Considering the downlink, let the signal power transmitted by the base station be P, the signal-to-noise ratio of the signal transmitted from the kth base station received by the UAV at the position v(t) is:

The channel gain γ _k,s (t) of the channel from the kth base station to the UAV is calculated as:

才能满足通信稳定性的要求，所以可得第k个基站(k∈[1,K])的信号覆盖区域为：The coverage area of each base station is defined as a series of points that are consistent with the flying height of the UAV, and the signal-to-noise ratio of the signals transmitted by the base station received by the UAV at these points is not less than the resolution of the UAV

In order to meet the requirements of communication stability, the signal coverage area of the kth base station (k∈[1,K]) can be obtained as:

To find the coverage area of the base station, we can start with finding the boundary of the coverage area of the base station. The points on the boundary of the signal coverage area of the base station satisfy:

Simultaneous equations (3) (4) (6) get the distance d _k (t) from the UAV to the k-th base station:

Therefore, in the two-dimensional plane at the height of the drone, the square of the farthest distance d _s from the point that the base station can cover to the base station is:

It is used as the calculation model of the farthest distance from each base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located, and is used to further determine the coverage point within the boundary range to Get the signal coverage area of the base station.

In one of the embodiments, as shown in FIG. 4 , according to the signal transmission mode between each grid point and each of the base stations and the coverage point of the base station in the two-dimensional plane where the UAV is located, to the base station in the UAV The farthest distance calculation model of the projection in the two-dimensional plane, the step S260 of determining the signal coverage area of each base station includes:

According to the following formula and the calculation model of the farthest distance from the base station coverage point in the two-dimensional plane where the UAV is located to the projection of the base station in the two-dimensional plane where the UAV is located, determine the grid points located in the signal coverage area of the base station Coordinates (x,y,h):

(xx _k ) ² +(yy _k ) ² ≤d _s ² (9)

Wherein, (x _k , y _k ) represents the projected coordinates of the base station on the two-dimensional plane at the height of the UAV. According to the above steps to determine the base station coverage area, we get the base station coverage of the task area shown in Figure 3 as shown in Figure 5 (due to the wide coverage area, it is inconvenient to indicate, and the uncovered area is marked).

表示无人机轨迹上的一系列点，相邻两个点之间无人机所走路径为直线，则可以将原优化问题(1)和(2)转化如下优化条件：Because in practice, we plan the path of the UAV by determining which points the UAV passes through, so after determining the coverage area of each base station, the trajectory of the UAV can be discretized and set

represents a series of points on the trajectory of the UAV, and the path taken by the UAV between two adjacent points is a straight line, then the original optimization problems (1) and (2) can be transformed into the following optimization conditions:

In the formula, L(v _n , v _n+1 ) represents the connection between the two points v _n , v _n+1 .

In one of the embodiments, the step of constructing a two-dimensional path planning space model of the UAV in the mission area according to the signal coverage area of each base station includes:

Based on the signal coverage area of each of the base stations (the area outside the polygon in Figure 6) and the MAKLINK graph theory method, a plurality of MAKLINK connection lines are generated in the mission area, and a two-dimensional UAV path planning space in the mission area is established. Model; the MAKLINK connecting line refers to the connecting line between the two areas not covered by the base station signal that does not intersect with the area where the base station signal is not covered, and the connection between the vertex of the area not covering the base station signal and the boundary of the task area. Wire.

By using the MAKLINK graph theory method to discretize the signal coverage area of the base station, multiple MAKLINK connecting lines are obtained, and a two-dimensional path planning space model of the UAV is constructed, and the shortest path in the above equations (10) and (11) are further solved. question.

In one embodiment, the step of obtaining the preliminary planned path of the UAV located in the signal coverage area of the base station according to the starting point position information, the end position information and the two-dimensional path planning space model of the UAV includes:

Use the Dijkstra algorithm and the starting point position information and the ending point position information to solve the two-dimensional path planning space model of the UAV, and obtain the shortest path from the starting point position of the UAV to the midpoint of each MAKLINK connecting line and the end position of the UAV. The preliminary planned path of the UAV is the shortest path from the starting point of the UAV to the midpoint of each MAKLINK connection line and the end position of the UAV.

After determining the coverage area of the base station, when the starting point S and the ending point T of the drone are known, find a shortest path for drone driving in the task area, and the path cannot pass through the uncovered area of the base station (inside the polygon in Figure 6). area), you can use the MAKLINK graph theory to build the UAV 2D path planning space model (as shown in Figure 7), in order to further reduce the complexity, first use the Dijkstra algorithm to solve a UAV preliminary planning path On this basis, the ant colony algorithm is used to solve the optimal planning path, which is beneficial to improve the efficiency of path planning.

There are L free connecting lines on the MAKLINK diagram. The positions of the midpoints of the connecting lines are v ₁ , v ₂ ,..., v _L in order. The midpoints connecting the adjacent MAKLINK lines plus the starting point S and the ending point T constitute the initial The undirected network diagram of path planning is shown in Figure 8. After the connection is completed, the midpoint connection matrix is obtained. The dimension is (L+2)×(L+2). Any two midpoints and the starting point S and the ending point T are connected as 1, otherwise it is 0, so the solution space of the two-dimensional path planning of the UAV is obtained.

In practice, the optimal path may pass through any connecting line, and may pass through any position of the connecting line, but it is more complicated to directly discretize all the above connecting lines, so we consider using the Dijkstra algorithm first to determine the connections the path passes through. Then, the obtained connecting line is subdivided, and the ant colony algorithm is used to obtain the optimal solution, which can ensure the planning efficiency and find the planning accuracy of the shortest flight path.

The basic idea of Dijkstra's algorithm is to divide all the nodes in the weighted graph into two groups, the first group S is the nodes whose shortest path has been determined, and the second group U is the node whose shortest path has not been determined. The nodes of the second group are added to the first group one by one in the increasing order of the shortest path, until all the nodes reachable from the source point are included in the first group. Based on the above ideas, use the Dijkstra algorithm and the starting point position information and the ending point position information to solve the two-dimensional path planning space model of the UAV (that is, solve the connecting line that the path passes through), and obtain the starting point position of the UAV into each MAKLINK connecting line. The steps of the shortest path to the point and the end position of the drone are shown in Figure 9:

Initialize the node set V for which the shortest path is not determined and the node set S for which the shortest path has been determined;

Calculate the distance from the starting point to each point by calculating the current starting point position, ending point position and the midpoint position of each MAKLINK connection line of the pattern;

If the midpoint of the connecting line is directly connected with the starting point, the shortest path D _ij =d _ij between the point and the starting point is obtained; if the end point of the connecting line is not directly connected with the starting point, the shortest path D _ij between the point and the starting point is obtained =∞;

Take out the node i that satisfies i=find (D=min(D)) from the set V and put it into the set S;

Update the path length from the starting point in the path D to each point in the set V according to the node i;

即遍历完所有点，则根据集合S中的节点i确定无人机起点位置到各MAKLINK连接线中点以及无人机终点位置的最短路径；if set

That is, after traversing all points, determine the shortest path from the starting point of the UAV to the midpoint of each MAKLINK connection line and the end position of the UAV according to the node i in the set S;

即未遍历完所有点，则跳转执行将满足i＝find(D＝min(D))的节点i从集合V中取出放入集合S中的步骤，直至遍历完所有点。if set

That is, if all the points have not been traversed, the jump executes the step of taking out the node i satisfying i=find (D=min(D)) from the set V and putting it into the set S, until all the points are traversed.

The above algorithm obtains the shortest path from the start point to the midpoint of each connecting line and the end point. A schematic diagram of the shortest path from the start point to the end point in a possible situation is shown in Figure 10. At this time, the shortest path from the start point to the end point is a time Optimal solution, because the real UAV path can pass through any position of the connecting line, the above sub-optimal solution (UAV preliminary planning path) only determines the connecting line that the UAV optimal path passes through, so next we need The ant colony algorithm is used to find the optimal solution based on the suboptimal solution.

Using the dijkstra algorithm to generate a preliminary planned path of the UAV through the nodes S, P ₁ , P ₂ ,...P _d , T on the two-dimensional path planning space model (ie MAKLINK graph) of the UAV. Let the connection lines corresponding to the nodes be L _i (i=1, 2,...,d) respectively. Using the ant colony algorithm requires a discretized workspace. Considering the different lengths of each connection line, the fixed distance method is used for the connection lines. Divide, set the division length as δ, then the number of divisions of each connecting line is:

表示向上取整，l_i表示连接线L_i的长度。in the formula

Indicates that it is rounded up, and _{li represents} the length of the connecting line Li.

分别表示连接线L_i的两个端点，

分别表示连接线L_i的两个端点坐标。那么将L_i分为π_i份后，其第n_i(i＝1,2,…,d)个π_i等分点的坐标为：After each connecting line is divided into π _i equal parts, there are (π _i +1) paths from the connecting line L _i-1 to the connecting line L _i . Assume

respectively represent the two _endpoints of the connecting line Li,

represent the coordinates of the two _endpoints of the connecting line Li, respectively. Then after dividing Li into π _i parts, the coordinates of the n _i ( _i =1, 2,..., d) π _i equal parts are:

Based on the above analysis, a schematic diagram of dividing the connecting lines found by the Dijkstra algorithm is shown in FIG. 11 . It can be seen from this that, given a set of n _i values, we know where the UAV path passes through each connecting line, and we can also get a path from the starting point to the end point. Therefore, the most searched result obtained by the ant colony algorithm The optimal solution can be expressed as (n ₁ ,n ₂ ,…,n _d ).

Specifically, in one of the embodiments, as shown in FIG. 12 , the step of obtaining the optimal planned path of the UAV according to the preliminary planned path of the UAV and the ant colony algorithm includes:

Initialize the number of ants m, the maximum number of iterations, the pheromone of each path, the parameter α that reflects the pheromone trajectory of the ants in the process of activity, the parameter β that reflects the relative importance of visibility in the ants’ path selection, and the attenuation coefficient of the pheromone trajectory ρ;

Each ant selects the node j on the next connecting line L _i+1 at the starting position S according to the following formula, until it reaches the end position T of the UAV:

Among them, I represents the set of all points on the next connecting line _Li+1 , τ _ik represents the pheromone intensity on the path (i, k), η _ik =1/d _ik represents the visibility on the path (i, k) , d _ik represents the length of the path ( _i , k), q is a random number between [0, 1], q ₀ is an adjustable parameter between [0, 1]; J represents the last connection line Li (i=1,2,...,d) node i is the probability of selecting the node j of the next connecting line, τ _ij represents the pheromone intensity on the path (i,j), η _ij =1/d _ij represents the path The visibility on (i, j), d _ij represents the length of the path (i, j), τ _is the pheromone intensity on the path from node i to the next connecting line _Li+1 , η _is =1/d _is represents the visibility on the path from node i to the next connection line L _i+1 , and d _is represents the length of each node path from node i to the next connection line L _i+1 ;

Each ant updates the pheromone of each path traversed by the ant according to the following formula:

τ _ij =(1-ρ)τ _ij +Δτ _ij (17)

In order to use the overall information to update the pheromone, the ant-week system is used to calculate

表示第k只蚂蚁在本次循环中留在路径(i,j)上的信息素量，其值视蚂蚁的优劣程度而定，路径越短，释放的信息素就越多；Δτ_ij表示本次循环中路径(i,j)的信息素量的增量，L_k为第k只蚂蚁在本次循环中所走的路径长度，Q为设定的常数；in,

Represents the amount of pheromone left by the kth ant on the path (i, j) in this cycle, and its value depends on the pros and cons of the ant. The shorter the path, the more pheromone released; Δτ _ij represents The increment of the pheromone amount of the path (i, j) in this cycle, L _k is the length of the path taken by the kth ant in this cycle, and Q is a set constant;

Record and update the shortest path traversed by all ants in this iteration as the global optimal path;

If the number of iterations plus 1 is not greater than the maximum number of iterations, each ant on the current connection line Li selects node _j on the next connection line _Li+1 at node i according to the following formula , until the steps to reach the end position of the drone;

If the number of iterations plus 1 is greater than the maximum number of iterations, the updated global optimal path is output as the optimal planned path of the UAV.

A possible optimal path obtained by the above algorithm is shown in Figure 13. The dotted line is the suboptimal solution (preliminary planning path of the UAV) found by the Dijkstra algorithm, and the solid line is the optimal solution found by the ant colony algorithm on this basis. Therefore, we use the ant colony algorithm to better solve the path planning problem of cellular networked UAVs in the urban environment.

Among them, the attenuation coefficient ρ<1 of the pheromone trajectory is usually set to avoid infinite accumulation of pheromone on the path.

It should be understood that although the steps in the flowcharts of FIGS. 1 , 2 , 4 , 9 and 12 are shown in sequence according to the arrows, these steps are not necessarily executed in the sequence shown by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in Figures 1, 2, 4, 9, and 12 may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but may be executed at different times, The order of execution of these steps or stages is also not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the steps or stages in the other steps.

On the other hand, an embodiment of the present application also provides a UAV path planning device, as shown in FIG. 14 , the device includes:

a base station coverage area acquisition module 200, configured to acquire the signal coverage areas of each base station in the task area;

A two-dimensional path planning space building module 400, configured to construct a two-dimensional path planning space model of the UAV in the task area according to the signal coverage area of each base station;

The preliminary path planning module 600 is configured to obtain the preliminary planned path of the UAV located in the signal coverage area of the base station according to the starting point position information, the end position information and the two-dimensional path planning space model of the UAV;

The optimal path planning module 800 is configured to obtain the optimal planned path of the UAV according to the preliminary planned path of the UAV and the ant colony algorithm.

For the specific limitation of the UAV path planning device, please refer to the definition of the UAV path planning method above, which will not be repeated here. Each module in the above-mentioned UAV path planning device can be implemented in whole or in part by software, hardware and combinations thereof. The above modules may be embedded in or independent of the processor in the controller in the form of hardware, or may be stored in the memory in the controller in the form of software, so that the processor can call and execute operations corresponding to the above modules.

In one embodiment, a controller is provided, and the controller may be a server, and its internal structure diagram may be as shown in FIG. 15 . The controller includes a processor, memory, and a network interface connected through a system bus. Among them, the processor of the controller is used to provide computing and control capabilities. The memory of the controller includes a non-volatile storage medium and an internal memory. The nonvolatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The controller's database is used to store data such as the maximum number of iterations. The network interface of the controller is used to communicate with external terminals through a network connection. The computer program, when executed by the processor, implements a method of path planning for an unmanned aerial vehicle.

Those skilled in the art can understand that the structure shown in FIG. 15 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the controller to which the solution of the present application is applied. The specific controller may be Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.

In one embodiment, a controller is provided, comprising a memory and a processor, a computer program is stored in the memory, and the processor implements the following steps when executing the computer program:

S200: Acquire the signal coverage area of each base station in the task area;

S400: Build a two-dimensional path planning space model of the UAV in the task area according to the signal coverage area of each base station;

S600: According to the starting point position information, the ending point position information and the two-dimensional path planning space model of the UAV, obtain a preliminary planning path of the UAV located in the signal coverage area of the base station;

S800: According to the preliminary planning path of the UAV and the ant colony algorithm, obtain the optimal planning path of the UAV.

In the controller provided by the embodiment of the present application, when the processor of the controller executes the computer program, other steps of the above-mentioned UAV path planning method can also be implemented, and corresponding beneficial effects can be achieved.

In one embodiment, a computer-readable storage medium is provided on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

S200: Acquire the signal coverage area of each base station in the task area;

S400: Build a two-dimensional path planning space model of the UAV in the task area according to the signal coverage area of each base station;

S600: According to the starting point position information, the ending point position information and the two-dimensional path planning space model of the UAV, obtain a preliminary planning path of the UAV located in the signal coverage area of the base station;

S800: According to the preliminary planning path of the UAV and the ant colony algorithm, obtain the optimal planning path of the UAV.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage In the medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database or other media used in the various embodiments provided in this application may include at least one of non-volatile and volatile memory. The non-volatile memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, and the like. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, the RAM may be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM).

The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features It is considered to be the range described in this specification.

The above-mentioned embodiments only represent several embodiments of the present application, and the descriptions thereof are specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the scope of protection of the patent of the present application shall be subject to the appended claims.

Claims (10)

1. An unmanned aerial vehicle path planning method, the method comprising:

acquiring a signal coverage area of each base station in a task area;

according to the signal coverage area of each base station, constructing a two-dimensional path planning space model of the unmanned aerial vehicle in the task area;

obtaining an unmanned aerial vehicle preliminary planning path in a signal coverage area of a base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;

and obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.

2. The method of claim 1, wherein the step of obtaining signal coverage areas of base stations in the task area comprises:

gridding the task area;

determining a signal transmission mode between each grid point and each base station according to the position relation between each grid point and each base station in the task area, wherein the signal transmission mode comprises line-of-sight transmission and non-line-of-sight transmission;

and determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point of the unmanned aerial vehicle in the two-dimensional plane to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle.

3. The method of claim 2, wherein the building process of the farthest distance calculation model from the coverage point of the base station in the two-dimensional plane of the unmanned aerial vehicle to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle comprises:

obtaining a signal-to-noise ratio model of signals transmitted from each base station and received by the current unmanned aerial vehicle by using the signal power transmitted by each base station and the current position information of the unmanned aerial vehicle according to the following formula:

where ρ is_k(v (t)) represents the signal-to-noise ratio of the signal transmitted from the kth base station and received by the unmanned aerial vehicle at present, P represents the signal power transmitted by the base station, v (t) represents the two-dimensional coordinate information of the unmanned aerial vehicle in the two-dimensional plane where the unmanned aerial vehicle is located, and gamma (t) represents the two-dimensional coordinate information of the unmanned aerial vehicle in the two-dimensional plane where the unmanned aerial vehicle is located_k,s(t) represents the channel gain from the kth base station to the unmanned aerial vehicle channel, s belongs to { LoS, NLoS }, LoS represents line-of-sight propagation, NLoS represents non-line-of-sight propagation, and sigma represents non-line-of-sight propagation²Representing the noise power of the drone;

wherein, the channel gain calculation model from the kth base station to the unmanned aerial vehicle channel is as follows:

wherein d is_k(t) is the distance, alpha, from the drone to the kth base station_sAnd beta_sTwo constant parameters which are dependent on the signal transmission mode between the base station and each base station;

the signal-to-noise ratio of the signals transmitted from each base station and received by the unmanned aerial vehicle is equal to the minimum receiving signal-to-noise ratio of the unmanned aerial vehicle

And combining the signal-to-noise ratio model and the channel gain calculation model to obtain a farthest distance calculation model from each base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:

wherein d is_sCovering points of all base stations in a two-dimensional plane of the unmanned aerial vehicle to the base stationsThe farthest distance of the projection of the unmanned plane in the two-dimensional plane, h represents the height of the unmanned plane, and h represents the height of the unmanned plane_gRepresenting the altitude of the base station.

4. The method of claim 3, wherein the step of determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point of the two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located comprises:

determining grid point coordinates (x, y, h) in a signal coverage area of the base station according to the following formula and a farthest distance calculation model from a base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to a projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:

(x-x_k)²+(y-y_k)²≤d_s ²

wherein (x)_k,y_k) And representing the projection coordinates of the base station on the two-dimensional plane of the height of the unmanned aerial vehicle.

5. The method according to any one of claims 1-4, wherein the step of constructing the two-dimensional path planning space model of the unmanned aerial vehicle for the mission area according to the signal coverage area of each base station comprises:

generating a plurality of MAKINK connecting lines in the task area based on the signal coverage area of each base station and the MAKINK graph theory method, and establishing an unmanned aerial vehicle two-dimensional path planning space model of the task area; the MAKLINK connecting line refers to a vertex connecting line which is not intersected with the area of the uncovered base station signal between the areas of the two uncovered base station signals and a connecting line which is intersected with the boundary of the task area by the vertex of the area of the uncovered base station signal.

6. The method of claim 5, wherein the step of obtaining a preliminary planned path of the UAV within a signal coverage area of a base station according to the starting location information, the ending location information and the two-dimensional path planning space model of the UAV comprises:

and solving the two-dimensional path planning space model of the unmanned aerial vehicle by utilizing a Dijkstra algorithm, starting point position information and end point position information to obtain the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle, wherein the primary planning path of the unmanned aerial vehicle is the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle.

7. The method of claim 6, wherein the step of obtaining the optimal planned path of the UAV according to the primary planned path of the UAV and the ant colony algorithm comprises:

initializing the number m of ants, the maximum iteration times, pheromones of all paths, a parameter alpha reflecting pheromone tracks of the ants in the activity process, a parameter beta reflecting the relative importance of visibility in ant selection paths and an attenuation coefficient rho of the pheromone tracks;

each ant selects the next connecting line L at the starting point position successively according to the following formula_i+1Node j above until reaching the unmanned aerial vehicle end position:

wherein the primary planned path of the unmanned aerial vehicle passes through nodes S and P₁,P₂,…P_dT; s represents a node of the starting position of the unmanned aerial vehicle in the unmanned aerial vehicle two-dimensional path planning space model, T represents a node of the destination position of the unmanned aerial vehicle in the unmanned aerial vehicle two-dimensional path planning space model, and P represents₁,P₂,…P_dRepresenting the midpoint of each MAKLINK connecting line through which the primary planned path of the unmanned aerial vehicle passes; i denotes the next connecting line L_i+1All aboveSet of points, τ_ikRepresenting intensity, η, of pheromones on the path (i, k)_ik＝1/d_ikRepresenting visibility on path (i, k), d_ikDenotes the length of the path (i, k), q is [0,1 ]]Random number between q₀Is [0,1 ]]Adjustable parameters therebetween; j denotes the last connecting line L_i(i ═ 1,2, …, d) probability, τ, of selecting node j of the next connection line_ijRepresenting intensity of pheromone, η, on path (i, j)_ij＝1/d_ijRepresenting visibility on path (i, j), d_ijDenotes the length of the path (i, j), τ_isRepresenting node i to the next connecting line L_i+1Intensity of pheromones, η, on each node path_is＝1/d_isRepresenting node i to the next connecting line L_i+1Visibility over each node path, d_isRepresenting node i to the next connecting line L_i+1The length of each node path;

each ant updates the pheromone of each path that the ant passes according to the following formula according to the path that the ant passes by:

τ_ij＝(1-ρ)τ_ij+Δτ_ij

wherein,

represents the pheromone quantity, delta tau, left on the path (i, j) by the kth ant in the current cycle_ijIndicates the increment of the pheromone quantity of the path (i, j) in the current cycle, L_kThe path length of the kth ant in the cycle is shown, and Q is a set constant;

recording and updating the shortest paths traveled by all ants in the iteration to be global optimal paths;

if the iteration times are not more than the maximum iteration times after adding 1, skipping to execute the current connecting line L_iSelecting the next connecting line L at the node i by each ant according to the following formula_i+1The node j is added until the destination position of the unmanned aerial vehicle is reached;

and if the iteration times are added by 1 and then are larger than the maximum iteration times, outputting the updated global optimal path as the optimal planning path of the unmanned aerial vehicle.

8. An unmanned aerial vehicle path planning apparatus, the apparatus comprising:

a base station coverage area acquisition module, configured to acquire a signal coverage area of each base station in a task area;

the two-dimensional path planning space construction module is used for constructing an unmanned aerial vehicle two-dimensional path planning space model of the task area according to the signal coverage area of each base station;

the primary path planning module is used for obtaining a primary planned path of the unmanned aerial vehicle in a signal coverage area of the base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;

and the optimal path planning module is used for obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.

9. A controller comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.

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